Six Sigma and Beyond:  Design for Six Sigma

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SIX SIGMA AND BEYOND Design for Six Sigma

SIX SIGMA AND BEYOND A series by D.H. Stamatis Volume I

Foundations of Excellent Performance

Volume II

Problem Solving and Basic Mathematics

Volume III

Statistics and Probability

Volume IV

Statistical Process Control

Volume V

Design of Experiments

Volume VI

Design for Six Sigma

Volume VII

The Implementation Process

D. H. Stamatis

SIX SIGMA AND BEYOND Design for Six Sigma

ST. LUCIE PRES S A CRC Press Company Boca Raton London New York Washington, D.C.

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Library of Congress Cataloging-in-Publication Data Stamatis, D. H., 1947Six sigma and beyond : design for six sigma, volume VI p. cm. -- (Six sigma and beyond series) Includes bibliographical references. ISBN 1-57444-315-1 (v. 1 : alk paper) 1. Quality control--Statistical methods. 2. Production management--Statistical methods. 3. Industrial management. I. Title. II. Series. TS156 .S73 2001 658.5′62--dc21

2001041635

This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage or retrieval system, without prior permission in writing from the publisher. The consent of CRC Press LLC does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from CRC Press LLC for such copying. Direct all inquiries to CRC Press LLC, 2000 N.W. Corporate Blvd., Boca Raton, Florida 33431. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe.

Visit the CRC Press Web site at www.crcpress.com © 2003 by CRC Press LLC St. Lucie Press is an imprint of CRC Press LLC No claim to original U.S. Government works International Standard Book Number 1-57444-315-1 Library of Congress Card Number 2001041635 Printed in the United States of America 1 2 3 4 5 6 7 8 9 0 Printed on acid-free paper

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To Christine

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Preface A collage of historical facts brings us to the realization that concerns about quality are present not only in the minds of top management when things go wrong but also in the minds of customers when they buy something and it does not work. We begin the collage 20 years ago, with Wayne’s (1982) proclamation in the New York Times of “management gospel gone wrong.” Wayne quoted two Harvard professors, Hays and Abernathy, as saying, “You may have your eye on the wrong ball.” In a discussion of the cost differential between American and Japanese companies, Wayne said that American business executives argue that the Japanese advantage is largely rooted in factors unique to Japan: lower labor costs, more automated and newer factories, strong government support, and a homogeneous culture. The professors, though, argue differently, Wayne said. They claim that Japanese businesses are better because they pay attention to such basics as a clean workplace, preventive maintenance for machinery, a desire to make their production process error free, and an attitude that “thinks quality.” Other authors writing in the early 1980s made similar points. Blotnick (1982) wrote, “If it’s American, it must be bad.” The headline of an anonymous article in The Sentinel Star (1982) referred to “retailers relearning lesson of customer’s always right.” Ohmae (1982) wrote an article titled “Quality control circles: They work and don’t work.” Imai (1982) wrote that unless organizations control (eliminate) their waste, they would have problems. He identified waste as: 1. 2. 3. 4. 5. 6. 7.

The The The The The The The

waste waste waste waste waste waste waste

of of of of of of of

making too many units waiting time at the machine transporting units processing itself inventory motion making defective units

Imai pointed out some of Toyota’s advantages, specifically its autonomation system. Autonomation means that the machine is equipped with human wisdom to stop automatically whenever something goes wrong with it. Wight (1982) urged management to “learn to live with the truth.” When Honda rolled out its first American-built car, Lewin (1982) wrote, “Japanese bosses ponder mysterious U.S. workers.” Among other things, the Japanese wondered why Americans have so many lawyers, Lewin pointed out. Lohr (1983) wrote that “it’s just wishful thinking to say that Japan cannot catch up software. That is what a lot of people were saying about semiconductor industry a few years ago and the auto industry a decade ago.”

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Holusha (1983) wrote of the “U.S. striving for efficiency.” Serrin (1983) described a study that showed that the work ethic is “alive but neglected.” Holloran (1983) wrote that an “army staff chief faults industry as producing defective materials.” Almost twenty years later, Zahary (2001) reported that Toyota strives to retain its benchmark status by continuing its focus on the Kaizen approach and genchi genbutso (go and see attitude). Winter (2001) wrote that GM is “now trying to show it understands importance of product.” McElroy (2001) wrote, “Customers don’t care how well your stock is performing. They do not care you are the lowest producer. They do not care you are the fastest to market. All they care about is the car they are buying. That is why it all comes down to product.” Morais (2001), quoting O’Connell (2000), claimed that over 100,000 focus groups were fielded in 1999, even though marketing and advertising professionals have mixed feelings about their value. Steel (1998 pp. 79 and 202–205) expressed industry’s ambivalence about focus groups. Among other things, he claimed that they are not very representative at all. The odd thing about focus groups is that we still use them to predict the sales potential of new products primarily because of their instant judgments, non-projectable conclusions, and comparatively low costs, even though we know better — that is, we know that we could do better by learning about consumers’ product needs and attitudes and understanding their lives. In the automotive industry, the evidence that something is wrong is abundantly clear, as Mayne et al. (2001) have reported. Here are some key points: 1. National Highway Traffic Safety Administration records showed more than 250 vehicle recalls as of mid-June 2001 — well on pace to exceed the previous year’s record 12-month total of 483. The 2000 total broke the previous high of 370 — set in 1999 — and shattered the next-highest mark of 328, set the year before. 2. Numbers of recalled vehicles have risen correspondingly — 23.4 million in 2000, 19.2 million the previous year, and 17.2 million in 1998. 3. The number of vehicles snared by non-compliance recalls — issued for failure to meet the Federal Motor Vehicle Safety Standards — increased to 4.5 million in 2000. This represents a 61% hike compared to 1999’s 2.8 million, and it is nearly three times the 1.6 million recorded in 1998. 4. A total of 18.9 million vehicles were recalled in 2000 because of safetyrelated defects. That is 81% of the overall recall total and a sharp increase compared to 1999 and 1998, when safety-related defects prompted recalls of 16.4 million and 15.6 million vehicles, respectively. Even more telling, perhaps, it is 9% more than the 17.3 million new light vehicles sold last year in the U.S. 5. A supplier executive, who wants to remain anonymous, bristles at the suggestion that quality problems fall at the feet of suppliers. He says quality has suffered because of the Big Three’s relentless pursuit of cost reduction. He also suggests that buyers at the Big Three are evaluated primarily on the basis of cost savings rather than on the quality of the parts they procure. In the final analysis, “Americans build to print and specification, whereas Japanese build to function.”

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Powerful statements indeed, yet I could go on with examples involving home appliances, food, electronics, health devices, and many other types of products. However, the point is that the problems we are having are not new. The actions necessary to fix these problems are not new. What we need is a new commitment to pursue customer satisfaction and mean it. We must put quality in the design of all our products and services in such a way that the customer sees value in them. We must become like a philologist who believes that there is truth and falsehood in a comma. The pleasures of philology are such that by merely changing the placement of a comma, you can make sense out of nonsense; you can claim a small victory over ignorance and error. So, we in quality must learn to persevere and learn as much as possible about the customer. We must make strides to identify what customers need, want, and expect and then provide them with that product or service. We must do what the French philosopher Etienne Souriau observed: pour inventer il faut penser a cote. To invent, you must think aside — that is, slightly askew. Or we must follow the lead of Emily Dickinson when she wrote, “My business is circumstances,” and her readers understood the serendipity of ideas and the rewards of looking aside to see those ideas’ unlikely, or at least less than obvious, connections. This is the essence of Design for Six Sigma (DFSS). The upfront analysis and investigation of the customer is of paramount importance. So is trying to identify what is really needed (trade-off analysis) to make the difference. The DFSS approach is based on a systems overhaul and a new mindset to cure the ailments of organizations (profitability) and provide satisfaction to the customer (functionality and value). It is a proactive approach rather than a reactive approach, unlike the regular six sigma methodology. DFSS is a methodology that works for the future, rather than the present or past. DFSS is a holistic system that is based on challenging the status quo and providing a product or a service that not only is accepted by the customer but is financially rewarding for the organization. To do this, of course, managers must take risks. They must allow their engineers to design robust designs — and that means that the traditional Y = F(x) is not good enough. Now we must look for Y = F(x,n). In these equations, x is the traditional customer characteristic (cascaded to smaller and precise characteristics), but now we add the n, which is the noise. In other words, we must design our products and services in the presence of noise for maximum satisfaction. The best way to predict the future is to invent it. This suggests that the best way to know what is coming is to put yourself in charge of creating the situation you want. Be purposeful. Look at what is needed now, and set about doing it. Action works like a powerful drug to relieve feelings of fear, helplessness, anger, uncertainty, or depression. Mobilize yourself as well as the organization because you will be the primary architect of your future. One of the keys to being successful in your efforts is to anticipate. Accept the past, focus on the future, and anticipate. Consider what is coming, what needs to happen, and how you can rise to the occasion. Stay loose. Remain flexible. Be light on your feet. Instead of changing with the times, make a habit of changing a little ahead of the times. This change can happen with Designing for Six Sigma and

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beyond. The only requirement is that we must take advantage of the future before we are ready for it. I am reminded of Flint’s (2001), Visnic’s (2001), and Mayne’s (2001) comments, respectively. American automotive companies, for example, have abandoned the car market because they do not make money on cars. They forget that the Japanese companies not only sell cars but make money from them. So what does Detroit do to sell? It focuses on price — rebates, discounts, 0% finance, lease subsidies, and so on. What does the competition do? Not only have they developed an engine/transmission with variable valve breathing, they are already using it. We are trying to perfect the five-speed, and the competition is installing six speeds; we talk about CVTs, and Audi is putting one in its new A4. We are focusing on 10 years and 150,000 miles reliability, and our competitors are pushing for 15 years and 200,000 miles reliability. In diesel technology, the Europeans and Americans are worlds apart. Even in this age of globalization, the light duty diesel markets in Europe have become more sophisticated and demanding to the point where policy makers have recognized the environmental advantages of diesel and have allowed new diesel vehicles to prove themselves as efficient, quiet, and powerful alternatives. What do we do? Our policy makers have created a regulatory structure that greatly impedes the widespread use of diesel vehicles. Consequently, Americans may be denied the performance, fuel economy, and environmental benefits of advanced diesel technology. A third example comes again from the automotive world in reference to fuel economy. One of the issues in fuel economy is the underbody design. Early on, American companies paid great attention to the design of the underbody. As time went on, the emphasis shifted to shapes that channel airflow over the bodywork, instead of what lies beneath. But while U.S. automakers were accustomed to being on top, BMW AG was redefining airflow from the ground up. Underbodies have been a priority with the Munich-based automaker since 1980. That is when BMW acquired its first wind tunnel and began development of the 1986 7-series — code named E32. Today, underbodies rank second behind rear ends, wheel housing and cooling airflow. As of right now, the initiative for BMW has gained them 2 miles per hour. When we talk about customer satisfaction we must do certain things that will help or improve the image of the organization in the perception of the customer. We are talking about prestige and reputation. Prestige and reputation differ from each other in three ways: 1. Reputation applies to individual products or services, while prestige is a characteristic of the organization as a whole. 2. Reputation can be measured on absolute scales, but prestige can only be judged in relative terms. 3. Prestige is judged relative to other organizations; reputation is not. It is prestige that we are interested from a Design for Six Sigma perspective. The reason for this is that prestige compels each organization to perform better than its competitors, thereby promoting excellence and continuously raising industry

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standards not only for the customer but also for the competitors. To achieve prestige, we must be cognizant of some basic yet inherent items, including the following: • Be ready to engage our customers in conversation every second of the day. In the digital age, this means having an interactive medium where people can tell you what they think about your brand and your product or service whenever they have an idea, a complaint, or a compliment, or when they just want to air some ideas with somebody who knows where you are going. Easy places to start are always-open discussion boards and focus groups. When you get more sophisticated, you can try regularly scheduled special events or special meetings. The best solution? Set up an Internet communication structure that lets you have a 24 × 7 open line of communication. • Make customer relations a two-way street. Today’s customers not only want to be heard, they want to respond. They want to engage you in conversation, brainstorming, and relationship building. To facilitate this, you may want to consider two-way communication into your Web site that provides means for real-time sharing of ideas, debate, and interaction. Another way to facilitate this is through moderated chat rooms or other more organized techniques. Online events and presentations allow you to show off new ideas or development to customers, then take questions in a moderated and controlled manner, across time zones and around the world. Online meetings allow you to have customers attend “by invitation only.” Keys to success: make sure your communication is honest and credible and that the “idea flow” is going both ways. In today’s world, an organization can design digital communication systems that can provide instant information. This system can be used to brainstorm, to test concepts and features, and more importantly, to consider trade-offs. • Get your customers to help design your products and services. Most organizations ignore the best product and service designers and consultants — people who know your product or service inside out and know intimately what the market needs, more often than not. They are your customers. They can tell you a lot more than just what is right and wrong with your current products. They can tell you what they really need in future products — in functional terms. • Let your customers get to know each other. Word of mouth is a concept that no one should ever underestimate in the Internet age. The power of conversation has the lightning-quick ability to create trends, fads, and brands. People talking to each other in a moderated environment and sharing unprompted, honest opinions about your brand of product or service remains the number one way for you to get new satisfied customers. • Make your customers feel special. When you get down to it, we have been talking about delighting the customer for at least 20 years, but that is where we have stopped. We have forgotten that relationships with customers should not be any different from relationships you have with

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close friends. You need to keep in touch. You need to be honest. You need to tell people they matter to you. To facilitate this “special attitude,” an organization may have special days for the customer, special product anniversaries and so on. However, in every special situation, representatives of the organization should be identifying new functionalities for new or pending products and shortcomings of the current products. • Never try to “understand” your customer. (This is not a contradiction of the above points. Rather, it emphasizes the notion of change in expectations.) Customers are fickle. They change. As a consequence, the organization must be vigilant in tracking the changes, the wants, and the expectations of customers. To make sure that customers are being satisfied and that they will continue to be loyal to your products and services, make sure you have a system that allows you to listen, listen, and then listen some more to what they have to say. • Shrink the globe. The world is shrinking. It has become commonplace to discuss the information revolution in terms of the creation of “global markets.” To “think global” is in vogue with the majority of large corporations. But global thinking presupposes that we also understand the “global customer.” Do we really understand? Or do we merely think we do? How do we treat all our customers as though they live right next door? One way, of course, is through a combination of modern communication technology and old-fashioned neighborliness. You need good, solid, two-way conversation with someone half a globe away that is as immediate, as powerful, and as intimate as a conversation with someone right in front of you. This obviously is difficult and demanding, and in the following chapters we are going to establish a flow of disciplines that perhaps can help us in formulating those “global customers” with their specific needs, wants, and expectations. • Design for customer satisfaction and loyalty. Some time ago I heard a saying that is quite appropriate here. The saying goes something like “everything is in a state of flux, including the status quo.” I happen to agree. Never in human history has so much change affected so many people in so many ways. The winds of change keep building, blowing harder than ever, hitting more people, reshaping all kinds of organizations. Incredible as it may sound, all these changes are happening even in organizations that think that they have understood the customer and the market. To their surprise, they have not. How else can we explain some of the latest statistics that tell us the following: 1. Business failures topped 400,00 in the first half of the 1990s and exceeded 500,000 by the end of the decade. That is double the number of the previous decade. The same trend is projected for the first decade of the new century.

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2. Eighty-five percent of all U.S. organizations now outsource services once performed in house. 3. More than three million layoffs have occurred in the last five years. What can be done to reverse this trend? Well, some will ride the wind based only on their size, some will not make it, and some will make it. The ones that will make it must learn to operate under different conditions — conditions that delight the customer with the service or the product that an organization is offering. The organization must learn to design services or products so that the customer will see value in them and cannot stand it until it has possession of either one. As the desire of the customer increases for the service or product, the demand for quality will increase. Designing for six sigma is not a small thing, nor should it be a lighthearted undertaking. It is a very difficult road to follow, but the results are worthwhile. The structure of this volume is straightforward and follows the pattern of the model of DFSS, which is Recognize, Define, Characterize, Optimize, and Verify (RDCOV). Specifically, with each stage of the model, we will explain some of the most important tools and methodologies. Our introduction is the stage where we address the basic and fundamental characteristics of any DFSS program. It is our version of the Recognize step. Specifically, we address: 1. 2. 3. 4.

Partnering Robust teams Systems engineering Advanced quality planning

We follow with the Define stage, where we discuss customer concerns by first explaining the notion of “function” and then continuing with three very important methodologies in the pursuit of satisfying the customer. Those methodologies are: 1. Kano model 2. Quality function deployment (QFD) 3. Conjoint analysis We move into a discussion of “Best in class” by discussing benchmarking. We continue the discussion with advanced topics relating to design, specifically: 1. 2. 3. 4. 5. 6. 7. 8.

Monte Carlo Finite element analysis Excel’s solver Failure mode and effect analysis (FMEA) Reliability and R&M DOE Parameter design Tolerance design

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We continue with relatively short discussions of manufacturing topics, specifically: 1. Design for manufacturing/assembly (DFM/DFA) 2. Mistake proofing Our discussion on miscellaneous topics is geared to enhance the overall design function and to sensitize readers to the fact that the pursuit of DFSS is a team orientation with many disciplines interwoven to produce the optimum design. Of course, we do not pretend to have exhaustively identified all methodologies and all tools, but we believe that we have identified the most critical ones. Specifically, we discuss: 1. 2. 3. 4. 5. 6. 7.

Theory of constraints Design review Trade-off analysis Cost of quality Reengineering GD&T Metrology

We follow with a chapter on innovative methodologies in pursuing DFSS such as signal process flow, axiomatic designs, and TRIZ, and then we return to classic discussions on value analysis, project management, an overview of mathematical concepts for reliability, and Taylor’s theorem and financial concepts. We conclude our discussion of Design for Six Sigma and Beyond with a formal summary in a matrix format of all the tools used, following the model of DCOV: 1. 2. 3. 4.

Define Characterize Optimize Verify

REFERENCES Anon., Retailers Relearning Lesson of Customer’s Always Right, The Sentinel Star, Jan. 17, 1982, p. 4. Blotnick, S., If It’s American, It Must Be Bad, Forbes, Feb. 1, 1982, p. 146. Flint, J., Where’s the Cars? You Can Make Money on Cars If You Really Want To, Ward’s AUTOWORLD, Sept. 2001, p .21. Halloran, R., Chief of Army Assails Industry on Arms Flaw, The New York Times, Aug. 9, 1983, p. 1. Holusha, J., Why G.M. Needs Toyota: U.S. Striving for Efficiency, The New York Times, Feb. 16, 1983, p. 1 (of business section). Imai, M., From Taylor to Ford to Toyota: Kanban System — Another Challenge from Japan, The Japan Economic Journal, Mar. 30, 1982, p. 12.

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Lewin, T., Japanese Bosses Ponder Mysterious U.S. Workers, The New York Times, Nov. 7, 1982, p. 2 (of business section). Lohr, S., Japan’s Hard Look at Software, The New York Times, Jan. 9, 1983, p. 3 (of business section). Mayne, E., Bottoms Up! Fuel Economy Pressure Underscores Underbody Debate. Ward’s AUTOWORLD, Sept. 2001, p. 58. Mayne, E. et al., Quality Crunch, Ward’s AUTOWORLD, July 2001, p. 14. McElroy, J., Rendezvous captures consumer interest, Wards AUTOWORLD, Jan. 2001, p. 12. Morais, R., The End of Focus Groups, Quirk’s Marketing Research Review, May 2001, p. 154. O’Connell, V., advertising column, Wall Street Journal, Nov. 27, 2000, p. B21. Ohmae, K., Quality Control Circles: They Work and Don’t Work, The Wall Street Journal, Mar. 29, 1982, p. 2. Serrin, W., Study Says Work Ethic Is Alive But Neglected, The New York Times, Sept. 5, 1983, p. 4. Steel, J., Truth, Lies and Advertising, Wiley, New York, 1998. Visnic, B., Super Diesel! Anyone in the Industry Will Tell You: Forget Hybrids; Diesels Are Our One Stop Cure All, Ward’s AUTOWORLD, Sept. 2001, p. 34. Wayne, L., Management Gospel Gone Wrong, The New York Times, May 30, 1982, p. 1 (of business section). Wight, O.W., Learning To Tell the Truth, Purchasing, May 13, 1982, p. 5. Winter, D., One last speed, Wards AUTOWORLD, July 2001, p. 9. Zachary, K., Toyota Strives To Retain Its Benchmark Status, Supplement to Ward’s AUTOWORLD, Aug. 6–10, 2001, p. 11.

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Acknowledgments I want to thank Dr. A. Stuart for granting me permission to use some of the material in Chapter 14. The summaries of the different distributions and reliability have added much to the volume. I am really indebted for his contribution. As with the other volumes in this series, many people have helped in many ways to make this book a reality. I am afraid that I will miss some, even though their help was invaluable. Dr. H. Hatzis, Dr. E. Panos, and Dr. E. Kelly have been indispensable in reviewing and commenting freely on previous drafts and throughout this project. I would like to thank Dr. L. Lamberson for his thoughtful comments and suggestions on reliability, G. Burke for his suggestions on R&M, and R. Kapur for his valuable comments about the flow and content of the material. I want to thank Ford Motor Company and especially Richard Rossier and David Kelley for their efforts to obtain permission for using the introductory material on “robust teams.” I want to thank Prentice Hall for granting me permission to use the material on conjoint and MANOVA analysis in Chapter 2. That material was taken from the 1998 book Multivariate Data Analysis, 5th ed., by J.F. Hair, R.E. Anderson, R.L. Tatham, and W.C. Block. I want to thank McGraw-Hill and D.R. Bothe for granting me permission to use some material on six sigma taken from the 1977 book Measuring Process Capability, by D.R. Bothe. I want to thank J. Wiley and the Buffa Foundation for granting me permission to use material on the Monte Carlo method from the 1973 book Modern Production Management, 4th ed., by E.S. Buffa. I want to thank the American Supplier Institute for granting me permission to use the L8 interaction table as well as some of their OA and linear graphs. I want to thank M.A. Anleitner, from Livonia Technical Services, for his contribution to the topic of “function” in Chapter 2, for helping me articulate some of the key points on APQP, and for serving as a sounding board on issues of value analysis. Thanks, Mike. I also want to thank J. Ondrus, from General Dynamics — Land System Division, for introducing me to Value Analysis and serving as a reviewer for earlier drafts on this topic. I want to thank T. Panson, P. Rageas, and J. Golematis, all of them certified public accountants, for their guidance and help in articulating the basics of accounting and financial concerns presented in Chapter 15. Of course, the ultimate responsibility for interpreting their guidance is solely mine. Special thanks go to the editors at CRC for putting up with me, as well as for transforming my notes and the manuscript into a user-friendly product.

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I want to thank the participants in my seminars for their comments and recommendations. They actually piloted the material in their own organizations and saw firsthand the results of some of the techniques and methodologies discussed in this particular volume. Their comments were incorporated with much appreciation. Finally, as always, this volume would not have been completed without the support of my family and especially my navigator, chief editor, and supporter — my wife, Carla.

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About the Author D. H. Stamatis, Ph.D., ASQC-Fellow, CQE, CMfgE, is president of Contemporary Consultants, in Southgate, Michigan. He received his B.S. and B.A. degrees in marketing from Wayne State University, his master’s degree from Central Michigan University, and his Ph.D. degree in instructional technology and business/statistics from Wayne State University. Dr. Stamatis is a certified quality engineer for the American Society of Quality Control, a certified manufacturing engineer for the Society of Manufacturing Engineers, and a graduate of BSI’s ISO 9000 lead assessor training program. He is a specialist in management consulting, organizational development, and quality science and has taught these subjects at Central Michigan University, the University of Michigan, and Florida Institute of Technology. With more than 30 years of experience in management, quality training, and consulting, Dr. Stamatis has served and consulted for numerous industries in the private and public sectors. His consulting extends across the United States, Southeast Asia, Japan, China, India, and Europe. Dr. Stamatis has written more than 60 articles and presented many speeches at national and international conferences on quality. He is a contributing author in several books and the sole author of 20 books. In addition, he has performed more than 100 automotive-related audits and 25 preassessment ISO 9000 audits, and has helped several companies attain certification. He is an active member of the Detroit Engineering Society, the American Society for Training and Development, the American Marketing Association, and the American Research Association, and a fellow of the American Society for Quality Control.

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List of Figures Figure 2.1 Figure 2.2 Figure 2.3 Figure 2.4 Figure 2.5 Figure 2.6 Figure 2.7 Figure 2.8 Figure 2.9 Figure 2.10 Figure 2.11 Figure 2.12 Figure 2.13 Figure 2.14 Figure 2.15 Figure 2.16 Figure 2.17 Figure 3.1 Figure 5.1 Figure 5.2 Figure 5.3 Figure 5.4 Figure 5.5 Figure 5.6 Figure 5.7 Figure 5.8 Figure 5.9 Figure 6.1 Figure 6.2 Figure 6.3 Figure 6.4 Figure 6.5 Figure 6.6 Figure 6.7 Figure 6.8 Figure 6.9 Figure 6.10 Figure 6.11

Paper pencil assembly. Function diagram for a mechanical pencil. Ten symbols for process flow charting. Process flow for complaint handling. Kano model framework. Basic quality depicted in the Kano model. Performance quality depicted in the Kano model. Excitement quality depicted in the Kano model. Excitement quality depicted over time in the Kano model. A typical House of Quality matrix. The initial “what” of the customer. The iterative process of “what” to “how.” The relationship matrix. The conversion of “how” to “how much.” The flow of information in the process of developing the final “House of Quality.” Alternative method of calculating importance. The development of QFD. The benchmarking continuum process. Trade-off relationships between program objectives (balance design). Sequential approach. Simultaneous approach. Tomorrow’s approach … if not today’s. The product development map/guide. Manufacturing system schematic. Approaches to mistake proofing. Major inspection techniques. Function of mistake-proofing devices. Types of FMEA. Payback effort. Kano model. A Pugh matrix — shaving with a razor. Scope for DFMEA — braking system. Scope for PFMEA — printed circuit board screen printing process. Typical FMEA header. Typical FMEA body. Function tree process. Example of ballpoint pen. FMEA body.

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Figure 6.12 Figure 6.13 Figure 6.14 Figure 6.15 Figure 6.16 Figure 6.17 Figure 6.18 Figure 6.19 Figure 6.20 Figure 6.21 Figure 6.22 Figure 7.1 Figure 7.2 Figure 7.3 Figure 7.4 Figure 7.5 Figure 7.6 Figure 7.7 Figure 9.1 Figure 9.2 Figure 9.3 Figure 9.4 Figure 9.5 Figure 9.6 Figure 9.7 Figure 9.8 Figure 9.9 Figure 9.10 Figure 9.11 Figure 9.12 Figure 9.13 Figure 9.14 Figure 9.15 Figure 9.16 Figure 9.17 Figure 9.18 Figure 9.19 Figure 9.20 Figure 9.21 Figure 9.22 Figure 10.1 Figure 10.2 Figure 11.1 Figure 11.2 Figure 11.3

Transferring the failure modes to the FMEA form. Transferring severity and classification to the FMEA form. Transferring causes and occurrences to the FMEA form. Transferring current controls and detection to the FMEA form. Area chart. Transferring the RPN to the FMEA form. Action plans and results analysis. Transferring action plans and action results on the FMEA form. FMEA linkages. The learning stages. Pen assembly process. Bathtub curve. A series block diagram. A parallel reliability block diagram. A complex reliability block diagram. The Weibull distribution for the example. Control factors and noise interactions. An example of a parameter design in reliability usage. An example of a partially completed fishbone diagram. An example of interaction. Example of cause-and-effect diagram. Plots of averages (higher responses are better). A linear example of a process with several factors. Contrasts shown in a graphical presentation. First round testing. Second round testing. Linear graph for L4. The orthogonal array (OA), linear graph (LG), and column interaction for L9. Three-level factors in a L8 array. Traditional approach. Nominal the best. Smaller the better Larger the better. A comparison of Cpk and loss function. Plots of averages (higher responses are better). ANOVA decomposition of multi-level factors. Factors not linear. Plots of the average standard deviation by factor level. Factor effects. Factor effects. Quality cost: The quality control system. Costs. A typical branching using signal flow graph. A simple example with signal flow graph. A hypothetical design process.

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Figure 11.4 Figure 11.5 Figure 11.6 Figure 11.7 Figure 11.8 Figure 11.9 Figure 12.1 Figure 12.2 Figure 12.3 Figure 12.4 Figure 12.5 Figure 12.6 Figure 12.7 Figure 15.1 Figure 15.2 Figure 15.3 Figure 16.1

The graph transmission. First few terms of the probability. The effect of a self loop. Node absorption. Order of design matrix showing functional coupling between FRs and DPs. Relationship of axiomatic design framework and other tools. Relationship of savings potential to time. Project identification sheet. Cost visibility sheet. Cost function worksheet. A form that may be used to direct effort. Second step in the FAST diagram block process. A partial cost function FAST diagram. Life cycle of a typical company or product. A pictorial approach of duPont’s formula. Breakeven analysis. The DFSS model.

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List of Tables Table I.1 Table 1.1 Table 1.2 Table 1.3 Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 2.5 Table 2.6 Table 4.1 Table 4.2 Table 4.3 Table 5.1 Table 5.2 Table 5.3 Table 5.4 Table 6.1 Table 6.2 Table 6.3 Table 6.4 Table 6.5 Table 6.6 Table 6.7 Table 6.8 Table 6.9 Table 7.1 Table 7.2 Table 7.3 Table 7.4 Table 7.5 Table 8.1 Table 8.2 Table 8.3

Probability of a completely conforming product. Customer/supplier expanded partnering interface meetings. A typical questionnaire. A general questionnaire. Characteristic matrix for a machining process. Benefits of improved total development process. Stimuli descriptions and respondent rankings for conjoint analysis of industrial cleanser. Average ranks and deviations for respondents 1 and 2. Estimated part-worths and factor importance for respondents 1 and 2. Predicted part-worth totals and comparison of actual and estimated preference rankings. Simulated samples of 20 performance time values for operations A and B. Simulated operation of the two-station assembly line when operation A precedes operation B. Simulated operation of the two-station assembly line when operation B precedes operation A. Customer attributes for a car door. Relative importance of weights. Customer’s evaluation of competitive products. Examples of mistakes and defects. DFMEA — severity rating. PFMEA — severity rating. DFMEA — occurrence rating. PFMEA — occurrence rating. DFMEA detection table. PFMEA detection table. Special characteristics for both design and process. Manufacturing process control matrix. Machinery guidelines for severity, occurrence, and detection. Failure rates with median ranks. Median ranks. Five percent rank table. Ninety-five percent rank table. Department of Defense reliability and maintainability — standards and data items. Activities in the first three phases of the R&M process. Cost comparison of two machines. Thermal calculation values.

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Table 8.4 Table 9.1 Table 9.2 Table 9.3 Table 9.4 Table 9.5 Table 9.6 Table 9.7 Table 9.8 Table 9.9 Table 9.10 Table 9.11 Table 9.12 Table 9.13 Table 9.14 Table 9.15 Table 9.16 Table 9.17 Table 9.18 Table 9.19 Table 9.20 Table 9.21 Table 9.22 Table 9.23 Table 9.24 Table 9.25 Table 9.26 Table 9.27 Table 9.28 Table 9.29 Table 9.30 Table 9.31 Table 9.32 Table 9.33 Table 9.34 Table 9.35 Table 9.36 Table 9.37 Table 9.38 Table 9.39 Table 9.40 Table 9.41 Table 9.42 Table 9.43 Table 9.44 Table 9.45

Guidelines for the Duane model. One factor at a time. Test numbers for comparison. The group runs using DOE configurations. Comparisons using DOE. Comparisons of the two means. The test matrix for the seven factors. Test results. An example of contrasts. L4 setup. The L8 interaction table. An L9 with a two-level column. Combination method. Modified L8 array. An L8 with an L4 outer array. Recommended factor assignment by column. Formulas for calculating S/N. Concerns with NTB S/N ratio. L8 with test results. ANOVA table. Higher order relationships. Inner OA (L8) with outer OA (L4) and test results. The STB ANOVA table. The LTB ANOVA table. The NTB ANOVA table. Raw data ANOVA table. Combination design. L9 OA with test results. ANOVA table. Second run of ANOVA. L8 with test results and S/N values. ANOVA table for data from Table 9.30. Significant figures from Table 9.31. Observed versus cumulative frequency. Attribute test setup and results. ANOVA table (for cumulative frequency). The effect of the significant factors. Rate of occurrence at the optimum settings. Door closing effort: test set up and results. ANOVA table for door closing effort. The effects of the door closing effort. Rate of occurrence at the optimum settings. OA and test setup and results. ANOVA for the raw data. ANOVA table for the NTB S/N ratios. Typical ANOVA table setup.

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Table 9.46 Table 9.47 Table 9.48 Table 9.49 Table 9.50 Table 9.51 Table 9.52 Table 9.53 Table 9.54 Table 9.55 Table 9.56 Table 9.57 Table 9.58 Table 9.59 Table 9.60 Table 9.61 Table 9.62 Table 9.63 Table 9.64

L4 OA with test results. ANOVA table raw data. ANOVA table (S/N ratio used as raw data). Level averages — raw data. OA setup and test results for Example 2. ANOVA table (S/N ratio used as raw data). Transformed data. ANOVA table for the transformed data. Components and their levels. L8 inner OA with L8 outer OA and test results. ANOVA table (NTB) and level averages for the most significant factors. Variation runs using recommended factor target values. Calculated response variance. Cost of reducing tolerances. The impact of tightening the tolerance. Reduction of 20% in the tolerance limits of component A. Reduction of tolerance limits for component D. Reduction of tolerance limits for component C. L8 OA used for the confirmation runs with the levels set, test setup, ANOVA table and level averages. Table 9.65 Response variance. Table 10.1 Design review objectives. Table 10.2 Design review checklist. Table 10.3 Comparison between traditional and concurrent engineering. Table 10.4 Typical monthly quality cost report (values in thousands of dollars). Table 10.5 Prevention costs. Table 10.6 Appraisal costs. Table 10.7 Internal failure costs. Table 10.8 External failure costs. Table 10.9 Seven-step process redesign model. Table 10.10 GD&T characteristics and symbols. Table 12.1 Project identification checklist. Table 12.2 Idea needlers or thought stimulators. Table 12.3 The worksheet for setting the list. Table 12.4 Evolution summary. Table 12.5 Ranking and weighting. Table 12.6 Criteria affecting car purchase XXXX — pair comparison. Table 12.7 Criteria weighing. Table 12.8 Criteria comparison. Table 12.9 Criteria weight comparison — completed matrix. Table 13.1 Key integrative processes. Table 13.2 The characteristics of the DFSS implementation model using project management. Table 13.3 The process of six sigma and DFSS implementation using project management. Table 14.1 Possibilities of selecting a DFSS problem.

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Table 15.1 Table 15.2 Table 15.3

A summary of debits and credits. Summary of normal debit/credit balances. The Z score.

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Contents Introduction Understanding the Six Sigma Philosophy.......................................1 A Static versus a Dynamic Process ..........................................................................1 Products with Multiple Characteristics .....................................................................2 Short- and Long-Term Six Sigma Capability ...........................................................4 Design for Six Sigma and the Six Sigma Philosophy..............................................5 Design Phase.........................................................................................................5 Internal Manufacturing .........................................................................................5 External Manufacturing ........................................................................................6 References..................................................................................................................7 Chapter 1 Prerequisites to Design for Six Sigma (DFSS) ...................................9 Partnering ...................................................................................................................9 The Principles of Partnering...............................................................................11 View of Buyer/Supplier Relationship: A Paradigm Shift ..................................11 Characteristics of Expanded Partnering .............................................................12 Evaluating Suppliers and Selecting Supplier Partners.......................................14 Implementing Partnering ....................................................................................14 1. Establish Top Management Enrollment (Role of Top Management — Leadership)....................................................14 2. Establish Internal Organization.................................................................14 Option 1: Supplier Partnering Manager....................................................14 Option 2: Supplier Council/Team .............................................................15 Option 3: Commodity Management Organization ...................................15 3. Establish Supplier Involvement ................................................................15 4. Establish Responsibility for Implementation ...........................................15 5. Reevaluate the Partnering Process............................................................17 Ratings.......................................................................................................17 Terms Used in Specific Questions ............................................................19 Major Issues with Supplier Partnering Relationships........................................19 How Can We Improve?.......................................................................................20 Basic Partnering Checklist..................................................................................21 1. Leadership .................................................................................................21 2. Information and Analysis..........................................................................22 3. Strategic Quality Planning ........................................................................22 4. Human Resource Development and Management ...................................22 5. Management of Process Quality...............................................................23 6. Quality and Operational Results...............................................................23 7. Customer Focus and Satisfaction .............................................................23 Expanded Partnering Checklist ..........................................................................23 1. Leadership .................................................................................................23 2. Information and Analysis..........................................................................24

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3. Strategic Quality Planning ........................................................................24 4. Human Resource Development and Management ...................................24 5. Management of Process Quality...............................................................25 6. Quality and Operational Results...............................................................25 7. Customer Focus and Satisfaction .............................................................25 The Robust Team: A Quality Engineering Approach .............................................25 Team Systems .....................................................................................................26 Input ...............................................................................................................27 Signal..............................................................................................................27 The System.....................................................................................................27 Output/Response ............................................................................................28 The Environment............................................................................................28 External Variation...........................................................................................28 Internal Variation............................................................................................29 The Boundary.................................................................................................29 Controlling a Team Process: Conformance in Teams........................................29 Strategies for Dealing with Variation .................................................................30 Controlling or Eliminating Variation .............................................................30 Compensating for Variation ...........................................................................30 System Feedback............................................................................................31 Minimizing the Effect of Variation................................................................31 Monitoring Team Performance...........................................................................33 System Interrelationships...............................................................................33 Systems Engineering ...............................................................................................34 “Systems“ Defined ..............................................................................................34 Implications of the Systems Concept for the Manager .....................................35 Defining Systems Engineering ...........................................................................37 Pre-Feasibility Analysis ......................................................................................38 Requirement Analysis .........................................................................................38 Design Synthesis.................................................................................................38 Verification ..........................................................................................................39 Advanced Quality Planning.....................................................................................40 When Do We Use AQP?.....................................................................................42 What Is the Difference between AQP and APQP? ............................................43 How Do We Make AQP Work?..........................................................................43 Are There Pitfalls in Planning? ..........................................................................43 Do We Really Need Another Qualitative Tool to Gauge Quality?....................44 How Do We Use the Qualitative Methodology in an AQP Setting?.................44 APQP Initiative and Relationship to DFSS .......................................................45 References................................................................................................................47 Selected Bibliography..............................................................................................47 Chapter 2 Customer Understanding....................................................................49 The Concept of Function.........................................................................................52 Understanding Customer Wants and Needs .......................................................54

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Creating a Function Diagram .............................................................................55 The Product Flow Diagram and the Concept of Functives ...............................56 The Process Flow Diagram ................................................................................61 Using Function Concepts with Productivity and Quality Methodologies.........64 Kano Model .............................................................................................................68 Basic Quality.......................................................................................................69 Performance Quality ...........................................................................................69 Excitement Quality .............................................................................................69 Quality Function Deployment (QFD) .....................................................................71 Terms Associated with QFD...............................................................................73 Benefits of QFD..................................................................................................73 Issues with Traditional QFD...............................................................................75 Process Overview................................................................................................76 Developing a “QFD” Project Plan .....................................................................76 The Customer Axis ........................................................................................77 Technical Axis................................................................................................79 Internal Standards and Tests ..........................................................................79 The QFD Approach ............................................................................................79 QFD Methodology..............................................................................................80 QFD and Planning ..............................................................................................84 Product Development Process ............................................................................86 Conjoint Analysis ....................................................................................................88 What Is Conjoint Analysis?................................................................................88 A Hypothetical Example of Conjoint Analysis..................................................89 An Empirical Example .......................................................................................90 The Managerial Uses of Conjoint Analysis .......................................................95 References................................................................................................................95 Selected Bibliography..............................................................................................95 Chapter 3 Benchmarking ....................................................................................97 General Introduction to Benchmarking...................................................................97 A Brief History of Benchmarking......................................................................97 Potential Areas of Application of Benchmarking ..............................................97 Benchmarking and Business Strategy Development ..............................................99 Least Cost and Differentiation............................................................................99 Characteristics of a Least Cost Strategy ..........................................................100 Characteristics of a Differentiated Strategy .....................................................101 Benchmarking and Strategic Quality Management ..............................................102 Benchmarking and Six Sigma ..........................................................................105 National Quality Award Winners and Benchmarking......................................107 Example — Cadillac ....................................................................................107 A Second Example — Xerox ......................................................................108 Third Example — IBM Rochester...............................................................109 Fourth Example — Motorola.......................................................................110 Benchmarking and the Deming Management Method ....................................110

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Benchmarking and the Shewhart Cycle or Deming Wheel.............................111 Plan...............................................................................................................111 Do .................................................................................................................111 Study — Observe the Effects ......................................................................111 Act ................................................................................................................111 Why Do People Buy? .......................................................................................111 Alternative Definitions of Quality ....................................................................112 Determining the Customer’s Perception of Quality.........................................117 Quality, Pricing and Return on Investment (ROI) — The PIMS Results .......119 Benchmarking as a Management Tool..................................................................119 What Benchmarking Is and Is Not...................................................................120 The Benchmarking Process ..............................................................................121 Types of Benchmarking....................................................................................122 Organization for Benchmarking .......................................................................123 Requirements for Success.................................................................................124 Benchmarking and Change Management .............................................................126 Structural Pressure ............................................................................................128 Aspiration for Excellence .................................................................................128 Force Field Analysis .........................................................................................128 Identification of Benchmarking Alternatives ........................................................129 Externally Identified Benchmarking Candidates..............................................129 Industry Analysis and Critical Success Factors ..........................................129 PIMS Par Report ..........................................................................................130 Financial Comparison ..................................................................................130 Competitive Evaluations ..............................................................................131 Focus Groups ...............................................................................................131 Importance/Performance Analysis ...............................................................131 Internally Identified Benchmarking Candidates — Internal Assessment Surveys..........................................................................................132 Nominal Group Process: General Areas in Greatest Need of Improvement............................................................................................132 Pareto Analysis.............................................................................................132 Statistical Process Control ...........................................................................133 Trend Charting .............................................................................................133 Product and Company Life Cycle Position.................................................133 Failure Mode and Effect Analysis ...............................................................134 Cost/Time Analysis ......................................................................................134 Need to Identify Underlying Causes ................................................................134 Problem, Causes, Solutions .........................................................................134 The Five Whys .............................................................................................134 Cause and Effect Diagram ...........................................................................134 Business Assessment — Strengths and Weaknesses.............................................135 Prioritization of Benchmarking Alternatives — Prioritization Process................139 Prioritization Matrix .........................................................................................139 Quality Function Deployment (House of Quality) ..........................................140 Importance/Feasibility Matrix ..........................................................................141

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Paired Comparisons .....................................................................................141 Improvement Potential .................................................................................141 Prioritization Factors....................................................................................141 Are There Any Other Problems? What Is the Relative Importance of Each of These? .............................................................................................142 Identification of Benchmarking Sources...............................................................142 Types of Benchmark Sources ...........................................................................142 Internal Best Performers ..............................................................................143 Competitive Best Performers .......................................................................143 Best of Class ................................................................................................143 Selection Criteria ..............................................................................................144 Sources of Competitive Information ................................................................144 Gaining the Cooperation of the Benchmark Partner........................................148 Making the Contact ..........................................................................................149 Benchmarking — Performance and Process Analysis..........................................149 Preparation of the Benchmarking Proposal......................................................149 Activity before the Visit....................................................................................149 Understanding Your Own Operations..........................................................149 Activity Analysis..........................................................................................150 1. Define the Activity .............................................................................150 2. Determine the Triggering Event ........................................................150 3. Define the Activity .............................................................................150 4. Determine the Resource Requirements .............................................151 5. Determine the Activity Drivers ..........................................................151 6. Determine the Output of the Activity ................................................151 7. Determine the Activity Performance Measure ..................................151 Model the Activity .......................................................................................152 Examples of Modeling.................................................................................152 Flow Chart the Process ................................................................................153 Activities during the Visit.................................................................................155 Understand the Benchmark Partner’s Activities..........................................155 Identification of All of the Factors Required for Success ..........................155 Activities after the Visit ....................................................................................156 1. Functional Analysis.................................................................................156 2. Cost Analysis...........................................................................................156 3. Technology Forecasting ..........................................................................156 4. Financial Benchmarking .........................................................................157 5. Sales Promotion and Advertising ...........................................................157 6. Warehouse Operations ............................................................................157 7. PIMS Analysis.........................................................................................157 8. Purchasing Performance Benchmarks ....................................................157 Motorola Example........................................................................................158 Gap Analysis..........................................................................................................158 Definition of Gap Analysis ...............................................................................158 Current versus Future Gap ...............................................................................158

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Goal Setting ...........................................................................................................159 Goal Definition .................................................................................................159 Goal Characteristics ..........................................................................................159 Result versus Effort Goals................................................................................159 Goal Setting Philosophy ...................................................................................159 Best of the Best versus Optimization ..........................................................159 Kaizen versus Breakthrough Strategies .......................................................160 Guiding Principle Implications.........................................................................160 Goal Structure ...................................................................................................160 Cascading Goal Structure ............................................................................160 Interdepartmental Goals...............................................................................161 Action Plan Identification and Implementation ....................................................161 A Creative Planning Process ............................................................................162 Action Plan Prioritization .................................................................................162 Action Plan Documentation..............................................................................162 Monitoring and Control ....................................................................................162 Financial Analysis of Benchmarking Alternatives................................................163 Managing Benchmarking for Performance...........................................................164 References..............................................................................................................166 Selected Bibliography............................................................................................167 Chapter 4 Simulation ........................................................................................169 What Is Simulation? ..............................................................................................169 Simulated Sampling...............................................................................................170 Finite Element Analysis (FEA) .............................................................................175 Types of Finite Elements ..................................................................................175 Types of Analyses .............................................................................................176 Procedures Involved in FEA.............................................................................178 Steps in Analysis Procedure .............................................................................178 Overview of Finite Element Analysis — Solution Procedure .........................179 Input to the Finite Element Model ...................................................................180 Outputs from the Finite Element Analysis.......................................................180 Analysis of Redesigns of Refined Model ........................................................181 Summary — Finite Element Technique: A Design Tool .................................182 Excel’s Solver ........................................................................................................182 Design Optimization..............................................................................................182 How To Do Design Optimization.....................................................................184 Understanding the Optimization Algorithm .....................................................184 Conversion to an Unconstrained Problem........................................................185 Simulation and DFSS ............................................................................................185 References..............................................................................................................186 Selected Bibliography............................................................................................186 Chapter 5 Design for Manufacturability/Assembly (DFM/DFA or DFMA) ..........................................................................................187

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Business Expectations and the Impact from a Successful DFM/DFA.................189 The Essential Elements for Successful DFM/DFA ..............................................192 The Product Plan ..............................................................................................194 Product Design.............................................................................................194 Criteria for Decision between Crash Program and Perfect Product...........195 Case #1 — Crash Program .....................................................................195 Case #2 — Perfect Product Design ........................................................196 The Product Plan — Product Design Itself.................................................196 Define Product Performance Requirement ..................................................198 Available Tools and Methods for DFMA .............................................................198 Cookbooks for DFM/DFA................................................................................199 Use of the Human Body ..............................................................................199 Arrangement of the Work Place ..................................................................200 Design of Tools and Equipment ..................................................................200 Mitsubishi Method............................................................................................200 U-MASS Method..............................................................................................202 MIL-HDBK-727 ...............................................................................................203 Fundamental Design Guidance .............................................................................204 The Manufacturing Process...................................................................................206 Mistake Proofing....................................................................................................208 Definition ..........................................................................................................208 The Strategy ......................................................................................................208 Defects ..............................................................................................................209 Mistake Proof System Is a Technique for Avoiding Errors in the Workplace ...............................................................................................210 Types of Human Mistakes ................................................................................210 Forgetfulness ................................................................................................210 Mistakes of Misunderstanding.....................................................................210 Identification Mistakes .................................................................................210 Amateur Errors.............................................................................................211 Willful Mistakes...........................................................................................211 Inadvertent Mistakes ....................................................................................211 Slowness Mistakes .......................................................................................211 Lack of Standards Mistakes.........................................................................211 Surprise Mistakes .........................................................................................211 Intentional Mistakes .....................................................................................212 Defects and Errors ............................................................................................212 Mistake Types and Accompanying Causes ......................................................213 Signals that Alert ..............................................................................................215 Approaches to Mistake Proofing ......................................................................215 Major Inspection Techniques.......................................................................216 Mistake Proof System Devices....................................................................216 Devices Used as “Detectors of Mistakes” ..............................................217 Devices Used as “Preventers of Mistakes”.............................................217 Equation for Success ........................................................................................218 Typical Error Proofing Devices ...................................................................219

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References..............................................................................................................219 Selected Bibliography............................................................................................219 Chapter 6 Failure Mode and Effect Analysis (FMEA) ....................................223 Definition of FMEA ..............................................................................................224 Types of FMEAs....................................................................................................224 Is FMEA Needed? .................................................................................................225 Benefits of FMEA .................................................................................................226 FMEA History .......................................................................................................226 Initiation of the FMEA..........................................................................................227 Getting Started .......................................................................................................228 1. Understand Your Customers and Their Needs ............................................228 2. Know the Function ......................................................................................230 3. Understand the Concept of Priority ............................................................230 4. Develop and Evaluate Conceptual Designs/Processes Based on Customer Needs and Business Strategy......................................................230 5. Be Committed to Continual Improvement ..................................................231 6. Create an Effective FMEA Team ................................................................231 7. Define the FMEA Project and Scope ..........................................................234 The FMEA Form ...................................................................................................235 Developing the Function...................................................................................238 Organizing Product Functions ..........................................................................239 Failure Mode Analysis......................................................................................240 Understanding Failure Mode .......................................................................240 Failure Mode Questions...............................................................................240 Determining Potential Failure Modes..........................................................242 Failure Mode Effects ........................................................................................243 Effects and Severity Rating .........................................................................244 Severity Rating (Seriousness of the Effect) ................................................245 Failure Cause and Occurrence..........................................................................246 Popular Ways (Techniques) to Determine Causes ......................................247 Occurrence Rating........................................................................................249 Current Controls and Detection Ratings .....................................................249 Detection Rating ..........................................................................................250 Understanding and Calculating Risk................................................................251 Action Plans and Results.......................................................................................253 Classification and Characteristics.....................................................................254 Product Characteristics/“Root Causes” .......................................................255 Process Parameters/“Root Causes”..............................................................255 Driving the Action Plan ....................................................................................255 Linkages among Design and Process FMEAs and Control Plan.........................258 Getting the Most from FMEA...............................................................................260 System or Concept FMEA ....................................................................................262 Design Failure Mode and Effects Analysis (DFMEA).........................................262 Objective ...........................................................................................................263 Timing ...............................................................................................................263

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Requirements ....................................................................................................263 Discussion .........................................................................................................263 Forming the Appropriate Team....................................................................263 Describing the Function of the Design/Product..........................................264 Describing the Failure Mode Anticipated ...................................................264 Describing the Effect of the Failure ............................................................264 Describing the Cause of the Failure ............................................................264 Estimating the Frequency of Occurrence of Failure ...................................265 Estimating the Severity of the Failure.........................................................265 Identifying System and Design Controls ....................................................265 Estimating the Detection of the Failure ......................................................266 Calculating the Risk Priority Number .........................................................267 Recommending Corrective Action...............................................................267 Strategies for Lowering Risk: (System/Design) — High Severity or Occurrence ..........................................................................................267 Strategies for Lowering Risk: (System/Design) — High Detection Rating ......................................................................................................267 Process Failure Mode and Effects Analysis (FMEA)...........................................268 Objective ...........................................................................................................268 Timing ...............................................................................................................268 Requirements ....................................................................................................268 Discussion .........................................................................................................269 Forming the Team ........................................................................................269 Describing the Process Function .................................................................269 Manufacturing Process Functions...........................................................269 The PFMEA Function Questions............................................................270 Describing the Failure Mode Anticipated ...................................................270 Describing the Effect(s) of the Failure........................................................271 Describing the Cause(s) of the Failure........................................................272 Estimating the Frequency of Occurrence of Failure ...................................273 Estimating the Severity of the Failure.........................................................273 Identifying Manufacturing Process Controls...............................................273 Estimating the Detection of the Failure ......................................................274 Calculating the Risk Priority Number .........................................................275 Recommending Corrective Action...............................................................275 Strategies for Lowering Risk: (Manufacturing) — High Severity or Occurrence ..........................................................................................275 Strategies for Lowering Risk: (Manufacturing) — High Detection Rating ......................................................................................................276 Machinery FMEA (MFMEA) ...............................................................................277 Identify the Scope of the MFMEA ..................................................................277 Identify the Function ........................................................................................277 Failure Mode.....................................................................................................277 Potential Effects ................................................................................................278 Severity Rating..................................................................................................279 Classification .....................................................................................................279

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Potential Causes................................................................................................279 Occurrence Ratings...........................................................................................282 Surrogate MFMEAs..........................................................................................282 Current Controls...........................................................................................282 Detection Rating ..........................................................................................282 Risk Priority Number (RPN)............................................................................282 Recommended Actions .....................................................................................283 Date, Responsible Party....................................................................................283 Actions Taken/Revised RPN.............................................................................283 Revised RPN.....................................................................................................284 Summary ................................................................................................................284 Selected Bibliography............................................................................................284 Chapter 7 Reliability .........................................................................................287 Probabilistic Nature of Reliability ........................................................................287 Performing the Intended Function Satisfactorily..................................................288 Specified Time Period.......................................................................................288 Specified Conditions .........................................................................................289 Environmental Conditions Profile ....................................................................289 Reliability Numbers ..........................................................................................290 Indicators Used to Quantify Product Reliability..............................................290 Reliability and Quality ..........................................................................................291 Product Defects.................................................................................................291 Customer Satisfaction .......................................................................................292 Product Life and Failure Rate ..........................................................................293 Product Design and Development Cycle ..............................................................295 Reliability in Design.........................................................................................296 Cost of Engineering Changes and Product Life Cycle....................................297 Reliability in the Technology Deployment Process.........................................298 1. Pre-Deployment Process .........................................................................298 2. Core Engineering Process.......................................................................299 3. Quality Support .......................................................................................300 Reliability Measures — Testing ............................................................................300 What Is a Reliability Test? ...............................................................................300 When Does Reliability Testing Occur?............................................................301 Reliability Testing Objectives...........................................................................301 Sudden-Death Testing ..................................................................................302 Accelerated Testing ......................................................................................305 Accelerated Test Methods .....................................................................................305 Constant-Stress Testing.....................................................................................305 Step-Stress Testing............................................................................................306 Progressive-Stress Testing ................................................................................306 Accelerated-Test Models ..................................................................................306 Inverse Power Law Model ...........................................................................307 Arrhenius Model ..........................................................................................308

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AST/PASS..............................................................................................................310 Purpose of AST.................................................................................................310 AST Pre-Test Requirements .............................................................................311 Objective and Benefits of AST.........................................................................311 Purpose of PASS...............................................................................................311 Objective and Benefits of PASS .......................................................................312 Characteristics of a Reliability Demonstration Test .............................................312 The Operating Characteristic Curve.................................................................313 Attributes Tests .................................................................................................313 Variables Tests ..................................................................................................314 Fixed-Sample Tests ...........................................................................................314 Sequential Tests ................................................................................................314 Reliability Demonstration Test Methods...............................................................314 Small Populations — Fixed-Sample Test Using the Hypergeometric Distribution ...........................................................315 Large Population — Fixed-Sample Test Using the Binomial Distribution ......................................................................315 Large Population — Fixed-Sample Test Using the Poisson Distribution.........................................................................316 Success Testing ......................................................................................................316 Sequential Test Plan for the Binomial Distribution .........................................317 Graphical Solution ............................................................................................318 Variables Demonstration Tests ..............................................................................318 Failure-Truncated Test Plans — Fixed-Sample Test Using the Exponential Distribution ..................................................................318 Time-Truncated Test Plans — Fixed-Sample Test Using the Exponential Distribution ..................................................................319 Weibull and Normal Distributions....................................................................320 Sequential Test Plans .............................................................................................321 Exponential Distribution Sequential Test Plan.................................................321 Weibull and Normal Distributions....................................................................323 Interference (Tail) Testing ................................................................................323 Reliability Vision ..............................................................................................323 Reliability Block Diagrams ..............................................................................323 Weibull Distribution — Instructions for Plotting and Analyzing Failure Data on a Weibull Probability Chart ................................................................325 Instructions for Plotting Failure and Suspended Items Data on a Weibull Probability Chart.........................................................................331 Additional Notes on the Use of the Weibull....................................................334 Design of Experiments in Reliability Applications ..............................................335 Reliability Improvement through Parameter Design ............................................336 Department of Defense Reliability and Maintainability — Standards and Data Items.......................................................................................................337 References..............................................................................................................342 Selected Bibliography............................................................................................343

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Chapter 8 Reliability and Maintainability ........................................................345 Why Do Reliability and Maintainability?.............................................................345 Objectives...............................................................................................................346 Making Reliability and Maintainability Work ......................................................346 Who’s Responsible? ..............................................................................................347 Tools.......................................................................................................................347 Sequence and Timing ............................................................................................348 Concept ..................................................................................................................349 Bookshelf Data .................................................................................................349 Manufacturing Process Selection .....................................................................350 R&M and Preventive Maintenance (PM) Needs Analysis ..............................350 Development and Design.......................................................................................350 R&M Planning..................................................................................................350 Process Design for R&M .................................................................................351 Machinery FMEA Development ......................................................................351 Design Review ..................................................................................................352 Build and Install ....................................................................................................352 Equipment Run-Off ..........................................................................................352 Operation of Machinery....................................................................................352 Operations and Support .........................................................................................353 Conversion/Decommission ....................................................................................353 Typical R&M Measures ........................................................................................353 R&M Matrix .....................................................................................................353 Reliability Point Measurement .........................................................................354 MTBE................................................................................................................354 MTBF................................................................................................................355 Failure Rate.......................................................................................................355 MTTR................................................................................................................355 Availability ........................................................................................................356 Overall Equipment Effectiveness (OEE)..........................................................356 Life Cycle Costing (LCC) ................................................................................356 Top 10 Problems and Resolutions....................................................................357 Thermal Analysis ..............................................................................................357 Electrical Design Margins ................................................................................359 Safety Margins (SM) ........................................................................................359 Interference .......................................................................................................360 Conversion of MTBF to Failure Rate and Vice Versa .....................................361 Reliability Growth Plots ...................................................................................361 Machinery FMEA .............................................................................................361 Key Definitions in R&M .......................................................................................362 DFSS and R&M ....................................................................................................364 References..............................................................................................................365 Selected Bibliography............................................................................................365

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Chapter 9 Design of Experiments.....................................................................367 Setting the Stage for DOE.....................................................................................367 Why DOE (Design of Experiments) Is a Valuable Tool..................................367 Taguchi’s Approach ..........................................................................................370 Miscellaneous Thoughts ...................................................................................371 Planning the Experiment .......................................................................................372 Brainstorming....................................................................................................372 Choice of Response ..........................................................................................373 Miscellaneous Thoughts ...................................................................................379 Setting Up the Experiment ....................................................................................380 Choice of the Number of Factor Levels...........................................................380 Linear Graphs ...................................................................................................382 Degrees of Freedom..........................................................................................383 Using Orthogonal Arrays and Linear Graphs ..................................................383 Column Interaction (Triangular) Table.............................................................384 Factors with Three Levels ................................................................................385 Interactions and Hardware Test Setup..............................................................385 Choice of the Test Array...................................................................................387 Factors with Four Levels ..................................................................................389 Factors with Eight Levels .................................................................................389 Factors with Nine Levels..................................................................................390 Using Factors with Two Levels in a Three-Level Array .................................390 Dummy Treatment .......................................................................................390 Combination Method ...................................................................................390 Using Factors with Three Levels in a Two-Level Array .................................391 Other Techniques ..............................................................................................391 Nesting of Factors ........................................................................................392 Setting Up Experiments with Factors with Large Numbers of Levels.......392 Inner Arrays and Outer Arrays .........................................................................393 Randomization of the Experimental Tests .......................................................394 Miscellaneous Thoughts ...................................................................................394 Loss Function and Signal-to-Noise.......................................................................397 Loss Function and the Traditional Approach ...................................................397 Calculation of the Loss Function .....................................................................398 Comparison of the Loss Function and Cpk .....................................................402 Signal-to-Noise (S/N) .......................................................................................403 Miscellaneous Thoughts ...................................................................................404 Analysis..................................................................................................................405 Graphical Analysis ............................................................................................405 Analysis of Variance (ANOVA) .......................................................................407 Estimation at the Optimum Level ....................................................................408 Confidence Interval around the Estimation......................................................409 Interpretation and Use ......................................................................................410 ANOVA Decomposition of Multi-Level Factors .............................................410

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S/N Calculations and Interpretations................................................................411 Smaller-the-Better (STB) .............................................................................412 Larger-the-Better (LTB) ...............................................................................413 Nominal the Best (NTB) .............................................................................413 Combination Design .........................................................................................415 Miscellaneous Thoughts ...................................................................................418 Analysis of Classified Data ...................................................................................421 Classified Responses.........................................................................................422 Classified Attribute Analysis.............................................................................422 Class 1 ..........................................................................................................425 Class 2 ..........................................................................................................426 Classified Variable Analysis..............................................................................426 Discussion of the Degrees of Freedom ............................................................428 Miscellaneous Thoughts ...................................................................................429 Dynamic Situations................................................................................................430 Definition ..........................................................................................................430 Discussion .........................................................................................................431 Conditions ....................................................................................................431 Analysis ........................................................................................................432 Miscellaneous Thoughts ...................................................................................439 For Example 1..............................................................................................440 For Example 2..............................................................................................440 Parameter Design...................................................................................................441 Discussion .........................................................................................................441 Example........................................................................................................441 Tolerance Design ...................................................................................................447 Discussion .........................................................................................................447 Example........................................................................................................448 Humidity..................................................................................................454 Testing .....................................................................................................454 DOE Checklist .......................................................................................................454 Selected Bibliography............................................................................................455 Chapter 10 Miscellaneous Topics — Methodologies .......................................457 Theory of Constraints (TOC) ................................................................................457 The Goal ...........................................................................................................457 Strategic Measures ............................................................................................458 Net Profit, Return on Investment, and Productivity.........................................459 Measurement Focus ..........................................................................................460 Throughput versus Cost World.........................................................................461 Obstacles to Moving into the Throughput World ............................................461 The Foundation Elements of TOC ...................................................................463 The Theory of Non-Constraints .......................................................................463 The Five-Step Framework of TOC...................................................................464 Selected Bibliography............................................................................................465

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Design Review .......................................................................................................465 Failure Mode and Effect Analysis (FMEA).....................................................467 References..............................................................................................................470 Selected Bibliography............................................................................................470 Trade-Off Studies...................................................................................................470 How to Conduct a Trade-Off Study: The Process ...........................................471 1. Construct the Preliminary Matrix ...........................................................471 2. Select and Assemble the Cross-Functional Team ..................................472 3. Assign Team Members’ Roles and Responsibilities ..............................472 4. Assign Ranking Teams To Evaluate the Alternatives ............................473 Identification of Ranking Methods .........................................................473 Development of Standardized Documentation .......................................474 Timing for Report out of Selection Process...........................................474 5. Weight the Various Categories................................................................474 6. Compile the Evidence Book ...................................................................475 7. Present the Results ..................................................................................475 Glossary of Terms.............................................................................................476 Selected Bibliography............................................................................................477 Cost of Quality ......................................................................................................477 Cost Monitoring System...................................................................................478 Standard Cost ...............................................................................................478 Actual Costs .................................................................................................478 Variance ........................................................................................................480 Cost Reduction Efforts.................................................................................480 Concepts of Quality Costs................................................................................480 J. Juran .........................................................................................................480 W.E. Deming ................................................................................................480 P. Crosby ......................................................................................................481 G. Taguchi ....................................................................................................481 Definition of Quality Components ...................................................................481 Methods of Measuring Quality.........................................................................483 Complaint Indices .............................................................................................484 Processing and Resolution of Customer Complaints.......................................484 Techniques for Analyzing Data ........................................................................484 Format for Presentation of Costs......................................................................485 Laws of Cost of Quality ...................................................................................485 Data Sources .....................................................................................................487 Inspection Decisions .........................................................................................487 Prevention Costs (See Table 10.5) ...................................................................487 Appraisal Costs (See Table 10.6) .....................................................................487 Internal Failure Costs (See Table 10.7)............................................................487 External Failure Costs (See Table 10.8)...........................................................487 Diagnostic Guidelines to Identify Manufacturing Process Improvement Opportunities ..............................................................................489 Diagnostic Guidelines to Identify Administrative Process Improvement Opportunities ..............................................................................490

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Steps for Quality Improvement — Using Cost of Quality ..............................492 Procedure......................................................................................................492 Examples ......................................................................................................492 Guideline Cost of Quality Elements by Discipline .........................................502 Cost of Quality and DFSS Relationship ..........................................................509 References..............................................................................................................511 Selected Bibliography............................................................................................511 Reengineering ........................................................................................................511 Process Redesign ..............................................................................................511 The Restructuring Approach.............................................................................512 The Conference Method ...................................................................................513 The OOAD Method ..........................................................................................515 Reengineering and DFSS..................................................................................516 References..............................................................................................................517 Selected Bibliography............................................................................................518 Geometric Dimensioning and Tolerancing (GD&T) ............................................518 References..............................................................................................................523 Selected Bibliography............................................................................................523 Metrology...............................................................................................................524 Understanding the Problem ..............................................................................524 Metrology’s Role in Industry and Quality .......................................................525 Measurement Techniques and Equipment........................................................527 Purpose of Inspection .......................................................................................528 How Do We Use Inspection and Why? ...........................................................529 Methods of Testing ...........................................................................................529 Interpreting Results of Inspection and Testing ................................................530 Technique for Wringing Gage Blocks..............................................................531 Length Combinations........................................................................................532 References..............................................................................................................533 Chapter 11 Innovation Techniques Used in Design for Six Sigma (DFSS)....535 Modeling Design Iteration Using Signal Flow Graphs as Introduced by Eppinger, Nukala and Whitney (1997) ............................................................535 Rules and Definitions of Signal Flow Graphs as Introduced by Howard (1971) and Truxal (1955) ..............................................................538 Basic Operations on Signal Flow Graphs ........................................................538 The Effect of a Self Loop.................................................................................538 Solution by Node Absorption ...........................................................................539 References..............................................................................................................539 Selected Bibliography............................................................................................540 Axiomatic Designs ................................................................................................541 So, What Is an Axiomatic Design? ..................................................................542 Axiomatic and Other Design Methodologies...................................................542 Applying Axiomatic Design to Cars ................................................................543 New Designs ................................................................................................544

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Diagnosis of Existing Design ......................................................................544 Extensions and Engineering Changes to Existing Designs ........................544 Efficient Project Work-Flow ........................................................................545 Effective Change Management ....................................................................545 Efficient Design Function ............................................................................545 References..............................................................................................................547 Selected Bibliography............................................................................................547 TRIZ — The Theory of Inventive Problem Solving ............................................548 References..............................................................................................................551 Selected Bibliography............................................................................................551 Chapter 12 Value Analysis/Engineering ...........................................................553 Introduction to Value Control — The Environment .............................................553 History of Value Control .......................................................................................555 Value Concept........................................................................................................556 Definition ..........................................................................................................556 Planned Approach .............................................................................................556 Function ............................................................................................................557 Value..................................................................................................................557 Develop Alternatives.........................................................................................558 Evaluation, Planning, Reporting, and Implementation ....................................559 The Job Plan .....................................................................................................559 Application.............................................................................................................560 Value Control — The Job Plan .............................................................................561 Value Control — Techniques versus Job Plan ......................................................562 Techniques.........................................................................................................562 Information Phase..................................................................................................563 Define the Problem ...........................................................................................563 Information Development ............................................................................564 Information Collection ............................................................................564 Cost Visibility..........................................................................................564 Project Scope...........................................................................................565 Function Determination ...............................................................................567 Function Analysis and Evaluation ...............................................................567 Cost Visibility ...................................................................................................568 Definitions ....................................................................................................568 Sources of Cost Information........................................................................570 Cost Visibility Techniques ...........................................................................570 Technique 1 — Determine Manufacturing Cost.....................................571 Technique 2 — Determine Cost Element ...............................................571 Technique 3 — Determine Component or Process Costs ......................571 Technique 4 — Determine Quantitative Costs .......................................572 Technique 5 — Determine Functional Area Costs.................................573 Function Determination ....................................................................................573 What Is Function?........................................................................................574

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Basic and Secondary Functions...................................................................574 Basic Functions .......................................................................................574 Secondary Functions ...............................................................................575 Function Analysis and Evaluation ....................................................................575 Technique 1 — Identify and Evaluate Function..........................................575 Technique 2 — Evaluate Principle of Operation ........................................576 Technique 3 — Evaluate Basic Function ....................................................576 Technique 4 — Theoretical Evaluation of Function ...................................576 Technique 5 — Input Output Method .........................................................577 Technique 6 — Function Analysis System Technique................................577 Cost Function Relationship..........................................................................580 Evaluate the Function ..................................................................................580 Creative Phase........................................................................................................582 Phase 1. Blast....................................................................................................584 Phase 2. Create .................................................................................................584 Phase 3. Refine .................................................................................................584 Evaluation Phase....................................................................................................585 Selection and Screening Techniques ................................................................585 Pareto Voting ................................................................................................585 Paired Comparisons .....................................................................................586 Evaluation Summary....................................................................................587 Matrix Analysis ............................................................................................587 Example...................................................................................................589 Rank and Weigh Criteria....................................................................589 Evaluate Each Alternative ..................................................................590 Analyze Results ..................................................................................591 Implementation Phase............................................................................................591 Goal for Achievement.......................................................................................592 Developing a Plan.............................................................................................592 Evaluation of the System..................................................................................593 Understanding the Principles............................................................................593 Organization ......................................................................................................594 Attitude..............................................................................................................596 Value Council....................................................................................................596 Audit Results.....................................................................................................597 Project Selection ....................................................................................................597 Concluding Comments ..........................................................................................598 References..............................................................................................................598 Selected Bibliography............................................................................................598 Chapter 13 Project Management (PM).............................................................599 What Is a Project? .................................................................................................599 The Process of Project Management.....................................................................601 Key Integrative Processes......................................................................................602 Project Management and Quality..........................................................................603

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A Generic Seven-Step Approach to Project Management....................................603 Phase 1. Define the Project ..............................................................................603 Step 1. Describe the Project ........................................................................603 Step 2. Appoint the Planning Team.............................................................604 Step 3. Define the Work...............................................................................604 Phase 2. Plan the Project ..................................................................................604 Step 4. Estimate Tasks .................................................................................604 Step 5. Calculate the Schedule and Budgets...............................................604 Phase 3. Implement the Plan ............................................................................605 Step 6. Start the Project ...............................................................................605 Phase 4. Complete the Project..........................................................................605 Step 7. Track Progress and Finish the Project ............................................605 A Generic Application of Project Management in Implementing Six Sigma and DFSS ...............................................................................................................605 The Value of Project Management in the Implementation Process ................607 Planning the Process ....................................................................................607 Goal Setting..................................................................................................608 PM and Six Sigma/DFSS .................................................................................608 Project Justification and Prioritization Techniques .....................................610 Benefit-Cost Analysis..............................................................................610 Return on Assets (ROA).....................................................................610 Return on Investment (ROI)...............................................................610 Net Present Value (NPV) Method......................................................611 Internal Rate of Return (IRR) Method ..............................................611 Payback Period Method .....................................................................612 Project Decision Analysis .......................................................................612 Why Project Management Succeeds .....................................................................613 References..............................................................................................................615 Selected Bibliography............................................................................................615 Chapter 14 Limited Mathematical Background for Design for Six Sigma (DFSS) ...........................................................................................617 Exponential Distribution and Reliability...............................................................617 Exponential Distribution...................................................................................617 Probability Density Function and Cumulative Distribution Function ........618 Probability Density Function (Decay Time)...........................................618 Cumulative Distribution Function (Rise Time) ......................................618 Reliability Problems.....................................................................................618 Constant Rate Failure .......................................................................................619 Example........................................................................................................619 Probability of Reliability ..................................................................................621 Control Charts...................................................................................................621 Continuous Time Waveform ........................................................................621 Discrete Time Samples ................................................................................621 Digital Signal Processing ........................................................................622

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Sample Space ....................................................................................................622 Assigning Probability to Sets ...........................................................................624 Gamma Distribution ..............................................................................................625 Gamma Distribution (pdf) ................................................................................625 Gamma Function...............................................................................................626 Properties of Gamma Functions ..................................................................626 Gamma Distribution and Reliability............................................................627 Example 1: Time to Total System Failure.......................................................627 Gamma Distribution and Reliability............................................................628 Reliability Relationships ..............................................................................632 Reliability Function......................................................................................632 Data Failure Distribution ..................................................................................633 Failure Rate or Density Function .....................................................................633 Hazard Rate Function .......................................................................................634 Relations between Reliability and Hazard Functions ......................................634 Poisson Process.................................................................................................635 Characteristics of Poisson Process ..............................................................636 Poisson Distribution .....................................................................................636 Example........................................................................................................639 Weibull Distribution...............................................................................................640 Three-Parameter Weibull Distribution..............................................................643 Taylor Series Expansion ........................................................................................644 Taylor Series Expansion ...................................................................................645 Partial Derivatives ........................................................................................649 Taylor Series in Two-Dimensions................................................................649 Taylor Series of Random Variable (RV) Functions.....................................650 Variance and Covariance..............................................................................650 Functions of Random Variables...................................................................651 Division of Random Variables .....................................................................651 Powers of a Random Variable .....................................................................652 Exponential of a Random Variable..............................................................652 Constant Raised to RV Power .....................................................................653 Logarithm of Random Variable ...................................................................653 Example: Horizontal Beam Deflection........................................................654 Example: Difference between Two Means..................................................655 Miscellaneous ........................................................................................................656 Closing Remarks....................................................................................................658 Selected Bibliography............................................................................................658 Chapter 15 Fundamentals of Finance and Accounting for Champions, Master Blacks, and Black Belts ............................................................................661 The Theory of the Firm.........................................................................................661 Budgets ..................................................................................................................662 Our Romance with Growth ...................................................................................663 The New Industrial State.......................................................................................663

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Behavioral Theory .................................................................................................663 Accounting Fundamentals .....................................................................................664 Accounting’s Role in Business.........................................................................664 Financial Reports ..............................................................................................664 The Balance Sheet .......................................................................................664 Current Assets and Liabilities .................................................................665 Fixed Assets.............................................................................................665 Other Slow Assets ...................................................................................666 Current Liabilities ...................................................................................666 Working Capital Format..........................................................................666 Noncurrent Assets ...................................................................................667 Noncurrent Liabilities .............................................................................667 Shareholders’ Equity ...............................................................................667 The Income Statement ............................................................................667 Gross Profit..............................................................................................668 A Gaggle of Profits .................................................................................668 Earnings per Share ..................................................................................669 The Statement of Changes ......................................................................669 Sources of Funds or Cash .......................................................................669 Use of Funds ...........................................................................................670 Changes in Working Capital Items .........................................................670 The Footnotes ..........................................................................................670 Accountants’ Report.....................................................................................671 How to Look at an Annual Report ..............................................................671 Recording Business Transactions .....................................................................672 Debits and Credits........................................................................................673 Sources and Uses of Cash.......................................................................673 How Debits and Credits Are Used .........................................................673 The Balance Sheet Equations ......................................................................673 Classification of Accounts ................................................................................674 Recording Transactions................................................................................675 The Two Books of Account.........................................................................675 The Trial Balance ....................................................................................676 The Mirror Image....................................................................................676 Accrual Basis of Accounting............................................................................676 Accrual Basis versus Cash Basis.................................................................677 Details, Details .............................................................................................677 Birth of the Balance Sheet...........................................................................678 Profits versus Cash.......................................................................................678 Things Are Measured in Money..................................................................678 Values Are Based on Historical Costs.........................................................678 Understanding Financial Statements .....................................................................679 Assets ................................................................................................................679 The Inflation Effect...........................................................................................679 Summary of Valuation Methods .......................................................................679 Historical Cost..............................................................................................679

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Liquidation Value .........................................................................................679 Investment or Intrinsic Value .......................................................................680 Psychic Value ...............................................................................................680 Current Value or Replacement Cost ............................................................680 Assets versus Expenses................................................................................680 Types of Assets .................................................................................................681 Financial Assets............................................................................................681 Physical Assets.............................................................................................681 Operating Leverage .................................................................................682 Determining the Value of Inventory .......................................................682 FIFO....................................................................................................682 LIFO ...................................................................................................682 Weighted Average...............................................................................683 Depreciation ............................................................................................683 Useful Life Concept ................................................................................683 Depreciation as an Expense ...............................................................684 Depreciation as a Valuation Reserve..................................................684 Depreciation as a Tax Strategy ..........................................................684 Depreciation as Part of Cash Flow ....................................................685 Straight Line .......................................................................................685 Sum-of-the-Years’ Digits (SYD)........................................................686 Double Declining Balance (DDB) .....................................................686 Unit of Production..............................................................................687 Replacement Cost...............................................................................687 Advantages of Accelerated Depreciation ...........................................687 Financial Statement Analysis ................................................................................688 Ratio Analysis ...................................................................................................688 Liquidity Ratios............................................................................................691 Financial Leverage .......................................................................................692 Coverage Ratios ...........................................................................................692 Earnings........................................................................................................692 Earnings Ratios ............................................................................................693 Le ROI .....................................................................................................693 ROE: Return on Equity ......................................................................694 ROA: Return on Assets ......................................................................694 ROS: Return on Sales ........................................................................694 Other Return Ratios............................................................................694 Financial Rating Systems ......................................................................................695 Bond Rating Companies...................................................................................695 Moody’s et al. ..............................................................................................695 Moody’s...................................................................................................696 Standard and Poor’s ................................................................................696 Ratings on Common Stocks .............................................................................696 The S&P Rating Method .............................................................................697 The Value Line Method ...............................................................................697 Good Ole Ben Graham ................................................................................697

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Commercial Credit Ratings ..............................................................................698 Dun & Bradstreet .........................................................................................698 Other Systems ..............................................................................................699 Company and Product Life Cycle.........................................................................699 Cash Flow .........................................................................................................700 A Final Thought about Cash Flow...................................................................701 A Handy Guide to Cost Terms.........................................................................703 Useful Concepts for Financial Decisions..............................................................704 The Modified duPont Formula .........................................................................704 Breakeven Analysis...........................................................................................705 Contribution Margin Analysis ..........................................................................706 Price–Volume Variance Analysis ......................................................................707 Inventory’s EOQ Model....................................................................................707 Return on Investment Analysis.........................................................................708 Net Present Value (NPV) .............................................................................709 Internal Rate of Return (IRR)......................................................................709 Profit Planning .......................................................................................................710 The Nature of Sales Forecasting ......................................................................710 The Plans Up Form......................................................................................710 Statistical Analysis .......................................................................................711 Compound Growth Rates........................................................................711 Regression Analysis ................................................................................711 Revenues and Costs.................................................................................711 Departmental Budgets .............................................................................711 How to Budget ........................................................................................712 Zero-Growth Budgeting ..........................................................................712 Selected Bibliography............................................................................................712 Chapter 16 Closing Thoughts about Design for Six Sigma (DFSS) ...............715 Appendix The Four Stages of Quality Function Deployment ..........................725 Stage 1: Establish Targets......................................................................................725 Stage 2: Finalize Design Timetables and Prototype Plans ...................................725 Stage 3: Establish Conditions of Production ........................................................725 Stage 4: Begin Mass Production Startup ..............................................................726 Tangible Benefits ...................................................................................................726 Intangible Benefits .................................................................................................727 Summary Value......................................................................................................727 The QFD Process...................................................................................................727 Managing the Process............................................................................................728 Selected Bibliography ..........................................................................................731 Index ......................................................................................................................737

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Introduction — Understanding the Six Sigma Philosophy Much discussion in recent years has been devoted to the concept of “six sigma” quality. The company most often associated with this philosophy is Motorola, Inc., whose definition of this principle is stated by Harry (1997, p. 3) as follows: A product is said to have six sigma quality when it exhibits no more than 3.4 npmo at the part and process step levels.

Confusion often exists about the relationship between six sigma and this definition of producing no more than 3.4 nonconformities per million opportunities. From a typical normal distribution table, one may find that the area underneath the normal curve beyond six sigma from the average is 1.248 × 10–9 or .001248 ppm, which is about 1 part per billion. Considering both tails of the process distribution, this would be a total of .002 ppm. This process has the potential capability of fitting two six sigma spreads within the tolerance, or equivalently, having 12 σ equal the tolerance. However, the 3.4 ppm value corresponds to the area under the curve at a distance of only 4.5 sigma from the process average. Why this apparent discrepancy? It is due to the difference between a static and a dynamic process. (The reader is encouraged to review Volume I of this series.)

A STATIC VERSUS A DYNAMIC PROCESS If a process is static, meaning the process average remains centered at the middle of the tolerance, then approximately .002 ppm will be produced. But under the six sigma concept, the process is considered to be dynamic, implying that over time, the process average will move both higher and lower because of many small changes in material, operators, environmental factors, tools, etc. Most small shifts in the process average will go undetected by the control chart. For an n of 4, there is only a 50 percent chance a 1.5-sigma shift in µ is detected by the next subgroup after this change. By the time this next subgroup is collected, it may have returned to its original position. Thus, this process change will never be noticed on the chart, which means that no corrective action will be implemented. However, this movement has caused the actual long-term process variation to increase somewhat because betweensubgroup variation is greater than within-subgroup variation. Note that estimates of short-term process variation are not impacted because they are determined only from within-subgroup variation. 1

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2

Six Sigma and Beyond

Based on studies analyzing the effect of these changes on process variation (Bender, 1962, 1968; Evans, 1970, 1974, 1975a and b; Gilson, 1951), the six sigma principle acknowledges the likelihood of undetected shifts in the process average of up to ±1.5 sigma. Because shifts in the average greater than 1.5 sigma are expected to be caught, and six is assumed not to change, the worst case for the production of nonconforming parts happens when the process average has shifted either the full 1.5 sigma above the middle of the tolerance or the full 1.5 sigma below it. For this worst case, there would be only 4.5 sigma (6 sigma minus 1.5 sigma) remaining between the process average and the nearest specification limit. This reduced Z value of 4.5 for the dynamic model corresponds to 3.4 ppm. When this size of shift occurs, the Z value for the other specification limit becomes 7.5, which means essentially 0 ppm are outside this limit. Because the process average can shift in only one direction at a time, the maximum number of nonconforming parts produced is 3.4 ppm. Notice that most of the time the average should be closer to the middle of the tolerance, resulting in far fewer than 3.4 ppm actually being manufactured. To achieve a goal of 3.4 ppm, the process average must be no closer than 4.5 sigma to a specification limit. Assuming the average could drift by as much as 1.5 sigma, potential capability must be at least 6.06 (4.56 plus 1.5 sigma) to compensate for shifts in the process average of up to 1.56, yet still be able to produce the desired quality level. The required 4.56 plus this added buffer of 1.5 sigma create the 6 σ requirement, and thereby generate the label “six sigma.” (Here it must be noted that the 4.5 shift is allegedly an empirical value for the electronic industry. In the automotive industry, for years the shift has been identified as only 1 sigma — a shift from a Ppk of 1.33 to a Cpk of 1.67 i.e., from 4 sigma to 5 sigma. The point is that every industry should identify its own shift and use it accordingly. It is unfortunate that the 4.5 shift has become the default value for everything. For a detailed explanation on the difference between Ppk and Cpk, the reader is encouraged to review Volumes I and IV of this series.) To counter the effect of shifts in µ, a buffer of 1.5 standard deviations can be added to other capability goals as well. If no more than 32 ppm are desired outside either specification, the goal would be to have ±4.06 fit within the tolerance, assuming no change in the process average. This target equates to a Cp of 1.33 (4.0/3). Under the static model, this potential capability goal translates into 32 ppm outside each specification when the average is centered at M. But with the inevitable 1.56 drifts in µ occurring with the dynamic process model, the average could move as close as 2.56 (4.5 sigma minus 1.5 sigma) to a specification limit before triggering any type of corrective action. This change in centering would cause as many as 6210 ppm to be produced, quite a bit more than the desired maximum of 32 ppm.

PRODUCTS WITH MULTIPLE CHARACTERISTICS Extremely low ppm levels are imperative for producing high quality products possessing many characteristics (or components). Table I.1 compares the probability of manufacturing a product with all characteristics inside their respective specifications when each is produced with ±4 sigma (Cp = 1.33) versus ±6 sigma (Cp = 2.00)

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Introduction — Understanding the Six Sigma Philosophy

3

TABLE I.1 Probability of a Completely Conforming Product With 1.56 Shift Number of Characteristics

C, = 1.33 (±46)

C,. = 2.00 (±6a)

1 2 5 10 25 50 100 150 250 500

99.3790 98.7618 96.9333 93.9607 85.5787 73.2371 53.6367 39.2820 21.0696 4.4393

99.99966 99.99932 99.9983 99.9966 99.9915 99.9830 99.9660 99.9490 99.9150 99.8301

capability. The processes producing the features are assumed to be dynamic, with up to a 1.5-sigma shift in average possible. Suppose a product has only one feature, which is produced on a process having ±4 sigma potential capability. We can then calculate that a maximum of .6210 percent of these parts will be non-conforming under the dynamic model. Conversely, at least 99.3790 percent will be conforming, as is listed in the first line of Table I.1. If this single characteristic is instead produced on a process with ±6 sigma potential capability, at most .00034 percent of the finished product will be out of specification, with at least 99.99966 percent within specification. If a product has two characteristics, the probability that both are within specification (assuming independence) is .993790 times .993790, or 98.7618 percent when each is produced on a ±4 sigma process. If they are produced on a ±6 sigma process, this probability increases to 99.99932 percent (.9999966 times .9999966). The remainder of the table is computed in a similar manner. When each characteristic is produced with ±4 sigma capability (and assuming a maximum drift of 1.5 sigma), a product with 10 characteristics will average about 939 conforming parts out of every 1000 made, with the 61 nonconforming ones having at least one characteristic out of specification. If all characteristics are manufactured with ±6 sigma capability, it would be very unlikely to see even one nonconforming part out of these 1000. For a product having 50 characteristics, 268 out of 1000 parts will have at least one nonconforming characteristic when each is produced with ±4 sigma capability. If these 50 characteristics were manufactured with ±6 sigma capability, it would still be improbable to see one nonconforming part. In fact, with ±6 sigma capability, a product must have 150 characteristics before you would expect to find even one nonconforming part out of 1000. Contrast this to the ±4 sigma capability level, where 60.7 percent of these parts would be rejected, and the rationale for adopting the six sigma philosophy becomes quite evident.

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Six Sigma and Beyond

SHORT- AND LONG-TERM SIX SIGMA CAPABILITY The six sigma approach also differentiates between short- and long-term process variation. Just as in the past, the short-term standard deviation has been estimated from within-subgroup variation, usually from R, and the long-term standard deviation incorporates both the short-term variation and any additional variation in the process introduced by the small, undetected shifts in the process average that occur over time. Although no exact relationship between these two types of variation applies to every kind of process, the six sigma philosophy ties them together with this general equation (Harry and Lawson, 1992, pp. 6–8). σ LT = cσ ST

As c is affected by shifts in the process average, it is related to the k factor, which quantifies how far the process average is from the middle of the tolerance.

c=

µ−M 1 k= (USL − LSL ) / 2 1− k

If a process has a Cp of 2.00 and is centered at the middle of the tolerance, then there is a distance of 6σST from the average to the USL. When the process average shifts up by 1.5σST, it has moved off target by 25 percent of one-half the tolerance (1.5/6.0 = .25). For this k factor of .25, c is calculated as 1.33. C = 1/(1 – .25) = 1/.75 = 1.33 The long-term standard deviation for this process would then be estimated from σST, as: ˆ LT = cσ ST = 1.33σ ST σ

The value 1.33 is quite commonly adopted as the relationship between shortand long-term process variation (Koons, 1992). This factor implies that long-term variation is approximately 33 percent greater than short-term variation. Other authors are more conservative and assume a c factor between 1.40 and 1.60, which translates to a k factor ranging from .286 to .375 (Harry and Lawson, 1992, pp. 6–12, 7–6). For a c factor of 1.50, k is .333. 1.50 = 1/(1 – k) 1 – k = 1/1.50 k = 1 – (1/1.50) = .333

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5

This assumption expects up to a 33.3 percent shift in the process average. With six sigma capability, there is 6σST from M to the specification limit, a distance that equals one-half the tolerance. A k factor of .333 represents a maximum shift in the process average of 2.0σST, a number derived by multiplying one-half the tolerance, or 6σST, by .333.

DESIGN FOR SIX SIGMA AND THE SIX SIGMA PHILOSOPHY The six sigma philosophy is becoming more and more popular in the quality field, especially with companies in the electronics industry (de Treville et al., 1995). Organizations striving to attain the quality levels required with the six sigma system usually adopt the following three recommended strategies for accomplishing this goal (Tomas (1991) offers a six-step approach). Improving an existing process to the six sigma level of quality would be very difficult, if not impossible. That is why Fan (1990) insists this type of thinking must already be incorporated into the original design of new products and the processes that will manufacture them if there is to be any chance of achieving six sigma quality. The three recommended strategies are as follows:

DESIGN PHASE 1. Design in ±6σ tolerances for all critical product and process parameters. For additional information on this topic, read Six Sigma Mechanical Design Tolerancing by Harry and Stewart (1988). 2. Develop designs robust to unexpected changes in both manufacturing and customer environments (see Harry and Lawson, 1992). 3. Minimize part count and number of processing steps. 4. Standardize parts and processes. Knowing the process capability of current manufacturing operations will greatly aid designers in accomplishing this first step. And of course, good designs will positively influence the capability of future processes. Once a new product is released for production, the designed-in quality levels must be maintained, and even improved upon, by working to reduce (or eliminate) both assignable and common causes of process variation. McFadden (1993) lists several additional key components of a six sigma quality program specifically targeted at manufacturing.

INTERNAL MANUFACTURING 1. Standardize manufacturing practices. 2. Audit the manufacturing system. Pena (1990) provides a detailed audit checklist for this purpose.

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3. Use SPC to control, identify, and eliminate causes of variation in the manufacturing process. Mader et al. (1993) have written a book entitled Process Control Methods to help with this step. The reader may also review Volume IV of this series. 4. Measure process capability and compare to goals. Koons’ (1992) and Bothe’s (1997) books on capability indices are useful here. 5. Consider the effects of random sampling variation on all six sigma estimates and apply the proper confidence bounds. The reference by Tavormina and Buckley (1992) would be helpful here. 6. Kelly and Seymour (1993), Bothe (1993), and Delott and Gupta (1990) reveal how the application of statistical techniques helped achieve six sigma quality levels for copper plating ceramic substrates. Harry (1994) provides several examples of applying design of experiments to improve quality in the electronics industry. A special warning here is appropriate. Even if the first two strategies are adopted, a company will never achieve six sigma quality unless it has the full cooperation and participation of all its suppliers.

EXTERNAL MANUFACTURING 1. 2. 3. 4. 5.

Qualify suppliers. Minimize the number of suppliers. Develop long-term partnerships with remaining suppliers. Require documented process control plans. Insist on continuous process improvement.

Craig (1993) shows how Dupont Connector Systems utilized this set of strategies to introduce new products into the data processing and telecommunications industries. Noguera (1992) discusses how the six sigma doctrine applies to chip connection technology in electronics manufacturing, while Fontenot et al. (1994) explain how these six sigma principles pertain to improving customer service. Daskalantonakis et al. (1990–1991) describe how software measurement technology can identify areas of improvement and help track progress toward attaining six sigma quality in software development. As all these authors conclude, the rewards for achieving the six sigma quality goals are shorter cycle times, shorter lead times, reduced costs, higher yields, improved product reliability, increased profitability, and most important of all, highly satisfied customers. We have reviewed the principles of six sigma here to make sure the reader understands the ramifications of poor quality and the significance of implementing the six sigma philosophy. In Volume I of this series, we discussed this philosophy in much more detail. However, it is imperative to summarize some of the inherent advantages, as follows:

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7

1. As quality improves to the six sigma level, profits will follow with a margin of about 8% higher prices. 2. The difference between a six sigma company and a non–six sigma company is that the six sigma company is three times more profitable. Most of that profitability is through elimination of variability — waste. 3. Companies with improved quality gain market share continuously at the expense of companies that do not improve. The focus of all these great results is in the manufacturing. However, most of the cost reduction is not in manufacturing. We know from many studies and the experience of management consultants that about 80% of quality problems are actually designed into the product without any conscious attempt to do so. We also know that about 70% of a product’s cost is determined by its design. Yet, most of the “hoopla” about six sigma in the last several years has been about the DMAIC model. To be sure, in the absence of anything else, the DMAIC model is great. But it still focuses on after-the-fact problems, issues, and concerns. As we keep on fixing problems, we continually generate problems to be fixed. That is why Stamatis (2000) and Tavormina and Buckley (1994) and the first volume of this series proclaimed that six sigma is not any different from any other tool already in the tool box of the practitioner. We still believe that, but with a major caveat. The benefit of the six sigma philosophy and its application is in the design phase of the product or service. It is unconscionable to think that in this day and age there are organizations that allow their people to chase their tails and give accolades to so many for fixing problems. Never mind that the problems they are fixing are repeatable problems. It is an abomination to think that the more we talk about quality, the more it seems that we regress. We believe that a certification program will do its magic when in fact nothing will lead to real improvement unless we focus on the design. This volume is dedicated to the Design for Six Sigma, and we are going to talk about some of the most essential tools for improvement in “real” terms. Specifically, we are going to focus on resource efficiency, robust designs, and production of products and services that are directly correlated with customer needs, wants, and expectations.

REFERENCES Bender, A, Bendarizing Tolerances — A Simple Practical Probability Method of Handling Tolerances for Limit-Stack-Ups. Graphic Science, Dec. 1962, pp. 17–21. Bender, A., Statistical Tolerancing as It Relates to Quality Control and the Designer, SAE Paper No. 680490, Society of Automotive Engineers, Southfield, MI, 1968. Bothe, D.R., Reducing Process Variation, International Quality Institute., Inc., Sacramento, CA, 1993. Bothe, D.R., Measuring Process Capability, McGraw-Hill. New York, 1997. Craig, R.J., Six-Sigma Quality, the Key to Customer Satisfaction, 47th ASQC Annual Quality Congress Transactions, Boston, 1993, pp. 206–212.

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Daskalantonakis, M.K., Yacobellis, R.H., and Basili, V.R., A method for assessing software measurement technology, Quality Engineering 3, 27–40, 1990–1991. Delott, C. and Gupta, P., Characterization of copperplating process for ceramic substrates, Quality Engineering, 2, 269–284, 1990. de Treville, S., Edelson, N.M., and Watson, R., Getting six sigma back to basics, Quality Digest, 15, 42–47, 1995. Evans, D.H., Statistical tolerancing formulation, Journal of Quality Technology, 2, 188–195, 1970. Evans, D.H., Statistical tolerancing: the state of the art, Part I: Background, Journal of Quality Technology, 6, 188–195, 1974. Evans, D.H., Statistical tolerancing: the state of the art, Part II: Methods for estimating moments, Journal of Quality Technology, 7, 1–12. 1975 (a). Evans, D.H., Statistical tolerancing: the state of the art, Part III: Shifts and drifts, Journal of Quality Technology, 7, 72–76, 1975 (b). Fan, John Y. (May 1990). Achieving Six Sigma in Design, 44th ASQC Annual Quality Congress Transactions, San Francisco, May 1990, pp. 851–856. Fontenot, G., Behara, R., and Gresham, A., Six sigma in customer satisfaction, Quality Progress, 27, 73–76, 1994. Gilson, J., A New Approach to Engineering Tolerances, Machinery Publishing Co., London, 1951. Harry, M., The Nature of Six Sigma Quality, Motorola Univ. Press, Schaumburg, IL, 1997. Harry, M. and Stewart, R., Six Sigma Mechanical Design Tolerancing, Motorola University Press, Schaumburg, IL, 1988. Harry, M., The Vision of Six Sigma: Case Studies and Applications, 2nd ed., Sigma Publishing Co., Phoenix, 1994. Harry, M. and Lawson, J.R., Six Sigma Producibility Analysis and Process Characterization, Addison-Wesley Publishing Co., Reading, MA, 1992. Kelly, H.W. and Seymour, L.A., Data Display. Addison-Wesley Publishing Co., Reading, MA, 1993. Koons, J., Indices of Capability: Classical and Six Sigma Tools, Addison-Wesley Publishing Co., Reading, MA, 1992. Mader, D.P., Seymour, L.A., Brauer, D.C., and Gallemore, M.L., Process Control Methods, Addison-Wesley Publishing Co., Reading, MA, 1993. McFadden, F.R., Six-sigma quality programs, Quality Progress, 26, 37–42, 1993. Noguera, J., Implementing Six Sigma for Interconnect Technology, 46th ASQC Annual Quality Congress Transactions, Nashville, TN, May 1992, pp. 538–544. Pena, E., Motorola’s secret to total quality control, Quality Progress, 23, 43–45, 1990. Stamatis, D.H., Six sigma: point/counterpoint: who needs six sigma anyway, Quality Digest, 33–38, May, 2000. Tadikamalla, P.R., The confusion over six-sigma quality, Quality Progress, 21, 83–85, 1994. Tavormina, J.J., and Buckley, S., SPC and six-sigma, Quality, 31, 47, 1992. Tomas, S., Motorola’s Six Steps to Six Sigma, 34th International Conference Proceedings, APICS, Seattle, WA, 1991. pp. 166–169.

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1

Prerequisites to Design for Six Sigma (DFSS)

So far in this series we have presented an overview of the six sigma methodology (DMAIC) and some of the tools and specific methodologies for addressing problems in manufacturing. Although this is a commendable endeavor for anyone to pursue — as mentioned in Volume I of this series — it is not an efficient way to use resources to pursue improvement. The reason for this is the same as the reason you do not apply an atomic bomb to demolish a two-story building. It can be done, but it is a very expensive way to go. As we proposed in Volume I, if an organization really means business and wants quality improvement to go beyond six sigma constraints, it must focus on the design phase of its products or services. It is the design that produces results. It is the design that allows the organization to have flexibility. It is the design that convinces the customer of the existence of quality in a product. Of course, in order for this design to be appropriate and applicable for customer use, it must be perceived by the customer as functional, not by the organization’s definition but by the customer’s personal perceived understanding and application of that product or service. Design for Six Sigma (DFSS) is an approach in which engineers interpret and design the functionality of the customer need, want, and expectation into requirements that are based on a win-win proposition between customer and organization. Why is this important? It is important because only through improved quality and perceived value will the customer be satisfied. In turn, only if the customer is satisfied will the competitive advantage of a given organization increase. There are four prerequisites to DFSS and beyond. The first is the recognition that improvement must be a collaboration between organization and supplier (partnering). The second is the recognition that true DFSS and beyond will only be achieved if in a given organization there are “real” teams and those teams are really “robust.” The third prerequisite is that improvement on such a large scale can only be achieved by recognizing that systems engineering must be in place. Its function has to be to make sure that the customer’s needs, wants, and expectations are cascaded all the way to the component level. The fourth prerequisite is the implementation of at least a rudimentary system of Advanced Quality Planning (AQP). In this chapter we will address each of these prerequisites in a cursory format. (Here we must note that these prerequisites have also been called the “recognize” phase of the six sigma methodology.) In the follow-up chapters, we will discuss specific tools that we need in the pursuit of DFSS and beyond.

PARTNERING Partnering and cooperation must be our watchwords. In any industry, better communication up and down the supply chain is mandatory. In the past — in few 9

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instances even today — U.S. companies have bought almost solely on the basis of price through competitive bidding. We need to change our attitude. Price is important, but it is not the only consideration. Partnering with both customers and suppliers is just as important. The Japanese have created a competitive edge through vertical integration. We can learn from them by establishing “virtual” vertical integration through partnering with customers and suppliers. Just as in a marriage, we need to give more than we get and believe that it will all work out better in the end. We need to give preferential treatment to local suppliers. We should take a long-term view, understanding their need for profitability and looking beyond this year’s buy. To begin our thinking in that direction we must change our current paradigm. The first paradigm shift must be in the following definitions: 1. Vendors must be viewed as suppliers. 2. Procurement must be viewed as business strategy. These are small changes indeed but they mean totally different things. For example: “supplier” implies working together in a win-win situation, while “vendor” implies a one-time benefit — usually price. “Procurement” implies price orientation based on bidding of some sort, while “business strategy” takes into account the concern(s) of the entire organization. We all know that price alone is not the sole reason we buy. If we do buy on the basis of price alone, we pay the consequences later on. So, what is partnering? Partnering is a business culture that fosters open communication and mutually beneficial relationships in a supportive environment built on trust. Partnering relationships stimulate continuous quality improvement and a reduction in the total cost of ownership. Partnering starts with: 1. An attitude and behavioral change at the top of the organization 2. Recognition of long-term mutual dependencies internal and external to the organization 3. A commitment to this change being understood and valued at all levels within the organization At the core or basic level, partnering: 1. Fosters excellence throughout the organization 2. Encourages open communication in a beneficial, supportive, and nonadversarial environment of mutual trust and respect 3. Carries this positive environment outward from the organization to its customers and suppliers At an expanded level, partnering involves:

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1. 2. 3. 4.

11

Teaming Sharing resources Melding of customer and supplier Eliminating the we/they approach to conducting business

By the same token, partnering is not: 1. A negotiation or purchasing tool to be used as leverage against the supplier 2. A business guarantee However, in all cases, partnering promotes: 1. 2. 3. 4.

Customer satisfaction Mutual profitability Improved product, service, and operational quality A desire for and a commitment to excellence through continuous improvements in communication skills, quality, delivery, administration, and service performance 5. The factors that contribute to customer satisfaction and the lowest total cost of ownership 6. A situation in which each partner enhances its own competitive position through the knowledge and resources shared by the other

THE PRINCIPLES

OF

PARTNERING

Effective partnering has its foundation in the basic principles of economics, marketing, business, humanities, and sociology. The customer develops a set of business and technical desires, needs, requirements, and expectations in a competitive global market. The supplier most closely meeting those business and technical needs will be successful. The supplier asks the customer what is wanted rather than telling the customer what is available. The customer recognizes and understands the supplier’s business and technical requirements, allowing the supplier to be a viable and successful source to the industry. All transactions are honorable and fair. The parties are not trying to take advantage of each other. Functioning interchangeably each day as customer and supplier, internally within the organization and externally with customers and suppliers, every person in a strong supply chain recognizes mutual dependencies. All transactions must be mutually beneficial, with each person encouraging open communication and operating with integrity, mutual trust, cooperation, and respect.

VIEW

OF

BUYER/SUPPLIER RELATIONSHIP: A PARADIGM SHIFT

Partnering involves an expanded view of the buyer/supplier relationship, as shown here:

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Traditional

Expanded

Lowest price Specification-driven Short-term, reacts to market Trouble avoidance Purchasing’s responsibility Tactical Little sharing of information on both sides

Total cost of ownership End customer–driven Long-term Opportunity maximization Cross-functional teams and top management involvement Strategic Both supplier and buyer share short- and long-term plans Share risk and opportunity Standardization Joint venture Share data

How can this partnership develop? There are prerequisites. Some are listed here. The prerequisites for basic partnering include: 1. 2. 3. 4. 5.

Mutual respect Honesty Trust Open and frequent communication Understanding of each other’s needs

Additional prerequisites for expanded partnering include: 6. 7. 8. 9. 10. 11. 12.

Long-term commitment Recognition of continuing improvement — objective and factual Passion to help each other succeed High priority on relationship Shared risk and opportunity Shared strategies/technology road maps Management commitment

CHARACTERISTICS

OF

EXPANDED PARTNERING

Expanded partnering promotes dedication, desire, and commitment to product and service excellence through improvements in technology, skills, quality, delivery, administration, responsiveness, and total cost of ownership. All these are imperative requirements for DFSS. In other words, expanded partnering: 1. 2. 3. 4.

Builds on basic partnering Is a long-term relationship process Provides focus on mutual strategic and tactical goals Includes customer/supplier team support to promote mutual success and profitability.

Of course, there are different levels of partnering just as there are different levels of results. For example:

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Prerequisites to Design for Six Sigma (DFSS) Results

Partnering Focus

Sale only Loyalty/trust Secured volumes Mutual improvements Mutual breakthrough

Short term Product Product and service Process or system Continual improvement

13 Stage 1 2 3 4 5

Why is partnering so important in the DFSS even though it may mean different things to different people? It is because the purposes or goal of most customers who advocate “partnerships” are to reduce the time to get a new product to market by eliminating the bid cycle and to extend the customer’s capability without adding personnel. Partnering is joining together to accomplish an objective that can best be met by two individuals or corporations rather than one. For a partnership to work well, it requires that both partners understand the objective, each partner complements the other in skills necessary to meet the objective, and each recognizes the value of the other in the relationship. A true partnership occurs when both partners make a conscious decision to enter into a unique relationship. As the partnership develops, trust and respect build to a degree that both share the joy and rewards of success and, when things do not go so well, both work hard together to resolve the issues to mutual satisfaction. In a customer/supplier partnership, the customer must define the objective (or the scope of the project) and identify the needs. The supplier must have the capability to meet the customer’s needs and become an extension of the customer’s resources. To be more specific, the customer must be able to quantify and share the desired needs in terms of the quantity of services required, the timeline or critical path desired, and targeted costs — including up-front engineering as well as unit cost and capital investment. The supplier must determine whether it can commit the resources required to meet those needs and whether it is capable of reaching the targets. A mutual commitment must be made early in the program, and it must be for the life of the program. In a more practical sense, the customer in a customer/supplier partnership must be the leader and be in a position to guide the partners to the objective — no different than a project leader or a team leader of a program that is 100 percent internal to the customer. The leader also must monitor the progress in terms of cost and time with input from the supplier. Our experience would indicate that longer projects should be broken into “phases” so that there are milestones that are mutually agreed to in advance by the partners and that mark the points at which the supplier is paid for its services. For a partnership to work well, customer/supplier communications must be open and frequent. With the availability of CAD, e-mail, Internet, Web sites, fax, and voice mail, there should be no reason not to communicate within minutes of recognition of an issue critical to the program, but there is also a need for regular meetings at predetermined intervals at either the customer’s or supplier’s location (probably with some meetings at each location to expose both partners to as many of the team players as possible).

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I am sure there is more to be said as to why partnership and DFSS work in tandem and why both strive for mutual benefits, but I hope these thoughts gave some idea of the significance that both have for each other.

EVALUATING SUPPLIERS

AND

SELECTING SUPPLIER PARTNERS

There are many schemes to evaluate suppliers, and each of them has advantages and disadvantages. We believe, however, that each organization should take the time to generate its own criteria in at least two dimensions. The first should be the supplier’s situation and the second the purchaser’s situation. Within each category, levels of satisfaction may be assessed as total dissatisfaction, partial satisfaction, or total satisfaction, or numerical values may be used. The higher the number, the more qualified the supplier is. This may be done with either a questionnaire or a matrix. In either case, this task should be performed by a team of people from various functional areas, such as purchasing, engineering, finance, quality, and legal. The important point is to evaluate key suppliers for a fit with your company’s needs.

IMPLEMENTING PARTNERING There are five steps to partnering. They are: 1. Establish Top Management Enrollment (Role of Top Management — Leadership) The senior management, in the role of an executive customer partner or executive supplier partner (champion): 1. 2. 3. 4. 5. 6. 7. 8. 9.

Serves in a long-term assignment for each expanded partnering relationship Is available to support prompt issue resolution Establishes strong counterpart relationships with key customers and suppliers Provides for and supports decision-making authority at the lowest practical levels Provides partnering progress updates for executive management review Encourages and supports prompt responsiveness to communications affecting customer/supplier relationships Maintains a rapid management approval cycle, providing an ombudsman when required Commits adequate time to the partnering process Ensures that cohesive internal, cross-functional teams are in place to support the partnering process

2. Establish Internal Organization There are several options in this phase. However, the most common are: Option 1: Supplier Partnering Manager A staff supplier partnering manager is appointed to a full-time position (for a minimum of two years). This manager will be responsible for:

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1. Working with purchasing/commodity team management 2. Instilling the partnering principles into the company culture 3. Implementing the partnering process with company management and suppliers 4. Reviewing progress during customer/supplier review sessions 5. Working the issues specific to the partnering process Option 2: Supplier Council/Team A supplier partnering council or team is established within the organizational and operational structure that “owns” the resources required to support the partnering process. The functions are the same as for the supplier partnering manager but are assigned to several individuals. Typically, the council or team is made up of purchasing, quality, product engineering, and manufacturing management with additional resources available from finance, law, training, and other departments as required. Option 3: Commodity Management Organization A line organization consisting of a commodity manager and staff is created to manage the commodity and the partnering activities described in Option 1. Support is received from the operational groups as required. 3. Establish Supplier Involvement To have an effective partnering involvement is of paramount importance. This involvement may be encouraged and helped to grow by having open communication. Communication may be conducted in a variety of forums or as scheduled periodic meetings — see Table 1.1. 4. Establish Responsibility for Implementation Identify roles and responsibilities of the partnering process manager: 1. 2. 3. 4. 5.

Serve as customer representative. Serve as supplier advocate. (Avoid conflict of interest.) Focus participants on long-term success. Accelerate and route communications (good news, bad news). Perform meeting planning (with supplier) and facilitation function.

Perhaps one of the most important functions in this step is to establish credibility with each other as well as confidentiality requirements. The process of this exchange must be truthful and full of integrity. Some characteristics of this exchange are: 1. Each party provides the other with the information needed to be successful. 2. The supplier needs to know the customer’s requirements and expectations in order to meet them on a long-term basis.

Establish/update mutual key results, goals, objectives, action plans Discuss issues Review performance Review/discuss on-time deliveries Required actions of both parties Quality indicators Quality action plan Business issues

Purchasing Technical Quality/reliability (Other team members)

Monthly Team Meeting

Major issues Performance review “Health check” Objectives Expectations Actual performance Technology trends Business trends Program direction

Purchasing Technical Quality/reliability (Other team members) Executive partnersb

Quarterly/Semiannual Management Meeting

At supplier location and tour Maintain key contacts Major performance review

Purchasing Technical Quality/reliability (Other team members) Executive partners

Annual Management Review

Team includes personnel from Purchasing, Quality, Material Control, Engineering. When needed, also can include personnel from Sales, Safety, Manufacturing, Process Area Management, Planning, Training, Legal, Risk Management, Finance, Project Management. b Optional as part of quarterly and semiannual meetings.

Introduce program Obtain mutual agreement and commitment Identify teams Introduce/suggest executive partners Present/discuss customer objectives Supplier objectives Proposed objectives Business objectives Definition of responsibilities Expectations

Customer team Supplier team Executive partners (if appointed)

Kick-off Meeting

Meetings

16

a

Meeting Topics Partner meeting Meeting purpose Objectives Issues Participant responsibilities

Participants Customer teama Supplier teama

Internal Preparation Meeting

TABLE 1.1 Customer/Supplier Expanded Partnering Interface Meetings

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To be successful in this exchange requires time. The reason for this is that building trust is a function of time. The longer you work with someone the more you get to know that person. To expedite the process of gaining trust, suppliers and customers may want to share in: 1. 2. 3. 4. 5. 6. 7. 8.

Non-disclosure agreements Quality improvement process Technology development roadmaps Specification development Should-cost/Total-cost model Forecasts/Frozen schedules Executive partners Job rotation with suppliers

Be aware of, adhere to, and respect the sensitive/confidential nature of proprietary information, both yours and your partner’s. Always remember: recognize the differences in company cultures. Find ways to do things without imposing your value system. Compromise... Find the common ground... Work out the differences... Move forward… Negotiate... COOPERATE! 5. Reevaluate the Partnering Process People cannot improve unless they know where they are. Evaluation of the partnering process is a way to benchmark the progress of the relationship and to set priorities for future improvement. Questionnaires with five-point rating criteria provide a means for this evaluation in which both customers and suppliers take an active role. A typical questionnaire may look like Table 1.2. Sometimes the questionnaires provide detailed definitions of certain words or criteria that are being used in the instrument. The following is a brief supplement to explain/define the rating categories and some of the terms used in Table 1.2: Ratings 1. Does not meet — Failing to satisfy requirements, unacceptable performance 2. Marginally meets — Performance is not fully acceptable, needs improvement 3. Meets — Fulfills basic requirements, satisfactory 4. Exceeds — Surpasses normal requirements 5. Superior — Consistently excels above and beyond expectations, “worldclass” performance

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Six Sigma and Beyond

TABLE 1.2 A Typical Questionnaire Please select one of the following ratings for each question: Ratings: (1) Does not meet (2) Marginally meets (3) Meets (4) Exceeds (5) Superior 1. Rate the relationship’s impact in focusing both parties on strategic and tactical goals to foster mutual success. Strategic Tactical

1 1

2 2

3 3

4 4

5 5

Comments: 2. Have all established communication channels within Intel, from executive sponsor down, enabled the partners to improve their effectiveness/competitiveness as a company? Technical Issues Business Issues

1 1

2 2

3 3

4 4

5 5

1 1 1

2 2 2

3 3 3

4 4 4

5 5 5

1

2

3

4

5

Comments: 3. Rate the effectiveness of the team structure. Management Team Working Team Performance Reviews (Both Parties) Follow-Up on Action Items Comments: 4. Rate the effectiveness of the Key Supplier Program team in generating high quality solutions. Time of Solutions Quality of Solutions Cost-Effective Solutions Comments:

1 1 1

2 2 2

3 3 3

4 4 4

5 5 5

1 1

2 2

3 3

4 4

5 5

4 4 4 4 4 4

5 5 5 5 5 5

5. Does the Executive Partner provide meaningful support? Customer Supplier Comments: 6. Is the Key Supplier Program process formally managed in an effective manner? Customer Resource Commitment Supplier Resource Commitment Formal Communication Tools Information Sharing Total Cost Focus Dealing with “Me Best“ Comments:

1 1 1 1 1 1

2 2 2 2 2 2

3 3. 3 3 3 3

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Terms Used in Specific Questions Question 1 Strategic Goals — Long-range objectives (i.e., next-generation technology) Tactical Goals — Operational, day-to-day problem solving, etc. Question 3 Management Team — Executive sponsors plus upper/middle managers Working Team — Commodity/product teams, task forces, user groups Performance Reviews — Grading joint MBOs, other indicators (e.g., quality, customer satisfaction survey) Question 4 Time of Solution — Meets or exceeds time requirements/expectations Quality of Solution — Meets or exceeds quality requirements/expectations Cost-Effective Solution — Improves total cost effectiveness/fosters mutual profitability Question 5 Meaningful Support — Active participation and involvement during and between business meetings Question 6 Resource Commitment — Adequate support (people, tools, space...) to allow successful results Formal Communication Tools — Meetings, reports, MBO’s technology exchange; correct topics, timely, worthwhile Information Sharing — Plans, technology, data; useful, timely, fosters profitability Total Cost Focus — Model in place and used to support decisions to apply resources Dealing with “The Best” — Process contributes to world-class performance Another general questionnaire evaluating the partnering process is shown in Table 1.3.

MAJOR ISSUES

WITH

SUPPLIER PARTNERING RELATIONSHIPS

In any relationship that one may think of, issues and concerns exist. Partnering is no different. Some of the areas that might be of general concern include the following: 1. 2. 3. 4. 5. 6.

Issues Issues Issues Issues Issues Other

or or or or or

concerns concerns concerns concerns concerns

within the customer’s company within the supplier’s company of a competitive nature of a political or legal nature of a technological nature

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TABLE 1.3 A General Questionnaire Evaluate the following categories based on a rating of 1 to 5, with 1 being low and 5 being excellent. (Yet another variation of the criteria may be 1 = Much improvement needed, 5 = Little or no improvement needed.) Executive commitment to the process Recognition of mutual dependencies Mutually defined and shared expectations/objectives Executive partners/sponsors Quick issue resolution (break down roadblocks) Understanding and sharing of risks Sharing of technical roadmaps/competitive analysis/business plans Openness, honesty, respect Formal and frequent communication/feedback process Access to data Establish clear definition of responsibility (project leadership)

Issues or concerns of specific nature may develop when any of the following situations exist: 1. Support on either side is insufficient. 2. Something has caused one party to consider abandoning the partnering relationship. 3. A “better deal” or innovation threatens the partnering relationship. 4. Unequal benefits or conflicting incentives exist. 5. There are forced requirements under the guise of a partnering relationship and fear on the part of the supplier to decline or dissent, particularly if the supplier is small. 6. Key players change or there is a change of ownership.

HOW CAN WE IMPROVE? A fundamental question that needs to be answered from a customer’s perspective is “How can we improve?” The answer is by establishing a process with strategic importance of “key” relationships. Once this process is identified then it needs recognition — the more the better. How do we do that? We can do it by: 1. 2. 3. 4.

Establishing upper management involvement Sharing information: technology exchanges Showing suppliers how to use the data Educating suppliers in tools and methodologies

We can benefit from creating a “mentoring” attitude toward our suppliers. Traditionally we say, “Do this because we need it.” Start saying (and thinking), “Do

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this because it will make you a stronger company, and that will in turn make us a stronger company.” Become a mentor in the Partnering for Total Quality assessment process with your suppliers. Clearly define expectations by: 1. Mutually developing short- and long-term objectives for each relationship 2. Increasing the concentration on areas for mutual success; reducing the concentration on terms and conditions 3. Making decisions based on total cost; increasing the involvement and awareness of suppliers in this process In the final analysis, in order for a successful partnership to flourish both partners — customer and supplier — must recognize that change is imminent, at least in the following areas: 1. 2. 3. 4. 5.

Organization itself Internal, interfunctional communication Customer orientation World-class definition Skills development

Are there indicators of a successful partnering process? We believe that there are. Typical indicators are the existence of: 1. 2. 3. 4. 5. 6. 7. 8. 9.

Formal communication processes Commitment to the suppliers’ success Stable relationships, not dependent on a few personalities Consistent and specific feedback on supplier performance Realistic expectations Employee accountability for ethical business conduct Meaningful information sharing Guidance to supplier in defining improvement efforts Non-adversarial negotiations and decisions based on total cost of ownership 10. Employees empowered to do the right thing

BASIC PARTNERING CHECKLIST The basic partnering principles below may be applied to any customer/supplier relationship, regardless of size of company and number of employees. The principles also apply to relationships within the organization. The investment is primarily an attitude and behavioral change to bring about six sigma quality and beyond. 1. Leadership Our management:

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1. Is personally committed to the principles of the partnering process 2. Has directed organization-wide commitment, adoption, and execution of the partnering principles and philosophy 3. Is committed to generating accurate forecasts to improve delivery schedule stability with our suppliers 4. Ensures that the partnering principles flourish even in stressful times 5. Seeks mutually profitable arrangements with our suppliers 6. Is involved in high-level review of the partnering process. 2. Information and Analysis Our organization: 1. Has standardized measurements and performance for products, processes, service, and administration 2. Respects the protection of intellectual property 3. Treats information gained in open exchanges with respect and confidentiality 4. Provides consistent and specific feedback on supplier performance 3. Strategic Quality Planning Our organization: 1. Avoids short-term solutions at the expense of long-term viability 2. Places more emphasis on overall needs and mutual expectations, less on legal or formal aspects of the relationship 3. Uses reasonable and realistic expectations and milestones with our customers and suppliers 4. Demonstrates a commitment to continuous improvement in all facets of our business 4. Human Resource Development and Management Our organization: 1. Promotes employee accountability for ethical business conduct through performance reviews, holding supervisors accountable for promoting such practices 2. Helps employees understand their roles as customer and supplier internal and external to the organization 3. Trains employees on business practices that are ethical, open, professional, and of high integrity 4. Provides position descriptions with a clear definition of responsibility 5. Supports decision-making authority at the lowest practical level

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5. Management of Process Quality Our organization: 1. Shares basic evaluation criteria with our customers and suppliers 2. Has methods for ensuring quality of components, processes, administration, service, and final product. 3. Checks periodically with our customers to verify that our quality meets their expectations 6. Quality and Operational Results Our organization: 1. Shares meaningful information and data with our customers and suppliers, with frequent and timely feedback on problems as well as successes 2. Provides guidance to suppliers in defining improvement efforts that address all problems 7. Customer Focus and Satisfaction Our organization: 1. Recognizes mutual dependencies with our customers and the need to work together; understands that partnering does not end with the signing of the purchase order. 2. Engages in win/win, non-adversarial negotiations and purchasing decisions based on total cost of ownership 3. Provides prompt disclosure to customers of any inability of the organization to meet current or future requirements; makes realistic commitments to customers

EXPANDED PARTNERING CHECKLIST In addition to the basic partnering principles, expanded partnering recognizes the need for mutual support based on such factors as cost, risk, criticalness, and actual performance. The investment involves an application of resources from both the customer and the supplier. Customer resource availability limits the number of expanded partnering relationships in which any organization can be simultaneously engaged. 1. Leadership Our senior management, in the role of an executive customer partner or executive supplier partner (champion):

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1. Serves in a long-term assignment for each expanded partner relationship 2. Is available to support prompt issue resolution 3. Establishes strong counterpoint relationships with our key customers and suppliers 4. Provides for and supports decision-making authority at the lowest practical levels 5. Encourages and supports prompt responsiveness to communications affecting customer/supplier relationships 6. Maintains a rapid management approval cycle, providing an ombudsman when required 7. Commits adequate time to the partnering process 8. Ensures that cohesive, internal, cross-functional teams are in place to support the partnering process 2. Information and Analysis Our organization, with our suppliers: 1. Uses positive encouragement and support to improve performance and total cost of ownership 2. Participates in joint information-sharing activities to develop value analysis models 3. Shares technical roadmaps, competitive analyses, and plans 4. Focuses on clearly defined, complete, achievable requirements, with less emphasis on contractual terms and conditions 5. Ensures that suppliers understand our long-term procurement strategy 3. Strategic Quality Planning Our organization: 1. Shares short- and long-term improvement plans and priorities with suppliers and customers 2. Works with customers and suppliers to understand their quality needs and plans for continuous improvements 4. Human Resource Development and Management Our company management: 1. Has established technical advisory boards to support supplier activities 2. Communicates regularly with customer and supplier management to understand mutual needs and possible areas for cooperation 3. Encourages employees to submit suggestions for continuous quality improvements 4. Offers the same quality training to supplier personnel as we provide to our own employees

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5. Management of Process Quality Our organization works with customers and suppliers to: 1. Share mutual joint performance measures that are written, measured, and tracked 2. Work toward standardization of quality and certification programs 3. Develop and implement valid quality assurance systems for products, processes, service, and administration 6. Quality and Operational Results Our organization works with customers and suppliers to: 1. Develop joint quality and yield improvement processes 2. Provide access to process data for tool and material development and refinement 7. Customer Focus and Satisfaction Our organization works with customers to: 1. Mutually define expectations, understand mutual requirements, and share risks 2. Ensure that partnering survives lapses in missed generation orders 3. Establish formal, frequent communications as part of the management process

THE ROBUST TEAM: A QUALITY ENGINEERING APPROACH In general, the traditional approach to evaluating the performance of groups in process has been twofold. The first has been to use a developmental model that provides a summary of the different phases or stages in the life cycle of a group. A popular example of this approach is the forming, storming, norming, performing model of group development. Each phase corresponds to a stage in the group life cycle — review Volume I, Part II of this series. The second model has emphasized structural patterns of a group or team. These may be construed in terms of gender, experience, length of service, or positional roles (leader, secretary, or assistant, for example). Using the structural approach, the team can also be analyzed in terms of process; the “peacemaker,” the “aggressor,” the “blocker,” or the “help-seeker,” for example, or Resource Investigator, Coordinator, and so on. Both these models have proven to be useful when trying to describe some aspects of group dynamics, and it may be possible to identify colleagues who fulfill some of these roles or identify teams that have passed through these different

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stages of development. Unfortunately, such a restricted approach to monitoring team process does not provide any feedback as to whether the team is producing predictable results, nor does it identify problems or opportunities for improvement — especially breakthrough opportunities. Specifically, no opportunity exists to determine whether team process is “in control” (capable) or whether the group is “out of control” (chaotic and falling far short of what it could achieve). Some of these issues were addressed in Volumes I and II of this series, and perhaps the reader may want to review them at this time. A further shortcoming in non-systems approaches to team building concerns team process improvement. As long as the team is operating within “acceptable” parameters, no opportunity or drive to improve or maximize the performance of the team exists. Furthermore, the team usually does not have the ability or training to self-regulate and, through self-regulation, to begin to change and adapt to the continual change taking place in the workplace. These and other considerations suggest that a systems approach to team building may have considerable advantages. The robust team involves an examination of teams as systems in conjunction with more detailed parallels between a team systems approach and the model put forward by Taguchi as part of his quality engineering methodology (see Volume V of this series). Using this viewpoint, a system is considered as a means by which a user’s intention is transformed into a perceived result. Therefore, if teams are considered in terms of how successfully they transfer energy when they function, it should be evident that there will be parallels between their functioning and the functioning of an engineered system — as in the P diagram for example. After all, in many ways, a team shares similar features to the manufacturing process of a particular product. Specifications are drawn up (objectives, time scales, etc. are established); the production machinery is put in place (team members are selected); the production process is designed and implemented (teams meet, establish norms, set agendas, and engage in problem solving, decision making, and planning activities, etc.) and the system is regulated by performance criteria (by the individual members’ expectations, assessments, performance appraisals, etc.). In manufacturing, it is important not to separate the performance of the component from its interaction with other components and its integration into large subsystems of the whole process or product. In teams, it is important not to separate the performance of the individual from his/her relationships to other team members, their interactions, and their membership in sub-teams and the team as a whole — rather it is of paramount importance to view them as a team system.

TEAM SYSTEMS Many social psychologists only consider a collection of people to be a group if their activities relate to one another in a systematic fashion. However, it is easier to define a group as a collection of individuals. The word “team,” however, as mentioned in Volume I, Part II, is reserved for those groups that constitute a system whose parts interrelate and whose members share a common goal. Some groups can easily be viewed according to this criterion. A soccer club, its manager, and its players constitute a set of parts necessary to the functioning of the whole — the common

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aim being to win soccer games. However, when does a newly established team become a good or effective team? To see the answer to this question let us examine the team from a systems approach. Input A team has an input or signal. The input is the information, energy, resources, etc., that enter into the system and are transformed through its structures and processes. A broad spectrum of inputs into the system can exist and, depending on the perspective one chooses to take, the boundaries that are drawn around the system can be more or less inclusive of these elements. A system in which the boundary is closely defined will have only the fixed structures and extant processes within it and will have a wide range of inputs, many of which may enter the system simultaneously. A system that has a very broad boundary might include people, materials, resources, and most information as a part of the system, with the input defined very narrowly as a discrete piece of information or energy. Signal The signal as developed in the Taguchi model has a more specific and limited definition. It is an input into the system, but it is limited to the means by which the user conveys to the system a deliberate intention to change (or adjust) the system output. In more general terms, it is the variable to which the system must respond in order to fulfill the user’s intent. From this perspective, most of what are traditionally considered inputs into the system, i.e., people, materials, information, and so on, are already part of the system itself, and the signal is the discrete piece of information that determines the amount of energy transformed by the system. The System The structure of a system comprises aspects of the system that are relatively static or enduring. Process, on the other hand, refers to the behavior of the system. Consequently, process refers to those relatively dynamic or transient aspects of a system that are observable by virtue of change or instability. Traditional models of a system are based upon an input-process-output model. The system acts to transform the energy from the input into the output. This process, once established, is subject to variation due to internal and external factors that produce “error states” or outputs other than the desired output. These outputs can simply be wasted energy or may actually reduce the functional ability of the system itself. If a particular team has a task to perform, e.g., solving a problem, you can consider the team to be a system that has inputs, output, and a process that allows the team members to transform their energy into the desired outputs. Team process can be defined as any activity (for example, meetings) that utilizes resources (the team) to transform inputs (ideas, skills, and qualities of team members) into outputs (discoveries, solutions to problems, proposals, actions, design ideas, products, etc.).

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Often the energy that the team brings to the process is not used to best effect. For example, in a meeting, time may be wasted reiterating points because individuals have not paid attention to what is being discussed or because there is cross talk. This in turn leaves people annoyed and frustrated. These are examples of “error states” or undesirable outputs from the team process. Output/Response In traditional systems models, the output is whatever the system transforms, produces, or expresses into the environment as a consequence of the impact its structures and processes have on the input. An output can be anything from a newborn baby to well done barbecued ribs to a presentation to a text return. This is very important to understand because teams, by their nature, are complex and multifunctional. They cannot and should not be configured to produce one kind of response. Most teams will have a whole range of outputs with accompanying measures that will be used to identify how successful they are and how effective they are in transferring energy. The key is to identify appropriate measures that can be used to monitor the team’s progress. The Environment It is important in attempting to maximize the performance of a team to identify factors that may have an impact on the performance of the team and its ability to maximize the transfer of its input into desired output and over which the team has little or no control. (Remember, the output of the team will be a new design — however defined — and it is up to the team to make that design “wanted” in the preset environment. This is not a small feat.) These factors are designated as internal or external to the system. It is these factors that cause energy to be wasted and undesirable output (error states) to occur. External Variation In teams, external variation factors may include such things as change in team membership, the environment in which the team is working, changing demands from management, corporate cultural, racial, and gender factors, and so on. In developing a group process, it is important to develop group systems and processes that are robust to these factors. In addition, team goals exert a considerable influence on the behavior of individual members, and goals can vary enormously. They could be output targets that will vary in accordance with the team’s task — problem-solving teams puzzling over the root cause of a problem; design teams considering the optimization of a particular system design to achieve robustness; a marketing team attempting to understand the exact details of customer requirements; or sports teams, each of which will have an entirely different set of performance goals depending upon the sport: soccer, football, tennis, golf, and so on. Any analysis of working teams should take into account the objectives of the team and the situation in which the team performs because both will have a profound effect on the team functioning.

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Internal Variation Internal variation, on the other hand, relates to factors that are in the team system and its members. People may bring predetermined ideas about the correct design solution. They may have biases about other team members depending on their race, gender, function, grade, and so on. Certain team members may not get along with other team members and will regularly question, challenge, or contradict the others for no apparent reason. The team may not manage its time well and consequently may find itself chronically short of time at the end of meetings. Team members may not know how to ask open questions that will open up fresh avenues of information. Closed questions will result in familiar dead ends or nonproductive and previously rejected ideas. Team members may not know how to build on the ideas of other team members and, consequently, good ideas may be regularly lost. If the reader needs help in this area, we recommend a review of Volume I, Part II. The Boundary At the simplest level, boundaries can be put around almost anything, thereby defining it as a system. In practice, the identification of the boundary is the key to successful system analysis. The classification of factors (signal, control, and variation) that impact on the system is dependent on the way in which the boundary is defined. For example, by setting the boundary of the system fairly wide, to include the team members, environment, resources, information, and so on, leaving only the directive from the champion or the monthly output target outside, more factors would be considered as control factors and fewer as variation. In this case, the directive from the champion would be the signal factor. The team members, environment (or aspect of it), and so on would be control factors. External variations would then include disruptions to the team process from sources outside the team boundary. Internal variations would include attitudinal, cultural, and intellectual variations among and between team members and variations in environmental conditions (e.g., temperature). By setting a narrower boundary, many of the factors such as environment and resources would be considered external to the system and therefore would become noise factors rather than control factors. These issues are important because they determine the team’s strategy for dealing with variations and establishing a means of becoming robust to them.

CONTROLLING

A

TEAM PROCESS: CONFORMANCE

IN

TEAMS

A tale in Hellenic mythology describes the behavior of Procrustes — an innkeeper by the Corinthean peninsula. Procrustes took his clientele, people of definite natural shape and size, and either stretched or truncated their limbs so that they might fit the mattresses he provided. There are many echoes here of the original approach to quality, “We know what you want, we will design it, you will buy it and you will like it.” Or the now famous quality euphemism, “We are not sure of what is really quality, but we sure know it, if we ever see it.” Fortunately, this philosophy is being transformed into a “customer-driven approach” and the pursuit of Total Quality Excellence through DFSS. It is not entirely

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unreasonable, therefore, when it comes to monitoring groups or teams, to identify an alternative to the current emphasis on fitting the behavior of team members into behavioral roles through a “Procrustean” method, that is, by squeezing identity and function into personality models like those of Belbin, Myers-Briggs, Bion, and so on, through normalization and pressure to conform. Remember, one of the diversity issues is the fact that everyone is different and we are all much better because of that difference. This is particularly the case when old norms are not questioned and challenged regularly or when personality models are used to avoid genuine personal contact or in place of a genuine understanding of the uniqueness of others.

STRATEGIES

FOR

DEALING

WITH

VARIATION

There are four basic strategies for dealing with variation and its effect on the performance of a system: ignore the variation, attempt to control or eliminate the variation, compensate for the variation, or minimize the effect of the variation by making the system robust to it. Adopting the first of these strategies would mean accepting that teams will never function efficiently, but hoping that they will “do the best they can under the circumstances.” As with an engineering system, this strategy would result in a lot of unhappy customers. Generally, with engineering systems, you are encouraged to adopt strategy four first, reverting only to strategies two and three as a last resort because they are difficult and expensive to implement. While strategy four should also be chosen in the case of the team system wherever possible, you have greater flexibility in many cases to consider the other two options. Controlling or Eliminating Variation Procrustes’ behavior is an example of controlling inner variation. While this approach to variation might be considered extreme, you may have some scope for selecting team members with the right characteristics for effective teamwork as well as the necessary technical expertise. External variations are perhaps a little easier to deal with. For example, you could ensure that meetings are held away from the shop floor to reduce distractions due to noise (in the audible sense!) or hold them at an off-site location to minimize interruption. Compensating for Variation The principal means of compensating for variation is by providing some feedback on its effect on system output. The link between structure and process —the way in which structure determines process, and for your purposes perhaps more importantly, the way that process determines structure — is found in the concept of feedback loops. Feedback loops are so named because they are circular interrelationships that feed information from output back to input. Information is transmitted within the system and is used to maintain stability, to bring about structural changes, and to facilitate interaction with other systems.

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Even the simplest model of the effective team includes this concept of feedback loops. By employing information feedback loops, systems may behave in ways that can be described as “goal seeking” or “purposive.” Negative feedback allows a system to maintain stability as in the case of the most commonly quoted example, a thermostat. A thermostat is controlled by negative feedback so that when the temperature increases above a certain level the heating is switched off, but when the temperature decreases sufficiently the heating is switched on. The process of maintaining stability is called “homeostasis.” The capacity for such control is engineered into some mechanical systems and occurs naturally in all biological and social systems. Threats to the stability of the system will be countered in a powerful attempt to maintain homeostasis. System Feedback One alternative approach is to monitor those aspects of team behavior that are observable (i.e., gather “the voice of the process”). Descriptive Feedback offers a non-judgmental method of monitoring what happens in working groups. It allows team members to notice when team process is in control and meeting or exceeding predetermined expectations or drifting out of control and reducing potential. Descriptive Feedback provides three basic functions: 1. It makes explicit what is happening during team process. 2. It describes those characteristics of team process behavior, relationships, and feelings that may degrade or go out of control and inhibit the potential of the team. 3. It determines what, if anything, needs to be changed in order to facilitate continuous improvement in team process. Feedback over time enables a team to establish performance-based control limits. By using these data, specific characteristics or variables relating to team process can be plotted over time. This will identify patterns that emerge and that can be used to identify and capture the degree of variability of the team. Some patterns are related to “in control” conditions, others to “out of control” conditions, just as the patterns of points on a control chart can be used to establish whether a manufacturing process is in control or out of control. Based on feedback that describes what people notice and how they feel, the team is able to regulate its process and identify opportunities for improvement. Minimizing the Effect of Variation The Parameter Design approach used in quality engineering — see Volume V of this series — is concerned with minimizing the effect of variation factors by making the system robust. This involves identifying control factors — in this case, aspects of the team process that are within the control of the team and that can be used to reduce the impact of variation factors without eliminating or controlling the variation factors themselves. An example of a “control factor” functioning in this way is the

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use of Warm-Up and its consideration of “place” (layout, heating, lighting, ventilation) so that best use is made of the facility provided and distractions are minimized, even though the place itself and many of its features cannot be changed. The key to a successful team lies not only in identifying those parameters that are critical for the efficient transformation of inputs to the team process into outputs but also in doing this with minimal loss of energy in error states and maximum robustness to variation factors in the environment. Different types of teams with different outputs required of them would have different parameters established for their most efficient performance. Many of the structures, processes and skills that could be used as control factors in a team process have been identified in Volume I, Part II of this series. Through this process of observation, it is possible to establish control limits in a wider area of team performance. A number of the factors that have an impact on team performance can be observed and regulated through feedback, and “tolerance” for them can be established depending upon the makeup and objective of the team. These factors include warming up and down, place, task, maintenance, process management, team roles, agenda management, communication skills, speaking guidelines, meeting management, exploratory thinking guidelines, experimental thinking guidelines, change management, action planning, and team parameters. The traditional approach to engineering waits until the end of the design process to address the optimization of a system’s performance — in other words, after parameter values are selected and tolerances determined, often at the extremes of conditions and often without considering interactions among different components or subsystems. When the components and subsystems are integrated, and if performance does not meet the target value or the customer’s requirements, parameter values are altered. Consequently, though the system may be adjusted to operate within tolerance, this process does not guarantee that the system is producing its ideal performance. Similarly, traditional approaches to building teams have selected team members according to a number of factors: predetermined skills and knowledge, established roles for team members, and implemented structured norms. They also have waited until the end of the process of team design in order to optimize performance. If the team does not perform within the accepted values of these parameters, then it is adjusted: team members are changed, roles are redefined, norms are more strictly enforced. This, however, is against performance criteria that do not necessarily optimize the team’s performance nor add to the motivation or job satisfaction of the team members. The shift suggested in Parameter Design in engineering (and that may be applied to teams as well) is to move from establishing parameter values to identifying those parameters that are most important for the function of the process and then determine through experimental design the correct values for those parameters. The key is to establish the values that use the energy of the system most efficiently and that are resistant to uncontrollable impact from other factors internal or external to the system itself.

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MONITORING TEAM PERFORMANCE One way of monitoring team performance has already been suggested, namely the use of Descriptive Feedback. Gathering “the voice of the process” enables the team to evaluate its performance and to continuously improve its efficiency and hence its effectiveness, before completing the task. Preliminary work in using process-control charting from Statistical Process Control suggests that there is opportunity for application to group process. This provides a second means of monitoring and continuously improving the team’s performance. Critical “control factors,” identified using the Parameter Design approach, could be measured and monitored in this way. Based upon further refinement, it may be possible to establish control limits, targets, and tolerances for these factors. System Interrelationships A systems model of processes differs from traditional models in many ways, one of which is the notion of circular causality. In the non-systems view, every event has its cause or causes in preceding events and its effects on subsequent events: the scientist seeks the cause or effect. Using the linear method of causality, ultimate causes are sought by tracing back through proximate causes. However, many phenomena do not “fit” the linear model: the relationships between them — and the relationships between the attributes or characteristics of the elements — do not conform to this linear approach to causality. In engineering systems, a direct cause and effect relationship often exists between the component of the system and the transformation of the input into an output. A steering wheel channels the input of the vehicle operator directly into the output of the system. That is, turning the steering wheel to the right or left actually turns the wheels of the vehicle to the right or the left. However, it is equally clear that error states or phenomena are nowhere near as simple or linear in the causal relationship. Feedback loops and circular causality create very complex interactions. Similarly, the choice of lubricants may not affect the performance of the system until months or years later, when early deterioration of a transmission would result in difficulty shifting gears. Similarly, in teams, some cause and effect relationships are clearly related in time and others are not. Interventions by a timekeeper will affect the ability of the team to stick to its agenda. But other factors have more circular relationships. In a global problem-solving team, changing seating arrangements from the long-tabled boardroom style to a circular arrangement will result in more universal eye contact among team members, which may increase the team’s communication. This leads to enhanced exchange of information, which may lead to a clearer identification of the problem which will, in turn, lead to a more targeted search for relevant data, which will finally lead to a root-cause identification for the problem. Changing the seating arrangement may enhance finding a root cause more quickly than might have been the case in boardroom seating, and the cause and effect chain may be quite intricate.

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SYSTEMS ENGINEERING An emerging basis for unifying and relating the complexities of managerial problems is the system concept and its methodology. This concept has been applied more to the analysis of productive systems than to other fields, but it is clear that the value of the concept in management is pervasive. The word “system” has become so commonplace in the general literature as well as in the field that one often wants to scream, for its common use almost depreciates its value. Yet the word itself is so descriptive of the general interacting nature of the myriad of elements that enter managerial problems that we can no longer talk of complex problems without using the term “systems.” Indeed, we must learn to distinguish the general use of the term from its specific use as a mode of structuring and analyzing problems. One of the great values of the system concept is that it helps us to take a very complex situation and lend order and structure to it by using statistics, probability, and mathematical modeling. A major contribution of the concept is the reduction of complexity in managerial problems to a block diagram showing the relationship and interacting effects of the various elements that affect the problem at hand. At its present state of development and application, the systems concept is most useful in helping us gain insight into problems. At a second and very powerful level of contribution, however, systems analysis is gaining prominence as a basis for generating solutions to problems and evaluating their effects, and for designing alternate systems.

“SYSTEMS“ DEFINED We have been using the term systems without defining it. Though nearly everyone may have a general understanding of the term, it may be useful to be more precise. Webster defines a system as a regularly interacting or interdependent group of items forming a unified whole. Thus a system may have many components and objects, but they are united in the pursuit of some common goal. They are in some sense unified, organized, or coordinated. The components of a system contribute to the production of a set of outputs from given inputs that may or may not be optimal or best with respect to some appropriate measure of effectiveness. Systems are often complex, although the definition does not specify that they need to be. It is probably correct to say that some of the most interesting systems for study are complex and that a change in one variable within the system will affect many other variables of the system. Thus in productive systems, a change in production rate may affect inventories, hours worked per week, overtime hours, facility layout, and so on. Understanding and predicting these complex interactions among variables is one of our main objectives in this section. One of the elusive aspects of the systems concept is in the definition of a specific system. The fact that we can define the system that we wish to consider and draw boundaries around it is important. We can then look inside the defined system to see what happens, but it is just as important to see how the system is affected by its environment.

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Thus, invariably, every system can be thought of as a part of an even larger system. One of the dangers of defining systems that are too narrow in scope is that we may fail to see broader implications. On the other hand, a broad definition runs the risk of leaving out important details involved in the functioning of the system. Obviously, there is a large element of “art” in the application of systems concepts. Systems can be open or closed. An open system is one characterized by outputs that respond to inputs but where the outputs are isolated from and have no influence on the inputs. An open system is not aware of its own performance. In an open system, past performance does not control future performance. A closed system (sometimes called a feedback system), on the other hand, is influenced by its own behavior. A feedback system has a closed loop structure that brings results from past action of the system back to control future action. There are two types of feedback systems: the negative feedback, which seeks a goal and responds as a consequence of failure to achieve the goal, and the positive feedback, which generates growth processes wherein action builds a result that generates still greater action. Unfortunately most of the feedback systems in managerial problems are of the negative feedback type where the objective is to control a process.

IMPLICATIONS

OF THE

SYSTEMS CONCEPT

FOR THE

MANAGER

Managers who put the systems concept to work are rewarded initially by the development of a deeper understanding of the systems that they manage. By developing the structure of the interacting effects of system components and the various feedback control loops in the system, managers can see better which “handles” to twist in order to keep themselves in control. Indeed, with a knowledge of the system structure, a manager can see how it might be possible to restructure the system in order to create the most effective feedback control mechanisms. With the availability of large-scale system models (simulation, statistical, reliability, and mathematical models) a manager is better able to assess the effects of changes in one division component on another and on the organization as a whole. Furthermore, the managers of any of the productive operations are better able to see how their units fit into the whole and to understand the kinds of trade-offs that are often made by higher level management and that sometimes seemingly affect one unit adversely. Perhaps one of the most important contributions of systems thinking is in the concept of suboptimization. Suboptimization often occurs when one views a problem narrowly. For example, one can construct mathematical formulas to determine the minimum cost (optimum) quantity of products or parts to manufacture at one time, which results in a supposedly optimum inventory level. If one broadens the definition of the system under study, however, and includes not just the inventory and reorder subsystem but the production and warehousing subsystems as well, it may turn out that the inventory-connected costs are a measure of only part of the problem. If the product exhibits seasonal sales, the costs of changing production levels may be significant enough to warrant carrying extra inventories to smooth production and employment. In such a situation, the minimum cost inventory model would be a suboptimal policy.

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Organizational suboptimization often occurs when the production and distribution functions of an enterprise are operated as essentially two different businesses. The factory manager will be faced with minimizing production cost while the sales/distribution manager will be faced mainly with an inventory management, shipping, and customer service problem. Each suborganization attempting to optimize separately will likely result in a combined cost somewhat larger than if the attempt were made to optimize the combined system. The reasons are fairly obvious, since in minimizing the costs of inventories, the sales function transmits directly to the factory most of the effects of sales fluctuations instead of absorbing these fluctuations through buffer inventories. Suboptimization is the result. By coordinating the efforts of the production and distribution managers, however, it may be possible to achieve some balance between inventory costs and the costs of production fluctuation. Another way to view suboptimization is through the “hidden factory” — the terminology of six sigma. If we take for example the issue of safety, let us examine what is really at stake. No one will deny that the bottom line of all safety programs is injury prevention, more often called “loss control.” To appreciate the concept of “loss control,” however, we must look at the direct and indirect costs (often called the hidden costs) associated with an on-the-job injury. The direct costs are: 1. Medical 2. Compensation The indirect costs are: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.

Time lost from work by injured Loss in earning power Economic loss to injured’s family Lost time by fellow workers Loss of efficiency due to breakup of crew Lost time by supervision Cost of breaking in new worker Damage to tools and equipment Time damaged equipment is out of service Spoiled work Loss of production Spoilage — fire, water, chemical, explosives, and so on Failure to fill orders Overhead cost (while work was disrupted) Miscellaneous (There are at least 100 other items of cost that appear one or more times with every incident in which a worker is injured.)

The point here is that with most injuries the focus becomes the direct cost, thereby dismissing the indirect costs. It has been estimated time and again that the cost relationship of direct to indirect cost is one to three, yet we continue to ignore the real problems of injury. An appropriate system design for injury prevention would

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minimize if not eliminate the hidden costs. Generally speaking, the system should include (a) engineering, (b) education, and (c) enforcement considerations. Some specific considerations should be: 1. 2. 3. 4.

Workers will not be injured or killed Property and materials will not be destroyed Production will flow more smoothly You will have more time for the other management duties of your job

DEFINING SYSTEMS ENGINEERING A simple definition of systems engineering is: A customer/requirements–driven engineering and management process which transforms the voice of the customer(s) into a feasible and verified product/process of appropriate configuration, capability, and cost/price. A system is always greater than the sum of its parts and is no better than the weakest link. The derivative of that, of course, is that optimizing a part does not optimize the whole. This was brought out by Mayne et al. (2001), when they reported that 37% of all the automotive warranty for model year 2000 was in interfacing of parts rather than individual components. The message of Mayne and coworkers and most of us in the quality field has been and continues to be: interactions determine the performance of the system. We cannot, no matter how hard we try, fully understand the whole by breaking down and analyzing parts — yet design is historically done precisely that way. Systems engineering builds on the fact that the whole is the most important entity and that integration to meet cost, schedule, and technical performance is dependent on both technical and management intervention. Ultimately, systems engineering is a team-based activity. This is very important because as we move into the future we see that: 1. Quality is becoming more customer dependent rather than definitional from the provider’s point of view. In other words, we must specify what the product or service must do and how well it must do it, then verify the design to those requirements. 2. Products/services are becoming more sophisticated (complex). 3. Traditionally, product development has been very serial with designs thrown over the imaginary wall to manufacturing — something that today is not working very well. This has resulted in late changes and ultimately higher costs. Systems engineering is based on the notion that design may be on a parallel development process and with a strong consideration for its total life cycle — manufacture, delivery, maintenance, decommission, and recycling. For systems engineering to be effective in any organization, that organization must be committed to integration of several items including timing of development and specific delivery(ies) at specific milestones. A generic approach to facilitate this is the following model, involving the steps of pre-feasibility analysis, requirement analysis, design synthesis, and verification.

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PRE-FEASIBILITY ANALYSIS Before the actual analysis takes place there is a preliminary trade-off analysis as to what the customer needs and wants and what the organization is willing to provide. This is done under the rubric of preliminary feasibility. When the feasibility is complete, then the actual requirement analysis takes place.

REQUIREMENT ANALYSIS The requirement analysis involves the following steps: 1. Collect the requirements — the customer’s needs, wants, and expectations are collected at every level. 2. Organize requirements — group the information in such a way that requirements are easy to address. Determine if the requirements are complete. 3. Translate into more precise terms — cascade the definitions to precise terms, honing their definition to the best possible correlation of real world usage. 4. Develop verification requirements — preliminary verification tests are discussed and proposed here to make sure that they are in fact doable. At the end of the requirement analysis the results are moved to the second stage of the system engineering model, which is design synthesis. However, before the synthesis actually takes place, another feasibility analysis is completed to find out whether the organization is capable of designing the requirements of the customer. This feasibility analysis takes into consideration the organization’s knowledge from previous or similar designs and incorporates it into the new. The idea of this feasibility analysis is to make sure the designers optimize reusability and carry over parts and/or complete designs.

DESIGN SYNTHESIS Design synthesis involves the following steps: 1. Generate alternatives — the more alternatives the better. The alternatives are generated with functionality in mind from the customer’s perspective as reflected in the system specifications. Remember that the ultimate design is indeed a trade-off design. 2. Evaluate alternatives — the generated alternatives are evaluated with appropriate benchmarking data and integrated into the design based on the customer’s requirements. 3. Generate sub element requirements — big chunks or sub elements are chosen and requirements cascaded to each sub element. As the cascading process continues, verification requirements are also developed to test the overall system integrity as more and more sub elements are integrated into the total system.

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At the end of the design synthesis a very important analysis takes place. This analysis tests the integrity of the design against the customer’s requirements. If it is found that the requirements are not addressed (design gap), a redesign or a review takes place and a fix is issued. If, on the other hand, everything is as planned, the process moves to the third link of the model — verification.

VERIFICATION The final stage is verification. It involves the following: 1. Verify that requirements are complete — a review of all requirements from both design and the customer takes place with appropriate tests and correlated to real world usage. 2. Verify that design meets customer’s requirements — CAE tools, labs, rigs, simulations, and key life testing are some of the verification methodologies used at this stage. The intent here is to verify that the selected system and cascaded requirements will meet the customer’s requirements and provide a balanced optimum design from the customer’s perspective. At the end of this stage, if problems are found they (the problems) revert back to the design; if there are no problems, the design goes to manufacturing, with a design ready to fulfill the customer’s expectations. This final stage in essence tests the integrity of the design against the actual hardware. In other words, the questions often heard in verification are: Does the design work? Can you prove it? The beauty of this model is that it is an iterative model, meaning that the process — no matter where you are in the model — iterates until a balanced optimum design is achieved. This is because the goal is to design a customer-friendly design with compatibility, carryover, reusability, and low complexity requirements compared to other, similar designs. Iterations happen because of human oversights, poorly defined requirements, or an increase in knowledge. Another special characteristic of systems engineering is the notion of traceability. Traceability is reverse cascading and is used throughout the design process to make sure that the voices of the customer, regulator, and corporate or lower-level design are heard and accounted for in the overall design. With traceability, extra caution is given to the trade-off analysis. This is because by definition trade-off analysis accounts for designs with certain priority levels among the needs and wants of the customer. In a trade-off analysis, we choose among stated design alternatives. However, a trade-off analysis is also an iterative process, and usually none of the alternatives is perfect [R(t) = 1 – F(t)]. This is important to remember because all trade-off analyses assess risk, both external and internal, of the given alternatives so as to make robust designs. A final word about verification and systems engineering: As we already mentioned, the intent of verification is to make sure that the hardware meets the requirements of the design. The process for conducting this verification is done — generally — in five steps, which are:

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1. Plan — Review all requirements and make a preliminary assessment as to their impact. At the end of this evaluation, take ownership of important requirements and begin the assessment of specific tests and methods. It is not unusual at this stage to review the plan again and perhaps combine, consolidate, or even adjust the plan completely. In this stage we select attribute data, as well, monitor the “unselected” requirement, schedule preliminary tests, and approve the testing schedule. As you begin to zero in on specific targets, you may want to take into consideration features of the proposed design and benchmarking data so that your targets become of value to the customer. If this plan is rich in information, it is possible to begin predicting and formulating prototype(s). 2. Execute — In this stage, the engineer in charge will determine which test(s) to run, when to run them, what the data should look like, and what to expect. Proper test execution is of importance here. 3. Analyze/revise — Analyze the test results, and see if the design has changed in any way. Determine whether to redo the test if the design changed during the test for any reason. At this stage you expect no design changes, only testing revisions and modifications. 4. Sign-off — This is the most common ending for a verification process. In this stage final approvals are given, usually several months before production begins. 5. Archive — This is a step that most engineers do not do, yet it is a very important step in the process. The idea of archiving or documenting is to make sure that key events are appropriately documented for future use. You may want to document unusual tests, time frames of specific tests, or any specific requirements that you had the intention of verifying but could not verify using the planned method. In essence, this phase of verification consists of lessons learned that need to be carried forward to the next design. The focus of this process is to make sure that the requirements are driving the process and not the tests regardless of how sophisticated they are. To be sure, tests are an integral part of verification, but they are the means not the end. The intent of the tests is to verify each requirement, and there is no wrong way as long as the testing method is linked to real world usage. The reason for doing all this is to: 1. 2. 3. 4.

Reduce workload in design verification Improve prototype and testing efficiency by avoiding duplication Improve testing quality resulting in higher sign-off confidence Improve communication and stronger relationships between customer and suppliers

ADVANCED QUALITY PLANNING Before we address the “why” of planning, we assume that things do go wrong. But why do they go wrong? Obviously, many specific answers address this question. Often the answer falls into one of these four categories:

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1. We never have enough time, so things are omitted. 2. We have done this, this way, in order to minimize the effort. 3. We assume that we know what has been requested, so we do not listen carefully. 4. We assume that because we finish a project, improvement will indeed follow, so we bypass the improvement steps. In essence then, the customer appears satisfied, but a product, service, or process is not improved at all. This is precisely why it is imperative for organizations to look at quality planning as a totally integrated activity that involves the entire organization. The organization must expect changes in its operations by employing cross-functional and multidisciplinary teams to exceed customer desires — not just meet requirements. A quality plan includes, but is not limited to: • • • • • • •

A team to manage the plan Timing to monitor progress Procedures to define operating policies Standards to clarify requirements Controls to stay on course Data and feedback to verify and to provide direction An action plan to initiate change

Advanced quality planning (AQP), then, is a methodology that yields a quality plan for the creation of a process, product, or service consistent with customer requirements. It allows for maximum quality in the workplace by planning and documenting the process of improvement. AQP is the essential discipline that offers both the customer and the supplier a systematic approach to quality planning, to defect prevention, and to continual improvement. Some specific uses are: 1. In the auto industry, demand is so high that Chrysler, Ford, and General Motors have developed a standardized approach to AQP. That standardized approach is a requirement for the QS-9000 and/or ISO/TS19469 certification. In addition, each company has its own way of measuring success in the implementation and reporting phase of AQP tasks. 2. Auto suppliers are expected to demonstrate the ability to participate in early design activities from concept through prototype and on to production. 3. Quality planning is initiated as early as possible, well before print release. 4. Planning for quality is needed particularly when a company’s management establishes a policy of “prevention” as opposed to “detection.” 5. When you use AQP, you provide for the organization and resources needed to accomplish the quality improvement task. 6. Early planning prevents waste (scrap, rework, and repair), identifies required engineering changes, improves timing for new product introduction, and lowers costs.

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7. AQP is used to facilitate communication with all individuals involved in a program and to ensure that all required steps are completed on time at acceptable cost and quality levels. 8. AQP is used to provide a structured tool for management that enforces the inclusion of quality principles in program planning.

WHEN DO WE USE AQP? We use AQP when we need to meet or exceed expectations in the following situations: 1. 2. 3. 4. 5.

During the development of new processes and products Prior to changes in processes and products When reacting to processes or products with reported quality concerns Before tooling is transferred to new producers or new plants Prior to process or product changes affecting product safety or compliance to regulations

The supplier — as in the case of certification programs such as ISO 9000, QS9000, ISO/TS19469, and so on — is to maintain evidence of the use of defect prevention techniques prior to production launch. The defect prevention methods used are to be implemented as soon as possible in the new product development cycle. It follows then, that the basic requirements for appropriate and complete AQP are: 1. Team approach 2. Systematic development of products/services and processes 3. Reduction in variation (this must be done, even before the customer requests improvement of any kind) 4. Development of a control plan As AQP is continuously used in a given organization, the obvious need for its implementation becomes stronger and stronger. That need may be demonstrated through: 1. Minimizing the present level of problems and errors 2. Yielding a methodology that integrates customer and supplier development activities as well as concerns 3. Exceeding present reliability/durability levels to surpass the competition’s and customer’s expectations 4. Reinforcing the integration of quality tools with the latest management techniques for total improvement 5. Exceeding the limits set for cycle time and delivery time 6. Developing new and improving existing methods of communicating the results of quality processes for a positive impact throughout the organization

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WHAT IS

THE

DIFFERENCE

BETWEEN

AQP

AND

43

APQP?

AQP is the generic methodology for all quality planning activities in all industries. APQP is AQP; however, it emphasizes the product orientation of quality. APQP is used specifically in the automotive industry. In this book, both terms are used interchangeably.

HOW DO WE MAKE AQP WORK? There are no guarantees for making AQP work. However, three basic characteristics are essential and must be adhered to for AQP to work. They are: 1. Activities must be measured based on who, what, where, and when. 2. Activities must be tracked based on shared information (how and why), as well as work schedules and objectives. 3. Activities must be focused on the goal of quality-cost-delivery, using information and consensus to improve quality. As long as our focus is on the triad of quality-cost-delivery, AQP can produce positive results. After all, we all need to reduce cost while we increase quality and reduce lead time. That is the focus of an AQP program, and the more we understand it, the more likely we are to have a workable plan.

ARE THERE PITFALLS

IN

PLANNING?

Just like everything else, planning has pitfalls. However, if one considers the alternatives, there is no doubt that planning will win out by far. To be sure, perhaps one of the greatest pitfalls in planning is the lack of support by management and a hostile climate for its practice. So, the question is not really whether any pitfalls exist, but why such support is quite often withheld and why such climates arise in organizations that claim to be “quality oriented.” Some specific pitfalls in any planning environment may have to do with commitment, time allocation, objective interpretations, tendency toward conservatism, and an obsession with control. All these elements breed a climate of conformity and inflexibility that favors incremental changes for the short term but ignores the potential of large changes in the long run. Of these, the most misunderstood element is commitment. The assumption is that with the support of management, all will be well. This assumption is based in the axiom of F. Taylor at the turn of the 20th century, which is “there is one best way.” Planning is assumed to generate the one best way not only to formulate, but to implement, a particular idea, product, and so on. Sometimes, this notion is not correct. In today’s “agile world,” we must be prepared to evaluate several alternatives of equal value. (See the section on system engineering). As a consequence, the issue is not simply whether management is committed to planning. It is also, as Mintzberg (1994) has observed, (1) whether planning is committed to management, (2) whether commitment to planning engenders commitment

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to the process of strategy making, to the strategies that result from that process, and ultimately to the taking of effective actions by the organization, and (3) whether the very nature of planning actually fosters managerial commitment to itself. Another pitfall, of equal importance, is the cultural attitude of “fighting fires.” In most organizations, we reward problem solvers rather than planners. As a consequence, in most organizations the emphasis is on low-risk “fire fighting,” when in fact it should be on planning a course of action that will be realistic, productive, and effective. Planning may be tedious in the early stages of conceptual design, but it is certainly less expensive and much more effective than corrective action in the implementation stage of any product or service development.

DO WE REALLY NEED ANOTHER QUALITATIVE TOOL TO GAUGE QUALITY? While quantitative methods are excellent ways to address the “who,” “what,” “when,” and “where,” qualitative study focuses on the “why.” It is in this “why” that the focus of advanced quality planning contributes the most results, especially in the exploratory feasibility phase of our projects. So, the answer to the question is a categorical “yes” because the aim of qualitative study is to understand rather than to measure. It is used to increase knowledge, clarify issues, define problems, formulate hypotheses, and generate ideas. Using qualitative methodology in advanced quality planning endeavors will indeed lead to a more holistic, empathetic customer portrait than can be achieved through quantitative study, which in turn can lead to enlightened engineering and production decisions as well as advertising campaigns.

HOW DO WE USE

THE

QUALITATIVE METHODOLOGY

IN AN

AQP SETTING?

Since this volume focuses on the applicability of tools rather than on the details of the tools, the methodology is summarized in seven steps: 1. Begin with the end in mind. This may be obvious; however, it is how most goals are achieved. This is the stage where the experimenter determines how the study results will be implemented. What courses of action can the customer take and how will they be influenced by the study results? Clearly understanding the goal defines the study problem and report structure. To ensure implementation, determine what the report should look like and what it should contain. 2. Determine what is important. All resources are limited and therefore we cannot do everything. However, we can do the most important things. We must learn to use the Pareto principle (the vital few as opposed to the trivial many). To identify what is important, we have many methods, including asking about advantages and disadvantages, benefits desired, likes and dislikes, importance ratings, preference regression, key driver analysis, conjoint and discrete choice analysis, force field analysis, value analysis, and many others. The focus of these approaches is to improve performance in areas in which a competitor is ahead or in areas where

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3.

4.

5.

6.

7.

45

your organization is determined to hold the lead in a particular product or service. Use segmentation strategies. Not everyone wants the same thing. Learn to segment markets for specific products or services that deliver value to your customer. By segmenting based on wants, engineering and product development can develop action oriented recommendations for specific markets and therefore contribute to customer satisfaction. Use action standards. To be successful, standards must be used, but with diagnostics. Standards must be defined at the outset. They are always considered as the minimum requirements. Then when the results come in, there will be an identified action to be taken, even if it is to do nothing. List the possible results and the corresponding actions that could be taken for each. Diagnostics, on the other hand, provide the “what if” questions that one considers in pursuing the standards. Usually, they provide alternatives through a set of questions specific to the standard. If you cannot list actions, you have not designed an actionable study. Better design it again. Develop optimals. Everyone wants to be the best. The problem with this statement is that there is only room for one best. All other choices are second best. When an organization focuses on being the best in everything, that organization is asking for failure. No one can be the best in everything and sustain it. What we can do is focus on the optimal combination of choices. By doing so, we usually have a usable recommendation based on a course of action that is reasonable and within the constraints of the organization. Give grasp-at-a-glance results. The focus of any study is to turn people into numbers (wants into requirements), numbers into a story (requirements into specifications), and that story into action (specifications into products or services). But the story must be easy to understand. The results must be clear and well-organized so that they and their implications can be grasped at a glance. Recommend clearly. Once you have a basis for an action, recommend that action clearly. You do not want a doctor to order tests and then hand you the laboratory report. You want to be told what is wrong and how to fix it. From an advanced quality planning perspective, we want the same. That is, we want to know where the bottlenecks are, what kind of problems we will encounter, and how we will overcome them for a successful delivery.

APQP INITIATIVE

AND

RELATIONSHIP

TO

DFSS

The APQP initiative in any organization is important in that it demonstrates our continuing effort to achieve the goal of becoming a quality leader in the given industry. Inherent in the structure of APQP are the following underlying value-added goals:

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1. Reinforces the company’s focus on continuous improvement in quality, cost, and delivery 2. Provides the ability to look at an entirely new program as a single unit • Preparing for every step in the creation • Identifying where the greatest amount of effort must be centered • Creating a new product with efficiency and quality 3. Provides a better method for balancing the targets for quality, cost, and timing 4. Allows for deployment of the targets using detailed practical deliverables with specific timing schedule requirements 5. Provides a tool for program management to follow up all program planning processes. The APQP initiative explicitly focuses on basic engineering activities to avoid concerns rather than focusing on the results in the product throughout all phases. Based on the fact that the deliverables are clearly defined between departments (supplier/customer relationships), program concerns and issues can be solved efficiently. The APQP initiative also is forceful in viewing the review process at the end of the cycle as unacceptable. Rather, the review must be done at the end of each planning step. This provides a critical step-by-step review of how the organizations are following best possible practices. Also, the APQP initiative has a serious impact on stabilizing the program timing and content. Stabilization results in cost improvement opportunities including reduction of special sample test trials. Understanding the program requirements for each APQP element from the beginning provides the following advantages: • • • • •

Clarifies the program content Controls the sourcing decision dates Identifies customer-related significant/critical characteristics Evaluates and avoids risks to quality, cost, and timing Clarifies for all organizations product specifications using a common control plan concept

Application of APQP in the DFSS process provides a company with the opportunity to achieve the following benefits: 1. It provides a value-added tool allowing program management to track and follow up on all the program planning processes — focusing on engineering method and quality results. 2. It provides a critical review of how each organization is following best possible practices by focusing on each planning step. 3. It identifies the complete program content upon program initiation, viewing all elements of the process as a whole (AIAG 1995; Stamatis 1998).

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Once program content has been clarified, the following information can be discerned: 1. 2. 3. 4.

Sourcing decision dates are identified. Customer-related significant/critical characteristics are specified. Quality, cost, and timing risks are evaluated and avoided. Product specifications are established for all organizations using a common control plan concept.

Using the APQP process to stabilize program timing and content, the opportunities for cost improvement are dramatically increased. When we are aware of the timing and concerns that may occur during the course of a program, it provides us the opportunity to reduce costs in the following areas: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Product changes during the program development phase Engineering tests Special samples Number of verification units to be built (prototypes, first preproduction units, and so on) Number of concerns identified and reduced Fixture and tooling modification costs Fixture and tooling trials Number of meetings for concern resolution Overtime Program development time and deliverables (an essential aspect of both APQP and DFSS)

For a very detailed discussion of APQP see Stamatis (1998).

REFERENCES Automotive Industry Action Group (AIAG), Advanced Product Quality Planning and Control Plan. Chrysler Co., Ford Motor Co., and General Motors. Distributed by AIAG, Southfield, MI, 1995. Mayne, E. et al., Quality Crunch, Ward’s AUTOWORLD, July 2001, pp. 14–18. Mintzberg, H., The Rise and Fall of Strategic Planning, New York Free Press, New York, 1994. Stamatis, D.H., Advanced Quality Planning. A Commonsense Guide to AQP and APQP, Quality Resources, New York, 1998.

SELECTED BIBLIOGRAPHY Bossert, J., Considerations for Global Supplier Quality, Quality Progress, Jan. 1998, pp. 29–34. Brown, J.O., A Practical Approach to Service: Supplier Certification, Quality Progress, Jan. 1998, pp. 35–40.

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Forcinio, H., Supply Chain Visibility: Is It Really Possible? Managing Automation, July 2001, pp. 24–28 Gurwitz, P.M., Six Questions to Ask Your Supplier About Multivariate Analysis, Quirk’s Marketing Review, Feb. 1991, pp 8–9, 23. Mehta, P.V. and Scheffler, J.M., Getting Suppliers in on the Quality Act, Quality Progress, Jan. 1998, pp. 21–28. Schoenfeldt, T., Building Effective Supplier Relationships, Automotive Excellence, Winter 1999, pp.17–25.

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2

Customer Understanding

In Volume I of this series, we made a point to discuss the difference between “customer satisfaction” and “loyalty.” We said that they are not the same and that most organizations are interested in loyalty. We are going to pursue this discussion in this chapter because, as we have been saying all along, understanding the difference between customer service and customer satisfaction can provide marketers with the competitive advantage necessary to retain existing customers and attract new ones. “Understanding” what satisfaction is and what the customer is looking for can provide the engineer with a competitive advantage to design a product and or service second to none. At first glance, service and satisfaction may appear to mean the same thing, but they do not; service is what the marketer provides and what the customer gets, and satisfaction is the customer’s evaluation of the level of service received based on preconceived assumptions and the customer’s own definition of “functionalities.” The satisfaction level is determined by comparing expected service to delivered service. Four outcomes are possible: 1. 2. 3. 4.

Delight — positive disconfirmation (a pleasant surprise) Dissatisfaction — negative disconfirmation (an unpleasant surprise) Satisfaction — positive confirmation (expected level of service) Negative confirmation, which suggests that you are neither managing expectations properly nor delivering good service

In managing service delivery, relying solely on the objective aspects of service is a mistake. Customers base future behavior on their evaluation of the experience they actually had, which is in effect their degree of satisfaction or dissatisfaction. In addition to determining that satisfaction degree, marketers should seek to learn the reasons underlying customers’ feelings (the insight) in order to tell the engineers what, how and when to make changes and maintain high satisfaction levels when they are achieved. In researching these areas, marketers should note that the why is not the what; nor is it the how. That is, what happened and how it made customers feel does not tell us why they felt as they did. And not knowing that, managing not only the service that customers experience but also their expectations becomes difficult, if not impossible. At times, service providers and customers tend to think differently. Consider this dealership example: After conducting 10 focus groups for an automotive company in a medium-size Midwestern city, the researchers discussed the findings in a review meeting with the head of marketing for the company. The researchers noted that, after having spoken with more than 100 recent customers, they had learned

49

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that the vast majority were frustrated and unhappy about having to wait more than 15 minutes before getting attended to. The marketing executive interrupted, saying, “Those customers should consider themselves lucky; if they were in the dealerships of one of our competitors, they would have to wait 20 to 30 minutes before they were seen by the service manager.” This example includes all the information needed to explain the difference between customer service and customer satisfaction. The customers in this example defined their personal expectations about the service — their waiting time experience — and clearly, a conflict existed between their service expectation (a short wait before being seen by a service manager) and their service experience (waits of more than 15 minutes). Customers then were dissatisfied with the waiting rooms and the dealerships in general. The marketing manager’s response to customer dissatisfaction was to note that the waits could have been worse: He knew that competitors’ dealerships were worse. He also knew customer waits of more than 30 minutes were not uncommon. In light of these data, he judged the 15- to 20-minute waits in the waiting room acceptable. Herein lies the conflict between service delivery and customer satisfaction. The important concept for this marketing executive — and for all marketers — is that customers define their own satisfaction standards. The customers in this example did not go to the competitors’ dealerships; instead, they came to the marketer’s dealership with a set of their own expectations in a preconceived environment. When the marketer used his service delivery criteria to defend the waiting time, he simply missed the point. Unfortunately, this illustration is typical of how many marketers think about customer satisfaction. They tend to relate customer satisfaction directly to their own service standards and goals rather than to their customers’ expectations, whether or not those expectations are realistic. To assess satisfaction, marketers must look beyond their own assessments, tapping into the customers’ evaluations of their service experience. Consider, for example, a bank that thought it was doing a good job of measuring service satisfaction but really was too focused on service delivery. This bank had developed a policy that time spent in the lobby room should be less than 15 minutes for all customers. A customer came into the office and waited 12 minutes in the reception area for a mortgage application. Then she waited another five minutes for the loan officer to clear all the papers from his desk from the previous customer and an additional three minutes for him to get the file and all the pertinent information for the current application. As this customer was leaving, she was asked to fill out a customer satisfaction questionnaire. Under the category for reception area waiting time, she checked off that she had waited less than 15 minutes. Based on this response, the bank’s marketing director assumed the customer was satisfied, but she was not; the customer had been told that if she came in for the mortgage loan during her lunch hour, she would be taken care of right away. Instead, she waited a total of 20 minutes for her application process to begin. She did not have time to shop for the gift her son needed that night for a birthday party, and her entire schedule was in disarray. She left dissatisfied.

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Understanding the difference between service and satisfaction is the first step in developing a successful customer satisfaction program, and all marketers must share the same understanding. Only customers can define what satisfaction means to them. Here are some practical ways to understand customers’ expectations: • Ask customers to reflect on their experiences with your services and their needs, wants, and expectations. • Talk to customers face to face through focus groups, as well as through questionnaires. A wealth of information can be collected this way. • Talk with your staff about what they hear from customers about their expectations and experience with service delivery. • Review warranty data. Remember the three words that can help you learn from your customers: What, how and why. That is, what service did you experience, how did it make you feel, and why did you feel that way? Continual probing with these three perspectives will deliver the answers you need to better manage service to generate customer satisfaction. As Harry (1997 p. 2.20) has pointed out: • • • • • •

We do not know what we do not know We cannot act on what we do not know We do not know until we search We will not search for what we do not question We do not question what we do not measure Hence, we just do not know

Therefore, part of this understanding is to identify a transfer function. That is, you need a bridge (quantitatively or qualitatively) that will define and explain the dependent variable (the customer’s needs, wishes, and excitements) with the independent variable(s) (the actual requirements that are needed from an engineering perspective to satisfy the dependent variable). The transfer function may be a linear one (the simplest form) or a polynomial one (a very complex form). Typical equations expressing transfer functions may look something like: Y = a + bx Y = f(x1, x2…xn) − sin θ  df a  r  a sin θ  sin cos + + =r θ + θ     dθ g cos θ  cos θ  cos2 θ  g cos2 θ  They can be derived from:

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• • • • • •

Known equations that describe the function Finite element analysis and other analytical models Simulation and modeling Drawing of parts and systems Design of experiments Regression and correlation analysis

In DFSS, the transfer function is used to estimate both mean (sensitivity) and variance (variability) of Ys and ys. When we do not know what the Y is, it is acceptable to use surrogate metrics. However, it must be recognized from the beginning that not all variables should be included in a transfer function. Priorities should be set based on appropriate trade-off analysis. This is because DFSS is meant to emphasize only what is critical, and that means we must understand the mathematical concept of measurement. The focus of understanding customer satisfaction has been captured by Rechtin and Hair (1998), when they wrote that “an insight is worth a thousand market surveys.” It is that insight that DFSS is looking for before the requirements are discussed and ultimately set. This will help us in identifying what is really going on with the customer. Let us look at the function first.

THE CONCEPT OF FUNCTION In any business environment, there may be no more powerful concept than that of function. To understand why this is a potent notion, we need to consider what we mean by “function.” What is “function?” Let us start with a common definition: “The natural, proper, or characteristic action of any thing...” This is the Webster’s New Collegiate Dictionary definition, and it is quite representative of what you will find in most dictionaries. This is actually a powerful and insightful definition. Think about any product or service that you purchase. What is it about the product or service (I will use the term “product” from here on, although every issue that will be discussed will be equally valid for services) that causes you to exchange money, goods, or some other scarce resource for it? Ultimately, it is because you want the “characteristic actions” that the product provides. These “actions” may be simple or complex, utilitarian or capricious, Spartan or gilded — but in each transaction, you enter with a set of unfulfilled wants and needs that you attempt to satisfy. Moreover, if the product you purchase actually manages to fulfill the wants and needs that you perceive, you are more likely to be satisfied with your purchase than when the product fails to satisfy your desires. Within these few short sentences, we have the fundamental principles that underpin three of the most powerful tools in the modern pantheon of quality, productivity, and profitability: Quality Function Deployment, Value Analysis, and Failure Modes & Effects Analysis. To put the concept of function into action, we need to refine our definition. The expanded definition we would like you to consider is “The characteristic actions that a system, part, service, or manufacturing process generates to satisfy customers.”

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In this expanded definition, you cannot only see the concept of function at work, but you may be able to recognize the essential abstraction of a process. In a process, some type of input is transformed into an output. As a simple equation, we might say that Input(s) + Transformation = Output(s) In the case of function, the inputs are the unfulfilled wants and needs that a customer or a prospective customer has. These can be and often are intricate; this is why the discipline of marketing is still more art than science. (We will have more to say about this issue in just a moment.) Nevertheless, there exist multiple sets of unfulfilled wants and needs that are open to the lures and attractions provided by the marketplace. In this very broad model, the transformation is provided by the producer. With one, ten, or hundreds of internal processes (within any discussion of process, there is always the “Russian doll” image: processes within processes within processes), business organizations attempt to determine the unmet wants and needs that customers have. The producer then must design and develop products and delivery processes that will provide tangible and/or intangible media of exchange that will assuage the unmet needs or need sets. Finally, the external processes that involve exchange of the producer’s goods for money or other barter provide the customer with varying degrees of satisfaction. The gratification (or lack of satisfaction) that results can then be viewed as the output of the general process. In business, the inputs are not within the control of producers. As a result, producers need powerful tools to understand, delineate, and plan for ways to meet these needs. This can be thought of as the domain of the Kano model or Quality Function Deployment. The transformational activities, however, are within the control of the producer. These “controlled” activities include planning efforts to deliver “function” at a satisfactory price; the nuances and subtleties of this activity can be strongly influenced or even controlled by the discipline of Value Analysis. In addition, fulfillment of marketplace “need sets” also implies that this fulfillment will occur without unpleasant surprises. Unwanted, incomplete, or otherwise unacceptable attempts to produce “function” often result in failure. This implies that producers have a need to systematically analyze and plan for a reduction in the propensity to deliver unpleasant surprises. This planning activity can be greatly aided by the application of Failure Modes and Effects Analysis techniques. To see how these ideas mesh, we need to consider how “function” can be comprehensively mapped. This will require several steps. To apply what will be discussed in the rest of this section, we need to emphasize the importance of choosing the proper scale for any analysis. The probability is that you will choose too broad a view or too much detail; we will try to provide guidance on this issue during our discussion of methods.

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UNDERSTANDING CUSTOMER WANTS

Six Sigma and Beyond AND

NEEDS

The nature of customer wants and needs is complex, deceptive, and difficult to discern. Nevertheless, the prediction of future wants and needs in the marketplace is perhaps the most important precursor to financial success that exists. Knowing and doing something that is profitable are two very separate (but not completely independent) aspects of this challenge. The first task that must be undertaken is to list the customers that we are interested in. Virtually no business is universal in terms of target market. Moreover, in today’s highly differentiated world, it is likely an act of folly to suggest that any product would have universal appeal. (Even an idealized product such as a capsule that, when ingested, yields immortality would have its detractors and would be rejected by some elements of humanity.) So, we need to start by cataloging the customers that we might wish to serve. In this effort, however, we need to recognize that there is a chain of customers. This is often seen in discussions of the “value chain,” a concept explored in detail by Porter (1985). For example, Porter discusses the concept of “channel value,” wherein channels of distribution “perform additional activities that affect the buyer, as well as influence the firm’s own activities.” This means that there are several dimensions on which we will discover important customers. First, there are market segments and niches. These can be geographic, demographic, or even psychographic in nature. Second, there are many intermediary customers, who have an important influence on ultimate purchases in the marketplace. Finally, there are what might be called “overarching” customers — persons or entities that must be satisfied even in the absence of any purchasing power — to enable or permit the sale of goods and services. This is readily visible in the auto industry. From the standpoint of a major parts manufacturer, say United Technologies, Johnson Controls, Dana Corporation, or Federal-Mogul, there are legions of important customers. In the market segment category, there are the vehicle manufacturers, including GM, Ford, Toyota, and all of the others. Contained within this category of customers are many sub-customers, including purchasing agents, engineers, and quality system specialists. As far as intermediary customers are concerned, we can consider perhaps a dozen or more important players. We need to consider the transportation firms that carry the parts from the parts plant to the assembly plant. We also need to think about the people and the equipment within the assembly plant that facilitate the assembly of the part into a vehicle. (If anyone doubts this is important, they have never tried to sell a part to an assembly plant where the assembly workers truly dislike some aspect of the part.) The auto dealer is yet another step in this array of hurdles, and mechanics and service technicians constitute still one more customer who must, in some way, be reasonably satisfied if commercial success is to spring forth. In addition, the auto industry has a web of regulatory and statutory requirements that govern its operation. These include safety regulations, emission standards, fleet mileage laws, and the general requirements of contract law. Behind these government requirements are still more governmental prerequisites, including occupational safety law, environmental law as applied to manufacturing, and labor law. This means that

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the governmental agencies and political constituencies that administer these laws can be seen as the “overarching” customers described previously. Ultimately, vehicle purchasers themselves are the critical endpoint in this chain of evaluation. And within this category of customers are the many segments and niches that car makers discuss, such as entry level, luxury, sport utility, and the many other differentiation patterns that auto marketers employ. Only when a product passes through the entire sequence will it have a reasonable chance of successfully and repeatedly generating revenue for the producer. This provides a critical insight about function. Function is only meaningful through some transactional event involving one or more customers. Only customers can judge whether a product delivers desired or unanticipated-yet-delightful function. In many cases (in fact, most), firms simply do not consider all of these customers. As a result, they are often surprised when problems arise. Moreover, they suffer financial impediments as a result — even though they may simply budget some degree of failure expectation into overhead calculations. A rational assessment of this situation means that the first requirement for understanding function is a comprehensive listing of customers. Frankly, this is very hard work, and it requires time, dedication, and effort. Regardless, understanding the customers that you wish to serve is an essential prerequisite to comprehension of function.

CREATING

A

FUNCTION DIAGRAM

If you want to understand function, the first requirement is the use of a special language. Function must be described using an active verb and a measurable noun. Fowler (1990) calls this linguistic construction a “functive” — a function described in direct terms that are, to the greatest degree possible, unambiguous. In a functive, the verb should be active and direct. How can you tell if the verb meets this test? Can you subject the action described by the verb to reasonable verification? One of the difficulties with this approach is the widespread affinity for ambiguity, the evil spawn of corporate life. To reduce ambiguity, you must avoid “nerd” verbs such as provide, be, supply, facilitate, and allow. Since most people pepper their business speech with these verbs, how can you avoid using them? If you cannot avoid “nerd verbs,” then you might try to convert the noun to a verb. Instead of “allow adjustment” think about what it is that you are adjusting. For example, you could easily restate this “nerd verb” functive with “adjust clearance.” Whenever a “nerd verb” comes up, try converting the noun that goes with the nerd verb to a verb, and then select the appropriate measurable noun. Most of the time, this will reduce the ambiguity. The measurable noun also must be reasonably precise. In particular, it should be relatively unchanging in usage and should rarely be the name of a part, operation, or activity used to generate the product or service under consideration. The test for a measurable noun is very simple: can you measure the noun? Bear in mind, however, that the measurement may be as simple as counting — or it can be a detailed statement of a technical or engineering expectation of the degree to which a function can be fulfilled. Ultimately, the combination of an active verb and measurable noun will give rise to an extent — the degree to which the functive is executed.

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For example, let us consider a simple mechanical pencil. The mechanism of the pencil must feed lead at a controlled rate. This also means that there must be a specific position for the lead. If the lead is fed too far, the lead will break. If the feed is not far enough, the pencil may not be able to make marks. As a result, one function that we can consider is “position lead.” The measurement is the length of exposed lead, and the desired extent of the positioning function may be 5 mm from the barrel end of the pencil. If there are limits on the extent in the form of tolerance, this is a good time to think about these limits as well.* While you are describing function in terms of an active verb and measurable noun, it is very important to maintain a customer frame of reference. Do not forget that function is only meaningful in terms of customer perception. No matter how much you may be enamored of a product feature or service issue, you must decide if the target customer will perceive your product in the same way.**

THE PRODUCT FLOW DIAGRAM

AND THE

CONCEPT

OF

FUNCTIVES

Now that we understand the essential issues involved in describing function, we can learn more about techniques for understanding the many complex functions that exist in a product. If products had just one or two functions, it would be easy to understand the issues that motivate purchase behavior. In today’s complex world, though, products seem to have more features (and hence more functions) nearly every day. How can we understand this complexity? Fortunately, there are common patterns that exist in the functionality of any product. We can see this through the creation of a product flow diagram. A product flow diagram uses simple, direct language to delineate function. This is a valuable aid to help you understand what your product provides to customers. We can start our efforts to develop this diagram by identifying functions. In practice, this is best done by a group or team, and it should be done after all participants have become familiar with the list of customers at whom the product is aimed. A general list of functions can then be developed using brainstorming techniques or other group-based creativity tools. There are a few issues that you should keep in mind while simply listing functions. Functions must describe customer wants and needs from the viewpoint of the customer. A common problem is to confuse product functions with functions being performed by the customer, the designer, the engineer who created the product, or the manufacturer who produces the product. Again, think about a mechanical pencil. Many people will start by describing the function of the pencil as “write notes.” However, the pencil, by itself, cannott write anything. (If you can invent a * When you do this, you have created a “specification” for this function. ** One of the most common and debilitating errors in market analysis is to assume that others will respond the same way that you do. This is a simple but profound delusion. Most of us think that we are normal, typical people. When we awaken in the morning, we look in the mirror and see a normal (although perhaps disheveled if we look before the second cup of coffee) person. Thus, we think, “I like this widget. Since I am normal, most other people will like this widget, too. Therefore, my tastes are likely to be a good guideline to what my customers will want.” In most cases, even if you really are “normal” and even “typical,” this easy generalization is dangerously false.

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pencil that will write notes without a writer attached you will probably become rich.) The function that is more appropriate for the pencil is “make marks.” The best way to start is to simply brainstorm as many functions as you can using active verbs and measurable nouns. There are many ways to brainstorm; in this case, it is usually easiest to have everyone involved use index cards or sticky notes to record their ideas. Remember that brainstorming should not be interrupted by criticism; just let the ideas flow. You will get things that do not apply, and, until you gain experience, you will not always use the “functive” structure that is ultimately important. Do not worry about these issues during the idea-generation phase of this process. Once you have a nice pile of cards or notes, start by sifting and sorting the ideas into categories. In any pile of ideas, there will be natural “groupings” of the cards. Determine these categories and then sort the cards. This can be thought of as “affinity diagramming” of the ideas. You will find some duplicates and some weird things that probably do not belong in the pile.* Discard the excess baggage and look at the categories. Are there any important functions you have missed? Do not hesitate to add new ideas to the categories, either. Finally, you are ready to bear down on the linguistic issues. Make sure that all of the ideas are expressed in terms of active verbs and measurable nouns. Change the idea to a “functive” construction, and then look for the “nerd” verb cards. Convert all of the “nerd verb” functions into true functives, with fully active verbs and measurable nouns. When you are done, you will have an interesting and important preliminary output. Now, count the cards again. If you have more than 20 to 30 cards, you have probably tackled too complex a subject or viewpoint. For example, a commercial airline has thousands — even hundreds of thousands — of functions. If you wanted to analyze function on the widest scale, you would probably be guilty of too much detail if you listed more than 30 functives. On the broadest scales of view, you may only list a handful of functions. Nothing is wrong with a short list, especially for the broadest view. If you have trouble, we can suggest some “function questions” that can assist you in your brainstorming. Try these questions: • What does it do? • If a product feature is deleted, what functions disappear? • If you were this element, what are you supposed to accomplish? Why do you exist? Ask the function questions in this order: • The entire scope of the project • A “system” view • Each element of the project * Do not automatically toss out strange ideas — see if the team can reword or express more clearly the idea that underlies the oddball cards or notes. Some percentage of these cards will have important information. Many will be eventual discards, but do not jump to conclusions.

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• A “part” view • Each sub-element of the project • A “component” view Finally, we can start our next task, which consists of arranging functions into logical groups that show interrelationships. In addition, this next “arranging” step will allow us to test for completeness of function identification and improve team communication. We start by asking “What is the reason for the existence of the product or service?” This function represents the fundamental need of the customer. Example: a vacuum cleaner “sucks air” but the customer really needs “remove debris.” Whatever this reason for being is, we need to identify this particular function, which we call the task function. You must identify the task function from all of the functions you have listed. If you happen to find more than one task function, it is quite likely that you have actually taken on two products. For example, a clock-radio has two task functions: tell time and play music. However, you would be far better served by breaking your analysis into two components — one for telling time, the other for playing music. Alternatively, this product could be considered on a broader basis, as a system — in which case the task function might be “inform user,” with subordinate functions of “tell time” and “play music.” In any event, once you have identified the task function, you will realize that there are many functions other than the task function. Divide the remaining functions by asking: “Is the function required for the performance of the task function?”

If the answer to this question is yes, then the function can be termed essential. If the answer is no, then the function can be considered enhancing. All functions other than the task function must be either essential (necessary to the task function) or enhancing. So, your next task is to divide all of the remaining functions into these two general categories. You can further divide the enhancing functions — the functions that are not essential to the task function. Enhancing functions influence customer satisfaction and purchase decisions. Enhancing functions always divide into four categories: 1. 2. 3. 4.

Ensure dependability Ensure convenience Please senses Delight the customer*

* “Delight the customer” is actually quite rare — most enhancing functions fit one of the other three categories. If you do find a “delight the customer” function, try comparing this with an “excitement” feature in a Kano analysis; you should find that the function fits both descriptions.

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None of these categories is needed to accomplish the task function. In fact, if you do not have a task function (and the associated essential functions), you probably do not have a product. The enhancing functions are those issues that purchasers weigh once they have determined that the task function will likely be fulfilled by your product. So, divide all of the enhancing functions into these four categories. Your next challenge is to create a function hierarchy that will, in finished form, be a function diagram. Start by asking this question: how does the product perform the task function? Primary essential functions provide a direct answer to this question without conditions or ambiguity. Secondary functions explain how primary functions are performed. Continue until the answer to “how” requires using a part name, labor operation, or activity, or you deplete your reserve of essential function cards. Now, you must reverse this process. Ask “why” in the reverse direction. For example, for a mechanical pencil, the task function is “make marks.” One of the functions you must perform to make marks is “support lead.” How do you support the lead? You do it by supporting the internal barrel tube (support tube) that carries the lead and by positioning this tube (position tube). Why do you support the tube and position the tube? You do this to support the lead. Why do you support the lead? You support the lead in order to make marks. The “chain” of function is driven by the how questions from the task function to primary then secondary functions — while this same chain is driven in reverse by why questions from secondary to primary to task function. As you progress, you will notice that you may be missing functions. If you find that you are, add additional functions as needed. After you have completed building “trees” of functions with the essential functions, repeat this process with the enhancing functions. The only difference is that the primary enhancing functions — ensure convenience, ensure dependability, please the senses, and delight the customer — have already been chosen. When you have finished, you will have a completed product flow diagram. At this point, try to delineate the extent of each function (range, target, specification, etc.) for each of the functions. Do not forget: Extent also tests “measurability” of each active verb–measurable noun combination. For example, for the mechanical pencil, the assembly may look like Figure 2.1. The sorted brainstorm list of functives may look like this: Entire project scope: • Make marks • Erase marks • Fit hand • Fit pocket • Show name • Display advertising • Convey message • Maintain point Tube assembly: • Store lead • Position lead

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Eraser

Tube Assembly Lead Barrel EAT AT JOE’S

0,7 mm

Clip FIGURE 2.1 Paper pencil assembly.

• Feed lead • Reposition lead • Support lead • Locate eraser • Position tube • Generate force • Hold eraser Lead: • Make marks • Maintain point Eraser: • Erase marks • Locate eraser Barrel: • Support tube • Support lead • Position lead • Protect lead • Position tube • Position eraser • Show name • Display advertising • Convey message • Fit hand • Enhance feel • Provide instructions Clip: • Generate force • Position clip • Retain clip And, finally, the function diagram (only one possibility among many, many different results) may look like Figure 2.2.

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Maintain point Support lead Basic Functions

Position lead

Support tube Position tube Store lead Feed lead Re-position lead

Make Marks

Ensure convenience

Erase marks

Hold eraser Retain clip

Fit pocket

Supporting Functions

Position clip

Ensure dependability

Provide Instructions

Please the senses

Enhance feel

Fit hand

Delight the customer

Display advertising Convey message

Show name

Position eraser Locate eraser Generate force

FIGURE 2.2 Function diagram for a mechanical pencil.

THE PROCESS FLOW DIAGRAM If you are working with a process rather than a product, you need to create a broad viewpoint “map” that shows how the activities in the process are accomplished. This can be done quickly and easily with a process flow diagram. The difficulty with most process flow diagrams is that they quickly bog down in too much detail. Whenever the detail gets too extensive, people lose interest (except for those who created the chart, but they are only part of the audience). Even though we need detail, we must avoid placing all of the details into one flow chart — at least if we want people to use the resulting charts. So, we will employ a “10 × 10” method that will aid in both communicating and managing the level of detail in a flow chart. If you keep the number of boxes in a flow chart to ten or fewer, most people will find your chart easy to read and understand. You can also use a “standard” symbol set for flow charting. After a great deal of trial and error from our experience, we have found that a simple set of ten symbols will explain almost any business process and provide enough options so that any team can easily illustrate what is going on — see Figure 2.3. By using some of the American National Standards Institute (ANSI) symbols and judiciously mixing in some easy-to-remember shapes, anyone can learn to flow chart a process in just a few minutes. The first step is to select a simple basis or point of view for your flow charts. This could be the view of the process operator, the work piece, or the process owner. (Be careful — if you

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Significant

Incidental

move

move

Output

Process

Input

Decision

Store

Delay

Inspect

Document

FIGURE 2.3 Ten symbols for process flow charting.

confuse your viewpoint while developing a flow chart, you will quickly become confused about the process functions.) Inputs and outputs are the easiest steps to understand. You start with an input and you end with an output. A document may be a special kind of input or output — it can appear at the beginning, at the end, or during the overall process. The process box is the most common box; it describes transformations that occur within the process. Decisions are represented by a diamond shape, and an inspection step (in the shape of a stop sign) is just a special kind of decision. If you delay a process, you use a yield sign. If you store information, you use an inverted yield sign — a pile. Movement is also important. If a move is incidental, you tie the associated boxes together with a simple arrow. However, if a movement is complex (say, sending a courier package to Hong Kong as opposed to handing it to your next-cubicle neighbor), then you may have a special transformation or process step that we call a “significant” move, i.e., a large horizontal arrow. Let us look at a simple process for handling complaints. Your office deals with customer complaints, but you have a local factory (where your office is) and a factory in Japan. How you handle a complaint might look like Figure 2.4. This flow chart shows many of the symbols noted above, but it is not the only way that the process could be flow charted. However, if the team that developed the chart (once again, a team approach is likely to be the most effective technique) can reach a high level of consensus, then the communication of these ideas to others will be powerful and comprehensive. Now that the basics of 10 × 10 (ten steps or fewer using ten or fewer symbols) are apparent, it becomes possible to construct a “hierarchy” of flow charts that will fill in missing details that may have been skirted with the “10 step limit.” The next step is to create a new 10 × 10 flow chart for each box in the top level flow chart that requires additional explanation to reach the desired level of detail. These next flow charts (typically three to five of the boxes require additional detail) make up the second level flow charts. Wherever necessary, go to another level of flow charts; continue creating 10 × 10 flow charts until you have a hierarchy of flow charts that directly addresses all of the details that you feel are important. Finally, for each process box on each flow chart, you will have a process purpose. Why did you do this step? Simple — you had one (or possibly two) purposes in

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Phone notice of customer complaint

Local or overseas factory?

Log complaint into database

Compile information for notice

Pending file

Local

Local factory is notified of complaint

Complaint notice

Overseas Send by courier to Japan

Factory is notified of complaint

FIGURE 2.4 Process flow for complaint handling.

mind when you designed this step into your process. Process purpose can be easily described using the language of function. Once again, you must use an active verb and a measurable noun. Often, a team can move directly to listing process functions from the flow charts. However, especially in manufacturing, it is common for the level of detail hidden in flow charts to be large, especially with intricate or subtle fabrication procedures. You may need to use an additional tool for teasing the “function” information from a flow chart called a “characteristic matrix.” A characteristic matrix is a reasonably simple analysis tool. The purpose of the matrix is to show the relationships between product characteristics and manufacturing steps. The importance of product characteristics in this matrix is significant; by considering the impact of a manufacturing step on product characteristics, we again focus our attention on customer requirements. Too often, manufacturing emphasis turns inward; it is critical that the focus be constantly directed at customers. Of course, there are “internal” customers as well. It is certainly important that intermediate characteristics, necessary for facilitating additional fabrication or assembly activities, be included in the analysis of function. For example, a simple machining process could have the characteristic matrix shown in Table 2.1. In this example, a simple machining step could be shown on a process flow chart with a process box that describes the machining operation as “CNC Lathe” or something similar. However, the lathe operation creates several important dimensions, or product characteristics, that are needed to meet customer expectations. These characteristics are sufficiently varied and complex that an additional level of detail is necessary. Some of these characteristics are important to the end customer; some are important to internal or “next step” process stations. For this example, the three left hand columns establish important functional information. The product characteristic is essentially the “measurable noun” (an

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TABLE 2.1 Characteristic Matrix for a Machining Process Product Characteristic

Target Value

Tolerance

Process Operations Lathe Turn 10

Diameter “A”

6.22 mm

±0.25 mm

Diameter “B”

3.25 mm

±0.1 mm

Shoulder “C”

12.2 mm

±0.5 mm

Radius “D”

0.5 mm

±0.05 mm

Lathe Turn 20

X

Face Cut 30

Deburr 40

C

L

X

C

L

X

C

Cut Radius 50

L X

X = Characteristic Created By This Operation C = Characteristic Used For Clamp Down In This Operation L = Characteristics Used As Locating Datum In This Operation

occasional adjective is acceptable in a functive if there are several identical nouns, such as diameter in this case). The extent is shown in the target dimension and tolerance columns, and the “active verbs” can be constructed or deduced from the “code letters” inserted in the matrix cells in the “Process Operation” columns. In any event, whether you are able to determine functions directly from a process flow chart or whether you find the use of characteristic matrices important, you need to end with a comprehensive listing of function. The important aspect of process function is to use a flow charting technique of some type to assist in reaching the comprehensive assessment of function that is similar to the point-by-point listing that can be achieved by the product flow diagram technique.

USING FUNCTION CONCEPTS QUALITY METHODOLOGIES

WITH

PRODUCTIVITY

AND

Earlier, we suggested that function concepts form a powerful fundamental basis for three major productivity and quality methodologies: • Quality Function Deployment (QFD) • Failure Modes and Effects Analysis (FMEA) • Value Analysis (VA) While we do not intend to explain these techniques fully in this context (however, they will be explained later), we would like to address the usefulness of function concepts in these methodologies. In these discussions, we are assuming that you have a passing or even detailed familiarity with these tools. If not, you may wish to pass over to the discussion of QFD later in this chapter or to Chapters 6 and 12 for lengthy discussions of FMEA and VA.

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For Quality Function Deployment, the most challenging issue is the one that we have just explored: how can one determine the functions that must be analyzed for deployment? In other forms, this is the same question facing practitioners in FMEA and VA. Clearly, the product flow diagram provides several instrumental techniques for improving these activities. A major difficulty in QFD is the often overwhelming complexity of the “House of Quality” approach. Constructing the first house, using conventional QFD techniques, is often the start of the complexity. Many different customer “wants” are listed. This is occasionally done as a “pre-planning” matrix. Moreover, the linguistic construction for these “wants” is undisciplined and subjective. Similarly, in FMEA, the initial list of failure modes is difficult to obtain. In VA, determining the “baseline” value assessment can also be difficult.* The techniques for developing a function diagram, especially the informal suggestions about “sizing” a project, can be very helpful in this regard. QFD, like FMEA and VA, typically fails to deliver the results expected because the project selected is too complex. A QFD study on a car or truck, for example, could easily contain hundreds of thousands of pages of information. That is not to say that the information in this study would not be valuable or that it should not be done; the issue is how complexity of this type should be dealt with. If you start with a systemwide view and construct a function diagram of the limited size previously discussed (20–30 functions maximum, even fewer are better), then this will provide a first level in a “hierarchy” of function diagrams. Subsequent analysis of various subsystems, then components and parts, and finally processes will complete the analysis. While the end result (for a car) would conceivably be of the same magnitude, the belief that all of the work must be done within the same team or by the same organization would be quickly abandoned. Moreover, the knowledge and understanding that is developed is generated at the hierarchical level (in the supply chain) of greatest importance, utility, and impact. Moreover, using the “functive” combination of active verbs and measurable nouns will assist in making QFD a useful tool. The vague, imprecise, or even confusing descriptions of function that are often used in QFD contribute to the difficulty in usage. A vehicle planning team may carry out a QFD study on the overall vehicle, assessing the major issues regarding the vehicle; these could include size, styling motifs, performance themes, and target markets. Subsequently, a study of the powertrain (engine, transmission, and axles) could be completed by another team. The engine itself could then be divided into major components: block, pistons, electronic controls, and so forth. Ultimately, suppliers of major and minor components alike would be asked to carry out QFD studies on each element. The multiplicity of information is still present, but it is no longer generated in some centralized form. This means that accessibility, usefulness, and the likelihood of beneficial deployment of the findings are much greater.** * In Value Analysis, the Function Analysis System Technique or “FAST,” a close cousin of the function diagram, is typically used to establish the initial functional baseline for value calculations. ** If the reader sees an “echo” of the hierarchy of flow charts, this is not coincidental.

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As an added benefit, starting QFD using this approach provides benefits in the completion of FMEA and VA studies, since a consistent set of functions will be used as a basis for each technique. We will next consider each of these in turn. We will start with FMEA, because the importance of function in this methodology is not widely understood or appreciated. In FMEA, determining all of the appropriate failure modes is usually a great challenge. This obstacle is reflected in the widespread difficulty in understanding what is a failure mode and what is an effect. For example, the effect “customer is dissatisfied” is often found in FMEA studies. While this is likely to be true, it is an effect of little or no worth in developing and improving products and processes. Similarly, failure modes are often confused with effect. This can be illustrated with another common product, a disposable razor. How can we determine a comprehensive list of failure modes? Simply start with an appropriate function diagram. For each function, we need to consider how these functions can go astray. There are a limited number of ways that this can occur, all related to function. If you consider the completion of a function (at the desired extent) to be the absence of failure, then pose these questions about each function in the function diagram: • • • • • •

What would constitute an absence of function? What would occur if the function were incomplete? What would demonstrate a partial function? What would be observed if there was excess function? What would a decayed function consist of? What would happen if a function occurs too soon or too late (out of desired sequence)? • Could there be an additional unwanted function?

Each of these conditions establishes a possible failure mode. For the disposable razor, the task function is generally understood to be “cut hair” (not, of course, to shave). The failure mode that is most obvious is an additional unwanted function, namely “cut skin.” Notice that the mode of failure is not “feel pain” or “bleed;” these are failure effects. To make use of these ideas in the context of the function diagram, we must next define “terminus” functions. Terminus functions are simply those functions at the right hand (or “how”) end of any function chain in the function diagram. In the mechanical pencil example, two terminus functions would be “position eraser” and “locate eraser.” Why do you position and locate the eraser? To hold the eraser. Why do you hold the eraser? To erase marks. Why do you erase marks? To ensure correctness. Since this chain is one of enhancing functions, we do not directly modify the task function. Start your analysis of failure modes by testing each of the possible conditions listed above against the terminus functions. After you have completed the terminus functions, move one step in the “why” direction. However, as you move to the left, you will find that you frequently discover the same modes for the other functions. Since the function chain shows the interrelated nature of the functions, this should

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not be surprising. As a rule, you will get most (if not all) of the relevant failure modes from the terminus functions.* So, starting with the terminus functions will speed your work and reduce redundancy. By working through each function chain in the function diagram, a comprehensive list of failure modes can be developed. This listing of failure modes then alters the approach to FMEA substantially; modes are clear, and cause-effect relationships are easier to understand. Moreover, developing FMEA studies using function diagrams that were originally constructed as part of the QFD discipline assures that product development activities continue to reflect the initial assumptions incorporated in the conceptual planning phase of the development process.** Once you have identified failure modes in association with functions, the remainder of the FMEA study — though still involved — is rather mechanical. For each failure mode, you must examine the likely effects that will result from this mode. With a clear mode statement, this is much simpler, and you are much less likely to confuse mode and effect issues. The effects can then be rated for severity using an appropriate table. With the effects in hand, causes can next be established and the occurrence rating estimated. Notice that this sequence of events makes the confusion of cause and effect much more difficult; in many cases, the logical improbability of reversal of cause and effect statements is so obvious that you simply cannot reverse these two issues. Finally, you can conclude the fundamental analysis with an evaluation of controls and detection. Once again, starting with a statement of function makes this clearer and less subject to ambiguity. Understanding the progression from function to mode to cause to effect sets the stage. What is it that you expect to detect? Is it a mode? In practice, detecting modes is extremely unlikely. You are more likely to detect effects. However, are effects what you want to detect? Once an effect is seen the failure has already occurred, and costs associated with the failure must already be absorbed. Let us return to the disposable razor to understand this. If the failure mode is “cut skin,” we must recognize that detecting “cut skin” is extremely difficult. You are much more likely to detect an effect — namely, pain or bleeding. Now, we recognize that we really do not want to detect failures at this point. Instead, we need to ask what are the possible causes of this failure mode. In this simple example, two different causes are readily apparent. From a design standpoint, the blades of the razor could be designed at the wrong angle to the shaver head. Even if the manufacturing were 100% accurate, a design that sets the blade angle incorrectly would have terrible consequences. On the other hand, the design could be correct; the blade angle could be specified at the optimum angle, but it could be assembled at an incorrect angle. Detection would best be aimed at testing the design angle*** and

* This is even more true for a system FMEA than for a design FMEA study. ** Of course, any change that is made in concept during development activities requires a continuous updating of the function diagrams under consideration. *** In the ISO and QS-9000 systems, we can think of this in terms of design verification.

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at controlling the manufacturing process so that the optimum design angle would be repeatable (within limits) in production.* Finally, the Value Analysis process can also make use of the function diagrams that serve in the QFD and FMEA processes. In VA, the essence of the technique is the association of cost with function. Once this is accomplished, the method of functional realization can be considered in a variety of “what if” conditions. If there is a comprehensive statement of function, VA teams can be reasonably sure that ongoing value assessments, based on the ratio of function to cost, have a consistent and rational foundation. Moreover, the teams have a much higher confidence that these “what if” questions take customer issues into proper account. Too often, VA activities are carried out as if function is well understood and only cost matters. In too many cases, no function analysis is even performed. Despite the long-standing cautions against this, this alluring shortcut is often taken to save time, money, or both. The shortcomings of skipping function analysis in VA are not trivial. More disappointing results in usage of the VA methodology have probably been obtained because function was not fully and comprehensively understood. At a very fundamental level, how can a value ratio analysis be performed without a full statement of function? This is like calculating a return on investment without knowing the investment. Moreover, the analysis of value ratio can be misleading if the function issue is not well defined. It is easy to reduce cost. You simply eliminate features and functions from a product. Soon, you will not even be able to accomplish the task function. (In practice, “functionless” VA studies typically eliminate important enhancing functions that make a critical difference in the marketplace, and customers consequently pronounce unfavorable judgments on “decontented” products. VA then gets the blame.) Since value studies typically occur subsequent to QFD and FMEA in product development activities, the difficulty of understanding function is eliminated if function is fully defined and even specified during these earlier activities. By using function as the basis for product and manufacturing activities, a degree of focus and understanding of customer wants and needs is preserved not only during VA activities but throughout the product life cycle.

KANO MODEL The tool of choice that is preferred for understanding the “function” is the Kano model. A typical framework of the model is shown in Figure 2.5. The Kano model identifies three aspects of quality, each having a different effect on customer satisfaction. They are: 1. Basic quality — take for granted they exist 2. Performance quality — the more principle 3. Excitement quality — the wow * This is the issue of “process control” in the ISO and QS-9000 systems — in QS-9000, it goes to the heart of the control plan itself. Also, this is a simplified example. In more detail, the failure mode of “cut skin” can even occur when the blade angle is correct both in design and execution. A deeper examination of these issues quickly leads to the consideration of “robustness” in the design itself.

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+ Y – axis (Customer satisfaction)

-

+ X – axis (product functionality)

-

FIGURE 2.5 Kano model framework.

The more we find out about these three aspects from the customer, the more successful we are going to be in our DFSS venture. (Caution: It is imperative to understand that the customer talks in everyday language, and that this language may or may not be acceptable from a design perspective. It is the engineer’s responsibility to translate the language data into a form that may prove worthwhile in requirements as well as verification. A good source for more detailed information is the 1993 book by Shoji.)

BASIC QUALITY “Basic” quality refers to items that the customer is dissatisfied with when the product performs poorly but is not more satisfied with when the product performs well. Fixing these items will not raise satisfaction beyond a minimum point. These items may be identified in the Kano model as in Figure 2.6. Some sources for the basic quality characteristics are: things gone right, things gone wrong, surrogate data, surveys, warranty, and market research.

PERFORMANCE QUALITY “Performance” quality refers to items that the customer is more satisfied with more of. In other words, the better the product performs the more satisfied the customer. The worse the product performs, the less satisfied the customer. Attributes that can be classified as linear satisfiers fall into this category. A typical depiction is shown in Figure 2.7. Some sources for performance quality characteristics are: internal satisfaction analysis, customer interviews, corporate targets/goals, competition, and benchmarking.

EXCITEMENT QUALITY “Excitement” quality refers to items that the customer is more satisfied with when the product is more functional but is not less satisfied with when it is not. This is the area where the customer can be really surprised and delighted. A typical depiction of these attributes is shown in Figure 2.8. Some sources for excitement quality characteristics are: customer insight, technology, interviews with comments such as high % or better than expected.

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+ Y – axis (Customer satisfaction)

-

+ product functionality

Brakes Horn Windshield wipers -

FIGURE 2.6 Basic quality depicted in the Kano model.

Performance Customer satisfaction + Quiet gear shift

+ X – axis (product functionality)

Wind noise Power -

Fuel economy

FIGURE 2.7 Performance quality depicted in the Kano model.

Customer satisfaction + Style Ride Features -

+ X – axis (product functionality)

-

FIGURE 2.8 Excitement quality depicted in the Kano model.

Items that are identified as surprise/delight candidates are very fickle in the sense that they may change without warning. Indeed, they become expectations. The engineer must be very cautious here because items that are identified as excitement items now may not predict excitement at some future date. In fact, we already know that over time the surprised/delighted items become performance items, the performance items become basic, and the basic items become inherent attributes of the product. A classic example is the brakes of an automobile. The traditional brakes were the default item. However, when disc brakes came in as a new technology, they were indeed the excitement item of the hour. They were replaced, however,

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71 Customer satisfaction +

-

Performance

+ product functionality

Excitement quality over time -

FIGURE 2.9 Excitement quality depicted over time in the Kano model.

with the ABS brake system, and now even this is about to be displaced by the electronic brake system. This evolution may be seen in the Kano model in Figure 2.9. Developing these “surprised and delighted” items requires activities that gain insight into the customers’ emotions and motivations. It requires an investment of time to talk with and observe the customer in the customer’s own setting, and the use of the potential product. Above all, it requires the ability to read the customer’s latent needs and unspoken requirements. Is there a way to sustain the delight of the customer? We believe that there is. Once the attributes have been identified, a robust design must be initiated with two objectives in mind. 1. Minimize the degradation of these items. 2. Preserve the basic quality beyond expectations. These two steps will create an outstanding reliability and durability reputation.

QUALITY FUNCTION DEPLOYMENT (QFD) Now that we have finished the Kano analysis, and we know pretty much what the customer sees as functional and value added items, we are ready to organize all these attributes and then prioritize them. The methodology used is that of QFD. QFD is a planning tool that incorporates the voice of the customer into features that satisfy the customer. It does this by portraying the relationships between product or process whats and hows in a matrix form. The matrix form in its entirety is called the House of Quality — see Figure 2.10. One of the reasons why QFD is used is because it allows us to organize the Ys and ys and xs into a workable framework of understanding. QFD does not generate the Ys, ys, or xs. Ultimately, however, QFD will help in identifying the transfer function in the form Y = f(x, n) QFD was developed in Japan, with the intent to achieve competitive advantage in quality, cost, and timing. To understand this need, one must comprehend what quality control is all about from Japan’s point of view. Japan’s industrial standards define Quality Control (QC) as a system of means to economically produce goods and/or services that satisfy customer requirements. It is this definition of QC that

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Correlation matrix HOW

Importance What

I M P O R T A N C E

Relationship matrix

I M P O R T A N C E

Competitive assessment

Technical difficulty How much Competitive assessment Important control items Importance

FIGURE 2.10 A typical House of Quality matrix.

propelled the Japanese to find not only a tool but a planning tool that implements the business objectives, of which the right application is product development. The definition of QFD is a systematic approach for translating customer wants/requirements into company-wide requirements. This translation takes place at each stage from research and development to engineering and manufacturing to marketing and sales and distribution. The QFD system concept is based on four key documents: 1. Overall customer requirement planning matrix. This document provides a way of turning general customer requirements into specified final product control characteristics. 2. Final product characteristic deployment matrix. This document translates the output of the planning matrix into critical component characteristics. 3. Process plan and quality control charts. These documents identify critical product and process parameters as well as benchmarks for each of those parameters.

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4. Operating instructions. These documents identify operations to be performed by plant personnel to assure that the important parameters are achieved.

TERMS ASSOCIATED

WITH

QFD

There are six key terms associated with QFD: Quality function deployment — An overall concept that provides a means of translating customer requirements into the appropriate technical requirements for each stage of product development and production (i.e., marketing strategies, planning, product design and engineering, prototype evaluation, production process development, production, sales). This concept is further broken down into “product quality deployment” and “deployment of the quality function” (described below). Voice of the customer — The customers’ requirements expressed in their own terms. Counterpart characteristics — An expression of the voice of the customer in technical language that specifies customer-required quality; counterpart characteristics are critical final product control characteristics. Product quality deployment — Activities needed to translate the voice of the customer into counterpart characteristics. Deployment of the quality function — Activities needed to ensure that customer-required quality is achieved; the assignment of specific quality responsibilities to specific departments. (The phrase “quality function” does not refer to the quality department, but rather to any activity needed to ensure that quality is achieved, no matter which department performs the activity.) Quality tables — A series of matrices used to translate the voice of the customer into final product control characteristics.

BENEFITS

OF

QFD

QFD certainly appears to be a sensible approach to defining and executing the myriad of details embodied in the product development process, but it also appears to be a great deal of extra work. What is it really worth? Setting the logical arguments aside, there are a number of demonstrated benefits resulting from the use of QFD: • • • • • • • •

Demonstrated results Preservation of knowledge Fewer startup problems Lower startup cost Shorter lead time Warranty reduction Customer satisfaction Marketing advantage

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Preservation of knowledge — The QFD charts form a repository of knowledge, which may (and should) be used in future design efforts. For example: Toyota is convinced that the QFD process will make good engineers into excellent engineers. An American engineering expert once commented, “There isn’t anything in the QFD chart I don’t already know.” Upon reflection, he realized that few other engineers knew everything on that chart. The QFD charts can be a knowledge base from which to train engineers. Fewer startup problems/lower startup cost — Toyota and other Japanese automobile manufacturers have found that the use of QFD more effectively “front loads” the engineering effort. This has substantially reduced the number of costly engineering changes at startup through a marked reduction of problems at startup. QFD has helped to identify potential problems early in design or avoid oversights through its disciplined approach. Shorter lead time — Toyota has reduced its product development cycle to less than 24 months. Warranty reduction — The corrosion problems with Japanese cars of the 1960s and 1970s led to enormous warranty expenses, significantly impacting profitability. The Toyota rust QFD study resulted in virtually eliminating corrosion and the resulting warranty expense. Customer satisfaction — The Japanese automobile manufacturers tend to focus on products that satisfy customers (as opposed to eliminating problems). The QFD approach has greatly facilitated the satisfying of customer wants. Domestic customer satisfaction surveys show that Japanese products have consistently scored higher than many American products. Marketing advantage — A Japanese manufacturer of earth moving equipment introduced a series of five new models that offered substantial advantages over their Caterpillar corporation counterparts, resulting in redistribution of market share. QFD brings several benefits to companies willing to undertake the study and training required to put the system in place. Some of these benefits as they relate to marketing advantage are: • Product objectives based on customers’ requirements are not misinterpreted at subsequent stops. • Particular marketing strategies’ “sales points” do not become lost or blurred during the translation process from marketing through planning and on to execution. • Important production control points are not overlooked. Everything necessary to achieve the desired outcome is understood and in place. • Tremendous efficiency is achieved because misinterpretation of program objectives, marketing strategy, and critical control points is minimized. See Figure 2.2. All of the above translate into significant marketing advantages, that is, speedy introduction of products that satisfy customers without problems. In addition to all the benefits already mentioned, Table 2.2 shows some of the benefits from the total development process perspective, which is a synergistic result starting with QFD.

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TABLE 2.2 Benefits of Improved Total Development Process Cash Drain

Old Process

Technology push, but where’s the pull? Disregard for voice of the customer

Concepts with no needs, needs with no concept The voice of the engineer and other corporate specialists is emphasized Mad dash with singular concept, usually vulnerable Initial design is not production intent and emphasizes newness rather than superior design Make it look good for demonstration Large number of highly overlapped prototype iterations leaves little time for improvement Product is developed, then factory reacts to it Old process parameters used repetitiously without design improvement Inspection creates scrap, rework, adjustments, and field quality loss Lack of teamwork

Eureka concept Pretend designs

Pampered product Hardware swamps

Here is the product; where is the factory? We have always made it this way Inspection

Give me my targets, let me do my thing

ISSUES

WITH

Improved Process Technology strategy and technology transfer bring right technology to the product House of Quality and all steps of QFD deploy the voice of the customer throughout the process Pugh process converges on consensus and commitment to invulnerable concept Two step design and design competitive benchmarking lead to superior design

Taguchi optimization positions product as far as possible away from potential problems Only four iterations, each planned to make maximum contribution to optimization

One total development process, product, and production capability Taguchi process parameter improves quality, reduces cycle times Taguchi’s optimal checking and adjusting minimizes costs of inspection Teamwork and competitive benchmarking beat contracts, and targets lead the process, do not manage problems

TRADITIONAL QFD

The use of traditional QFD raises several issues for business people, including the following: 1. Change is uncomfortable. Counterpoint: There is an old saying, “If we do what we have done, we will get what we have.” To truly improve, we must explore new patterns of logical thinking and let go of outdated ways. We must be willing to change. 2. Success is not realized until the product is released. Counterpoint: The truest measure of customer satisfaction comes after the product or service is introduced. It is easy to lose sight of improvements that do not materialize until years after the improvement effort. We

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3.

4.

5.

6.

would be remiss not to seek ways to achieve the end goal of customer satisfaction in our design and development process. QFD is a long process. Counterpoint: QFD saves the team’s time and resources with new approaches and tools. Avoiding multiple redesigns and multiple prototype levels in response to customer input recovers the time spent on QFD. The upstream time saves multiples of downstream time. It is not as much fun as “fire fighting.” Counterpoint. Finding and fixing problems may be personally gratifying. It is the stuff from which heroes/heroines are made. But emergencies are not in the company’s best interest and certainly not in the customer’s interest. Management must provide a system that rewards problem prevention as well as problem solving. The relation to the traditional product development process is not understood. Counterpoint: QFD replaces some traditional product design and development events, i.e., target setting and functional assumptions, and thereby does not add time. It is difficult to accept customer input when the “voice of the engineer” contradicts. Counterpoint: Engineering has delivered about 80% customer satisfaction; getting to 90–95% is a tough challenge requiring enhancements to current methods for achieving quality.

PROCESS OVERVIEW The easiest way to think of QFD is to think of it as a process consisting of linked spreadsheets arranged along a horizontal (Customer) axis and intersecting vertical (Technical) axis. Important details include the following: • From a macro perspective, the horizontal arrangement is referred to as the Customer Axis because it organizes the Customer Wants. • Customers are the people external to the organization who purchase, operate, and service your products. Customers can also be internal, i.e., the end users of your work within the organization. • The vertical arrangement is referred to as the Technical Axis Customer Wants into technical metrics. • The intersection of the axes (referred to as the Relationship Matrix) identifies how well engineering metrics correlate to customer satisfaction. • A closer look reveals that the interrelated matrices build upon one another beginning with a validated list of Customer Wants.

DEVELOPING

A

“QFD” PROJECT PLAN

Perhaps one of the most important issues in QFD is the selection of appropriate teams. Teams must share a common vision and mission to accomplish their objectives. Some of the reasons are:

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• Building a project plan is the first critical team-building exercise • The project plan has been standardized in QFD, so all teams follow a basic strategy that includes the following steps: • Develop Project Plan to include safety standards and any governmental regulations, as well as timing. • Review Project Plan with program management for buy-in. • Complete the Customer Axis. • Review Customer Axis interim report with program management. • Complete Technical Axis. • Develop corporate strategy. • Develop final report. • Develop Deployment Plan for integrating into business cycle. • Communicate results to all programs and affected activities. The Customer Axis The steps necessary for completion of the customer axis include the following: Determining Customer Wants a. Obtain Customer Wants. b. Select relevant Customer Wants — about 30% of total Wants. c. Add applicable Wants. d. Set up focus groups, interviews, surveys, etc. e. Refine Customer Wants list. f. Enter Customer Wants into QFD net. g. Give Customer Wants to strategic standardization organization (SSO). Obtaining customer competitive evaluations a. Submit Customer Wants to market research (team). b. Develop mail-out questionnaire and/or clinic (market research). c. Send mail-out questionnaire and/or conduct clinic (market research). d. Report results to project team (market research). e. Enter customer competitive evaluation data into the internal team base. Setting customer targets a. Identify Customer Want (team). b. Review its Customer Desirability Index (CDI) rating and rank (team). c. Identify baseline product (team). d. Review customer competitive evaluations (team). e. Identify corporate strategy (team). Calculate image ratio for each Customer Want: customer target/baseline product. Calculate strategic CDI for each Customer Want: CDI × image ratio × sales point. f. Enter corporate strategy into customer targets matrix (team). g. Set customer targets — either opportunity to copy or sales point. h. If opportunity to copy, enter symbol into customer targets matrix. i. If sales point, enter values into customer targets matrix (team). j. End.

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Determining Technical System Expectations (TSE) a. Review and adapt TSE template (team). b. Review past and current projects for additional TSEs (team). c. Identify and define new TSEs (team). d. Organize adapted list of TSEs (team). e. Enter TSEs into internal base (team). Determining relationships a. Review the relationship (team). b. Confirm/establish relationships (team and subject matter experts [SMEs]). c. Seek team consensus (team). d. Collect data and/or conduct experiments (team and SMEs) to find out whether disagreements exist. e. Check that each Want is satisfied by at least one TSE (team and SMEs). f. Enter into internal base. Technical competitive benchmarking • Buy, rent, lease or borrow competitive products (team). • Select TSEs to be benchmarked (team). • Establish inventory of benchmarking tests and data (team and SMEs). • Identify additional benchmarking tests required (team and SMEs). • Develop new tests (team and SMEs). • Conduct benchmark tests (team and SMEs). • Enter data into QFDNET (team). • Establish customer/engineer correlations (team and SMEs). Setting technical targets a. Develop technical targets (team and SMEs). b. Review existing program targets for existing TSEs (team). c. Recommend technical targets to program office (team and SMEs). d. Reconcile program targets and technical targets for existing TSEs (program office). e. Enter technical targets into QFDNET (team). The steps listed above will result in the following QFD deliverables for the Customer Axis: • Validated list of Customer Wants for the product, system, subsystem, or component • Customer Wants prioritized to focus engineering attention a. Customer Desirability Index of the most to least desirable Customer Wants b. Customer satisfaction targets for all Customer Wants, expressed as a percent over/under satisfaction of base product, system, subsystem, or component c. A final rank ordered strategic index of Customer Wants based on corporate strategies and competitive opportunities

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Technical Axis On the Technical Axis, the following items will need to be produced: • Rank ordered list of key Technical System Expectations that when correctly targeted will satisfy Customer Wants at a strategically competitive level • Target values for key TSEs derived from technical competitive benchmarking that correlate with customer’s competitive evaluations. These target values aid program management two ways: a. By driving the product and engineering program toward integrated business and technical propositions that program management can prove b. With managing the program team’s performance at program completion Internal Standards and Tests • New or modified tests or other verification methods that make certain basic and product performance wants achieved Institutionalizing revised tests and standards into real world usage — customer dependent, of course — customer requirements, corporate engineering test procedures, and other documents both generic and program specific that support the organization’s design verification system.

THE QFD APPROACH The first concern of QFD is the customer. Therefore, in planning a new product we start with customer requirements, defined through market research. Generally, we call this the product development process, and it includes the program planning, conceptualization, optimization, development, prototyping, testing, and manufacturing functions. One can see that this development process is indeed very complex. Quite often, it cannot be performed by one individual. This is because it consists of several tradeoffs, such as: • • • • • • • •

Shared responsibilities Interpretations Priorities Technical knowledge Long time experience Resource changes Communication Lots of work

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It is precisely this complexity that all too often causes the product development process to create a product that fails to meet the customer requirements. For example: Customer requirement → Design requirements → Part characteristics → Manufacturing operations → Production requirements Note: It is of paramount importance that the communication process within an organization does not fall victim to the use of jargon.

QFD METHODOLOGY QFD is accomplished through a series of charts that appear to be very complex. They do contain a great deal of information, however. That information is both an asset and a liability. All the charts are interconnected to what is called the House of Quality because of the roof-like structure at its top. Since this house is made up of distinct parts or “rooms,” let us find the function of each part, so that we can comprehend what QFD is all about — see Figure 2.10. QFD begins with a list of objectives or the “what” that we want to accomplish — see Figure 2.11. This is usually the voice of the customer and as such is very general, vague, and difficult to implement directly. It is given to us in raw form, that is, in the everyday language of the customer. (Example: “I don’t want a leaky window when it rains.”) For each what, we refine the list into the next level of detail by listing one or more “hows” for each what. The hows are an engineering task. Figure 2.11 shows the relationship between the what and the how. Figure 2.12 shows that it is possible to have an iterative process between the what and the how, with a possible refinement of the “old how” into the “new what” and ultimately to generate a very good “new how.” Even though this step shows greater detail than the original what list, it is by itself often not directly actionable and requires further definition. This is accomplished by further refinement until every item on the list is actionable. This level is important because there is no way of ensuring successful realization of a requirement that no one knows how to accomplish. (Note: Remember that our level of refinement within the how list may affect more than one how or what and can in fact adversely affect one another. That is why the arrows in Figure 2.11 are going in multiple directions.) To reduce possible confusion we represent the what and how in the following manner. The enclosed matrix becomes the relationships. The relationships are shown at the intersections of the what and how. Some common symbols are: □ Medium relationship  Weak relationship  Very strong relationship

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What

How

FIGURE 2.11 The initial “what” of the customer.

What

How/What

How

FIGURE 2.12 The iterative process of “what” to “how.”

The method of using symbols allows very complex relationships to be shown, and the interpretation is easy and is not dependent on experience. There are many variations of this, and readers are encouraged to use what is comfortable for them. Figure 2.13 presents a typical matrix. Once the what, how, and relationships have been identified, the next step is to establish a “how much” for each how — see Figure 2.14. The intent here is to provide specific objectives that guide the subsequent design and provide a means of objectivity to the process. The result is minimum interference from opinion. (Note: This how much is another cross check on our thinking process. It forces us to think in a very detailed, measurable fashion.) To summarize: The what identifies the customer’s requirements in everyday language. The how refines the customer’s requirements (from an engineering perspective). The relationship defines the relationship between what and how via a symbolic language. The how much provides an objective means of assessing that requirements have been met and provides targets for further detail development. Pictorially, the flow is shown in Figure 2.14.

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How What

Importance 4



5 •

1 3 •

2 How much Importance ratings

42

21

33

28

24

Where = 3 •

= 9 = 1

Therefore: (4x9) + (2x3) = 42 and so on. Make sure that the ratings differentiate to the point of discrimination between each other. Remember, you are interested in great differentiation rather than a simple priority.

FIGURE 2.13 The relationship matrix.

HOW What

How much

FIGURE 2.14 The conversion of “how” to “how much.”

At this point, even though a lot of information is at hand, it is not unusual to refine the hows even further until an actionable level of detail is achieved. This is done by creating a new chart in which the hows of the previous chart become the whats of the new chart. The “how much” information as a general rule is carried along to the next chart to facilitate communication. This is done to ensure that the objectives are not lost. The process is repeated as necessary. In the product development process, this means taking the customer requirements and defining design requirements that are carried on to the next chart to establish the basis for the part characteristic. This is continued to define the manufacturing operations and the production requirements — see Figure 2.15. (Note: The greatest gains using QFD can be realized only when

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Functional spec VOC

Requirements analysis Design System design

Where: VOC = Voice of the customer

Methods, tools, procedures Technical assessment

Resource plan Implementation plan

FIGURE 2.15 The flow of information in the process of developing the final “House of Quality.”

taken down to the work detail level of production requirements. The QFD process is well suited to simultaneous engineering in which product and process engineers participate in a team effort.) For more information on the cascading process of the QFD methodology, see the Appendix. So far, we have talked about the basic charts in the House of Quality, and as a result we have gained much information about the problem at hand. However, there are several useful extensions to the basic QFD charts that enhance their usefulness. These are used as required based on the content and purpose of each particular chart. One such extension is the correlation matrix. The correlation matrix — see Figure 2.10 — is a triangular table often attached to the “hows.” The purpose of such placement is to establish the correlation between each “how” item, i.e., to indicate the strength of the relationship and to describe the direction of the relationship. To do that, symbols are used, most commonly:  Positive  Strong positive

X Negative # Strong negative

A second extension is the competitive assessment — see Figure 2.10. This is a pair of graphs that shows item for item how competitive products compare with current company products. Its strength is the fact that it can be done for the whats, hows, and how muchs. The competitive assessment may also be used to uncover gaps in engineering judgment. What and how items that are strongly related should also exhibit a relationship in the competitive assessment. For example, if we believe superior dampening will result in an improved ride, the competitive assessment would be expected to show that products with superior dampening also have a superior ride. If this does not occur, it calls attention to the possibility that something significant may have been overlooked. If not acted upon, we may achieve superior performance

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to our “in house” tests and standards but fail to achieve expected results in the hands of our customers. Why are we doing this? Basically, for two reasons: 1. To establish the values of the objectives to be achieved 2. To uncover engineering judgment errors Remember that the correlation must be related to real world usage from the customer’s perspective. What and how items that are strongly related should also be shown to relate to one another in the competitive assessment. If the correlation does not agree, it may mean that we overlooked something very significant. A third extension is the importance rating — see Figure 2.10. This is a mechanism for prioritizing efforts and making trade-off decisions for each of the whats and hows. It is important to keep in mind that the values by themselves have no direct meaning; rather, their meaning surfaces only when they are interpreted by comparing their magnitudes. The importance rating is useful for prioritizing efforts and making trade-off decisions. (Some of the trade-offs may require high level decisions because they cross engineering group, department, divisional, or company lines. Early resolution of trade-offs is essential to shorten program timing and avoid non-productive internal iterations while seeking a nonexistent solution.) The rating itself may take the form of numerical tables or graphs that depict the relative importance of each what or how to the desired end result. Any rating scale will work, provided that the scale is a weighted one. A common method is to assign weights to each relationship matrix symbol and sum the weights, just as we did in Figure 2.13. Another more technical way is the following: w ′functioni =

∑w r

yj ij

j

wfunction i =

5(w ′functioni ) maxi (w ′functioni )

where w ′functioni = unnormalized function importance; wyj = importance rating; rij = individual rating of functions; and wfunction i = weighted function importance. Applying this methodology to Figure 2.13 yields Figure 2.16.

QFD

AND

PLANNING

Contrary to what the name implies, quality function deployment (QFD) is not just a quality tool. QFD was developed in Japan, growing out of the need to simultaneously achieve a competitive advantage in quality, cost, and timing. To better comprehend QFD, it is important to understand what the Japanese mean by the word “quality.” The word “quality,” which we generally define as conformance to requirements, fitness for use, or some other measure of goodness, takes on a much broader meaning

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HOW What

Importance 4



5



1 3



2 How much Importance 42 ratings – unnormalized Importance of 5 how

21

33

28

24

2.5 or 3

3.9 or 4

3.3 or 3

2.9 or 3

W’ function i = (4x9) + (2x3) = 42 and so on W function i =

5 (42) 42

= 5 and so on

Keep in mind that when you are addressing the “hows” in essence you are dealing with customer functionalities. Therefore, it is recommended to design for the average, based on each function’s importance according to its capability to supply each original Y.

FIGURE 2.16 Alternative method of calculating importance.

in Japan (there is probably no exact English translation of the Japanese version). However, according to Japanese industrial standard Z8101–1981, “quality control” is “a system of means to economically produce goods or services which satisfy customer requirements.” (Italics added.) Thus to the Japanese, “quality” means conducting the business effectively, not just producing a good product. In this context, QFD really becomes a planning tool for implementing business objectives, of which the most widely known application is to product development. In planning a new product, we start with customer needs, wants, and expectations, often defined through market research. We wish to design and manufacture a product that satisfies the customer’s perception of intended function, as well as or better than our competitors (subject to certain internal company constraints). In other words: CUSTOMER REQUIREMENTS ⇓ ⇓ ⇓ PRODUCT

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Let us call the process of translating these requirements into a viable product the “product development process.” This process includes program planning, concepting, optimization, development, prototyping, and testing, as well as the corresponding manufacturing functions. Thus: CUSTOMER REQUIREMENTS ⇓ ⇓ ⇓ PRODUCT DEVELOPMENT PROCESS ⇓ ⇓ ⇓ PRODUCT In a large organization, the product development process is so detailed that often no one individual can comprehend it all. For some, the process looks like a maze or a mysterious “black box.” For others the process is an intricate network of activities. Regardless of how it is represented, the product development process is exceedingly complex, consisting of numerous trade-offs. Shared responsibilities and interpretation differences often result in conflicting priorities. That is the reason the team must have ownership of the projects and must have a substantial body of technical knowledge over a relatively long time frame while enduring resource changes. This, of course, requires a great deal of communication and a substantial work effort.

PRODUCT DEVELOPMENT PROCESS The complexity of the product development process makes it a natural haven for Murphy’s law, with nearly an infinite number of opportunities for problems to occur. Despite the best of intentions and efforts, all too often the product development process creates a product that fails to meet the customer requirements. Such failures may occur due to: • • • • • • • •

Trade-offs Shared responsibilities Interpretations Priorities Technical knowledge Long time frame Resource changes Communication — lots of work

The QFD approach focuses on customer requirements in a manner that directs efforts toward achieving those requirements — see Figure 2.17. In Figure 2.17, for

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Design requirements

Product planning

Part characteristic Part deployment

Manufacturing operations

Process planning

Production requirements

Production planning

FIGURE 2.17 The development of QFD.

each of the customer requirements, a set of design requirements is determined, which if satisfied will result in achieving the customer requirements. In like manner, each design requirement is evolved into part characteristics, which in turn are used to determine manufacturing operations and specific production requirements. The flow is as follows: CUSTOMER REQUIREMENTS ⇓ ⇓ ⇓ DESIGN REQUIREMENTS ⇓ ⇓ ⇓

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PART CHARACTERISTICS ⇓ ⇓ ⇓ MANUFACTURING OPERATIONS ⇓ ⇓ ⇓ PRODUCTION REQUIREMENTS ⇓ ⇓ ⇓ So, for example: The customer requirement of “years of durability” may be achieved in part by the design requirement of no visible rust in three years. This in turn may be achieved in part by ensuring part characteristics that include a minimum paint film build and maximum surface treatment crystal size. The manufacturing process that provides these part characteristics consists of a three-coat process that includes a dip tank. The production requirements are the process parameters within the manufacturing process that must be controlled in order to achieve the required part characteristics (and ultimately the customer requirements). Therefore, we can present this in a summary form as: CUSTOMER REQUIREMENT: Years of durability DESIGN REQUIREMENT: No visible exterior rust in 3 years PART CHARACTERISTICS: Paint weight — 2–2.5 gm/m2; Crystal size — 3 max MANUFACTURING OPERATIONS: Dip tank; 3 coats PRODUCTION REQUIREMENTS: Time = 2.0 minutes; Acidity = 1.5 to 2.0; Temperature = 45–55ο C

CONJOINT ANALYSIS WHAT IS CONJOINT ANALYSIS? We introduced conjoint analysis in Volume III of this series. Recall that conjoint analysis is a multivariate technique used specifically to understand how respondents develop preferences for products or services. It is based on the simple premise that consumers evaluate the value of a product/service/idea (real or hypothetical) by combining the separate amounts of value provided by each attribute. It is this characteristic that is of interest in the DFSS methodology. After all, we want to know the bundle of utility from the customer’s perspective. (The reader is encouraged to review Volume III, Chapter 11.) So in this section, rather than dwelling on theoretical statistical explanations, we will apply conjoint analysis in a couple of hypothetical examples. The examples are based on the work of Hair et al. (1998) and are used here with the publisher’s permission.

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A HYPOTHETICAL EXAMPLE

89 OF

CONJOINT ANALYSIS

As an illustration of conjoint analysis, let us assume that HATCO is trying to develop a new industrial cleanser. After discussion with sales representatives and focus groups, management decides that three attributes are important: cleaning ingredients, convenience of use, and brand name. To operationalize these attributes, the researchers create three factors with two levels each: Factor Ingredients Form Brand name

Level Phosphate-free Liquid HATCO

Phosphate-based Powder Generic brand

A hypothetical cleaning product can be constructed by selecting one level of each attribute. For the three attributes (factors) with two values (levels), eight (2 × 2 × 2) combinations can be formed. Three examples of the eight possible combinations (stimuli) are: • HATCO phosphate-free powder • Generic phosphate-based liquid • Generic phosphate-free liquid HATCO customers are then asked either to rank-order the eight stimuli in terms of preference or to rate each combination on a preference scale (perhaps a 1-to-10 scale). We can see why conjoint analysis is also called “trade-off analysis,” because in making a judgment on a hypothetical product, respondents must consider both the “good” and “bad” characteristics of the product in forming a preference. Thus, respondents must weigh all attributes simultaneously in making their judgments. By constructing specific combinations (stimuli), the researcher is attempting to understand a respondent’s preference structure. The preference structure “explains” not only how important each factor is in the overall decision, but also how the differing levels within a factor influence the formation of an overall preference (utility). In our example, conjoint analysis would assess the relative impact of each brand name (HATCO versus generic), each form (powder versus liquid), and the different cleaning ingredients (phosphate-free versus phosphate-based) in determining the utility to a person. This utility, which represents the total “worth” or overall preference of an object, can be thought of as based on the part-worths for each level. The general form of a conjoint model can be shown as (Total worth for product)ij…,n = Part-worth of level i for factor 1 + Part-worth of level j for factor 2 +... + Part-worth of level n for factor m where the product or service has m attributes, each having n levels. The product consists of level i of factor 2, level j of factor 2, and so forth, up to level n for factor m.

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TABLE 2.3 Stimuli Descriptions and Respondent Rankings for Conjoint Analysis of Industrial Cleanser Stimuli Descriptions

1 2 3 4 5 6 7 8

Respondent Rankings

Form

Ingredients

Brand

Liquid Liquid Liquid Liquid Powder Powder Powder Powder

Phosphate-free Phosphate-free Phosphate-based Phosphate-based Phosphate-free Phosphate-free Phosphate-based Phosphate-based

HATCO Generic HATCO Generic HATCO Generic HATCO Generic

Respondent 1

Respondent 2

1 2 5 6 3 4 7 8

1 2 3 4 7 5 8 6

In our example, a simple additive model would represent the preference structure for the industrial cleanser as based on the three factors (utility = brand effect + ingredient effect + form effect). The preference for a specific cleanser product can be directly calculated from the part-worth values. For example, the preference for HATCO phosphate-free powder is: Utility = Part-worth of HATCO brand + Part-worth of phosphate-free cleaning ingredient + Part-worth of powder With the part-worth estimates, the preference of an individual can be estimated for any combination of factors. Moreover, the preference structure would reveal the factor(s) most important in determining overall utility and product choice. The choices of multiple respondents could also be combined to represent the competitive environment faced in the “real world.”

AN EMPIRICAL EXAMPLE To illustrate a simple conjoint analysis, assume that the industrial cleanser experiment was conducted with respondents who purchased industrial supplies. Each respondent was presented with eight descriptions of cleanser products (stimuli) and asked to rank them in order of preference for purchase (1 = most preferred; 8 = least preferred). The eight stimuli are described in Table 2.3, along with the rank orders given by two respondents. As we examine the responses for respondent 1, we see that the ranks for the stimuli with the phosphate-free ingredients are the highest possible (1, 2, 3, and 4), whereas the phosphate-based product has the four lowest ranks (5, 6, 7, and 8). Thus, the phosphate-free product is much more preferred than the phosphate-based cleanser. This can be contrasted to the ranks for the two brands, which show a mixture of high and low ranks for each brand. Assuming that the basic model (an

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additive model) applies, we can calculate the impact of each level as differences (deviations) from the overall mean ranking. (Readers may note that this is analogous to multiple regression with dummy variables or ANOVA.) For example, the average ranks for the two cleanser ingredients (phosphate-free versus phosphate-based) for respondent 1 are: Phosphate-free: (1 + 2 + 3 + 4)/4 = 2.5 Phosphate-based: (5 + 6 + 7 + 8)/4 = 6.5 With the average rank of the eight stimuli of 4.5 [(1 + 2 + 3 + 4 + 5 + 6 + 7 + 8)/8 = 36/8 = 4.5], the phosphate-free level would then have a deviation of –2.0 (2.5 – 4.5) from the overall average, whereas the phosphate-based level would have a deviation of +2.0 (6.5 – 4.5). The average ranks and deviations for each factor from the overall average rank (4.5) for respondents 1 and 2 are given in Table 2.4. In our example, we use smaller numbers to indicate higher ranks and a more preferred stimulus (e.g., 1 = most preferred). When the preference measure is inversely related to preference, such as here, we reverse the signs of the deviations in the part-worth calculations so that positive deviations will be associated with part-worths indicating greater preference. Deviation is calculated as: deviation = average rank of level – overall average rank (4.5). Note that negative deviations imply more preferred rankings. The part-worths of each level are calculated in four steps: • Step 1: Square the deviations and find their sum across all levels. • Step 2: Calculate a standardizing value that is equal to the total number of levels divided by the sum of squared deviations. • Step 3: Standardize each squared deviation by multiplying it by the standardizing value. • Step 4: Estimate the part-worth by taking the square root of the standardized squared deviation. Let us examine how we would calculate the part-worth of the first level of ingredients (phosphate-free) for respondent 1. The deviations from 2.5 are squared. The squared deviations are summed (10.5). The number of levels is six (three factors with two levels apiece). Thus, the standardizing value is calculated as .571 (6/10.5 = .571). The squared deviation for phosphate-free (22; remember that we reverse signs) is then multiplied by .571 to get 2.284 (22 × .571 = 2.284). Finally, to calculate the part-worth for this level, we then take the square root of 2.284, for a value of 1.1511. This process yields part-worths for each level for respondents 1 and 2, as shown in Table 2.5. Because the part-worth estimates are on a common scale, we can compute the relative importance of each factor directly. The importance of a factor is represented by the range of its levels (i.e., the difference between the highest and lowest values) divided by the sum of the ranges across all factors. For example, for respondent 1, the ranges are 1.512 [.756 – (–.756)], 3.022 [1.511 – (–1.511)], and .756 [.378 – (–.378)]. The sum total of ranges is 5.290. The relative importance for form, ingredients,

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TABLE 2.4 Average Ranks and Deviations for Respondents 1 and 2 Factor Level

Ranks Across Stimuli

Average Rank of Level

Deviation from Overall Average Rank

Respondent l Form Liquid Powder

1, 2, 5, 6 3, 4, 7, 8

3.5 5.5

–1.0 +1.0

Ingredients Phosphate-free Phosphate-based

1, 2, 3, 4 5, 6, 7, 8

2.5 6.5

–2.0 +2.0

Brand HATCO Generic

1, 3, 5, 7 2, 4, 6, 8

4.0 5.0

–.5 +.5

Respondent 2 Form Liquid Powder

1, 2, 3, 4 5, 6, 7, 8

2.5 6.5

–2.0 +2.0

Ingredients Phosphate-free Phosphate-based

1, 2, 5, 7 3, 4, 6, 8

3.75 5.25

–.75 +.75

Brand HATCO Generic

1, 3, 7, 8 2, 4, 5, 6

4.75 4.25

+.25 –.25

and brand is calculated as 1.512/5.290, 3.022/5.290, and .756/5.290, or 28.6, 57.1, and 14.3 percent, respectively. We can follow the same procedure for the second respondent and calculate the importance of each factor, with the results of form (66.7 percent), ingredients (25 percent), and brand (8.3 percent). These calculations for respondents 1 and 2 are also shown in Table 2.5. To examine the ability of this model to predict the actual choices of the respondents, we predict preference order by summing the part-worths for the different combinations of factor levels and then rank ordering the resulting scores. The calculations for both respondents for all eight stimuli are shown in Table 2.4. Comparing the predicted preference order to the respondent’s actual preference order assesses predictive accuracy. Note that the total part-worth values have no real meaning except as a means of developing the preference order and, as such, are not compared across respondents. The predicted and actual preference orders for both respondents are given in Table 2.6.

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TABLE 2.5 Estimated Part-Worths and Factor Importance for Respondents 1 and 2 Estimated Part-Worths Factor Level

Reversed Squared Deviationa Deviation

Standardized Deviationb

Calculating Factor Importance Estimated Range of Factor Part-Worthc Part-Worths Importanced

Respondent 1 Form Liquid Powder

+1.0 –1.0

1.0 1.0

+.571 –.571

+.756 –.756

1.512

28.6%

Ingredients Phosphate-free Phosphate-based

+2.0 –2.0

4.0 4.0

+2.284 –2.284

+1.511 –1.511

3.022

57.1%

+.5 –.5

.25 .25 10.5

+.143 –.143

+.378 –.378

.756

14.3%

Brand HATCO Generic Sum of squared deviations Standardizing valuee Sum of part-worth ranges

.571 5.290

Respondent 2 Form Liquid Powder Ingredients Phosphate-free Phosphate-based Brand HATCO Generic Sum of squared deviations Standardizing value Sum of part-worth ranges a

+2.0 –2.0

4.0 4.0

+2.60 –2.60

+1.612 –1.612

3.224

66.7%

+.75 –.75

.5625 .5625

+.365 –.365

+.604 –.604

1.208

25.0%

–.25 +.25

.0625 .0625 9.25

–.04 +.04

–.20 +.20

.400

8.3%

.649 4.832

Deviations are reversed to indicate higher preference for lower ranks. Sign of deviation used to indicate sign of estimated part-worth. b Standardized deviation equal to the squared deviation times the standardizing value. c Estimated part-worth equal to the square root of the standardized deviation. d Factor importance equal to the range of a factor divided by the sum of the ranges across all factors, multiplied by 100 to yield a percentage. e Standardizing value equal to the number of levels (2 + 2 + 2 = 6) divided by the sum of the squared deviations.

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TABLE 2.6 Predicted Part-Worth Totals and Comparison of Actual and Estimated Preference Rankings Stimuli Description Size

Part-Worth Estimates

Preference Rankings

Ingredients

Estimated

Size

Ingredients

Brand

Brand Total

Actual

Liquid Liquid Liquid Liquid Powder Powder Powder Powder

Phosphate-free Phosphate-free Phosphate-based Phosphate-based Phosphate-free Phosphate-free Phosphate-based Phosphate-based

HATCO Generic HATCO Generic HATCO Generic HATCO Generic

Respondent 1 .756 1.511 .756 1.511 .756 –1.511 .756 –1.511 –.756 1.511 –.756 1.511 –.756 –1.511 –.756 –1.511

.378 –.378 .378 –.378 .378 –.378 .378 –.378

2.645 1.889 –.377 –1.133 1.133 .377 –1.889 –2.645

1 2 5 6 3 4 7 8

1 2 5 6 3 4 7 8

Liquid Liquid Liquid Liquid Powder Powder Powder Powder

Phosphate-free Phosphate-free Phosphate-based Phosphate-based Phosphate-free Phosphate-free Phosphate-based Phosphate-based

HATCO Generic HATCO Generic HATCO Generic HATCO Generic

Respondent 2 1.612 .604 1.612 .604 1.612 –.604 1.612 –.604 –1.612 .604 –1.612 .604 –1.612 –.604 –1.612 –.604

–.20 .20 –.20 .20 –.20 .20 –.20 .20

2.016 2.416 .808 1.208 –1.208 –.808 –2.416 –2.016

2 1 4 3 6 5 8 7

1 2 3 4 7 5 8 6

The estimated part-worths predict the preference order perfectly for respondent 1. This indicates that the preference structure was successfully represented in the part-worth estimates and that the respondent made choices consistent with the preference structure. The need for consistency is seen when the rankings for respondent 2 are examined. For example, the average rank for the generic brand is lower than that for the HATCO brand (refer to Table 2.4), meaning that, all things being equal, the stimuli with the generic brand will be more preferred. Yet, examining the actual rank orders, this is not always seen. Stimuli 1 and 2 are equal except for brand name, yet HATCO is preferred. This also occurs for stimuli 3 and 4. However, the correct ordering (generic preferred over HATCO) is seen for the stimuli pairs of 5–6 and 7–8. Thus, the preference structure of the part-worths will have a difficult time predicting this choice pattern. When we compare the actual and predicted rank orders (see Table 2.6), we see that respondent 2’s choices are many times mispredicted but most often just miss by one position due to the brand effect. Thus, we would conclude that the preference structure is an adequate representation of the choice process for the more important factors, but that it does not predict choice perfectly for respondent 2, as it does for respondent 1.

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THE MANAGERIAL USES

OF

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CONJOINT ANALYSIS

It is beyond the scope of this section to discuss the statistical basis of conjoint analysis. However, in DFSS, we should understand the technique in terms of its role in decision making and strategy development. The simple example we have just discussed presents some of the basic benefits of conjoint analysis. The flexibility of conjoint analysis gives rise to its application in almost any area in which decisions are studied. Conjoint analysis assumes that any set of objects (e.g., brands, companies) or concepts (e.g., positioning, benefits, images) is evaluated as a bundle of attributes. Having determined the contribution of each factor to the consumer’s overall evaluation, the marketing researcher could then: 1. Define the object or concept with the optimum combination of features 2. Show the relative contributions of each attribute and each level to the overall evaluation of the object 3. Use estimates of purchaser or customer judgments to predict preferences among objects with differing sets of features (other things held constant) 4. Isolate groups of potential customers who place differing importance on the features to define high and low potential segments 5. Identify marketing opportunities by exploring the market potential for feature combinations not currently available The knowledge of the preference structure for each individual allows the researcher almost unlimited flexibility in examining both individual and aggregate reactions to a wide range of product- or service-related issues.

REFERENCES Fowler, T.C., Value Analysis in Design, Van Nostrand Reinhold, New York, 1990. Hair, J.F., Multivariate Data Analysis, 5th ed., Prentice Hall, Upper Saddle River, NJ, 1998. Harry, M.,The Vision of Six Sigma: A Roadmap for Breakthrough, 5th ed., Vol. 1, TriStar Publishing, Phoenix, 1997. Porter, M., Competitive Advantage, Free Press, New York, 1985. Rechtin, E. and Maier M., The Art of Systems Architecting, CRC, Boca Raton, FL, 1997. Shoji, S., A New American TQM, Productivity Press, Portland, OR, 1993.

SELECTED BIBLIOGRAPHY Afors, C. and Michaels, M.Z., A Quick, Accurate Way to Determine Customer Needs, Quality Progress, July 2001, pp. 82–88. Anon., Quality Function Deployment, American Supplier Institute, Inc., Dearborn, MI, 1988. Bialowas, P. and Tabaszewska E., How to Evaluate the Internal Customer Supplier Relationship, Quality Progress, July 2001, pp. 63–67. Carlzon, J., Moments of Truth, HarperCollins, New York, 1989. Fredericks, J. O. and Salter, J.M., What Does Your Customer Really Want? Quality Progress, Jan. 1998, pp. 63–70.

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Gale, B.T., Managing Customer Value: Creating Quality and Service that Customers Can See, Free Press, New York, 1994. Gobits, R., The Measurement of Insight, unpublished paper presented at the 2nd International Symposium on Educational Testing, Montreux, 1975. Goncalves, K.P. and Goncalves, M.P., Use of the Kano Method Keeps Honeywell Attuned to the Voice of the Customer, Quirk’s Marketing Research Review, Apr. 2001, pp. 18–25. Gutman, J. and Miaoulis, G., Past Experience Drives Future CS Behavior, Marketing News, Oct. 22, 2001, pp. 45–46. Harry, M., The Vision of Six Sigma: A Roadmap for Breakthrough, 5th ed., Vol. 2, TriStar Publishing, Phoenix, 1997. James, H.L., Sasser, W.E., and Schlesinger, L.A., The Service Profit Chain: How Leading Companies Link Profit and Growth to Loyalty, Satisfaction and Value, Free Press, New York, 1997. Mariampolski. H, Qualitative Market Research, Sage Publications, Newbury Park, CA, 2001. Morais, R., The End of Focus Groups, Quirk’s Marketing Research Review, pp. 153–154, May 2001. Mudge, A.E., Numerical Evaluation of Functional Relationships, Proceedings, Society of American Value Engineers, 1967. Murphy, B., Methodological Pitfalls in Linking Customer Satisfaction with Profitability, Quirk’s Marketing Research Review, Oct. 2001, pp. 22–27. Murphy, B., Qualitatively Speaking: Of Bullies, Friends and Mice, Quirk’s Marketing Research Review, Oct. 2001, pp. 16, 61. Saliba, M.T. and Fisher, C.M., Managing Customer Value, Quality Progress, June 2000, pp. 63–70. Shillito, M.L., Pareto Voting. Proceedings, Society of American Value Engineers, 1973. Stamatis, D.H., Total Quality Management: Engineering Handbook, Marcel Dekker, New York, 1997. Stamatis, D.H., Total Quality Service, St. Lucie Press, Delray Beach, FL, 1996. Sullivan, L.P., The Seven Stages in Company Wide Quality Control, Quality Progress, May 1986, pp. 77–83. Sullivan, L.P., Quality Function Deployment, Quality Progress, June 1986, 1986, pp. 39–50. Thomas, J. and Sasser, W.E., Why Satisfied Customers Defect, Harvard Business Review, Nov.-Dec. 1995, pp. 88–89. VanVierah, S. and Olosky, M., Achieving Customer Satisfaction: Registrar Satisfaction Survey Counterbalances the Myth About Registrars, Automotive Excellence, Winter 1999, pp. 10–15. Veins, M., Wedel, M., and Wilms, T., Metric conjoint segmentation methods: a Monte Carlo comparison, Journal of Marketing Research, 33, 73–85, 1996. Wittink, D.R. et al., Commercial use of conjoint analysis: an update, Journal of Marketing, 53, 91–96, 1989.

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3

Benchmarking

Benchmarking is a tool, a technique or process, a philosophy, and a new name for old practices. It involves operations research and management science for determining (a) what to do or “goal setting” and (b) how to do it or “action plan identification.” Benchmarking can be applied (a) systematically and comprehensively or (b) ad hoc project by project. In both cases it can require (a) sophisticated statistical analysis, (b) utilization of a wide variety of analytical tools, and (c) a wide range of data sources. The basic requirements for success are: • • • • • •

Time, effort, and resources A willingness to learn and to change Continuing, long-term top management support An external focus on customers and competitors A common-sense approach and active listening The ability to look at the old in a new way

GENERAL INTRODUCTION TO BENCHMARKING A BRIEF HISTORY

OF

BENCHMARKING

The term “benchmarking” was coined by Xerox in 1979. Xerox has now performed over 400 benchmark studies, and the process is totally integrated at all levels as part of the business planning process. The approach has actually been in use for a number of years — although it was often called by different names. (Reverse engineering is an approach used to study the design and manufacturing characteristics of competitive products. Benchmarking of computer hardware and software is a very common practice.) Benchmarking extends the concept to consider administrative and all management processes. There is a conscious attempt to compare with the “best of the best” even — especially — if that is not a direct competitor. The fundamental process in starting benchmarking is to think about the area to be benchmarked, which can be just about anything, and ask yourself, “Who is especially good at that? What can I creatively imitate?” A typical process for doing a benchmarking is shown in Figure 3.1.

POTENTIAL AREAS

OF

APPLICATION

OF

BENCHMARKING

Benchmarking is a methodology that can be used along with other systematic, comprehensive management approaches to improve performance. It is not an end unto itself. Some examples of applications of benchmarking include: 97

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Prepare and respond to surveys

Agree to site visits

Builds customer goodwill Builds network of benchmarking partners May provide “in” to target companies but time consuming Must be managed to gain long term benefit Must be viewed as an investment

Two-way site visits

Informal search for the best

Define a process A benchmarking study IS a project Make sure you have clearance with legal department Involve the process owner Avoid the following mentality: We are unique We know it all It was not invented here It is too complex We already tried it and it does not work here

Follow a model

Form consortium group

Provides true improvement opportunities Answers “How do the best do it? Provides actionable data But Time consuming, must be focused Disciplined approach builds results Must be treated as an ongoing way of doing business

FIGURE 3.1 The benchmarking continuum process.

Broad management focus • Cost reduction • Profit improvement • Business strategy development • Total quality management Individual management processes • Improving customer service • Reducing product development time • Market planning • Product distribution Highly specific focus • Invoice design • Sales force compensation • Fork lift truck maintenance The critical questions to ask are: • What are the areas that potentially could be benchmarked? • How do you prioritize and focus the efforts?

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BENCHMARKING AND BUSINESS STRATEGY DEVELOPMENT Hall (1980) observed that certain industry leaders had exceptional performance even in the bad times of 1979–1980. For example:

Goodyear Inland Steel Paccar Caterpillar General Motors Maytag G. Heilman Brewing Philip Morris Average

Company ROE

Industry ROE

9.2 10.9 22.8 23.5 19.8 27.8 25.8 22.7 20.2

7.4 7.1 15.4 15.4 15.4 10.1 14.1 18.2 12.9

How can this be so? What strategy did the more successful competitors follow?

LEAST COST

AND

DIFFERENTIATION

Hall’s study itself is an early example of successful benchmarking. By extensive interviewing and data analysis, Hall reached conclusions based on the performance and the experience of a group of highly successful companies. As determined by Hall and also described in the book Competitive Strategy by Michael Porter, the successful competitor tends to follow one of two strategies: • Least cost • Differentiation Those competitors who do not explicitly follow one strategy or the other tend to get “stuck in the middle” and do not have the highest return on investment. Hall’s findings do, however, indicate that some firms can successfully manage both strategy options. The generic strategies identified by Hall and Porter have been supported by a number of research studies (see Higgins and Vincze, 1989). For a successful business strategy to be developed, a company must decide what course it will follow. It must also be certain that it is, in fact, realistically able to pursue that alternative. Some questions to be asked include the following: • Does a company really have the least cost? How do they know? What is the basis for the claim? • Is the company really differentiated in the eyes of the customer? How do they know? What is the basis for the claim? • How might competitive conditions change in the future? Benchmarking can provide — in part — the information necessary to answer these questions by providing focus and insight on what the best companies are doing.

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In addition to making a choice relative to least cost versus differentiation, an important strategy choice is that of being a mass marketer versus supplying the needs of a specific market segment. Therefore, when benchmarking is performed the following must always be present: • • • • • •

Build a relationship with your benchmarking partner. Establish trust and mutual interest. Be worthy of trust. Make it last. Be open to reciprocity. Follow a code of conduct. • Principle of confidentiality • Principle of first party contact • Principle of preparation • Principle of third party contact

CHARACTERISTICS

OF A

LEAST COST STRATEGY

A firm following the least cost strategy must be able to deliver a product or service with acceptable quality at a lower total cost than any of its competitors. Note that total cost is the critical concern. The company does not have to be least cost in every aspect of the business. The fact that the total cost is the lowest does not necessarily mean that the price that is charged is the lowest. To determine if the least cost strategy is viable, it is necessary to perform competitive benchmarking and gain information relative to the following: • What is the relative market share of the company? Does the experience curve have a significant effect on cost reduction? • Is the industry one that can be affected by automation possibilities, conveyorized assembly, or new production technology? Is the capital available for investment in efficient scale facilities and product and process engineering innovation? • Do competitors have a different mix of fixed and variable costs? • What is the percent capacity utilization by competitive firms? • Are the competitive firms using activity-based accounting? • How critical is raw material supply? Does the firm have preemptive sources of supply? • Does the firm have a tight system of budgeting and cost control for all functions? • Are productions designed for low cost productions? Are products simplified and product lines reduced in number? Are bills of material standardized? • What is the level of product/service quality versus competition? • How labor intensive is the process? How effective are labor/management relations? • Are marginal accounts minimized?

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Improved quality through benchmarking can lead to lower costs. The cost of quality — really the cost of non-quality — consists of the costs of prevention, appraisal (inspection), internal quality failures, and external quality failures. This cost can amount to as much as 30–40% of the cost of goods sold. Costs include the following: Costs of prevention Training Equipment Costs of appraisal (inspection) Inspectors Equipment Cost of internal quality failures Scrap Rework Machine downtime Missed schedules Excess inventory Cost of external quality failures Warranty expense Customer dissatisfaction Studies have shown that the average quality improvement project results in $100,000 of cost reduction. The associated cost to diagnose and remedy the problem has averaged $15,000. Consequently, the payout from benchmarking in this area can be significant. Velcro reported a 50% reduction in waste as a percentage of total manufacturing cost in the first year and an additional 45% decrease in the second year of its quality program. Motorola achieved a quality level in 1991 that was 100 times better than it was in 1987. By 1992, this company was striving for six sigma quality. That means three defects per million or 99.9997 percent perfection. Motorola believes that super quality is the lowest cost way of doing things, if you do things right the first time. Their director of manufacturing — at that time — pointed out that each piece of equipment has 17,000 parts and 144,000 opportunities for mistakes. A 99 percent quality rate is equivalent to 1,440 mistakes per piece. The cost to hire and train people to fix those mistakes would put the company out of business.

CHARACTERISTICS

OF A

DIFFERENTIATED STRATEGY

A firm following the differentiation strategy must be able to provide a unique product or service to meet the customer’s expectations. The challenge of being unique is that of providing a sustainable source of differentiation. It is very difficult to create something that is totally sustainable. This may depend upon a corporate culture producing a positive attitude toward quality and customer service or perhaps the value of information or computer-to-computer linkages.

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Following a differentiation strategy does not mean that a company can be inefficient relative to costs. Although cost is not the primary driving force, costs still must be minimized for the degree of differentiation provided. To determine if the differentiation strategy is viable it is necessary to perform competitive benchmarking and gain information relative to segmentation. When developing corporate or marketing strategy, it is important to identify the different market segments that make up the total market. A market segment is a group of customers with similar or related buying motives. The members of the segment have similar needs, wants, and expectations. A focus on market segments allows a company to tailor its products, services, pricing, distribution, and communication message to meet the specific needs of a market. The opposite of market segmentation is mass marketing. Segmentation allows a smaller company to successfully compete with a larger company by concentrating resources at the specific point of competition. Any market can be segmented. The toothpaste market, for example, can be segmented into the sensory segment (principal benefit sought is flavor or product appearance), the sociable segment (brightness of teeth), the worriers (decay prevention), and the least cost buyer. To segment a market you need to know who the customers are, what they buy, how they buy, when they buy, why they buy, and where they buy. Some typical questions in this area are: • • • •

How do you segment your market? What do you do differently for each of these segments? How does the competition segment the market? What new segments are likely to develop due to changes in sociological factors, technology, legislation, economic conditions, or growing internationalism?

BENCHMARKING AND STRATEGIC QUALITY MANAGEMENT Strategic Quality Management (SQM) or Total Quality Management (TQM), as defined by J.M. Juran, W. Edwards Deming and others, consists of a systematic approach for setting and meeting quality goals throughout a company. Just as companies have set out to achieve financial goals through a process of corporate business planning, so also can companies achieve quality goals by SQM or TQM or six sigma. An overly simplified definition of TQM is “Doing the right thing, right the first time, on time, all the time; always striving for improvement, and always satisfying the customer.” This requires a focus on customer needs, people, systems and process, and a supportive cultural environment. But this really is not any different from what the six sigma methodology proposes. The essential steps of the quality management process consist of: Quality planning • Identifying target market segments • Determining specific customers’ needs, wants, and expectations

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• Translating the customer needs into product and process requirements • Designing products and processes with the required characteristics (Competitive benchmarking can assist in this part of the process.) Quality control • Measuring actual quality performance versus the design goals • Diagnosing the causes of poor quality and initiating the required corrective steps • Establishing controls to maintain the gains Quality improvement • Establishing a benchmarking process • Providing the necessary resources It is important to note that the process: • Is strategic in nature, proactive • Is competitively focused on meeting customer needs as opposed to techniques of analysis • Is goal oriented • Is comprehensive in terms of level and functions • Manages in quality, not simply defect reduction The following are very closely linked: • • • • • •

Six sigma Business strategic planning Strategy development (least cost versus differentiation) TQM Pricing strategy Benchmarking

The classical approach to benchmarking viewed as process — which has become the de facto process — has the following characteristics: • • • • •

Inspection to control defects is primary tool. Better quality means higher costs. Significant scrap and rework activity takes place. Quality control is found only in manufacturing. SPC is used as an example; other tools are used occasionally.

Top management commitment • Level 5: Continuous improvement is a natural behavior even for routine tasks. • Level 4: Focus is on improving the system. • Level 3: Adequate money and time are allocated to continuous improvement and training. • Level 2: There is a balance of long-term goals with short-term objectives.

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• Level 1: The traditional approach is in place. Note that the Level 1 commitment is the status quo, and not much is happening. It is the least effective way of demonstrating to the organization at large that management commitment is a way of life. On the other hand, Level 5 is the most effective and demands change of some kind. Obsession with excellence • Level 5: Constant improvement in quality, cost, and productivity • Level 4: Use of cross-functional improvement teams • Level 3: TQM and six sigma support system set up and in use • Level 2: Executive steering committee set up • Level 1: Traditional approach Organization is customer satisfaction driven • Level 5: Customer satisfaction is the primary goal. More customers desire a long-term relationship. • Level 4: Striving to improve value to customers is a routine behavior. • Level 3: Customer feedback is used in decision making. • Level 2: Customer rating of company is known. • Level 1: The traditional approach is in place. Supplier involvement • Level 5: Suppliers fully qualified in all benchmark areas • Level 4: Suppliers actively implementing TQM and aware of the six sigma demands • Level 3: Direct involvement in supplier awareness training; supplier criteria in place • Level 2: Suppliers knowledgeable about your TQM as well as the six sigma direction; supplier number reduction started • Level 1: Traditional approach Continuous learning • Level 5: Training in TQM and six sigma tools is common among all employees. • Level 4: Top management understands and applies TQM and the six sigma philosophy. • Level 3: Ongoing training programs are in place. • Level 2: A training plan has been developed. • Level 1: The traditional approach is in place. Employee involvement • Level 5: People involvement; self-directed work groups. • Level 4: Manager defines limits, asks group to make decisions. • Level 3: Manager presents problem, gets suggestions, makes decision. • Level 2: Manager presents ideas and invites questions, makes decision. • Level 1: The traditional approach is used. Use of incentives • Level 5: Gainsharing • Level 4: More team than individual incentives and rewards • Level 3: Quality-related employee selection and promotion criteria

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• Level • Level Use of tools • Level • Level • Level • Level • Level

105

2: Effective employee suggestion program used 1: Traditional approach 5: Statistics a common language among all employees 4: More team than individual incentives and rewards 3: SPC used for variation reduction 2: SPC used in manufacturing 1: Traditional approach

The Malcolm Baldrige National Quality Award encapsulates the essential elements of Strategic Quality Management. The key attributes considered when making this award are listed below. Many agree that the criteria provide the blueprint for a better company. The urgency to win the award can accelerate change within an organization. Some companies have told their suppliers to compete or else. These are the criteria: • Quality is defined by the customer. • The senior management of a business needs to have clear quality values and build the values into the way the company operates on a day-to-day basis. • Quality excellence derives from well-designed and well-executed systems and processes. • Continuous improvement must be part of the management of all systems and processes. • Companies need to develop goals, as well as strategic and operational plans, to achieve quality leadership. • Shortening the response time of all company operations and processes needs to be part of the quality improvement effort. • Operations and decisions of the company need to be based on facts and data. • All employees must be suitably trained and involved in quality activities. • Design quality and defect and error prevention should be major elements of the quality system. • Companies need to communicate quality requirements to suppliers and work with suppliers to elevate supplier quality performance. Achievement of the award requires extensive top management effort and support. All of the Quality Award winners have been in highly competitive industries and either had to improve or get out of the business. On a scale of 10 (best) to 1 (poor), how would you rate your company on each of these attributes? If you find yourself on the low end, there may be a need for benchmarking.

BENCHMARKING

AND

SIX SIGMA

Within the information and analysis part of the examination or survey, the practitioners of benchmarking look specifically at competitive comparisons and benchmarks. It has been reported in the literature that many companies do not do enough

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in the way of benchmarking. They compare themselves against other manufacturers but do not make comparisons with outside businesses or even “true” best-in-class companies. A six sigma company is expected to describe the company’s approach to selecting quality-related competitive comparisons and world-class benchmarks to support quality planning, evaluation, and improvement. The specific areas to address are: • Criteria and rationale the company uses for making competitive comparisons and benchmarks. These include: • The relationship to company goals and priorities for the improvement of product and service quality and/or company operations • The companies for comparison within or outside the industry • Current scope of competitive and benchmark involvement and data collection relative to: • Product and service quality • Customer satisfaction and other customer needs • Supplier performance • Employee data • Internal operations, business processes, and support services • Other • For each, the company is directed to list sources of comparisons and benchmarks, including companies benchmarked and independent testing or evaluation, and: • How each type of data is used • How the company evaluates and improves the scope, sources, and uses of competitive and benchmark data • The company must also indicate how this data is used to support: • Company planning • Setting of priorities • Quality performance review • Improvement of internal operations • Determination of product or service features that best predict customer satisfaction • Quality improvement projects Specific uses of benchmarking are to assist in: • • • •

Developing plans Goal setting Continuous process improvement Determining trends and levels of product and service quality, the effectiveness of business practices, and supplier quality • Determining customer satisfaction levels

A closer review of the criteria indicates several factors that are essential for effective quality excellence and benchmarking activities within a company, including:

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Customer-driven quality • Quality is judged by the customer. The customer’s expectations of quality dictate product design and this, in turn, drives manufacturing. • All product and service attributes that lead to customer satisfaction and preference must be taken into consideration. • Customer driven quality is a strategic concept. Why do people buy your product? How do you know? • Leadership is crucial. A company’s senior management must create clear quality values, specific goals, and well-defined systems and methods for achieving the goals. • Ongoing personal involvement is essential. The attitude must be changed from a “management control” focus to a “management committed to help you” focus. Continual improvement • Constant improvement in many directions is required: improved products and services, reduced errors and defects, improved responsiveness, and improved efficiency and effectiveness in the use of resources. All of this takes time. If you do not have the time, do not start. Fast response • An increasing need exists for shorter new product and service development and introduction cycles and a more rapid response to customers. Actions based on facts, data and analysis • A wide range of facts and data is required, e.g., customer satisfaction, competitive evaluations, supplier data, and data relative to internal operations. • Performance indicators to track operational and competitive performance are critical. These performance indicators or goals can act as the cohesive or unifying force within an organization. They can also provide the basis for recognition and reward. • Participation by all employees is important. Reward and recognition systems need to reinforce total participation and the emphasis on quality. • Factors bearing on the safety, health, and well being of employees need to be included in the improvement objectives. • Effective training is required. The emphasis must be on preventing mistakes, not merely correcting them. Employees must be trained to inspect their own work on a continuous basis. • Participation with suppliers is essential. It is important to get suppliers to improve their quality standards.

NATIONAL QUALITY AWARD WINNERS

AND

BENCHMARKING

Example — Cadillac To show the strong relationship between National Quality Award winners and benchmarking, we provide a historical perspective. The first example comes to us from

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Cadillac’s approach to excellence. (Cadillac was the 1990 winner of the National Quality Award.) The brief case study that follows indicates the integration of business planning, excellent quality management, and benchmarking. The Business Plan was the Quality Plan. The plan was designed to ensure that Cadillac is the “Standard of the World” in all measurable areas of quality and customer service. The major components of the plan were: • Mission • Objectives • Quality — Emphasis on six major vehicle systems: • Exterior component and body mechanical • Chassis/powertrain • Seats and interior trim • Electrical/electronics • Body in white • Instrument panel • Competitiveness • Disciplined planning and execution • Leadership and people • Goals For each objective, the following issues were addressed: • What are the measurable performance indicators of quality and customer service? When answering, consider both the product itself and the management process that led to the improved product or service. • What does the customer need or want? • What levels are achieved by the best-of-class companies considering both direct competitors and any other company? • What are the time-phased quality improvement goals? Action plans • Took appropriate and applicable action to fulfill all the requirements so that the customer could be satisfied. A Second Example — Xerox In the early 1980s, Xerox realized that Japanese competition was selling products for less than the Xerox cost. Many of the required reforms focused on Xerox suppliers because the cost of purchases amounted to 80% of the copiers cost of goods sold. Xerox asked suppliers to restate their company performance data so that the supplier could be compared with the best of class Xerox could find anywhere in the world. Some of the benchmarks Xerox used to measure operations proficiency included: • Ratio of functional cost to revenue (percent) • Headcount per unit of output • Overhead rate (dollars/hour)

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• • • • • • • • • • •

109

Cost per order entered Cost per engineering drawing Customer satisfaction rating (index value) Internal and external defect rates (parts per million) Service response time (hours) Billing error rates Days inventory on hand Total manufacturing lead time (days) New product development time (weeks) Percent of parts delivered on time New ideas per employee

Xerox reduced its number of suppliers from 5,000 in 1980 to 300 by 1986 based on performance data and attitude. Suppliers were classified as: (a) does not think improvement is necessary, (b) slow to accept or manage change, and (c) willing to go for it and strong enough to be a survivor. Xerox reallocated its internal efforts to concentrate on the companies in the third group. Xerox provided extensive training to these companies, and defect rates in incoming materials dropped 90 percent in three years. In addition to performance improvement, the suppliers were asked to participate in copier design, as early in the concept phase as possible, and to make suggestions so that overall quality could be improved and costs reduced. When this information was used, the cost of purchased material dropped by 50 percent. Third Example — IBM Rochester IBM Rochester describes its quality journey as follows: 1981 1984 1986

Vision Goal Vision Goal Vision Goals

1989

Vision

1990–1994

Goal Vision

Goal

Product reliability Zero defects Process effectiveness and efficiency All process rated Customer and supplier partnerships Competitive and functional benchmarks Best of competition Over 350 benchmarking teams are in place; scores of benchmarking studies have been completed; strategic targets are derived from the comprehensive benchmarking process Market-driven customer satisfaction Total business process focus Closed loop quality management system Total customer satisfaction Customer — the final arbiter Quality — excellence in execution Products and services — first with the best People — enabled, empowered, excited, rewarded Undisputed leadership in customer satisfaction

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Results: A 30 percent improvement in productivity occurred between 1986 and 1989. This was a period of extensive benchmarking activity. Product development time has been reduced by more than half, and manufacturing cycle time has been trimmed by 60 percent since 1983. Fourth Example — Motorola Each of the firm’s six major groups and sectors have “benchmarking” programs that analyze all aspects of a competitor’s products to assess their manufacturability, reliability, manufacturing cost, and performance. Motorola has measured the products of some 125 companies against its own standards, verifying that many Motorola products rank as “best in their class.” (It is imperative for the reader to understand that the result of a benchmarking study may indeed provide the researcher with data to support the assertion that the current practices of your own organization are the “best in class.”)

BENCHMARKING

AND THE

DEMING MANAGEMENT METHOD

There is a very close relationship between the approach of W. Edwards Deming and that specified by the requirements of the National Quality Award. The potential role of benchmarking to implement certain aspects of the Deming approach is apparent. Deming’s fourteen points are summarized below: 1. Create constancy of purpose for the improvement of product and services. 2. Adopt the new philosophy that quality is critical for the competitive survival of a company. 3. Cease dependence on mass inspection, and create the processes that build a quality product from the start. 4. End the practice of awarding business based on price alone, and take into consideration the quality of products and services received. 5. Improve constantly and forever the system of production and service. This begins with product design and goes through every phase of business operations. 6. Institute training and retraining. 7. Provide leadership and the resources required to get the job done. 8. Drive out the fear of admitting problems and suggesting new and different ways of doing things. Get around the not invented here syndrome. 9. Break down interdepartmental barriers so that all departments can work toward the common objective of satisfying the customer. 10. Eliminate slogans, exhortations, and targets for the workforce without providing the ways and means for accomplishment. Do not tell people what to do without telling them how to do it and providing the systems and support necessary.

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11. Eliminate numerical quotas. These often promote poor quality. Instead analyze the process to determine the systemic changes required to enable superior performance. 12. Remove barriers to pride in workmanship by providing the training, communication, and facilities required. 13. Institute a vigorous program of education and retraining. Help people to improve every day. 14. Take action to accomplish the transformation required.

BENCHMARKING

AND THE

SHEWHART CYCLE

OR

DEMING WHEEL

Plan Study a process to determine what changes might be made to improve it. What type of performance is achieved by the best of the best? What do they do that we are not doing? What results do they achieve? What changes would we have to make? What does the customer expect? What is the customer level of satisfaction? Is the change economically justified? Do Determine the specific plan for improvement and implement it. This involves the development of creative alternatives by work teams and the conscious choice of a strategy to be followed. This may require internal or external benchmarking. Study — Observe the Effects Was the root cause of the problem identified and corrected? Will the problem recur? Are the expected results being achieved? Act Study the results and repeat the process. Was the plan a good one? What was learned? This approach amounts to the application of the scientific method to the solution of business problems. It is the basis of organizational learning.

WHY DO PEOPLE BUY? Differentiation and quality management both focus on the need to meet customer needs, wants, and expectations. Why does a person buy a particular product? • One view: (marketing based) • A second view: (psychologically based) How can we define quality? This is a very critical question and may indeed prove the most important question in pursuing benchmarking. The importance of this question is that it will focus the research on “best” in a very customized fashion

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from the organization’s perspective. This is a question that must be addressed as early as possible.

ALTERNATIVE DEFINITIONS

OF

QUALITY

People buy a combination of products and services for a price that depends upon the perception of the value received. In order to conduct benchmarking studies relative to quality, it is important to define the elusive term “quality.” Garvin (1988) and Stamatis (1996, 1997) provide various definitions of quality as follows: Garvin’s eight dimensions of product quality are: Performance — Performance refers to the ability of the product to perform up to expectations relative to its primary operating characteristics. For example, a camera can be self-focusing and automatically adjust the lens opening. Products can often be ranked in terms of levels of performance, i.e., good, better, best. People’s expectations differ depending upon the task to be performed. Products are designed for different uses. Therefore, a failure to perform might simply indicate another product class or market segment focus and not inferior quality. Features — Features are secondary attributes that affect a product’s performance. For example, the camera mentioned above can weigh less than two pounds. A car can have power steering as a feature. Features can often be bundled or unbundled. The distinction between performance and features is arbitrary. One person’s performance characteristics can be another person’s features. Reliability — Reliability reflects the ability of a product to perform properly over a period of time. A car, for example, might perform without major repairs for 50,000 miles. Measures used to evaluate reliability are factors such as the mean time between failures, the mean time to first failure, and the failure rate per 1000 items. Conformance — Conformance measures whether product quality specifications have been met. Is a shaft the required diameter? Are the parts of impurity per million within the specified limits? Individual parts can be within tolerance; however, there can be a problem of tolerance stackup. Four parts, each 1.000 inch wide plus or minus .0005 inch, when stacked up will not be 4.000 inches tall plus or minus .0005 inch. Durability — Durability measures a product’s expected operating life. Product life can be limited due to technical failure (mechanical, electrical, hydraulic, pneumatic), technical obsolescence, or the economics of continued repair. For example, a light bulb has technical failure when the filament burns out. An automobile has economic failure when the owner decides it is no longer economically advantageous to repair it. Serviceability — Serviceability refers to the speed, ease, cost, certainty, and effectiveness of repair. Of critical concern are the courtesy of the repair people, the speed of getting the product back, and whether or not it is really fixed.

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Aesthetics — Aesthetics are concerned with the look, taste, feel, sound, and smell of an item. This can be critical for products such as food, paint, works of art, fashion, and decorative items. Perceived quality — Perceived quality is determined by factors such as image, advertising, brand identity, and word of mouth reputation. Stamatis, on the other hand, has introduced a modified version of the above points with some additional points — especially for service organizations. They are: Function — The primary required performance of the service Features — The expected performance (bells and whistles) of the service Conformance — The satisfaction based on requirements that have been set Reliability — The confidence of the service in relationship to time Serviceability — The ability to service if something goes wrong Aesthetics — The experience itself as it relates to the senses Perception — The reputation of the quality To be effective and efficient, the following characteristics must be present: • • • • • •

Be accessible Provide prompt personal attention Offer expertise Provide leading technology Depend — quite often — on subjective satisfaction Provide for cost effectiveness

What is interesting about these two lists is the fact that both Garvin and Stamatis recognize that design for optimum customer satisfaction is a design issue. Design, indeed, is the integrating factor. The designer has to make the tough trade-offs. Concurrent engineering and Quality Function Deployment suggest that the product designer, the manufacturing engineer, and the purchasing specialist work jointly during the product design phase to build quality in from the start. The focus, of course, is to design all the above characteristics as a bundle of utility for the customer. That bundle must address in holistic approach the following: Image Transcendent view — This view defines quality as that property that you will innately recognize as such once you have been exposed to it. Something about the product or service or the way it has been promoted/communicated to you causes you to recognize it as a quality offering — perhaps an excellent one. Performance Product-based view — This view defines quality in terms of a desirable attribute or series of attributes that a product contains. A high-quality fuel product could have a high BTU content and a low percentage of sulfur.

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User-based view — This view defines quality in terms of how well a product or service meets the expectation of the customer. If the product meets expectations, it is considered to be of high quality. Expectations vary widely, and meeting expectations may not lead to the best product. For example, a bestseller may not be the best literature. Manufacturing-based view — This view defines quality in terms of conformance to manufacturing specifications. This view may, however, promote manufacturing efficiencies at the expense of suitability to the user. For example, problems of tolerance stackup are particularly noteworthy. Value Value-based view — This view, which is gaining in popularity, looks at value as the trade-off between quality and price. From this perspective, quality consists of all of the non-price reasons to buy a product or service. To come up with reasonable definitions and actions for the above characteristics, a team must be in place and team dynamics at work. A very good approach for this portion of benchmarking that we recommend is the nominal group process: The process features are as follows: Group size: five to nine core individuals Group composition: multidisciplinary and cross-functional Reflection — 20 minutes: all participants allowed to express their views as to what the problem is and how the team should progress Sharing of ideas: Discussion of the presented ideas Voting: evaluation of ideas and selection of the “best” Tabulation: Final resolution of what is at stake and how to proceed so that success will result The discussion and direction of the nominal process must not focus on price alone because that is a very narrow point of view. Some examples of non-price reasons to buy are: Product non-price reasons to buy • Ease of product use • Performance • Features • Reliability • Conformance • Durability • Serviceability • Aesthetics/style • Perceived quality • Ability to provide a bundled package Service and image non-price reasons to buy • Speed of delivery • Dependability of delivery

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• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

Fill rate Fun to deal with Number/location of stocking warehouses Repair facilities and location Technical assistance Service — before, during, and after sale Willingness to hold inventory Flexibility Access to salespeople Access to multiple supply sources Reputation Life cycle cost Financing terms Turnkey operations Consulting/training Warehousing Guarantees/warranty Services provided by salespeople Ease of resale Computer placement of orders Professional endorsement Packaging Up front engineering Vendor financial stability Confidence in salespeople Backup facilities Courtesy Credibility Understandability Responsiveness Accessibility of key players Flexibility Confidentiality Safety Delivery Ease of installation Ability to upgrade Customer training Provision of ancillary services Product briefing seminars Repair service and parts availability Warranty Image Brand recognition Atmosphere of facilities Sponsor of special events

115

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The service and image features define the “augmented product.” They answer the questions: • What does your customer want in addition to the product itself? (the unspoken requirements) • What does your customer perceive to have value? • What does your customer view as “quality”? In order to focus benchmarking efforts, it is critical to define the unique selling proposition or the product concept. A statement of product concept requires the definition of both attribute(s) and benefit(s). Attributes consist of both form and features (specific product or service characteristics) and technology (how they are to be provided). For example, a new brewing technique brings a double-strength beer to add to your enjoyment by capturing the taste of the 1800s (technology, form, benefit). So what do you expect to get out of this team effort and integration? Simply put, you should get the answers to some very fundamental questions about your organization and the product/service you offer. Some typical questions are: • What are the non-price reasons to buy your product? How do they compare with the product and service attributes listed above? • How do your customers define quality? How does your company define quality? • What is more important? Product or service? • Can specific, measurable attributes be defined? • How does your competitor define quality? • How do you compare with your competitor? • What other companies or industries influence your customer as to what should be expected relative to each of these characteristics? • What does this suggest in the way of benchmarking opportunities? For example, here are some non-price reasons to buy that might apply to a supermarket: • • • • • • • • • • • •

Large parking lot Zoo in parking lot Lots of giveaways Makes shopping fun for the entire family Clowns Disneyland figures Well-stocked, attractive displays Rock hard containers of ice cream Complaint box (policy to respond the next day to the customer) Fast cash out “Forget your checkbook? Pay next time.” Trains all associates

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117

Uses Dale Carnegie courses Walt Disney people management One aisle that rambles through the store No question return policy Bus for senior citizens Customer focus groups every three weeks Associates who take the initiative to please customers In-store dairy and bakery

None of these, in itself, is earth-shaking. But they could make the difference in an industry that operates with a profit margin of less than 1%. We cannot pass up the opportunity to address non-price issues for the WALMart corporation, which allegedly spends 1% of 100 details in the following items: • • • • • • • • • • • • • • • • • •

Aggressive hospitality People greeters Associates not employees Tough people to sell to Weekly top management meetings Low cost, no frills environment Good computerized database Rapid communications by phone Managers in the field Monday through Thursday High-efficiency distribution centers Emphasis on training of people Department managers having cost and margin data Profit sharing if store meets goals Bonus if shrinkage goal is met Open door policy Grass-roots meetings Constant improvements Competitive ads shown in store

DETERMINING

THE

CUSTOMER’S PERCEPTION

OF

QUALITY

Differentiation is uniqueness in the eyes of the customer. Quality is meeting the unique needs, wants, and expectations of the customer in terms of the non-price reasons to buy. But who is the customer? Depending on (a) defining the customer for multiple channels of distribution or (b) identifying the multiple buying influences in a business-to-business sale, the customer may be: • • • •

User Technical buyer Economic buyer Corporate general interest buyer

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Who is the competitor? Assume for example a recreation environment. Here are some questions you might ask that would help you to determine who the competitors are: What • • What • • What • • What • •

is the desire I want to satisfy? (Desire competitor) Recreation Education kind of recreation do I enjoy? (Generic competitor) Baseball Boating kind of boating? (Form competitor) Power boat Sailboat brand boat? (Brand competitor) Bayliner Boston Whaler

Once these questions have been addressed, now we are ready to do the competitive evaluation in the following stages: Survey design • Attributes considered • Relative weight given to each • Direct competitors • Performance versus competition Approaches to making the survey Internal • Sales force • Sales management (Remember, the more accurate data you have, the better the survey. For example: Colgate Palmolive audits 75,000 customers for all products. “People know what they want and will not settle for happy mediums.”) External • Market research firms/universities • Attribution/non-attribution • Use of customer service hot line — GE progressed from receiving 1000 calls per week in 1982 to receiving 65,000 calls per week with the installation of an 800 number answer center. The 150 phone reps need a college degree and sales experience. They have been effective in spotting trends in complaints as well as increasing sales. The increase in sales has been estimated at more than twice the operating cost of the center. (Did this trigger off a benchmarking candidate for you?) Groups to be surveyed • Current customers • Lost customers • Prospects

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Survey frequency Comparison of company internal view versus the customer view

QUALITY, PRICING

AND

RETURN

ON INVESTMENT

(ROI) — THE PIMS RESULTS

Being perceived as being the best or having the product with the highest quality can have significant bottom line results. Buzzell and Gale (1987) introduced the PIMS (Profit Impact of Marketing Strategies) system, which is an elaborate benchmarking database developed by the Strategic Planning Institute in Cambridge, Mass. The database contains information for over 450 companies and over 3000 business experience pools in a wide variety of industries, including manufacturers, raw material producers, service companies, distributors, and durable and non-durable consumer products. Data are collected for independent business units, each with a defined served market. The objectives of the Strategic Planning Institute and benchmarking are to help organizations in the process of becoming excellent organizations. How do they do it? By: 1. Using the statistical analysis and modeling of business experience 2. Isolating the key factors that determine return on investment (ROI) ROI equals net income before interest and taxes divided by the total of working capital and fixed capital. As a result the Institute can help organizations with: • Understandability • Predictability of their own organization’s behavior and their own products and services. Of course, the choice of strategy depends upon several factors, including but not limited to: • • • • • • • •

Market growth rate and product life cycle Current market share Price/quality sensitivity by segment Competitive response profiles Current and planned capacity Cost and feasibility of quality improvements Market perception of quality improvements Financial and marketing goals — long and short term (The period described as “short term” and “long term” will differ widely among various strategies and organizations.)

BENCHMARKING AS A MANAGEMENT TOOL So far we have talked about benchmarking but we really have not defined it. A formal definition, then, is that benchmarking is a systematic, continual (ongoing)

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management process used to improve products, services, or management processes by focusing on and analyzing the best of the best practices, by direct competitors or any other companies, to determine standards of performance and how to achieve those standards, to provide least cost, quality or differentiation, in the eye of the customer. Key words in this definition are systematic and ongoing, which imply that in order to have a successful benchmarking one must be familiar with the Kano model, Shewhart-Deming cycle, and principle of Kaizen improvement. This systematic and ongoing pursuit of excellence is applicable to all aspects of business and in all methodologies including the six sigma. It is an integral part of the strategic, operational, and quality planning process. It is not an end in itself. Benchmarking identifies the best of class and determines standards of excellence based on the market — considering both customers and competitors. It is a challenge with a solution. It provides the what and how. (A narrow focus on what you want to get done — a results orientation that controls performance with a carrot and a stick — is not effective without a broader focus on how best to do it — a process orientation that identifies the process changes that need to be made in order for the results to be achieved consistently.) Benchmarking is a creative imitation because part of its goal setting process that encourages the development of proactive plans is the action to bring about change. To do that, of course, analysis is required to determine all of the factors necessary for a solution to work, as appropriate and applicable to a given organization. In addition, it is necessary to project the future performance of the competition to set improvement goals. Otherwise, a company is always playing catch-up. Some of the key factors in this analysis are: • • • •

People/culture/compensation Process/procedure Facilities/systems Material

WHAT BENCHMARKING IS

AND IS

NOT

Benchmarking is not: • A way to cut costs or headcount, necessarily • A quick fix or a panacea • A cookbook approach Rather, it is a methodology that is an integral part of the management process and provides the organization with many benefits including but not limited to: • Identifying the specific action plans required to achieve success in company growth and profitability • Assessing objectively strengths and weaknesses versus competition and the best in class

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• Improving quality as perceived by the customer (The customer can be external to the company or the next department in the company.) • Determining goals objectively and realistically based on the actual achievements of others • Providing a vision of what can be accomplished in terms of both what and how • Providing hard, reliable data as a basis for performance improvement • Causing people to think creatively and to look at proactive alternative solutions to a problem • Promoting an opportunity for personal and corporate growth, learning, and development • Raising the company level of awareness of the outside world and of customer needs • Stimulating change — “Others are doing this, why can’t/shouldn’t we?” • Identifying all of the factors required to get a job done • Promoting an in-depth analysis and quantification of operations and management processes • Encouraging teamwork and communication within an organization • Creating an awareness of problems and stimulating change • Documenting the fact that a good job is being done and that you are the best in class • Allowing a company to leapfrog competition by looking outside of an industry • Changing the rules of the game by breaking with the traditions of an industry

THE BENCHMARKING PROCESS The benchmarking process can differ from company to company. However, the tenstep process below is generally followed. I. Benchmark planning and prioritization 1. Identification of benchmarking alternatives 2. Prioritization of the benchmarking alternatives II. Benchmark data collection 3. Identification of the benchmarking sources 4. Benchmarking performance and process analysis — company operations • What do we do? • What is the process? • What are the resource inputs? • What are the outputs? • What is the resource cost per unit of output? • What are the limitations? • What are possible changes? 5. Benchmarking performance and process analysis — partner’s operations

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III. Benchmark implementation 6. Gap analysis 7. Goal setting 8. Action plan identification and implementation IV. Benchmark monitoring and control 9. Monitor company performance and action plan milestones 10. Identify the new “best in class”

TYPES

OF

BENCHMARKING

Benchmarking can be performed for any product, service, or process. Different classification schemes have been suggested. For example, Xerox classifies benchmarking in the following categories: • Internal benchmarking • Direct product competitor benchmarks • Functional benchmarking — This is a comparison with the best of the best even if from a different industry. • Generic benchmarking — This is an extension of functional benchmarking. It requires the ability to creatively imitate any process or activity to meet a specific need. For example, the technique used for high speed checking of paper currency (into the categories of good, mutilated, or counterfeit) by a bank could be adapted for high speed identification and sorting of packages in a warehouse. ATT, on the other hand, uses the classification indicated below. Specific examples of benchmarking studies for each are shown. These are not limited to ATT examples: Task • Invoicing • Order entry • Invoice design • Customer satisfaction • Supplier evaluation • Flow charting • Accounts payable Functional • Promotion by banks • Purchasing • Advertising by media type • Pricing strategy • Safety • Security Management process • PIMS par report • Profit margin/asset turnover

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• Strategic planning • Operational planning • Capital project approval process • Technology assessment • Research and development (R and D) project selection • Innovation • Training • Time-based competition • Benchmarking • Self-managed teams Operations process • Warehouse operations • Make versus buy Another classification of benchmarking projects is by: • • • •

Function — sales and marketing Process — missionary selling Activity — cold calling Task — preparation of target list

Still another classification is in terms of: • Overall financial performance • Department or functional benchmarking • Cost benchmarking

ORGANIZATION

FOR

BENCHMARKING

Ad hoc benchmarking studies can be helpful and productive. However, many companies are attempting to institutionalize benchmarking as part of the business planning and six sigma process. The business planning process consists of strategic planning followed by operational planning. Both phases require the development of functional area plans. However, the time periods considered, the alternatives of interest, and the level of detail are very different. The general flow of the planning process is: What should we do? • Situation analysis performed to determine critical success factors, strengths, weaknesses, opportunities, and threats • Mission development • Statement of objectives and goals How should we to do it? • Strategy determination • Tactics identified • Action plans specified

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What are the expected results? • Budgets and financial projections How did we do? • Monitoring and control Who should get rewarded? • Performance evaluation and compensation Benchmarking is often an integral part of the situation analysis. It can also have a major impact on the mission statement, the goals, the strategy, the tactics, and the identification and determination of action plans. Benchmarking can provide major guidance when determining what to do, how to do it, and what can be expected. Benchmarking for strategic planning might concentrate on the determination of the critical success factors for an industry (based on customer and competitive inputs) and identifying what has to be done to be the success factors. This then leads to the development of detailed action plan with effort and result goals. Benchmarking for operational planning might concentrate on the cost and cost structure for each functional area relative to the outputs produced. All quality initiatives — including six sigma — have a significant influence on the mission statement and the objectives and goals of an organization. As such, they can provide an added impetus to do benchmarking to satisfy the quality goals. Benchmarking can be centralized (ATT) or decentralized (Xerox). Xerox has several functional area benchmarking specialists, including specialists for finance, administration, marketing, and manufacturing. The big advantage of a decentralized approach is a greater likelihood of organizational buying of the final results of the benchmarking study. The effort required to perform a benchmarking study can vary significantly. For example, the L.L. Beam study performed by Xerox took one person year of effort. Generally, three to six companies are included in the benchmark. However, some companies use only one or two. Also, some studies are performed in depth, while others are fairly casual. The “One Idea Club” was a simple approach with a substantial reward.

REQUIREMENTS

FOR

SUCCESS

All initiatives have requirements for success. Benchmarking is no different. Some of these requirements are: • Management vision and support to ensure the conditions necessary for the success of the strategy — people, money, time. • Goal-focused management with a customer/competitive focus on continuously improved quality • Performance- or results-based compensation • Action plan prioritization and focus • Defined roles and responsibilities for a multidisciplinary approach • Defined organizational approach — central versus decentralized • Integration with other management processes

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• Ability to maintain focus on the continuous improvement of hundreds of small items a little bit at a time • Willingness to deal with the conflict caused by a lack of goal congruity and the need to share scarce resources and to make tough decisions • Tolerance to deal with the ambiguity of results as research is conducted to determine when, where, and how to improve operations • Openness to learn and to change; results can affect organization structure, allocation of resources, corporate culture, and individual work assignments • Use of the scientific method: hypothesis formation, data collection, testing, and learning • Humility and the willingness to admit weakness and the possibility for improvement • Identification of the impediments to change and the development of a plan for change • Patience and resources to perform the analytical studies and to complete the required documentation • Long-term commitment to achieving results • Flexibility and discipline to implement the required changes • Communication of intent and approach, findings, concerns, and apprehensions • Training and total employee involvement, empowerment, and teamwork • A process that starts slow, showcases, and picks up speed as experience and confidence are gained • Market segmentation focus and a defined corporate strategy It sounds good. But does benchmarking work? Let us see what the SAS Airlines did, as an example. When Jan Carlzon took over as president of Scandinavian Airlines (SAS) in 1980, the company was losing money. For several previous years, management had dealt with this problem by cutting costs. After all, this was a commodity business. Carlzon saw this as the wrong solution. In his view, the company needed to find new ways to compete and build its revenue. SAS had been pursuing all travelers with no focus on superior advantage to offer to anyone. In fact, it was seen as one of the least punctual carriers in Europe. Competition had increased so much that Carlzon had to figure out: • Who are the customers? • What are their needs? • What must we do to win their preference? Carlzon decided that the answer was to focus SAS’s services on frequently flying business people and their needs. He recognized that other airlines were thinking the same way. They were offering business class and free drinks and other amenities. SAS had to find a way to do this better if it was to be the preferred airline of the frequent business traveler.

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The starting point was market research to find out what frequent business travelers wanted and expected in the way of airline service. Carlzon’s goal was to be one percent better in 100 details rather than 100 percent better in only one detail. The market research showed that the number one priority was on-time arrival. Business travelers also wanted to check in fast and be able to retrieve their luggage fast. Carlzon appointed dozens of task forces to come up with ideas for improving these and other services. They came back with ideas for hundreds of projects, of which 150 were selected with an implementation cost of $40 million. One of the key projects was to train a total customer orientation into all of SAS’s personnel. Carlzon figured that the average passenger came into contact with five SAS employees on an average trip. Each interaction created a “moment of truth” about SAS. At that point of contact, the person was SAS. Given the 5 million passengers per year flying SAS, this amounted to 25 million moments of truth where the company either satisfied or dissatisfied its customer. To create the right attitudes toward customers within the company, SAS sent 10,000 front line staff to service seminars for two days and 25,000 managers to three-week courses. Carlzon taught many of these courses himself. A major emphasis was getting people to value their own self-worth so that they could, in turn, treat the customer with respect and dignity. Every person was there to serve the customer or to serve someone who was serving the customer. The results: Within four months, SAS achieved the record as the most punctual airline system in Europe, and it has maintained this record. Check-in systems are much faster, and they include a service where travelers who are staying at SAS hotels can have their luggage sent directly to the airport for loading on the plane. SAS does a much faster job of unloading luggage after landings as well. Another innovation is that SAS sells all tickets as business class unless the traveler wants economy class. The company’s improved reputation among business flyers led to an increase in its full fare traffic in Europe of 8 percent and its full fare intercontinental travel of 16 percent, quite an accomplishment considering the price cutting that was taking place and zero growth in the air travel market. Within a two-year period, the company became a profitable operation. Carlzon’s impact on SAS illustrates the customer satisfaction and profits that a corporate leader can achieve by creating a vision and target for the company that excites and gets all the personnel to swim in the same direction — namely, toward satisfying the target customers. As a leader, Carlzon created the conditions necessary to ensure the success of the strategy by implementing the projects required for the front line people to do their jobs well.

BENCHMARKING AND CHANGE MANAGEMENT Several behavioral models underscore the psychological requirements for change in a person or an organization. The classic equation for change is: D×V×F>R

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where D = dissatisfaction with current situation; V = vision of a better future; F = the first steps of a plan to convert D to V; and R = resistance to change. Typical attitudes/comments of resistance are: • • • • • • •

Perceived threat of loss — power, position Everything is OK. Why fix it? What should we change? How? What is management trying to tell me? Takes a long time to see results! We do not have time to do that “stuff.” If this is so good, why aren’t they doing it?

Benchmarking can accelerate the change process by offering the organization’s managers facts that relate to their needs and expectations, by understanding the psychology of change. For example, while the previous mathematical formula is a quantifiable entity on its own, it gives us little opportunity to explore change from the individual’s perspective. Change begins with an individual. That individual must: 1. Believe that he or she has the skill necessitated by the change. Can I do it? 2. Perceive a reasonable likelihood of personal value fulfillment as a result of making the change. What will I get out of it? 3. Perceive that the total personal cost of making the change is more than offset by the expectation of personal gain. Is it worth making the change? This model suggests that we manage change by education and communication to influence what a person thinks and that this, in turn, causes a change in behavior. Thought is affected by: • • • •

Beliefs Facts Values Feelings

Benchmarking can help implement change by providing the required facts and challenging beliefs, especially if there are data to be supported from other organizations. Other models to manage change are: • • • • •

Facilitation and support Participation and involvement Negotiation Manipulation Explicit and implicit coercion

Corporate culture is important:

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• Reward risk taking • Encourage passionate champions • Focus on base hits versus home runs Sources of dissatisfaction that can drive change include: • Financial pressure • Quarterly earnings • Cash flow (Need: to improve operational efficiency)

STRUCTURAL PRESSURE • • • •

Cyclical business mix Customer mix Cash flow conflicts Product life cycle mix (Need: To improve business mix or effectiveness)

ASPIRATION

FOR

EXCELLENCE

The need to improve is an internal perception. “You do not have to be bad to improve.” Organization positions can be viewed as having either innovation and/or maintenance responsibilities relative to change. How does the mix change for workers, supervisors, middle management, and top management in an organization that strives for excellence? Current success can mask underlying problems and can prevent or delay action from taking place when it should, i.e., when the company has the time and resources to do something. Consider the classic story of the “boiled frog” as an example. (If you recall, the frog was boiled when the temperature was increasing at a very slow rate. The frog was adapting. The frog could not differentiate the change and ultimately, was boiled. On the other hand, the frog that was thrown into hot water jumped out right away and saved its life.)

FORCE FIELD ANALYSIS Force field analysis is a systematic way of identifying and portraying the forces (often people) for or against change in an organization. The specific forces will differ depending upon the area where benchmarking is applied. Here is how the process works: 1. 2. 3. 4. 5. 6. 7.

Define the current situation. Define the desired position based on the results of the benchmarking study. Define the worst possible situation. What are the forces for change? What is their relative strength? What are the forces against change? What is their relative strength? What forces can you influence? Define the specific action to be taken relative to each of those forces that you can influence.

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One effective way to start the benchmarking process is to select one high visibility area of concern to the influence leaders in a company and produce results that can showcase the benchmarking process. This might start with a library search to highlight the results that are possible.

IDENTIFICATION OF BENCHMARKING ALTERNATIVES As indicated earlier, benchmarking candidates can be identified in a wide variety of ways. They can be detected, for example, during the business planning process, as part of a quality initiative, during a six sigma project, or during a profit improvement campaign. Both external and internal analysis can lead to potential candidates.

EXTERNALLY IDENTIFIED BENCHMARKING CANDIDATES Industry Analysis and Critical Success Factors Based on the structure of an industry and the dynamics of the customer/supplier interface, certain factors are critical to the success of a business. An identification of the critical success factors and an evaluation of the company’s current capabilities can lead to benchmarking opportunities. The competitive rivalry among firms in an industry has a significant impact on total demand and the level and stability of prices. Competitive rivalry is a function of several interrelated factors that affect the supply and demand for products and services. The balance of supply and demand at a particular time affects the percent capacity utilization in an industry. The percent capacity utilization directly affects price levels and price elasticity. The factors affecting demand are: • The strategy of the buyer to be least cost or differentiated — How well do you meet your customers’ specific needs? • The availability of and knowledge about substitute products — What are existing and new competitors likely to do? • The ease of switching from one product to another — How can you increase the cost of switching to another supplier? • Governmental regulations — What can you do to influence these? The factors affecting supply are: • The ease of market entry — What can you do that will make it hard to enter the business? • The barriers to market exit — What can you do to make it easy for a competitor to get out of the business? • Governmental regulations — What can you do to influence these?

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Based on an analysis of the industry as it exists now and might exist in the future, what are the factors absolutely critical for success? Five or six critical success factors can usually be identified for a company. Examples are: • • • • • • • • • • • • • • • • • • •

Customer service Distribution Technically superior product Styling Location Product mix Cost control Dealer system Product availability Supply source Production engineering Advertising and promotion Packaging Staff/skill availability Quality Convenience Personal attention Innovation Capital

Once the critical success factors have been identified, the company can assess its current position to determine whether benchmarking is required. One technique for performing this analysis is to make a tabulation showing how the major competitors in an industry rank for each critical success factor. As a cross check, there should be a correlation between the tabulated results, market share and return on equity. PIMS Par Report The PIMS par report indicates the financial results that companies in similar circumstances have been able to achieve. As such, it provides a quantitative benchmark. The PIMS report also indicates those factors that should enable you to earn greater than par and those factors that would cause you to earn less than par. Financial Comparison If PIMS data are not available, a comparison of the company’s financial performance versus that of other companies in the same industry can suggest the value of benchmarking in specific areas. Potential areas that might be identified are: • Gross margin improvement • Overhead cost reduction

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Fixed asset utilization Inventory or accounts receivable reduction Liquidity improvement Financial leverage Sales growth

Competitive Evaluations As discussed earlier, a competitive evaluation is a periodic assessment made to determine, objectively, what factors a buyer takes into consideration when deciding to buy from one supplier versus another, the relative weight given to each of those factors, the competing firms, and the relative performance of each firm with respect to each buying motive. Focus Groups Focus groups are used to determine what a customer segment thinks about a product or service and why it thinks that way. Participants are invited to join the group usually with some type of personal compensation. A focus group starts with a series of open-ended questions relative to a specific subject. Representatives of the sponsoring company view the entire process either through a one-way mirror or by closed circuit TV. As a second phase, the company representatives ask specific follow-up questions (through the facilitator), based on the open-ended probing. Importance/Performance Analysis The customer perception of performance versus importance can be used to identify benchmark alternatives. A list of attributes can be prepared using either a nominal group process or a focus group. The customer is asked to rate each attribute in terms of both importance and company performance. A matrix is then prepared showing high and low performance versus high and low importance. It has the following implications: • • • •

Continue with high importance, high performance. Reduce emphasis on low importance, low performance. Increase emphasis on high importance, low performance. Reduce emphasis on low importance, low performance.

In addition to determining the customer’s perception of performance versus importance, it is also valuable to determine the customer’s versus the company’s perception of importance. This can also be used to determine areas for intensification and reduction of effort and benchmarking possibilities, including: 1. Customer-oriented goals 2. Service/quality goals

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INTERNALLY IDENTIFIED BENCHMARKING CANDIDATES — INTERNAL ASSESSMENT SURVEYS An internal assessment of strengths and weaknesses can be used to identify benchmarking candidates. This determination can be made using a Business Assessment Form followed by group discussion or by using the Nominal Group Process. The assessment can be made by the owner of a product, process, or service and/or by the department being served. An internal assessment can also be approached from the viewpoint of the generic value added chain. For each block of the chain, two questions can be asked: 1. What are the alternatives for least cost operation? 2. What are the alternatives to provide differentiation? The value added chain can also provide customer perspective by suggesting the questions: 1. How does our product or service help customers to minimize their cost? 2. How does our product or service help customers to differentiate their product? Nominal Group Process: General Areas in Greatest Need of Improvement • • • • • • •

Improving the precision of the sales forecast Reducing the cycle time to bring out new products Increasing the success rate in bidding for new business Reducing the time required to fill customer orders Reducing the errors in invoices Major problems or issues Areas of competitive disadvantage

Pareto Analysis Pareto analysis is a form of data analysis that requires that each element or possible cause of a problem be identified along with its frequency of occurrence. Items are then displayed in order of decreasing frequency of probability of occurrence. This can help to identify the most significant problem to attack first. A common expression of the Pareto Law is the 80/20 rule, which states that 20% of the problem causes 80% of the difficulties. A Pareto analysis of setup delay might include factors such as: necessary material not available, tooling not ready, lack of gages, setup personnel not available, another setup has priority, material handling equipment not available, error — incorrect setup. Develop a Pareto analysis for the production of scrap. (There is a tremendous difference between knowing the facts and guessing).

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Statistical Process Control Statistical process control is a technique for identifying random (or common) causes versus identifiable (or special) causes in a process. Both of these are potential sources for improvement. The amount of random variation affects the capability of a machine to produce within a desired range of dimensions. Hence, benchmarking could be performed to determine machine processing capabilities and how to achieve those levels. The determination and correction of recurring systematic changes is also a benchmarking possibility. The reduction of the random variation or the uncertainty of the process and the identification and correction of special causes are critical aspects of the total quality management process. Correction often requires a change in the total manufacturing process, tooling, the equipment being used, and/or training in setup and operations. The first step in process improvement is to control the environment and the components of the system so that variations are within natural, predictable limits. The second step is to reduce the underlying variation of the process. Both of these are candidates for benchmarking. Trend Charting Historic data can be used to develop statistical forecasts and confidence intervals that depict acceptable random variation. When data fall within the confidence intervals, you have no cause to suspect unusual behavior. However, data outside of the confidence intervals could provide an opportunity for benchmarking. It might also be informative to pursue benchmarking as a device to reduce the range of variation or the size of the confidence interval. A simple trend analysis of your own past data can also provide a basis for improvement. The following data relative to the percent scrap and rework illustrate the improvement made and could provide the basis for benchmarking: 1987

1988

1989

1990

2.1%

3.0%

1.0%

0.7%

Product and Company Life Cycle Position Products tend to go through a defined life cycle starting with an introductory phase and proceeding through growth, maturity, and decline. The management style and business tactics are very different at each stage. Anticipating and managing the transitions can be important. This could lead to opportunities for benchmarking of product life cycle management and product portfolio management. Product portfolio management can lead to the need for new product identification and introduction. These areas have both been the subjects of benchmarking studies. In addition to the changes that products go through, companies tend to go through various stages of development and crises. Again, the management of the transitions can be an important benchmarking candidate.

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Failure Mode and Effect Analysis Failure Mode and Effect Analysis (FMEA) is a systematic way to study the operating environment for equipment or products and to determine and characterize the ways in which a product can fail. Benchmarking can be used to determine component and system design goals and alternatives (see Chapter 6). Cost/Time Analysis To evaluate its new product introduction process, a company may plot cost per unit produced versus elapsed time for each element of the process, e.g., design and engineering, production, sub-assembly, and assembly. The area under the curve represents money tied up (inventory), and smaller is better.

NEED

TO IDENTIFY

UNDERLYING CAUSES

Problem, Causes, Solutions When solving a problem, it is critical to attack the underlying cause of the problem and not the symptoms. The underlying cause can be identified by listing all possible causes and identifying the most probable cause based on data collection and a Pareto analysis. This sometimes leads to multiple benchmarking opportunities. Failure to diagnose a problem (ready, fire, aim) can lead to an inefficient use of resources and frustration. The Five Whys When identifying underlying causes, it can also be useful to ask five sequential “whys” to get to the heart of a problem. For example: Problem: The milling machine is down. Why? The chucking mechanism is broken. Why? A piece got jammed when being loaded. Why? There was excess flash from the stamping operation. Why? The stamping die was not changed. Why? The die usage control log was not updated daily. Cause and Effect Diagram The development of a Cause and Effect Diagram or Fishbone Diagram or Ishikawa Diagram is another way to identify and display the underlying causes of a problem. Causes are usually displayed in terms of major categories such as human or personnel, machines or technology, materials and methods or procedures. Once causes are identified, an analysis is made to determine actionable solutions. The determination of cause and effect can require the use of designed experiments to measure effects and interaction. For example, to reduce conveyor belt spillage, it was necessary to determine the effects of belt wipers, belt surface, dryness of the belt and material, and particle size in various combinations of each factor.

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BUSINESS ASSESSMENT — STRENGTHS AND WEAKNESSES You will be asked to evaluate the organization relative to sales and marketing, manufacturing and operations, R & D, and general management. A typical assessment is shown in Table 3.1.

TABLE 3.1 A Typical Assessment Instrument Please indicate how you evaluate the organization using the following key: (There are many ways to use a key. This is only one example.) ++ + E – •

Extremely strong, definite leaders Better than average Average Weak, should do better Extremely weak, area of major concern Sales and marketing Customer base Market share Market research Customer knowledge Brand loyalty Company business image Response to customers Breadth of product line Product differentiation Product quality Distributors Locations Size Warehousing Transportation Communication Influencing customers Sales force People and skills Size Type Location Productivity Morale Advertising

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TABLE 3.1 (Continued) A Typical Assessment Instrument National regional cooperative Promotion devices Prices/incentives Customer communication Service Before sale After sale Credit Long term Short term Trade allies Costs Selling Distribution Manufacturing/operations Materials management People and skills Sourcing Inventory P & C Production P & C Capability P & C Computer system Physical plant Capacity Utilization Flexibility Plant Size location Number Age Equipment Automation Maintenance Flexibility Processes Uniqueness Flexibility Degree of integration Engineering Process Tool design Cost improve

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TABLE 3.1 (Continued) A Typical Assessment Instrument Time standards Quality control People and skills Workforce Skills mix Utilization Availability Turnover Safety Unionization Costs Productivity Morale Direct/indirect Research and development Basic research Concepts and studies Emphasis People and skills Conversions to applications Patents Applied research Finding Emphasis People and skills Conversion to prototype Patents Basic engineering Prototypes Emphasis People and skills Convert to product Design engineering Designs Patents and copyrights Emphasis People and skills Design for production Funding Amount Consistency Sources Project selection

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TABLE 3.1 (Continued) A Typical Assessment Instrument General management Leadership Vision Risk/return profile Clarity of purpose Implementation skills Turnover Experience Motivation skills Leadership style Delegation Strategic emphasis Organization Type Size Location Communication Defined responsibility Coordination Speed of reaction Fix with strategy Commitment Planning and control Early alert system Forecasting Operational budget Control MBO program Capital planning Long range planning Contingency planning Cost analysis Resource allocation Accounting and finance Financial public relations Financial relations Auditing Decision making Style Techniques used Responsiveness Position in org. criteria used

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TABLE 3.1 (Continued) A Typical Assessment Instrument Personnel Effectiveness Hourly labor Clerical labor Sales people Scientists and engineers Supervisors Middle management Top management Comp. and reward Management development Management depth Turnover Morale Information systems Decision support system Customer data Product line data Fixed/variable costs Exception reporting Culture Shared values Pluralism Conflict resolution Openness Optional Information Name: Date: Title: Dept:

PRIORITIZATION OF BENCHMARKING ALTERNATIVES — PRIORITIZATION PROCESS A variety of prioritization approaches are available. Use the one most appropriate to a specific situation.

PRIORITIZATION MATRIX The following steps are required to complete a prioritization matrix:

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1. 2. 3. 4. 5.

List all items to be prioritized. List the goals or the prioritization criteria. Specify the goal weights. Indicate the impact score of each item relative to each goal. Determine the value index for each item by totaling the cross product of each goal weight times the impact score. 6. Sort the items from highest to lowest value index.

QUALITY FUNCTION DEPLOYMENT (HOUSE

OF

QUALITY)

Quality function deployment is an extension of the prioritization matrix described above. However, the rows and the columns are interchanged. The rows become the evaluation criteria (or goals) and the columns represent the alternative solutions to be prioritized. The following procedure is used to complete the Quality Function Deployment analysis: 1. List the items indicating “what” you want to accomplish. These are the evaluation criteria. 2. List “how” you will accomplish what you want to do. These are the alternatives to be evaluated. 3. Indicate the degree of importance for each of the “whats.” This is a number ranging from 1 to 10 (10 is most important). 4. Indicate the company and the competitive rating using a scale from 1 to 10 (10 is best). Plot the competitive comparison. 5. Specify the planned or desired future rating. 6. Calculate the improvement ratio by dividing the planned rating by the company current rating. 7. Select at most four items to indicate as “sales points.” Use a factor of 1.5 for major sales points and a factor of 1.2 for minor sales points. 8. Calculate the importance rate as the degree of importance times the improvement ratio times the sales points. 9. Calculate the relative weight for each item by dividing its importance rate by the total of the importance rates for all “whats.” 10. Indicate the relationship value between each “what” and “how.” Use values of 9, 3, and 1 to indicate a strong, moderate or light interrelationship. 11. Calculate the importance weight for each “how.” This is the total of the cross products of the relationship value and the relative weight of the “what.” 12. Indicate the technical difficulty associated with the “how.” Use a scale of 5 to 1 (5 is the most difficult). 13. Indicate the company, competitive values, and benchmark values for the “how”. 14. Specify the plan for each of the “hows.” Quality function deployment is usually applied at four different interrelated levels:

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1. Product planning What — customer requirements How — product technical requirements 2. Product design What — product technical requirements How — part characteristics 3. Process planning What — part characteristics How — process characteristics 4. Production planning What — process characteristics How — process control methods

IMPORTANCE/FEASIBILITY MATRIX Importance is a function of urgency and potential impact on corporate goals. It is expressed in terms of high, medium, and low. Feasibility takes into consideration technical requirements, resources, and the cultural and political climate. It is also expressed in terms of high, medium, and low. Paired Comparisons This approach is based on a pair-by-pair comparison of each set of alternatives to determine the most important. Count the total number of times each alternative was selected to determine the overall prioritization. Improvement Potential To determine how to prioritize cost improvement benchmarking alternatives, perform the following analysis: 1. Make a Pareto analysis of cost components 2. Assess the percent improvement possible for each of the most significant cost components. 3. Multiply the cost times the percent improvements possible to determine the improvement potential. 4. Prioritize the benchmark studies based on improvement potential. This approach can be used to prioritize other areas as well. Prioritization Factors When prioritizing benchmarking candidates, it is important to take into consideration many factors. Some of these factors are listed below. It is important to narrow projects down to the significant few and to choose a good starting project to showcase the value of the approach.

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The first project should be a winner. It should address a chronic problem, there should be a high likelihood of completion in several weeks, and the results should be (a) correlated to customer needs and wants, (b) significant to the company, and (c) measurable. Factors to be used subsequently are: • • • • • • • • • • • •

• • • • • •

Importance of business need long term Basis for a sustainable competitive advantage Percent improvement possible Customer impact Realism of expectations Urgency Ease of implementation/degree of difficulty Time to implement Consistency with mission, values, and culture Organizational buy in Passionate champion identified Resource requirements and availability • Capital expenditures • Working capital • Time by skill category Synergy Risk versus return Measurability of result Modularity of approach Anticipated problems Potential resistance

ARE THERE ANY OTHER PROBLEMS? WHAT IS OF EACH OF THESE?

THE

RELATIVE IMPORTANCE

The Japanese approach to improvement is called “Kaizen.” This philosophy espouses an innovative, small-step-at-a-time approach that is implemented by creating an awareness of need and empowerment throughout an organization. This contrasts to the Western approach, which tends to be higher tech, capital intense, and focused on major innovative changes. (Several studies have demonstrated that the U.S. is much better at discovery and invention than Japan, but that we lag in commercial development and implementation of the ideas.) Could the low tech, people-oriented focus work in your competitive situation? What does this suggest in terms of benchmarking prioritization?

IDENTIFICATION OF BENCHMARKING SOURCES TYPES

OF

BENCHMARK SOURCES

The benchmarking process often starts with a library search to identify alternative views, issues, approaches, and possible benchmarking sources. Benchmarking

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sources can be internal best performers, competitive best performers, or best in class worldwide. Internal Best Performers Xerox used internal benchmarking when it studied Fuji-Xerox’s manufacturing methods (but not until Florida Power and Light began to emulate them). Different divisions, plants, distribution outlets, and departments tend to do things differently. Much can often be learned by looking at these company operations. Competitive Best Performers The advantage of making comparisons with direct competitors is obvious. However, it can be difficult to get competitors to share their source of competitive advantage. When working with direct competitors, it can also be difficult to get out of the industry mind-set and come up with creative ideas. It could be that the competitors in an industry are not particularly good at what they do and hence provide little stimulus for improvement. Xerox regularly benchmarks all direct competitors, all their suppliers, and all major competitors to those suppliers. Updates are important. Knowing how fast competitors are moving is just as important as knowing where they are. Best of Class There is, in general, no way to know the “best” of the best. Companies generally pick the “best” based on reputation through publications, speeches, news releases, etc. A company might start out with four to ten “best” candidates and narrow them down based on initial discussions. Xerox looked at IBM and Kodak but also L.L. Bean, the catalog sales company, known for effective and efficient warehousing and distribution of products. Additional benchmarking partners used by Xerox were: Customer satisfaction, customer retention Financial stability and growth SPC and quality Customer care and training

USAA (Insurance Co.) A.G. Edwards & Sons Florida P&L Walt Disney

Milliken & Company, winner of the 1989 National Quality Award, provided the following partial list of benchmarks: Strategy Safety Customer satisfaction Innovation Education Strategic planning Time based competition

DuPont ATT, IBM 3M, KBC IBM, Motorola Frito-Lay, IBM, ATT Lenscrafters

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Benchmarking Self-managed teams Continuous improvement Heroic goals concept Role model evaluation Environmental practice Statistical methods Flow charting Quality process

Security Accounts payable Order handling

Quality Process Xerox Goodyear, P&G Japanese Motorola Xerox DuPont, Mobay, Ciba-Geigy Motorola Sara Lee FP&L, Westinghouse, Motorola Miscellaneous DuPont Mobay L.L. Bean

SELECTION CRITERIA How do you know who is the best? Here are some ways to get that information: • • • •

Library search Reputation Consultants Networking

Characteristics to be examined when seeking partners include: • • • • • • •

Company size Customer non-price reasons to buy Industry critical success factors Availability of data Data collection costs Innovation Receptivity

One hundred percent accuracy of information is not required. You only need enough to head you in the right direction.

SOURCES

OF

COMPETITIVE INFORMATION

Read everything and ask, “Has anyone faced this or a similar problem? What have they done?” Do not forget to ask people in your own organization, including: • • • •

Past employees of benchmark company Family members Market researchers Sales and marketing

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It is also helpful to make use of trade associations and consultants and to network. Review studies in which people have identified the characteristics of best performers. Good sources here are Clifford and Cavanagh (1988), Smith and Brown (1986), and Berle (1991). Another good source is the Encyclopedia of Business Information Sources, published frequently by Gale Research, Detroit, Michigan. This source contains references by subject to the following: • • • • • • • • • • • • •

Abstracting and indexing services Almanacs and yearbooks Bibliography Biographical sources Directories Encyclopedias Financial ratios Handbooks and manuals Online databases Periodicals and newsletters Research centers and institutes Trade associations/professional associations Other

Additional sources may also be found in the John Wiley publication entitled Where to Find Business Information, as well as the following: Books and periodicals • Trade journals • Functional journals • F.W. Dodge reports • Technical abstracts • Local newspapers, national newspapers • Nielson — Market Share • Yellow Pages • Textbooks • Special interest books • City, region, state business reviews • Standard and Poors industry surveys Directories • Trade show directory • Directory of Associations • Brands and Their Companies • Who Runs the Corporate 1000 • Corporate Technology Directory • American Firms in Foreign Countries • Corporate Affiliations • Foreign Manufacturers in U.S. • Directory of Company Histories

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• International Trade Names • Leading Private Companies • Marketing Economics Key Plants • Directory of Advertisers • Books of business lists • Thomas Register • Wards Directory • Lists of 9 Million Businesses — ABI Computer databases — CD-ROM or online Text databases • Business dateline — articles • BusinessWire — press releases • Intelligence Tracking Service — consumer trends • Dow Jones Business and Financial Report • Newsearch • Trade and Industry Index Statistical business information • BusinessLine • Cendata • Consumer Spending Forecast • Disclosure Database • CompuServe • Retail Scan Data • Moody’s 5000 Plus Demographic data • Census Projection 1989–1993 • Donnelley demographics Directories • Dun’s Million Dollar Directory • Thomas Register Company direct • Advertising • Benchmarking partner • Company newsletters • Minority interest partners • Speeches • Direct contact Financial sources • Annual reports, 10k, proxies, 13D • Investment reports • Prospectus • Filings with regulatory agencies • Dun and Bradstreet, Robert Morris • Moody’s Manuals • S&P Reports

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Individuals • Company employees • Past employees/retirees • Social events • Construction contractors • Landlords, leasing agents • Salesmen • Service personnel • Focus groups Professional societies • Professional society members • Trade shows/conventions • National associations • User groups • Seminars • Rating services • Newsletters Government • Public bid openings • Proposals • National Technical Information Center • Freedom of Information Act • Occupational Safety and Health Administration (OSHA) filings • Environmental Protection Agency (EPA) filings • Commerce Business Daily • Government Printing Office Index • Federal depository libraries • Court records • Bank filings • Chamber of Commerce • Government Industrial Program reviews • Uniform Commerce Code filings • State corporate filings • County courthouse • U.S. Department of Commerce • Federal Reserve banks • Legislative summaries • The Federal Database Finder • Patents Customers • New customers • Consumer groups Industry members • Suppliers • Equipment manufacturers

147

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• Distributors • Buying groups • Testing firms Snooping • Reverse engineering • Hire past employees • Interview current employees • Dummy purchases • Shopping • Request a proposal • Hire to do one job • Apply for a job • Mole • Site inspections • Trash • Chatting in bars • Surveillance equipment Schools and universities • Directories of case studies • Industry studies Consultants • Business schools on a consulting basis • Jointly sponsored studies • Information brokers • Industry studies • Market research studies • Seminars

GAINING

THE

COOPERATION

OF THE

BENCHMARK PARTNER

Without confidentiality, benchmarking will not work. Some items for consideration in gaining this confidentiality and cooperation are: • Use consultants or trade associations or universities to ensure confidentiality. • Make sure that there is mutual sharing — could be different areas. • Be prepared to give and receive. • Focus on mutual learning and self improvement. • Benefit of probing questions and debate • Opening up of a vision • Confirmation of good practice • Consider benchmarking a circuit of companies. • Important that all know in advance • Consider all security and legal implications of sharing data.

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MAKING

149

CONTACT

THE

When making the contact for benchmarking, follow these steps: • Call and express interest in meeting. • Send/receive a detailed list of questions. • Make sure that you have prepared your questions carefully. (The quality of the questions can be the signal for a worthwhile use of time.) • Follow up by telephone. • Visit — keep an open mind — document everything

BENCHMARKING — PERFORMANCE AND PROCESS ANALYSIS PREPARATION

OF THE

BENCHMARKING

PROPOSAL

Factors to be considered in the preparation of the benchmarking proposal include: • • • • • • • • • • •

Mission Objective/scope Statement of importance Information available Critical questions Ethical and legal issues Partner selection Roles and responsibilities Visit schedule Data analysis requirements Form of recommendation

ACTIVITY

BEFORE THE

VISIT

The approach that follows is very comprehensive. It might not be economical to follow all the steps in every study. Let practical common sense be the guide to action. Understanding Your Own Operations You need to understand your own operations very thoroughly before comparing them with the operations of others. Here are some steps you should take to make sure that you understand your current methods: Ask open-ended questions. For example, for “who”: • • • •

Who Who Who Who

does it per the job description? is actually doing it? else could do it? should be doing it?

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Ask similar questions for what, where, when, why, and how. Activity Analysis Activity analysis consists of the following steps: 1. Define the Activity Activities can be defined through: • • • •

Function Process Marketing and sales Sell products

Activity: These are the major action steps of a process. For example, make a proposal. Task: Prepare proposal draft Operation: Type proposal

2. Determine the Triggering Event Identify what happens to trigger the activity. Why does the activity get performed at a specific time? What is the status of material or information before the activity occurs? What documentation signifies that the activity is to start? Example: Receive material Material receipt document

3. Define the Activity Document how to perform the activity. Indicate what has to be done and the order in which it is done. This will define all business procedures, policies, and controls. Questions to ask include: • • • • •

What What What What What

are the key process variables? controls these variables? levels lead to optimum performance? are the causes of variation? are the limitations?

Activities should be classified in terms of repetitive versus non-repetitive, primary versus secondary, and required versus discretionary. It is important in this analysis to determine limitations, sources of error, rejects, and delays. Example: Raw material Inspection process manual

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4. Determine the Resource Requirements Identify the resources to perform the activity. Include factors such as direct material, direct labor (hours and grade), equipment requirements, information requirements, and space requirements. The resources might come from more than one department. It is crucial to trace all of the resources required to perform the activity. The resources can be determined by a careful analysis of the chart of accounts. When making the cost analysis, carefully choose among using actual, budgeted, standard, or planned cost information. Example: Inspector, material, handling equipment, inspection equipment, inspection area, inspection manual

5. Determine the Activity Drivers What are the factors external to the activity that cause more or less of the resources to be used? What drives the need for the activity and the level of resources required? Consider both efficiency and effectiveness, as follows: 1. Efficiency: Doing things right. 2. Effectiveness: Doing the right things Example: bad weather, poor product quality, automated equipment, workplace layout

6. Determine the Output of the Activity What units can be used to measure the output of the activity? This will be a measure of production level such as pieces produced, lots produced, invoices processed, checks written, or standard hours earned. Example: Lots of raw material inspected, pieces inspected, or material acceptance forms completed

7. Determine the Activity Performance Measure Identify that output measure that most closely controls the level of resources required. For example, when looking at clerical activities, the number of invoices is more significant than the dollar volume of the invoices. When moving material, the tons moved is more significant than the number of invoices represented. In general, the activity measure will be a resource input per unit of an output measure. Examples: • • • • • •

Cost/lot Pieces/hour Cost/unit Square foot per person Patents per engineer Drawings per engineer

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• • • •

Lines of code per programmer Contact labor/company labor Sales dollars/sales manager Machine changeover –% total

Model the Activity Modeling an activity involves the following: • • • •

Define the process Define the cost or resource requirements Define the output variables Determine the metric or resource per unit of output (This may require the use of regression analysis or the design of experiments.)

Critical considerations are: • What is the relationship between fixed and variable costs? • What determines the capacity limitations of the process? • How much does overhead change with a change in the volume of business? It is important to distinguish between the metric (resource per unit of output) and the cost drivers. The metric or activity measure for inserting pins might be cost per pin inserted. However, the cost drivers might be the product design and the technology used. A different design might require fewer insertions, and a different technology might avoid the need for any insertions. Examples of Modeling The modeling of raw material cost per unit produced might consider the following variables: • • • • • •

The number of parts to be produced The standard raw material per part The percent scrap produced Raw material unit price Raw material quantity discounts Exchange rates

Note that a simple comparison of raw material cost as a percentage of sales dollars provides little real basis for comparing costs and cost improvement. The number of units sold of an item could be modeled as the number of potential buyers times the percentage who become aware of the product if they can get it times the percentage of potential buyers who can get the product times the percentage of triers who will be repeat buyers times the number of units purchased by a repeat buyer.

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When working with salaries and wages, it is necessary to take into consideration factors such as headcount, rate by grade, straight time/overtime ratios, benefits, skill level, age, education, union vs. non-union, and incentives. Salary and wage ratios that can be benchmarked are: • • • •

Skilled/unskilled labor Direct/indirect labor Training cost per employee Overtime hours/straight time hours

Flow Chart the Process To determine the sales dollars from a new account, start by flow charting the steps required to sell a new account. Start with cold calls and work through to a close. Use of symbols in flow charting: • • • • • •

Start or stop Flow lines Activity Document Decision Connector

Then ask some key questions: • What are the major activities? • What are the ratios required to forecast sales? • What factors affect the selling cost per rep or the revenue per rep? Does looking at these ratios tell you very much? What would you benchmark? Here is an example of activity performance measures for warehouse operations: Picking operations Orders filled per person per day Line items per person per day Pieces per person per day Number of picks per order Standard hours earned per day Line items per order Receiving operations Number of trucks unloaded per shift Number of pallets received per day Number of cases received per day Number of errors per day Direct labor hours unloading trucks

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Incoming QC operations Number of inspections per period Number of rejects per period Direct inspection labor hours Putaway/storage operations Number of full pallet putaways per period Number of loose case putaways per period Direct labor hours putaway or storage Cube utilization Truck loading Number of units loaded per truck per period Number of trucks per period Time per trailer Customer service operations Fill rate Elapsed time between order and shipment Error rate Customer calls taken per day Number of problems solved per call Number requiring multiple calls Number of credits issued Number of backorders At this stage we are ready to identify information required when meeting with the benchmark partner. The following information is typical and may be used to focus the meeting with the benchmark partner and to highlight information requirements: 1. Description of company activity and results: 2. Alternative ways of performing the activity: Alternative 1: Alternative 2: Alternative 3: 3. The pros and cons of the alternatives are: Pros Cons Alternative 1: Alternative 2: Alternative 3: 4. Information required to reach a conclusion as to the best approach: Review the assumptions for the study to make sure that the outcomes are correlated to what you were studying. (At this stage, it is not unusual to find surprises. That is, you may find items that you overlooked or you thought were unimportant and so on.)

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DURING THE

155

VISIT

By far the most important characteristic of the visit is to: Observe, question, analyze and learn

Make sure to notice: • • • •

What are they doing? How is it different from what we are doing? Why are they doing it that way? How can the results be measured?

Ask open-ended questions, just as you did when observing your own operations. For example, for “who”: • • • •

Who Who Who Who

does it per the job description? is actually doing it? else could do it? should be doing it?

Ask similar questions for what, where, when, why, and how. Understand the Benchmark Partner’s Activities Follow the procedures described above for analyzing the company activities. You may encounter some analytical difficulties because of the following factors. Accounting differences Account definitions vary in terms of what is included in the account. For example, does the cost of raw material include the cost of freight in and insurance? Where is scrap accounted for? Cost allocations. Identification of all multi-department costs. Different economies of scale/learning curve Specialization Automation Time/unit Identification of All of the Factors Required for Success Factors to consider when trying to determine if you have identified all the factors required for success include the following: Analysis and intuition • MRP and inventory cycle count • Salary and wage comparisons — are the jobs really comparable?

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• The use of manufacturing work cells (This may require a change in socialization.) • Level of advertising per dollar of sales (Just knowing this may not be very helpful. The relevant question is, “How effectively are the advertising dollars spent?”) Regression analysis Warehouse study Design of experiments

ACTIVITIES

AFTER THE

VISIT

Key activities after the visit include the following: • • • • • •

Be sure to send a thank you note promptly. Document findings for each visit. Summarize all findings — analysis and synthesis. Compare current operations with findings. Gather more specific data if required. Identify opportunities for improvement — combine, eliminate, change order, etc. • Develop team recommendation. • Distribute benchmark report.

Benchmarking Examples 1. Functional Analysis Hours/1000 pcs Company Best Company

Functions Primary machining Heat training Grinding Assembly Packing

.75

.50

Gap .25

2. Cost Analysis

Cost Item Raw material Direct labor

% Total Cost

Cum % Total

Company

Cost per Unit Best

40 20

40 60

17.50 7.50

15.50 5.50

3. Technology Forecasting The benchmarking of competitive technologies can be very critical. This is particularly true when the product or technical life cycle is very short. Keys are:

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• Knowing the current technology and its limitations • Identifying the emerging technologies that become the new benchmarks. • Knowing what customers really buy and relating this to the emerging technology. • Having the courage and foresight to change. 4. Financial Benchmarking Financial benchmarking compares a company (or the major segments of a company) relative to the financial performance of other companies. The modified Du Pont chart provides a convenient way to do this. The idea of the modified Du Pont plan is to start with the return on equity and progressively calculate the return on assets, profit margin, gross margin, sales, cost of goods sold (COGS), sales per day, cost of goods sold per day, days inventory (COGS), days receivable (COGS), and days payable (COGS). Company results can be compared with data provided by: • Dun and Bradstreet Industry Norms and Key Business Ratios • Robert Morris Associates Annual Statement Summary • Prentice Hall Almanac of Business and Industrial Financial Ratios 5. Sales Promotion and Advertising The comparison of company strategy versus industry strategy can lead to the need for more specific benchmarking studies. 6. Warehouse Operations The performance of units engaged in essentially the same type of activity can be compared using statistical regression analysis. This technique can be used to determine the significant independent variables and their impact on costs. Exceptionally good and bad performance can be identified and this provides the basis for further benchmarking studies. 7. PIMS Analysis The PIMS analysis is a further application of multiple regression analysis. It can be used to identify the major determinants of company profitability. 8. Purchasing Performance Benchmarks The Center for Advanced Purchasing Studies (Tempe, Arizona) has benchmarked the purchasing activity for the petroleum, banking, pharmaceutical, food service, telecommunication services, computer, semiconductor, chemical, and transportation industries. For a wide variety of activity measures, the reports provide the average value, the maximum, the minimum, and the median value.

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Motorola Example Perhaps one of the most famous examples of benchmarking in recent history is the Motorola example. Motorola, through “Operation Bandit,” was able to cut the product development time for a new pager in half to 18 months based on traveling the world and looking for “islands of excellence.” These companies were in various industries: cars, watches, cameras, and other technically intensive products. The solution required a variety of actions: • • • •

Automated factories Removing barriers in the workplace Training of 100,000 employees Technical sharing alliance with Toshiba

Motorola was particularly impressed by the P200 program of a Hitachi plant. This stands for a 200% increase in productivity by year end. The plant set immutable deadlines for the solutions to problems. All departments had six sigma goals.

GAP ANALYSIS DEFINITION

OF

GAP ANALYSIS

There are at least two ways to view “gap.” 1. Result Gap — A result gap is the difference between the company performance and the performance of the best in class as determined by the benchmarking process. This gap is defined in terms of the activity performance measure. The gap can be positive or negative. 2. Practice or Process Gap — A practice or process gap is the difference between what the company does in carrying out an activity and what the best in class does as determined by the benchmarking process. This gap is measured in terms of factors such as organizational structure, methods used, technology used, or material used. The gap can be positive or negative. The determination of a gap can be a strong motivator toward the improvement of performance. It can create the tension necessary for change to occur.

CURRENT

VERSUS

FUTURE GAP

It is critical to distinguish between the current gap and the likely future gap. Remember that the benchmark partner’s performance will not remain at the current levels nor will the expectations of the marketplace. More likely than not, performance will improve as time goes on. A company that concentrates only on closing the current gap will find itself in a constant game of catch-up.

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The company that ignores likely improvements of the benchmark gets caught in the Z trap. The Z trap, of course, is the step-wise improvement that is OK for catching up but never good enough to be the best in class. To summarize the benchmark findings, it is often helpful to make a tabulation showing the current practice and metric and the expected future practice and metric for the company, the competition, and the best in class. In order for the benchmarking process to be effective, it is critical that management accept the validity of the gap and provide the resources necessary to close the gap.

GOAL SETTING GOAL DEFINITION Two terms that are often used interchangeably are “objective” and “goal.” There is, of course, no one correct definition. As long as the terms are used consistently within an organization, it does not really matter. For our purposes, however, objectives are broad areas where something is to be accomplished, such as sales and marketing or customer service. Goals, on the other hand, are specific and measurable and have a time frame. For example, “Answer all inquiries within 2 hours by the 3rd quarter of 2002.”

GOAL CHARACTERISTICS For best results, goals should be (a) tough (you need to stretch to attain them) and (b) attainable (realistic). When evaluating these two characteristics, always take into consideration the current capabilities of the company versus the benchmark candidate now and projected. A good way to monitor progress towards attainment is through trend charting.

RESULT

VERSUS

EFFORT GOALS

Result goals define the specific performance measure to be achieved. For example, “Sell $4 million of product x to company y in 2003.” Effort goals define specific accomplishments that are completely under the control of the goal setter. They are necessary to achieve the result goals. They can be thought of as action plans. For example, an effort goal would be, “Make x cold calls a week to new departments of x company”.

GOAL SETTING PHILOSOPHY Best of the Best versus Optimization There can be a clear difference between implementing an inventory control system that ensures that a company never runs out of stock and an inventory control system that optimizes the level of inventory. The optimum inventory balances off the cost of holding the inventory and the cost of carrying the inventory.

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A similar consideration is that of determining the optimum feature set for a product, taking into consideration what specific market segments value and will pay for. Differentiation that is not valued by the market could result in an unnecessary expenditure of funds. The determination of value has to be based on the underlying need of the customer. If this had not been done, there would be no need to have produced a ballpoint pen, only a better fountain pen. Who asked for electricity, the camera, or the copy machine before they existed? No one by name, but many in terms of desire and underlying need. There is a fundamental difference between working within the constraints facing a business and removing the constraints. For example, a company can either (a) optimize production given the setup time for a job or (b) reduce the setup time. Optimization within the constraints leads to larger lots, higher inventory, perhaps poorer quality, and delays. It is much more effective to remove the constraint. The key to manufacturing excellence is to remove the constraints that cause the tradeoffs between cost and customer satisfaction. Kaizen versus Breakthrough Strategies The Kaizen philosophy of management stresses making small, constant improvements as opposed to looking for the one magic silver bullet that will lead to success. Which company is likely to be more innovative: (a) a company that is looking for the one big idea or (b) a company that is constantly making small improvements? Both are appropriate strategies depending on the specific situation. However, if a company is in dire need of improvement there is no better way than to look at benchmarking. The benchmarking in this case will be a true breakthrough. On the other hand, the Kaizen approach tells us that we should not relax in our effort to be the best. There is always something that we can do better.

GUIDING PRINCIPLE IMPLICATIONS The decisions made regarding goals can have a profound interaction with the mission statement of the company and/or the values as defined in the statement of guiding principles. The statement of guiding principles generally consists of: 1. Mission statement — a description of the product and markets served or who, what, and how 2. Values — those human and ethical principles that guide the conduct of the business

GOAL STRUCTURE Cascading Goal Structure A consistent goal structure can provide focus and direction to the entire organization. In order to create this, start with the most important goal, as viewed by the president or chief executive officer, and decompose each of these by functional area working from one management level to the next. For example, starting with a return on equity

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goal, what does this mean each department has to do? What does this suggest in the way of specific benchmarking goals? Interdepartmental Goals One of the most elusive tasks of management is to get all departments to work together toward a common set of goals. One way to manage this is to have each department indicate its goals and what it requires in the way of performance from other departments to reach those goals. A cross tabulation can then be used to develop the total goals for a department or function.

ACTION PLAN IDENTIFICATION AND IMPLEMENTATION The benchmarking process has been used to identify the present and projected result and performance gap. The actual solution to closing the gap may be the synthesis of several of the benchmark partner’s ideas. In order to creatively identify new solutions, the following questions can be helpful: Put to other uses? New ways to use as is? Other uses if modified? Adapt? What else is this like? What other ideas does this suggest? Does the past offer a parallel? What could I copy? Whom could I emulate? Modify? New twist? Change meaning, color, motion, sound, odor, form or shape? Other changes? Magnify? What to add? More time? Greater frequency? Stronger? Higher? Longer? Thicker? Extra value? Plus ingredients? Duplicate? Multiply? Exaggerate? Minimize? What to subtract? Smaller? Condensed? Miniature? Lower? Shorter? Lighter? Omit? Streamline? Split up? Understate? Substitute? Who else instead? What else instead? Other ingredients? Other material? Other process? Other power? Other place? Other approach? Other tone of voice? Rearrange? Interchange components? Other pattern? Other layout? Other sequence? Transpose cause and effect? Change place? Change schedule? Reverse? Transpose positive and negative? How about opposites? Turn it backwards? Turn it upside down? Reverse roles? Change shoes? Turn tables? Turn other cheek?

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Combine? How about a blend, an alloy, an assortment, an ensemble? Combine units? Combine purpose? Combine appeals? Combine ideas?

A CREATIVE PLANNING PROCESS It is highly desirable that more than one alternative way to achieve a goal be identified. It is also critical that each viable alternative be fully evaluated on its own merits and that a conscious choice be made to select the best alternative. For each alternative, consider the following process: 1. 2. 3. 4. 5.

Develop a vision or a dream of what you would like to have happen. Identify the critical success factors for achieving the vision. Determine the required action programs. Match resource requirements and availability. Determine if the vision is feasible and either implement the required action programs or consciously decide to drop or modify the vision. 6. Implement the plan by assigning action plan responsibility. 7. Monitor performance versus expectations and revise the plan as required.

ACTION PLAN PRIORITIZATION If more action plans are identified than can be implemented, it will be necessary to prioritize the action plans relative to the corporate goals and customers’ needs, wants, and expectations. The process identified earlier in the discussion of prioritization of benchmark alternatives may be used for this purpose. One aspect of action plan prioritization is the determination of the most desirable plan from a financial point of view. Evaluations of this type often involve the comparison of cash flows that occur in different years. Consequently, the time value of money has to be taken into consideration when deciding which plan is most desirable.

ACTION PLAN DOCUMENTATION The action plan must be documented and the person(s) responsible for individual tasks must be identified: • Use of Critical Path Scheduling Tools • Action plan format • Technique for sequencing activities using Post It Notes • Importance of identifying milestones, deliverables, and decision-making roles

MONITORING

AND

CONTROL

A good way to maintain monitoring and control is through formalized periodic reporting of performance versus plan. Issues to keep in mind are:

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Need to assign responsibility for ongoing review and evaluation Use of a control chart for each variable with the responsible person identified Just because the official benchmarking study has been completed does not mean that you are done. To the contrary, you must be vigilant in monitoring your competitor’s activities by tracking the competitive performance versus plan. This is because things change and modifications must be made to recalibrate the results. Some items of interest are: • • • • • •

Benchmarks may need to be recalibrated. Changes may occur in industry, customers, or competitors. How fast are things moving and in what direction? Critical success factors may change. New competitors may enter the field. Competition may be better or worse than expected.

FINANCIAL ANALYSIS OF BENCHMARKING ALTERNATIVES When comparing benchmarking alternatives, it is often necessary to take into consideration the fact that cash is received and/or disbursed in different time periods for each of the alternatives. Cash received in the future is not as valuable as cash received today because cash received today can be reinvested and earn a return. In order to compare the current value of cash received or disbursed in different periods, it is necessary to convert a future dollar value to its present value. For example, the present value of $1100 received a year from now is $1000 if money can be invested at 10%. The alternative way to view this is to note that the future value of $1000 invested for one year at 10% is equal to 1000 times 1.10 or $1100. The following table can be used to determine the present value of a future cash flow depending upon the discount rate and the number of years from the present that the investment is made. To relate to the discussion above, note that the Present Value Factor for one year at 10% is .9091. Therefore, the present value of $1100 received a year from now is $1000, i.e., $1100 times .9091. A typical capital project of benchmark alternative evaluation is discussed in the following pages. The projected net income after tax as well as a summary of the investments made in the project, the after-tax salvage value, and the cash flow associated with the project are indicated. The assumptions used to generate the net income are indicated below the projection. Note the separation of fixed and variable cost and the relationship between specific assumptions and the level of capacity utilization. In this case, the investment is assumed to occur at the end of the first year. Also, there is no increase in working capital associated with the construction of the plant. The cash flow can be determined in one of two ways: (a) it is equal to the net income after tax plus depreciation or (b) it is equal to revenue minus operating

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Present Value Factors Discount Rate Year

10%

20%

30%

40%

1 2 3 4 5 6 7 8 9 10

0.9091 0.8264 0.7513 0.6830 0.6209 0.5645 0.5132 0.4665 0.4241 0.3822

0.8333 0.6944 0.5787 0.4823 0.4019 0.3349 0.2791 0.2326 0.1938 0.1615

0.7692 0.5917 0.4552 0.3501 0.2693 0.2072 0.1594 0.1226 0.0943 0.0725

0.7143 0.5102 0.3644 0.2603 0.1859 0.1328 0.0949 0.0678 0.0484 0.0346

expenses minus taxes. The net present value is indicated for several discount rates (10 to 40%). The net present value at 10% is determined, for example, as in Table 3.2. If the company cost of capital is 15%, then this project would be acceptable because the net present value is positive at that discount rate. A similar analysis can be used to determine a breakeven product price — see Table 3.3.

MANAGING BENCHMARKING FOR PERFORMANCE To summarize this chapter, here are some do’s and don’ts for successful benchmarking: Requirements for success • Use goal-oriented management — measure and monitor everything; link to compensation plan. • Start small and showcase. • Recognize that conflict is inevitable because of the need to share resources to reach conflicting goals. Management has to make tough decisions to resolve the healthy conflict. • Link goals to action plans. • Understand that adequate resources are necessary to ensure the success of the plan. • Ensure continuing top management support with the recognition that benchmarking does not necessarily supply a quick fix. • Place emphasis both on the result (what to do) and the process (how to do it). • Accept the concept of constant, incremental change. • A blend of analytical and intuitive skills requiring the ability to synthesize sometimes ambiguous data is needed. • Be willing to admit that change or improvement is possible and perhaps desirable. • Focus on the needs of specific target market segments and business strategy when setting the priorities to benchmark.

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TABLE 3.2 An Example of Cash Flow and Present Value End of Year

Cash Flow

Present Value Factor

Present Value

1 2 3 4

–1,000,000 246,680 597,764 1,008,814

0.9091 0.8264 0.7513 0.6830 Total Net Present Value

–909,091 203,868 449,109 689,034 432,919

TABLE 3.3 Benchmark Project Evaluations 2001 Sales (units) Unit price Revenue Operating expense Depreciation Net income before tax Tax Net income after tax Investment Salvage value Cash flow Interest rate (%) Net present value

1,000,000 10,000 –1,000,000 10 432,919

Assumptions Plant capacity (units) Unit price — start Tax rate (%) Depreciation (%) Capacity utilization (%) Price increase (%) Operating Expense Units Fixed Variable 10,000 20,000 30,000 40,000 50,000 60,000 70,000

200 200 200 400 400 500 500

20.00 20.00 20.00 21.00 21.00 21.00 21.00

2002

2003

2004

21,000 38.00 798,000 420,200 50,000 327,800 131,120 196,680

49,000 40.66 1,992,340 1,029,400 50,000 912,940 365,176 547,764

66,500 45.54 3,028,357 1,397,000 50,000 1,581,357 632,543 948,814

246,680 20 170,404

597,764 30 2,030

1,008,814 40 –107,982

70,000 38.00 40 5 30 7

70 7

95 12

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• Create a corporate culture that thrives on learning and self-improvement with constant, though gradual, change. Constantly apply the Plan, Do, Check, Act cycle. • Use Statistical Process Control to determine when events, results, or processes are out of control. • Change the role of middle management. The middle manager is no longer “the boss.” Middle managers must encourage and enable workers to think. Common mistakes • Giving lip service to the process and not providing the resources to get the job done properly • Failure to effectively communicate the benchmark findings and drive them to implementation: all analysis and no action • Failure to precisely define the expected results of benchmark improvement and to monitor actual performance (In the absence of this, no organizational learning occurs.) • Lack of a comprehensive prioritization of the benchmarking projects to ensure the best cost/benefit results • The expectation of quick results and a short-term focus on quarterly earnings • Lack of constant purpose, focus, and direction • Failure to implement results in small size, meaningful modules with specific deliverables; looking for “the” big win • Unwillingness to face the reality of a situation and recognize that change is necessary and that hard choices have to be made • Not drawing the correct balance between required accuracy and the practical ability to achieve better results; 100 percent accuracy, certainty, or performance is not required • Failure to recognize that the early follower is almost as profitable as the pioneer and sometimes even more so • Reliance on executive office analysis versus observation of the handson experience of others both within and outside the company • Focus on problem reduction and not problems avoidance • Failure to realize that, in most cases, benchmarking follows strategy • Failure to recognize the constantly rising level of expectations in the marketplace • Lack of contingency planning • Failure to get participation at all levels and to break down interdepartmental barriers so that the total resources of the organization can be focused on the solution to common problems

REFERENCES Berle, G., Business Information Sourcebook, Wiley, New York, 1991. Buzzell, R.D. and Gale B.T., The PIMS Principles, Free Press, New York, 1987.

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Clifford, D.K. and Cavanagh, R.E., The Winning Performance: How America’s High Growth Midsize Companies Succeed, Bantam Books, New York, 1988. Garvin, D.A, Managing Quality, Free Press, New York, 1988. Hall, W.K., Survival Strategies in a Hostile Environment, Harvard Business Review, Sept./Oct. 1980, pp. 34–38. Higgins, H. and Vincze, A., Strategic Management, Dryden Press, New York, 1989. Smith, G.N. and Brown, P.B., Sweat Equity, Simon and Schuster, New York, 1986. Stamatis, D.H., Total Quality Service, St. Lucie Press, Boca Raton, FL, 1996. Stamatis, D.H., TQM Engineering Handbook, Marcel Dekker, New York, 1997.

SELECTED BIBLIOGRAPHY Balm, G.J., Benchmarking: A Practitioner’s Guide for Becoming and Staying Best of the Best, Quality & Productivity Management Association, Schaumburg, IL, 1992. Barnes, B., Squeeze Play: Satisfaction Programs Are Key for Manufacturers Caught Between Declining and Increasing Raw Material Costs, Quirk’s Marketing Research Review, Oct. 2001, pp. 44–47. Bosomworth, C., The Executive Benchmarking Guidebook, Management Roundtable, Boston, MA, 1993. Boxsvell, R.J., Jr., Benchmarking for Competitive Advantage, McGraw-Hill, New York, 1994. Camp, R., Business Process Benchmarking: Finding and Implementing Best Practices, ASQC Quality Press, Milwaukee, WI, 1995. Chang, R.Y. and Kelly, P.K., Improving through Benchmarking, Richard Chang Associates, Publications Division, Irvine, CA, 1994. Karlof, B. and Ostblom, S., Benchmarking: A Signpost to Excellence in Quality and Productivity, John Wiley & Sons, New York, 1993. Lewis, S., Cleaning Up: Ongoing Satisfaction Measurement Adds to Japanese Janitorial Firm’s Bottom Line, Quirk’s Marketing Research Review, Oct 2001, pp. 20–21, 68–70.

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4

Simulation

As companies continue to look for more efficient ways to run their businesses, improve work flow, and increase profits, they increasingly turn to simulation, which is used by best-in-class operations to improve their processes, achieve their goals, and gain a competitive edge. Simulation is used by some of the world’s most successful companies, including Ford, Toyota, Honda, DaimlerChrysler, Volkswagen, Boeing, Delphi Automotive Systems, Dell Corp. Gorton Fish Co., and many others. Both design and process simulations have become increasingly important and integral tools as businesses look for ways to strip non-value-adding steps from their processes and maximize human and equipment effectiveness, all parts of the six sigma philosophy. The beauty of simulation is that, while it complements and aids in the six sigma initiative, it can also stand alone to improve business processes. In this chapter, we do not dwell on the mathematical justification of simulation; rather, we attempt to explain the process and identify some of the key characteristics in any simulation. Part of the reason we do not elaborate on the mathematical formulas is the fact that in the real world, simulations are conducted via computers. Also, readers who are interested in the theoretical concepts of simulation can refer to the selected bibliography found both at the end of the chapter and at the end of this volume.

WHAT IS SIMULATION? Simulation is a technology that allows the analysis of complex systems through statistically valid means. Through a software interface, the user creates a computerized version of a design or a process, otherwise known as a “model.” The model construction is a basic flow chart with great additional capabilities. It is the interface a company uses to build a model of its business process. Simulation technology has been around for a generation or more, with early developments mostly in the area of programming languages. In the last 10 to 15 years, a number of off-the-shelf software packages have become available. More recently, these tools have been simplified to the point that your average business manager with no industrial engineering skills can effectively employ this technology without requiring expert assistance. (Some companies have actually modified the commercial versions to adopt them into their own environments.) Simplicity is the key to today’s simulation software. The basic simulation structure is as follows: after flow charting the process, the user inputs information about how the process operates by simply filling in blanks. While completing a model, the user answers three questions at each step of the process: how long does the step 169

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take, how often does it happen, and who is involved? After the model is built and verified, it can be manipulated to do two critical things: analyze current operations to identify problem areas and test various ideas for improvement. The latest improvements in simulation software have made it an excellent tool for enhancing the design for six sigma (DFSS) process, which strives to eliminate eight wastes: overproduction, motion, inventory, waiting, transportation, defects, underutilized people, and extra processing. DFSS targets non-value-added activities — the same activities that contribute to poor product quality. In this chapter we are not going to discuss commercial packages; rather we are going to introduce three methodologies that facilitate simulation — Monte Carlo, Finite Element analysis, and Excel’s Solver approach.

SIMULATED SAMPLING The sampling method, known generally as Monte Carlo, is a simulation procedure of considerable value. Let us assume that a product is being assembled by a two-station assembly line. There is one operator at each of the two stations. Operation A is the first of the two operations. The operator completes approximately the first half of the assembly and then sets the half-completed assembly on a section of conveyor where it rolls down to operation B. It takes a constant time of 0.10 minute for the part to roll down the conveyor section and be available to operator B. Operator B then completes the assembly. The average time for operation A is 0.52 minute per assembly and the average time for operation B is 0.48 minute per assembly. We wish to determine the average inventory of assemblies that we may expect (average length of the waiting line of assemblies) and the average output of the assembly line. This may be done by simulated sampling as follows: 1. The distributions of assembly time for operations A and B must be known or procured. Usually this is done through historical data, sometimes with surrogate. A study was taken for both operations, and two frequency distributions were constructed (not shown here). In the case of operation A, the value 0.25 minute occurred three times, 0.30 occurred twice, and so on. For operation A, the mean was 0.52 min with N = 167 and for operation B the mean was 0.48 with N = 115. The two distributions do not necessarily fit mathematical distributions but this is not important. 2. Convert the frequency distributions to cumulative probability distributions. This is done by summing the frequencies that are less than or equal to each performance time and plotting them. The cumulative frequencies are then converted to percents by assigning the number 100 to the maximum value. The cumulative frequency distribution (not shown here) for operation A began at the lowest time, 0.25 minute; there were three observations. Three is plotted on the cumulative chart for the time 0.25 minute. For the performance time 0.30 minute, there were two observations, but there were five observations that measured 0.30 minute or

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less, so the value five is plotted for 0.30 minute. For the performance time 0.35 minute, there were 10 observations recorded, but there were 15 observations that measured 0.35 minute or less. When the cumulative frequency distribution was completed, a cumulative percent scale was constructed on the right, by assigning the number 100 to the maximum value, 167 in this case, and dividing the resulting scale into equal parts. This results in a cumulative probability distribution. We can use this distribution to say for example that 100 percent of the time values were 0.85 minute or less, 55.1 per cent were 0.50 minute or less and so on. 3. Sample at random from the cumulative distributions to determine specific performance times to use in simulating the operation of the assembly line. We do this by selecting numbers between 0 and 100 at random (representing probabilities or percents). The random numbers could be selected by any random process, such as drawing numbered chips from a box, using a random number table, or using computer-generated random numbers. For small studies, the easiest way is to use a table of random numbers. The random numbers are used to enter the cumulative distributions in order to obtain time values. In our example, we start with the random number 10. A horizontal line is projected until it intersects the distribution curve; a vertical line projected to the horizontal axis gives the midpoint time value associated with the intersected point on the distribution curve, which happens to be 0.40 minute for the random number 10. Now we can see the purpose behind the conversion of the original distribution to a cumulative distribution. Only one time value can now be associated with a given random number. In the original distribution, two values would result because of the bell shape of the curve. Sampling from the cumulative distribution in this way gives time values in random order, which will occur in proportion to the original distribution, just as if assemblies were actually being produced. Table 4.1 gives a sample of 20 time values determined in this way from the two distributions. 4. Simulate the actual operation of the assembly line. This is done in Table 4.2, which is very similar to waiting (queuing) line problems. The time values for operation A (Table 4.1) are first used to determine when the half-completed assemblies would be available to operation B. The first assembly is completed by operator A in 0.40 minute. It takes 0.10 minute to roll down to operator B, so this point in time is selected as zero. The next assembly is available 0.40 minute later, and so on. For the first assembly, operation B begins at time zero. From the simulated sample, the first assembly requires 0.60 minute for B. At this point, there is no idle time for B and no inventory. At time 0.40 the second assembly becomes available, but B is still working on the first so the assembly must wait 0.20 minute. Operator B begins

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TABLE 4.1 Simulated Samples of 20 Performance Time Values for Operations A and B Operation A

Random Number 10 22 24 42 37 77 99 96 89 85 28 63 9 10 7 51 2 1 52 7 Totals

Operation B

Performance Time from Cumulative Distribution for Operation A 0.40 0.40 0.45 0.50 0.45 0.60 0.85 0.75 0.65 0.65 0.45 0.55 0.40 0.40 0.35 0.50 0.30 0.25 0.50 0.35 9.75

Random Number 79 69 33 52 13 16 19 4 14 6 30 25 38 0 92 82 20 40 44 25

Performance Time from Cumulative Distribution for Operation B 0.60 0.50 0.40 0.45 0.35 0.35 0.35 0.30 0.35 0.30 0.40 0.35 0.40 0.25 0.70 0.60 0.35 0.40 0.45 0.35 8.20

work on it at 0.60. From Table 4.1, the second assembly requires 0.50 minute for B. We continue the simulated operation of the line in this way. The sixth assembly becomes available to B at time 2.40, but B was ready for it at time 2.30. He therefore was forced to remain idle for 0.10 minute because of lack of work. The completed sample of 20 assemblies is progressively worked out — see Table 4.2. The summary at the bottom of Table 4.4 shows the result in terms of the idle time in operation B, the waiting time of the parts, the average inventory between the two operations, and the resulting production rates. From the average times given by the original distributions, we would have guessed that A would limit the output of the line since it was the slower of the two operations. Actually, however, the line production rate is less than that dictated by A (116.5 pieces per hour compared to 123 pieces per hour for A as an individual operation). The reason is that the interplay

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TABLE 4.2 Simulated Operation of the Two-Station Assembly Line when Operation A Precedes Operation B Assemblies Available for Operation B Operation B at Begins at

Operation B Time in Ends at Operation B

Idle Waiting Time of Assemblies

Number of Parts in Line, Excluding Assembly Being Processed in Operation B

0.00 0.00 0.60 0 0 0.40 0.60 1.10 0 0.20 0.85 1.10 1.50 0 0.25 1.35 1.50 1.95 0 0.15 1.80 1.95 2.30 0 0.15 2.40 2.40 2.75 0.10 0 3.25 3.25 3.60 0.50 0 4.00 4.00 4.30 0.40 0 4.65 4.65 5.00 0.35 0 5.30 5.30 5.60 0.30 0 5.75 5.75 6.15 0.15 0 6.30 6.30 6.65 0.15 0 6.70 6.70 7.10 0.05 0 7.10 7.10 7.35 0 0 7.45 7.45 8.15 0.10 0 7.95 8.15 8.75 0 0.20 8.25 8.75 9.10 0 0.50 8.50 9.10 9.50 0 0.60 9.00 9.50 9.95 0 0.50 9.35 9.95 10.30 0 0.60 Idle time in operation B = 2.10 minutes Waiting time of parts = 3.15 minutes Avenge inventory of assemblies between A and B = 3.15/9.35 = 0.34 assemblies Average production rate of A = [20 × 60]/9.75 = 123 pieces/hour Average production rate of B (while working) = [20 × 60]/8.20 = 146 pieces/hour Average production rate of A and B together = [20 × 60]/10.30 = 116.5 pieces/hour

0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2

Note: In the above computations, 20 is the total number of completed assemblies; 9.75 is the total work time of operation A for 20 assemblies from Table 4.1; 8.20 is the total work time, exclusive of idle time, for operation B for 20 assemblies from Table 4.1.

of performance times for A and B does not always match up very well, and sometimes B has to wait for work. B’s enforced idle time plus B’s total work time actually determine the maximum production rate of the line. A little thought should convince us that, if possible, it would have been better to redistribute the assembly work so that A is the faster of the two operations. Then the probability that B will run out of work is reduced. This is demonstrated by Table 4.3, which assumes a simple reversal of the sequence of A and B. The same

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TABLE 4.3 Simulated Operation of the Two-Station Assembly Line when Operation B Precedes Operation A Assemblies Available for Operation A Operation A at Begins at

Operation A Time in Ends at Operation A

Idle Waiting Time of Assemblies

Number of Parts in Line, Excluding Assembly Being Processed in Operation A

0.00 0.00 0.40 0 0 0.50 0.50 0.90 0.10 0 0.90 0.90 1.35 0 0 1.35 1.35 1.85 0 0 1.70 1.85 2.30 0 0.15 2.05 2.30 2.90 0 0.25 2.40 2.90 3.75 0 0.40 2.70 3.75 4.50 0 1.05 3.05 4.50 5.15 0 1.45 3.35 5.15 5.80 0 1.80 3.75 5.80 6.25 0 2.05 4.10 6.25 6.80 0 2.15 4.50 6.80 7.20 0 2.30 4.75 7.20 7.60 0 2.45 5.45 7.60 7.95 0 2.15 6.05 7.95 8.45 0 1.90 6.40 8.45 8.75 0 2.05 6.80 8.75 9.00 0 1.95 7.25 9.00 9.50 0 1.75 7.60 9.50 9.85 0 1.90 Idle time in operation A = 0.10 minute Waiting time of parts = 25.75 minutes Average inventory of assemblies between A and B = 25.75/7.60 = 3.4 assemblies Average production rate of A (while working) = [20 × 60]/9.75 = 123 pieces/hour Average production rate of B = [20 × 60]/8.20 = 146 pieces/hour Average production rate of A and B together = [20 × 60]/9.85 = 122 pieces/hour

0 0 0 0 1 1 1 2 2 3 3 4 4 5 5 5 5 6 5 6

sample times have been used and the simulated operation of the line has been developed as before. With the faster of the two operations being first in the sequence, the output rate of the line increases and approaches the rate of the limiting operation, and the average inventory between the two operations increases. With the higher average inventory there, the second operation in the sequence is almost never idle owing to lack of work. Actually, this conclusion is a fairly general one with regard to the balance of assembly lines; that is, the best labor balance will be achieved when each succeeding operation in the sequence is slightly slower than the one before it. This minimizes the idle time created when the operators run out of work because of the variable performance times of the various operations. In practical

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situations, it is common to find safety banks of assemblies between operations in order to absorb these fluctuations in performance. We may have wanted to build a more sophisticated model of the assembly line. Our simple model assumed that the performance times were independent of other events in the process. Perhaps in the actual situation, the second operation in the sequence would tend to speed up when the inventory began to build up. This effect could have been included if we had knowledge of how inventory affected performance time. If we have followed this simulation example through carefully, we may be convinced that it would work but that it would be very tedious for problems of practical size. Even for our limited example, we would probably wish to have a larger run on which to base conclusions, and there would probably be other alternatives to test. For example, there may be several alternative ways to distribute the total assembly task between the two stations, or more than two stations could be considered. Which of the several alternatives would yield the smallest incremental cost of labor, inventory costs, etc.? To cope with the problem of tedium and excessive person-hours to develop a solution, the computer may be used. If a computer were programmed to simulate the operation of the assembly line, we would place the two cumulative distributions in the memory unit of the computer. Through the program, the computer would select a performance time value at random from the cumulative distribution for A in much the same fashion as we did by hand. Then it would select at random a time value from the cumulative distribution for B, make the necessary computations, and hold the data in memory. The cycle would repeat, selecting new time values at random, adding and subtracting to obtain the record that we produced by hand. A large run could be made easily and with no more effort than a small run. Various alternatives could be evaluated quickly and easily in the same manner.

FINITE ELEMENT ANALYSIS (FEA) This technique is not thought of as being a reliability improvement method, yet it can contribute significantly to its enhancement. Finite Element Analysis (FEA) is a technique of modeling a complex structure into a collection of structural elements that are interconnected at a given number of nodes. The model is subjected to known loads, whereby the displacement of the structure can be determined through a set of mathematical equations that account for the element interactions. The reader is encouraged to read Buchanan (1994) and Cook (1995) for a more complete and easy understanding of the theoretical aspects of FEA. In commercial use, FEA is a computer-based procedure for analyzing a complex structure by dividing it into a number of smaller, interconnected pieces (the “finite elements”), each with easily definable load and deflection characteristics.

TYPES

OF

FINITE ELEMENTS

The library of finite elements available in general purpose codes can be subdivided into the following categories:

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1. Point elements: An example of a point element is a lumped mass element or an element specifically created to represent a particular constraint or loading present at that point. 2. Line elements: Truss links, rods, beams, pipes, cables, rigid links, springs, and gaps are examples of line elements. This type of element is usually characterized by two grid points or nodes at each end. 3. Surface elements: Membranes, plates, shells and certain types of fluid and thermal elements fall into this category. The surface elements can be triangular or quadrilateral, and thin or thick; accordingly they are characterized by a connectivity of three or more grid points or nodes. 4. Solid elements: Examples of solid elements include wedges, prisms, cubes, parallelepipeds and three-dimensional fluid and thermal elements. Elements in this category are usually defined using six or more grid points or nodes. 5. Special purpose elements: Combinations of springs, gaps, dampers, electrical conductors, acoustic, fluid, magnetic, mass, superelement, crack tips, radiation links, etc., are included in this category. For example, commonly used elements in the automotive industry (body engineering) are: • • • • • •

TYPES

Beams Rigid links Thin plates — triangular and quadrilateral Solid elements Springs Gaps (contact or interface elements) OF

ANALYSES

There are many combinations of analyses one may perform with FEA as the driving tool. However, the two predominant types are nonlinear and dynamic. Using these types one may focus on specific analysis of — for example nonlinearities types such as: Geometric • Stress less than yield strength • Euler (elastic) buckling • Examples: quarter panel under jacking and towing; hood following front crash Material • Stress greater than yield strength or material is nonlinear elastic • Plastic flow • Examples: seat belt pull; door intrusion beam bending Combination of geometric and material • Stress is greater than yield strength and buckling takes place • Crippling • Examples: rails during crash; roof crush

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The reader should also recognize that combinations of these types exist as well, for example linear/static — the easiest and most economical. Most of the FEA applications involve this kind of analysis. Examples include joint stiffness and door sag. Nonlinear/static is less frequently used. Examples include door intrusion beam, roof crush, and seat belt pull. Linear/dynamic is rarely used. Examples include windshield wipers or latch mechanism. Nonlinear/dynamic is the most complex and most expensive. Examples include knee bolster crash, front crash, and rear crash. Let us look at these combinations a little more closely: Linear static analysis: This is the simplest form of analytical application and is used most frequently for a wide range of structures. The desired results are usually the stress contours, deformed geometry, strain energy distribution, unknown reaction forces, and design optimization. Typical examples are door sag simulation, margin/fit problems, joint stiffness evaluation, high stress location search for all components, spot weld forces, and thermal stresses. Euler buckling analysis: This analysis is also relatively simple to perform and is used to calculate critical buckling loads. Caution should be exercised when performing this analysis because it produces analytical results that are not conservative. In other words, the critical buckling load thus calculated is usually higher than the actual load that would be determined through testing. A typical application is hood buckling. Normal modes analysis: This is an extremely useful technique for determining the natural frequencies (eigenvalues) of components and also the corresponding eigenvectors which represent the modes of deformation. Strictly speaking, this category does not fall under dynamic analysis since the problem is not time dependent. Typical examples include instrument panels, total vehicle or component NVH evaluation, door inner panel flutter, and steering column shake. Nonlinear static analysis: In general, all nonlinear analysis requires advanced methodology and is not recommended for use by inexperienced analysts. Usually, a graduate degree or several graduate level courses in the theory of elasticity, plasticity, vibrations, and solid and fluid mechanics are required to understand nonlinear behavior. Nonlinear FEA tends to be as much an art as it is a science, and familiarity with the subject structure is essential. Typical examples are seat back distortion, door beam bending rigidity studies, underbody components such as front and rear rails and wheel housings, bumper design, and crush analysis of several components. Nonlinear dynamic analysis: This FEA category is the most advanced. It involves very complex ideas and techniques and has become practicable only due to the availability of super-high-speed computers. This class of analysis involves all the complexities of nonlinear static analysis as well as additional problems involved with iterative time step selection and contact simulation at impact. Typical applications are related to crash evaluation and energy management.

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PROCEDURES INVOLVED

IN

FEA

The procedures involved in FEA include: 1. Problem definition: Specification of concerns and expected results 2. Planning of analysis: Making decisions regarding the applicability of FEA, which code to use, and the size and the type of model to be constructed 3. Digitizing: The translation of a drawing into line data that is available to the modeler 4. Modeling: Creating the desired finite element model as planned (Many sophisticated tools are available such as the PDGS-FAST system, PATRAN, and so on.) 5. Input of data: Creating, editing and storing a formatted data file that includes a description of the model geometry, material properties, constraints, applied loading, and desired output 6. Execution: Processing the input data in either the batch or the interactive mode through the finite element code residing on the computer system and receiving the output in the form of a printout and/or post-processor data 7. Interpretation of output: A study of the output to check the validity of the input parameters as well as the solution of the structural problem 8. Feasibility considerations: Utilizing the output to make intelligent technical decisions about the acceptability of the structural design and the scope for design enhancement 9. Parametric studies: Redesign using parametric variation (The easiest changes to study are those involving different gages, materials, constraints, and loading. Geometric changes require repetition of steps 3 through 8; the same is true about remodeling of the existing geometry.) 10. Design optimization: An iterative process involving the repetition of steps 3 through 9 to optimize the design from considerations of weight, cost, manufacturing feasibility, and durability

STEPS

IN

ANALYSIS PROCEDURE

The steps in the analysis procedure are: 1. Establish objective. 2. What type of analysis? What program? Statics Mechanical Loads • Forces • Displacements • Pressure • Temperatures

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4.

5.

6.

179

Heat Transfer • Conduction • Convection • 1-D radiation Dynamics Mode frequency Mechanical load • Transient (direct or reduced) linear • Sinusoidal Shock spectra Heat transfer direct transient Special features Nonlinear • Buckling • Large displacement • Elasticity • Creep • Friction, gaps Substructuring What is minimum portion of system or structure required? Known forces or displacements at a point Allows for separation Structural symmetry Isolation through test data Cyclic symmetry What are loading and boundary conditions? Loading known Loading can be calculated from simplistic analysis Loading to be determined from test data Support of excluded part of system established on modeled portion Test data taken to establish stiffness of partial constraints Determine model grid. Choose element types. Establish grid size to satisfy cost versus accuracy criterion. Develop bulk data. Establish coordinate systems. Number node or order elements to minimize cost. Develop node coordinates and element connectivity description. Code load and B.C. description. Check geometry description by plotting.

OVERVIEW

OF

FINITE ELEMENT ANALYSIS — SOLUTION PROCEDURE

The process of FEA may be summarized with a flow chart of linear static structural analysis in seven steps. The steps are:

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1. Represent continuous structure as a collection of discrete elements connected by node points. 2. Formulate element stiffness matrices from element properties, geometry, and material. 3. Assemble all element stiffness matrices into global stiffness matrix. 4. Apply boundary conditions to constrain model (i.e., remove certain degrees of freedom). 5. Apply loads to model (forces, moments, pressure, etc.). 6. Solve matrix equation {F} = [K]{u} for displacements. 7. Calculate element forces and stresses from displacement results.

INPUT

TO THE

FINITE ELEMENT MODEL

Once the user is satisfied with the model subdivision, the following classes of input data must be prepared to provide a detailed description of the finite element model to typical FEA software such as MSC/NASTRAN (1998): Geometry: This refers to the locations of grid points and the orientations of coordinate systems that will be used to record components of displacements and forces at grid points. Element connectivities: This refers to identification numbers of the grid points to which each element is connected. Element properties: Examples of element properties are the thickness of a surface element and the cross-sectional area of a line element. Each element type has a specific list of properties. Material properties: Examples of material properties are Young’s modulus, density, and thermal expansion coefficient. There are several material types available in MSC/NASTRAN. Each has a specific list of properties. Constraints: Constraints are used to specify boundary conditions, symmetry conditions, and a variety of other useful relationships. Constraints are essential because an unconstrained structure is capable of free-body motion, which will cause the analysis to fail. Loads and enforced displacements: Loads may be applied at grid points or within elements.

OUTPUTS

FROM THE

FINITE ELEMENT ANALYSIS

Once the data describing the finite element model have been assembled and submitted to the computer, they will be processed by a software package such as MSC/NASTRAN to produce information requested by the user. The classes of output data are: 1. Components of displacements at grid points 2. Element data recovery: stresses, strains, strain energy, and internal forces and moments 3. Grid point data recovery: applied loads, forces of constraint, and forces due to elements

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It is the responsibility of the user to verify the accuracy of the finite element analysis results. Some suggested checks to perform are: Generate plots to visually verify the geometry. Verify overall model response for loadings applied. Check input loads with reaction forces. Perform hand checks of results whenever possible. Review and check results. Plot deformation and stress contour. Check equilibrium and reaction forces. Check concentration region for fineness of grid (compare calculated stress distribution with assumed element distribution). Check peak deflection and/or stress for ballpark accuracy. Special note: How a structure actually behaves under loading is determined by four characteristics: (a) the shape of the structure, (b) the location and type of constraints that hold the structure in place, (c) the loads applied to the structure — their magnitude, location and direction, and (d) the characteristics of the materials that comprise the structure. For example, glass, steel, and rubber have significantly different characteristics and different stiffnesses.

ANALYSIS

OF

REDESIGNS

OF

REFINED MODEL

At this stage, generally a correlation is attempted even though it is very difficult and presents many potential problems. These problems are about 60% associated with analysis and 40% associated with the actual testing. Remember that correlations at this stage commonly (over 50 projects) may run from 5 to 30%. Obviously, the focus should be on testing and test-related correlation with real world usage. Items of concern should be: Loads: • Isolation of single component of assembly • Hard to put assumed load in controlled lab test (linear loads causing moments) Strain gages: • Gage locations and orientation • Single leg gages versus rosettes • Improper gage lead hookup Non-linearities: • Plasticity • Pin joint clearance • Bolted joints In a typical analysis, the related correlation issues/problems/concerns examples are:

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• Mesh size (for localized stress concentration, isolate concentration region and refine mesh) • Element type • Load distribution and B.C. isolation • Input error/bad data • Weld details Common problems that may be encountered in the FEA are: • Part not to size • Misunderstanding or interpretation of results Therefore, to make sure that the FEA is worth the effort, the following steps are recommended: 1. Initially, take simple, well-isolated components, with simple well-defined loads. 2. Do not expect miracles. 3. Use a joint test/analysis program. It can improve the capabilities of each step and serves as a check on techniques. 4. Work together. This is the key. The test results supplement weakness of analysis and vice versa.

SUMMARY — FINITE ELEMENT TECHNIQUE: A DESIGN TOOL • • • • •

Proven tool — approximate but very accurate if applied properly. Fine enough grid to match true strain field. Need to know loads accurately. Are supports rigid? What spring stiffness? Do not let FEA become just a research tool searching for an absolute answer. Use in all stages of design cycle as relative comparison tool in conjunction with test. • FEA if nothing else forces someone to examine in detail a component design. • A check on geometry itself. • The experimenter must think in detail about loads and interaction with rest of system

EXCEL’S SOLVER Yet another simple simulation tool is found in the Tools (add in) category of the Excel software program. Its simplicity is astonishing, and the results may be indeed phenomenal. What is required is the transformation function. Once that is identified, then the experimenter defines the constraints and the rest is computed by Solver.

DESIGN OPTIMIZATION In dealing with DFSS, a frequent euphemism is “design optimization.” What is design optimization? Design optimization is a technique that seeks to determine an

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optimum design. By “optimum design,” we mean one that meets all specified requirements but with a minimum expense of certain factors such as weight, surface area, volume, stress, cost, and so on. In other words, the optimum design is one that is as efficient and as effective as possible. To calculate an optimum design, many methods can be followed. Here, however, we focus on the ANSYS program, as defined by Moaveni (1999), which performs a series of analysis-evaluation-modification cycles. That is, an analysis of the initial design is performed, the results are evaluated against specified design criteria, and the design is modified as necessary. This process is repeated until all specified criteria are met. Design optimization can be used to optimize virtually any aspect of the design: dimensions (such as thickness), shape (such as fillet radii), placement of supports, cost of fabrication, natural frequency, material property, and so on. Actually, any ANSYS item that can be expressed in terms of a parameter can be subjected to design optimization. One example of optimization is the design of an aluminum pipe with cooling fins where the objective is to find the optimum diameter, shape, and spacing of the fins for maximum heat flow. Before describing the procedure for design optimization, we will define some of the terminology: design variables, state variables, objective function, feasible and unfeasible designs, loops, and design sets. We will start with a typical optimization problem statement: Find the minimum-weight design of a beam of rectangular cross section subject to the following constraints: Total stress σ should not exceed σmax

[σ ≤ σmax]

Beam deflection δ should not exceed δmax

[δ ≤ δmax]

Beam height h is limited to hmax

[h ≤ hmax]

Design Variables (DVs) are independent quantities that can be varied in order to achieve the optimum design. Upper and lower limits are specified on the design variables to serve as “constraints.” In the above beam example, width and height are obvious candidates for DVs, since they both cannot be zero or negative, so their lower limit would be some value greater than zero. State Variables (SVs) are quantities that constrain the design. They are also known as “behavioral constraints” and are typically response quantities that are functions of the design variables. Our beam example has two SVs: σ(the total stress) and δ(the beam deflection). You may define up to 100 SVs in an ANSYS design optimization problem. The Objective Function is the quantity that you are attempting to minimize or maximize. It should be a function of the DVs, i.e., changing the values of the DVs should change the value of the objective function. In our beam example, the total weight of the beam could be the objective function (to be minimized). Only one objective function may be defined in a design optimization problem. A design is simply a set of design variable values. A feasible design is one that satisfies all specified constraints, including constraints on the SVs as well as constraints

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on the DVs. If even one of the constraints is not satisfied, the design is considered infeasible. An optimization loop (or simply loop) is one pass through the analysis-evaluation-modification cycle. Each loop consists of the following steps: 1. Build the model with current values of DVs and analyze. 2. Evaluate the analysis results in terms of the SVs and objective function. 3. Modify the design by calculating new values of DVs. These new values are calculated by ANSYS and are used to define the new version of the model. At the end of each loop, new values of DVs, SVs, and the objective function are available and are collectively referred to as a design set (or simply set).

HOW TO DO DESIGN OPTIMIZATION Design optimization requires a thorough understanding of the concept of ANSYS parameters, which are simply user-named variables to which you can assign numeric values. The model must be defined in terms of parameters (which are usually the DVs), and results data must be retrieved in terms of parameters (for SVs and the objective function). The usual procedure for design optimization consists of six main steps: 1. 2. 3. 4.

Initialize the design variable parameters. Build the model parametrically. Obtain the solution. Retrieve the results data parametrically and initialize the state variable and objective function parameters. 5. Declare optimization variables and begin optimization. 6. Review and verify optimum results. Details of these steps are beyond the scope of this volume. However, the reader may find the information in Moaveni (1999).

UNDERSTANDING

THE

OPTIMIZATION ALGORITHM

Understanding the algorithm used by a computer program is always helpful, and this is particularly true in the case of design optimization. Perhaps one of the most important issues is the notion of approximation. For simple mathematical functions that are continuously differentiable, minima can be found by analytical techniques such as solving for points of zero slope. The mathematical relationship between an arbitrary objective function and the DVs, however, is generally not known, so the program has to establish the relationship by curve fitting. This is done by calculating the objective function for several sets of DV values (i.e., for several designs) and performing a least squares fit among the data points. The resulting curve (or surface) is called an approximation. Each optimization loop generates a new data point, and the objective function is updated. It is this approximation that is minimized, not the actual objective function.

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State variables are handled in the same manner. An approximation is generated for each state variable and updated at the end of each loop. (Because approximations are used for the objective function and SVs, the optimum design will be only as good as the approximations.)

CONVERSION

TO AN

UNCONSTRAINED PROBLEM

State variables and limits on design variables are used to constrain the design and make the optimization problem a constrained one. The ANSYS program converts this problem to an unconstrained optimization problem because minimization techniques for the latter are more efficient. The conversion is done by adding penalties to the objective function approximation to account for the imposed constraints. You can think of penalties as causing an upturn of the objective function approximation at the constraints. The ANSYS program uses extended interior penalty functions. (For more information on penalty functions see sources in the selected bibliography for this chapter.) The search for a minimum is then performed on the unconstrained objective function approximation using the Sequential Unconstrained Minimization Technique (SUMT), which is explained in most texts on engineering design and optimization.

SIMULATION AND DFSS In summary, simulation is of value in connection with DFSS because: Design problems are discovered sooner. • Shortens development time • Provides better overall quality • Permits early optimization of the design Build-and-test is supplemented by computer simulations. • Permits lower testing budgets • Shortens development time • Permits evaluation of alternative designs • Minimizes overdesign by evaluating early in cycle Therefore, with the aid of simulation we are capable of: • Less time spent designing • Less time spent testing • Less time spent changing Result: Better products … in less time… at a lower cost. And that is what DFSS is all about.

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REFERENCES Buchanan G.R., Schaum’s Outline of Finite Element Analysis, McGraw-Hill Professional Publishing, New York, 1994. Cook, R., Finite Element Modeling for Stress Analysis, Wiley, New York, 1995. Moaveni, S., Finite Element Analysis: Theory and Applications with ANSYS, Prentice Hall, Upper Saddle River, NJ, 1999. Schaeffer, H.G., MSC/NASTRAN Primer: Static and Normal Modes Analysis, MSC, New York, 1998.

SELECTED BIBLIOGRAPHY Adams, V. and Askenazi, A., Building Better Products with Finite Element Analysis, OnWord Press, New York, 1998. Belytschko, T., Liu, W.K., and Moran, B., Nonlinear Finite Elements for Continua and Structures, Wiley, New York, 2000. Hughes, T.J.R., The Finite Element Method: Linear Static and Dynamic Finite Element Analysis, Dover Publications, New York, 2000. Malkus, D.S. et al., Concepts and Applications of Finite Element Analysis, 4th ed., Wiley, New York, 2001. Rieger, M. and Steele, J., Basic Course in FEA Modeling, Machine Design, June 6, 1981, pp. 7–8. Rieger, M. and Steele, J., Basic Course in FEA Modeling, Machine Design, July 9, 1981, pp. 8–10. Rieger, M. and Steele, J., Advanced Techniques in FEA Modeling, Machine Design, July 23, 1981, pp. 7–12. Shih, R., Introduction to Finite Element Analysis Using I-DEAS Master Series 7, Schroff Development Corp. Publications, New York, 1999. Zienkiewics, O.C. and Taylor, R.L., Finite Element Method: Volume 1, The Basis, ButterworthHeinsmann, London, 2000. Zienkiewics, O.C. and Taylor, R.L., Finite Element Method: Volume 2, Solid Mechanics, Butterworth-Heinsmann, London, 2000. Zienkiewics, O.C. and Taylor, R.L., Finite Element Method: Volume 3, Fluid Dynamics, Butterworth-Heinsmann, London, 2000.

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Design for Manufacturability/ Assembly (DFM/DFA or DFMA)

When we talk about design for manufacturability/assembly (DFM/DFA or DFMA), we describe a methodology that is concerned with reducing the cost of a product through simplification of its design. In other words, we try to reduce the number of individual parts that must be assembled and ultimately, increase the ease with which these parts can be put together. By focusing on these two items we are able to: 1. Design for a competitive advantage 2. Design for manufacturability, assembliability 3. Design for testability, serviceability, maintainability, quality, reliability, work-in-process (wip), cost, profitability, and so on. This, of course, brings us to the objectives of DFM/DFA, which are: To maximize a. Simplicity of design b. Economy of materials, parts, and components c. Economy of tooling/fixtures, process, and methods d. Standardization e. Assembliability f. Testability g. Serviceability h. Integrity of product features To minimize a. Unique processes b. Critical, precise processes c. Material waste, or scrap due to process d. Energy consumption e. Generation of pollution, liquid or solid f. Waste g. Limited available materials, components, and parts h. Limited available, proprietary, or long lead time equipment i. Degree of ongoing product and production support 187

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Producibility Trade-off

Trade-offs Reliability

Performance

a) Old Design Goals: Reliability Better performance

Life Cycle Costs

Trade-offs

Producibility

Trade-offs Reliability

Trade-offs

Trade-offs

Trade-offs

Performance

(b) New Design Goals: Balanced Design Low support cost Low acquisition cost

FIGURE 5.1 Trade-off relationships between program objectives (balance design).

Therefore, one may describe the DFM/DFA process as a common-sense approach consistent with the old maxim, “Get it done right the first time.” In DFM/DFA, we strive to get it done right the first time with the most practical and affordable methods in order to meet the customer’s expectations in terms of time, process, costs, value, needs, and wants. This approach is quite different from the old way of doing business. Figure 5.1 shows the old and new ways of design. So, in a formal way we can say that design for manufacturing and assembly is a way of focusing on designing the product with manufacturability and assembliability in mind, to ensure the product can be produced with an affordable manufacturing effort and cost and also, after the manufacturing process, to ensure that the original designed product reliability can be maintained, if not enhanced. This approach may seem time-consuming and not value added, but if we consider the possible alternatives available we can appreciate the benefit of any DFM/DFA initiative. For example, consider the following: • What good is the design, if nobody can produce it? • What good is the design, if nobody can produce it with an affordable effort (in terms of manufacturing cost, scrap, rework, production cycle/turn-around, wip, and so on)?

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• • • • •

What What What What What

good good good good good

is is is is is

189

the product, if nobody can afford it? the product, if we cannot market it in time? the product, if it does not sell? the product, if it is not profitable? it, if it does not work?

By doing a DFM/DFA, we are able to take into consideration many inputs with the intent of optimizing the design in terms of the following characteristics: • Design/development lead time vs. marketing time • Customer needs/wants vs. field application/performance vs. engineering specifications • Production launch efforts • Manufacturing cost • Flexibility and obsolescence of process and equipment • Maintainability/serviceability of product • Profitability Specifically, we are looking for the: 1. DFA to minimize total product cost by targeting: a. Part count — the major product cost driver b. Assembly time c. Part cost d. Assembly process 2. DFM to minimize part cost by: a. Optimizing manufacturing process b. Optimizing material selection c. Evaluating tooling and fabrication strategies d. Estimating tooling costs

BUSINESS EXPECTATIONS AND THE IMPACT FROM A SUCCESSFUL DFM/DFA Perhaps one of the major reasons why we do a DFMA is that in the final analysis we expect tremendous results with a measurable impact in the organization. Typical expectations are: • • • • • • •

Product development time improvement by 50–75% Product design cost reduction by 25–50% Product liability improvement by 10–25% Product field performance chosen to customer’s needs/wants Product production launch time reduction by 25–50% Total manufacturing cost reduction by 25–75% Reduction or even elimination of additional tooling/fixture cost

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• Reduction, if not total elimination, of the engineering change notice by 75–99% • Increase in engineering and technical personnel’s work morale, and also letting them feel and assume ownership • Ability to be competitive, be profitable, be successful The impact, of course, becomes obvious. The entire organization is impacted for the better — it becomes business focused. For example: marketing becomes focused on the customer; engineering becomes focused on design; and manufacturing becomes focused on process. Specifically, the impact may be in the following areas: • Product closer to what customer expects • Reduction of time to market • Enhanced product liability, not just from original product design point of view but also from a manufacturing process point of view • Improved profit margins by reducing product cost • Improved operating efficiency by reducing work-in-process • Enhanced return on assets • Reduced technical personnel turnover rate by improving group and individual satisfaction with the job/work • Making the organization be profitable Traditional Approach — In the past, product design/development, manufacturing process design/development, and equipment selection/capability assessment were typically discrete activities — a sequential and discrete approach. That approach may be shown as in Figure 5.2. New Way — In order to let the manufacturing process and equipment have a head start, all three activities of design, process, and equipment occur simultaneously — a simultaneous equipment approach. This is where DFMA can help. This process may be shown as in Figure 5.3. The business strategy here becomes a pursuit to articulate the: Customer needs, wants and expectations → product/process engineering specification by asking a series of specific questions such as: • • • • • • •

What is the voice of the customer (VOC)? What regulations have to be met? What is the relative importance of requirement? Which product characteristics impact the VOC? Which process characteristics impact the VOC? What price and profit margin impact to meet VOC? Are there delivery schedule impacts?

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Product selection and development assessment

Design/development manufacturing process

191

Equipment selection and capability assessment Time

Marketing specification and function confirmation

Engineering product design

Mfg process design

Mfg production Quality inspection

Product to customer FIGURE 5.2 Sequential approach.

Product design/development

Manufacturing process design/development assessment

Equipment design capability

FIGURE 5.3 Simultaneous approach.

• Any competition? Targeted competitor? • Continuing improvement opportunity? • Future cost reduction opportunity to meet future customer price reduction demands? Figure 5.4 shows the modern way of addressing these concerns. The arrows between product and process indicate possible alternatives. For example, if we

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Product alternative(s) Voice of the customer

Process alternative(s)

Business decision (cost and investment)

Manufacturing production and quality

Product

FIGURE 5.4 Tomorrow’s approach … if not today’s.

examine the producibility for a textile component, we could look at the following material considerations: • • • • •

Natural Synthetic Properties Processes Applications

On the other hand, if we were to evaluate the manufacturing process we might want to examine: • • • • • •

Pattern layout Cutting Sewing assembly Types Processes Characteristics

THE ESSENTIAL ELEMENTS FOR SUCCESSFUL DFM/DFA The very minimum requirements for a successful DFMA are: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Form a charter that includes all key functions. Establish the product plan. Define product performance requirement. Develop a realistic, agreed upon engineering specification. Establish product’s character/features. Define product architectural structure. Develop a realistic, detailed project schedule. Manage the project — schedule, performance, and results. Make efforts to reduce costs. Plan for continuing improvement.

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The details of some of these elements are outlined below: Form a DFMA charter With any charter there are two primary responsibilities: (a) to identify the roles and (b) to identify the functions. i. Roles A. Charter members — designer, manufacturing engineer, material/component engineer, product engineer, reliability/quality engineer, and purchasing. B. Team leader — program manager is a good candidate, but not necessary. Any one of the charter members can be an adequate team leader. Some companies/organizations assign an integrator to be the DFMA leader. ii. Charter’s functions A. Determining the character of the product, to see what it is and thus, what design and production methods are appropriate B. Subjecting the product to a product function analysis, so that all design decisions can be made with full knowledge of how the item is supposed to work and so that all team members understand it well enough to contribute optimally C. Carrying out a design for producibility, usability, and maintainability study to determine if these factors can be improved without impairing functionality D. Designing an assembly process appropriate to the product’s particular character (This involves creating a suitable assembly sequence, identifying subassemblies, control plan, and designing each part so that its quality is compatible with the assembly/manufacturing method.) E. Designing a factory system that fully involves workers in the production strategy, operates on adequate inventory, and is integrated with suppliers’/vendors’ capabilities and manufacturing processes Establish product’s character/feature • QFD approach • Value analysis • Effectiveness study on function and appearance/cosmetic • Product character risk assessment Define product architectural structure a. Functional block approach b. Hardware approach c. Software approach d. Component approach Develop a project schedule a. Agreed to by all functions on: • Tasks • Objectives

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• Duration • Responsibility b. Specific performance test: • Function • Appearance • Durability c. Use project management techniques. d. Concentrate on the concept of getting it done right the first time, not only doing it right the first time. e. Focus on the high leverage items — get some encouraging news first. f. Locate and prioritize the resource. g. Management commitment. h. Individual commitment. Manage the DFMA project • Ensure regular and formal review of the status by charter members. • Regularly prepare and formalize executive reports; get feedback. • Ensure total team inputs and contributions, not only involvement. • Utilize proven tools/methodologies. • Make adjustment with team consensus. • Ensure adequate resources with proper priorities. • Control the progress of the project.

THE PRODUCT PLAN It is imperative that the following considerations, all of which have a major impact on the manufacturing process, must be discussed and resolved as early as possible in the design cycle: 1. Nature of program — crash program, perfect design, or some other alternative 2. Product design itself 3. Production volume 4. Product life cycle 5. Funding 6. Cost of goods sold Product Design The focuses of marketing, engineering, manufacturing, and business/finance are quite different, yet they all push for the same interest for the organization. Our task then is to make sure that we balance out the different interests and priorities among the four functions of an organization. How do we do that? To make a long story short: How to decide between a crash program and a perfect product? When we talk about perfect product we mean it from a definitional perspective. There is no such a thing as a perfect product, but because of the operating definition we choose, we can indeed call something a perfect product.

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Criteria for Decision between Crash Program and Perfect Product There are three issues here: 1. Opportunity cost 2. Development risk 3. Manufacturing risk For a short life cycle product or a highly innovative product in a competitive environment that changes rapidly, a company must react quickly to each new product that enters the market. Getting the product to market fast is the name of the game. However, being fast to the market is no advantage if the company chooses inadequate technology, creates a product that cannot meet the potential customer’s wants/needs/expectations, designs a product that cannot be manufactured, or must set the price so high that nobody can afford the product. The opportunity cost of missing a fast-moving market window, the risk of entering a market with the wrong product, and the risk of introducing a product nobody can produce pulls managers in opposite directions. So, the choice of a crash program (CP) or a perfect product (PP) approach is a necessary step prior to any product design taking place. Two examples will make the point of a CP and a PP: Case #1 — Crash Program Company: IBM Product: Personal computer Environment: Forecasted annual growth rate of 60%. Competitors, i.e., Apple, Tandy are controlling market developments and are beginning to cut into IBM’s traditional office market. Analysis: Opportunity cost is high. Development cost is low ($10 million compared to IBM’s equity value of $18 billion). The technology of design and process are stable and internally available. Decision: Crash program approach — develop, design, manufacture, and market the product within 2 years. Approach details: Deviate the standard eight phases design procedure. Give the development team complete freedom in product planning; keep interference to a minimum; and allow the use of streamlined, relatively informational management system. Use a so-called zero procedure approach, focusing on development speed rather than risk reduction of product, manufacturing, and so on. Results: Introduce the product within 2 years. Customer acceptance is good. Cost overrun by 15%. Cost of goods sold is about 5% unfavorable to the original estimate. Market share is questionable. Long term effects — ??? (Does this sound familiar? Quite a few organizations take this approach and of course, they fail.)

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Case #2 — Perfect Product Design Company: Boeing Product: Boeing 727 replacement aircraft (767) Environment: Replacement within ten years is inevitable (may be speeded up to 5 years). Competitor, i.e., Airbus, has started its design. A new mid-range aircraft may take 727 replacement market away due to the operating/fuel inefficiency, comfortability, and Environmental Protection Agency (EPA) restraints. Analysis: Opportunity cost is high. (There is a need for 200–300 seat market; 727 is becoming obsolete.) Development cost is high (estimated $1.5 billion compared to entire company equity of $1.4 billion). Development and manufacturing risk is high. Technology and customer preferences are predictable but not yet crystallized. (Should it have two engineers or three? Should its cockpit allow for two people or three? Cruise range? Fuel consumption? Pricing?) Decision: Perfect product design approach. Complete the development of all new technologies of design and manufacturing processes in the early stages of research and development (R and D). Test everything in sight, and move product to launch only when success is nearly guaranteed. Eight-year design lead time. Approach details: Form an R and D team of 400 engineers/managers that includes designer, manufacturing engineer, quality, purchasing, and marketing. (The team member number goes up to 1000 right before go-ahead.) Apply concurrent engineering and DFMA process fully in the product R and D stage. Results: Introduce the 767 on schedule (which compares to Airbus’ 310 eight months behind schedule). Although Boeing had missed the 300–350 seat market and lost some of the 727 replacement market to Airbus 300, Boeing got to keep 200–300 seat market with a successful 767. Development costs were within budget and cost of goods sold was 4% favorable to the original estimates. No recall record so far. Long term effects — likely good. Most likely you are the in-betweens. The other approaches (see Figure 5.5) include: • • • •

Quantum leap — parallel program Acquisition Joint venture Leapfrog (Purchase a facility to maintain and manufacture current technology/design. Focus R and D on next generation technology/design.)

The Product Plan — Product Design Itself Product design has dedicated (whether one wants to admit it or not) the future of the product. About 95% of the material costs and 85% of the design/labor and

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Crash program

Acquisition

197

Leapfrog exit

Step-by-step design approach

Joint venture

Opportunity cost

Development risk and manufacturing risk

FIGURE 5.5 The product development map/guide.

overhead costs are controlled by the design itself. Once the design is complete, about 85% of the manufacturing process has been locked in. Design-related factors affecting the manufacturing process include: • • • • • • •

Product size/weight Reliability/quality requirement Architectural structure Fastener/joint methods Parts/components/materials Size, shape, and weight of parts/components Appearance/cosmetic requirement

Other factors affecting the manufacturing process include: • • • • • • • •

Floor space Material flow and process flow Power, compressed air, a/c and heating, and facility Quality plan Manual operation mandatory Mechanized operation or automation operation mandatory System interfacing requirement Manufacturing process concepts/philosophy — cpf vs. in-line vs. batch vs. cellar approaches

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• Management commitment • Production volume Volume requirements have the major influence on the choice of the manufacturing process. • Product life cycle As with volume requirements, product life has a significant influence on the manufacturing process. • Funding Since most of mechanization and automation are heavily capitalized, funding plays a major role in determining the product plan, which has a significant influence on the manufacturing process. • Cost of goods sold What is affordable capital/tooling/fixture amortization? What is the targeted cost of goods sold? Define Product Performance Requirement Minimum requirements are the collection and understanding of the following information: • • • •

Customer wants vs. customer needs vs. customer expectations Field condition and environment Performance standards Durability

The result of this understanding will facilitate the development of realistic and agreed upon specification(s). Some of the specific items that will guide realistic specifications are: • • • • • • • •

Engineering interpretation of customer needs Correlation between engineering specification and product specification Reliability study in terms of MTBF Manufacturing process reliability assessment in terms of maintaining original designed product standard Manufacturing cost assessment Option structure Control plan Qualification plan

AVAILABLE TOOLS AND METHODS FOR DFMA Infinite tools and methodologies may be used to accomplish the goal of a DFMA program. However, all of them fall into two categories: (a) approach alternatives and (b) mechanics. Some of the most important ones are listed below: Approach alternatives:

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Ongoing program/project manager approach Manufacturing engineering sign-off approach Design engineering use simulation software package approach Simultaneous engineering approach Concurrent engineering approach Integrator approach

Mechanics: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Quality function development (QFD) Design of experiments (DOE) Potential failure mode and effects analysis (FMEA) Value engineering and value analysis (VE/VA) Group technology (GT) Geometric dimensioning and tolerancing (GD&T) Dimensional assembly analysis (DAA) Process capability study (Cpk, Ppk, Cp, Cr, ppm indices) Just-in-time (JIT) Qualitative assembly analysis (QAA)

COOKBOOKS

FOR

DFM/DFA

There are no cookbooks for DFMA. However, three organized instruction manuals may be close to most engineers’ terms of guidelines. They are: 1. Mitsubishi method 2. U-MASS method 3. MIL-HDB-727 design guidance for producibility All of the above methods utilize the principles of Taylor’s motion economy, which have been proven to be quite helpful, especially in the DFA area. We identify some of these principles here that may be profitably applied to shop and office work alike. Although not all are applicable to every operation, they do form a basis or a code for improving efficiency and reducing fatigue in manual work: 1. Smooth continuous curved motions of the hands are preferable to straightline motions involving sudden and sharp changes in direction. 2. Ballistic movements are faster, easier, and more accurate than restricted (fixation) or “controlled” movements. 3. Work should be arranged to permit easy and natural rhythm wherever possible. Use of the Human Body 4. The two hands should begin as well as complete their motions at the same time.

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5. The two hands should not be idle at the same time except during rest periods. 6. Motions of the arms should be made in opposite and symmetrical directions and should be made simultaneously. 7. Hand and body motions should be confined to the lowest classification with which it is possible to perform the work satisfactorily. 8. Momentum should be employed to assist the worker wherever possible, and it should be reduced to a minimum if it must be overcome by muscular effort. 9. Eye fixations should be as few and as close together as possible. Arrangement of the Work Place 10. There should be a definite and fixed place for all tools and materials. 11. Tools, materials, and controls should be located close to the point of use. 12. Gravity feed bins and containers should be used to deliver material close to the point of use. 13. Drop deliveries should be used wherever possible. 14. Materials and tools should be located to permit the best sequence of motions. 15. Provisions should be made for adequate conditions for seeing. Good illumination is the first requirement for satisfactory visual perception. 16. The height of the work place and the chair should preferably be arranged so that alternate sitting and standing at work are easily possible. 17. A chair of the type and height to permit good posture should be provided for every worker. Design of Tools and Equipment 18. The hands should be relieved of all work that can be done more advantageously by a jig, a fixture, or a foot-operated device. 19. Two or more tools should be combined wherever possible. 20. Tools and materials should be pre-positioned whenever possible. 21. Where each finger performs some specific movement, such as in typewriting, the load should be distributed in accordance with the inherent capacities of the fingers. 22. Levers, crossbars, and hand wheels should be located in such positions that the operator can manipulate them with the least change in body position and with the greatest mechanical advantage.

MITSUBISHI METHOD The Mitsubishi method was developed and fine-tuned by Japanese engineers in Mitsubishi’s Kobe shipyard. The primary principle is the combination of QFD and Taylor’s motion economy. The Mitsubishi method is very popular in Japan’s heavy industries, i.e., shipbuilding industry, steel industry, and heavy equipment industry. There is also evidence of some application of this method in Japan’s automotive,

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motorcycle, and office equipment industries. More efforts are needed to promote and share these techniques, and some effort is needed to fine-tune the Mitsubishi method and make it practical to fit U.S. manufacturing companies’ cultures and traditions. The process is based on the following principles: • The Mitsubishi method focuses on the product design’s reflection of the customer’s desires and tastes. Thus, marketing people, design engineers, and manufacturing staff must work together from the time a product is first conceived. • The Mitsubishi method is a kind of conceptual map that provides the means for inter-functional planning and communications. People with different problems and responsibilities can thrash out design priority while referring to patterns of evidence on the house’s grid. • The method involves 12 steps for each part in design/manufacturing, as follows: 1. Customer attributes (CA) analysis — also called voice of customer (VOC) evaluation — is performed. 2. Relative-importance weights of CA are determined. 3. Data is collected on customer evaluations of competitive products. 4. Engineering characteristics tell how to change the product. 5. Relationship matrix shows how engineering decisions affect customer perceptions. 6. Objective measures evaluate competitive products. 7. Roof matrix facilitates engineering creativity. 8. QFD is finalized. 9. Parts development is based on manufacturing process planning and handling planning (i.e., start the basic manufacturing process with materials in liquid state, feeding raw materials with elevator feeder, handling the wip with center board hopper, and continuing the forthcoming sequential operation with carousel assembly machine). 10. Manufacturing process and handling operation are based on the principles of motion economy. 11. Process planning is guided by parts/component characteristics, which are based on engineering characteristics, and the latter are based on customer attributes (compare to step #9). 12. Integrator coordinates/controls the project. • Analysis procedure. • Continuing improvement: Voice of customer, design alternative, and process alternative continue to interface with each other. It is a dynamical situation — no ending improvement. • Software package. Table 5.1 shows an example of customer attributes (Cas) and bundles of CAs for a car door. An example of relative importance weights of customer attributes is

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TABLE 5.1 Customer Attributes for a Car Door Primary

Good operation and use

Secondary

Tertiary

Easy to open and close

Easy to close from outside Stays open on a hill Easy to open from outside Does not kick back Easy to close from inside Easy to open from inside Does not leak in rain No road noise Does not leak in car wash No wind noise Does not drip water or snow when open Does not rattle Soft, comfortable In right position Material will not fade Attractive (non-plastic look) Easy to clean No grease from door Uniform gaps between matching panels

Isolation

Arm rest Interior trim Good appearance

Clean Fit

TABLE 5.2 Relative Importance of Weights Bundles

Customer Attributes

Easy to open and close door

Easy to close from outside Stays open on a hill Does not leak in rain No road noise A complete list totals

Isolation

Relative Importance 7 5 3 2 100%

shown in Table 5.2. An example of customer evaluations of competitive products is shown in Table 5.3.

U-MASS METHOD The U-MASS method is named for the University of Massachusetts, where it was developed by two professors, Geoffrey Boothroyd and Peter Dewhurst, and their graduate students. It is the most common DFM/DFA approach used in the U.S. The

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TABLE 5.3 Customer’s Evaluations of Competitive Products Customer Attributes

Relative Importance

Customer Perceptions

Easy to close from outside Stays open on a hill Does not leak in rain No road noise

7 5 3 2

Worst Best 1 2 3 4 5 Worst Best 1 2 3 4 5 Comparison is based on individual attributes as compared to: Our car door Competitor A’s Competitor B’s And so on…

Bundles Easy to open and close door Isolation

A complete list totals

100%

primary principle is the conventional motion and time study, while keeping in mind the component counts and motion economy. This method is heavily promoted in academic communities or institute-related manufacturing companies located in the New England area, such as Digital Equipment Corp. and Westinghouse Electric Company. Other companies are using it as well, such as Ford Motor Co., DaimlerChrysler, and many others. Its appeal seems to be the availability of the software that may be purchased from Boothroyd and Dewhurst. (Some practitioners find the software very time-consuming in design efficiency calculation and believe that more work is needed to fine tune its efficiency, as well as make it more user friendly.) The process is based on the following principles: 1. Determine the theoretical minimum part count by applying minimum part criteria. 2. Estimate actual assembly time using DFA database. 3. Determine DFA Index by comparing actual assembly time with theoretical minimum assembly time. 4. Identify assembly difficulties and candidates for elimination that may lead to manufacturing and quality problems.

MIL-HDBK-727 This method was developed by the U.S. Army material command and published by the naval publications and forms center. The first edition was published in 1971, and the latest revision was published in April 1984. The primary principle is Taylor’s motion economy and some other design tools, i.e., DOE. This method is not too popular. Not many people know about it, and it is not used very much outside of the military. Some updates and revisions are needed to make it more practical to general manufacturing companies.

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FUNDAMENTAL DESIGN GUIDANCE The core of the DFM/DFA process is to make sure that the design and assembly are planned in terms of: 1. 2. 3. 4. 5. 6.

Simplicity (as opposed to complexity) Standardization (commonality) Flexibility Capability Suitability Carryover

So, a designer designing a product should be cognizant of the effects on product design. Some of these are: • Materials selection is based on the targeted manufacturing process. • The forms/shapes of parts are based on the targeted transportation, handling, and parts feeding system. • Field environment can affect the production durability, which contributes variation to the components/parts as well as the manufacturing process. • Shelf life. • Operating life. • Product MTBF and MTBR. In the development of the primary design, consideration must be given to whether to start with a basic process or to start with secondary process with purchased raw or semi-raw materials. If the decision is to start with a basic process, then the next question will be — what kind of materials to start with? There are three options: 1. Start with materials in liquid state, i.e., casting. 2. Start with materials in plastic state, i.e., forging. 3. Start with materials in solid state, i.e., roll forming (sheet), extrusion (rod, sheet), electroforming (powder), automatic screw machine work (rod). If a secondary process is needed, either as a sequential operation of a basic process or a fresh starting point, consideration must be given to the selection of the most favorable forming and sizing operations. A number of factors relating to a given design that need to be considered include: 1. 2. 3. 4. 5. 6. 7.

The The The The The The The

shape desired characteristics of the materials tolerance required surface finish quantity to be produced average run size cost

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The focus then of a product design is to: 1. Minimize parts/components: The fewer parts/components and the fewer manufacturing/assembly operations, the better, i.e., • Combine mating parts, unless isolation is needed. • Eliminate screws and loose pieces. Replace screws with snap-on parts or fasten rivet, if practical. If screws are a necessary evil, try to make them all the same type and size. • Do not use a screw to locate. Remember that a screw is a fastener. 2. Use common/popular components/parts: Off-the-shelf type components/parts usually are user friendly and less expensive. Tooling/setup charges also can be avoidable beyond the pilot try headache, i.e., • Use fasteners with common/popular/standard length and diameter. • Use common values of resistors, capacitors, diodes, etc. • Use standard color chip of paints and coatings, if possible. 3. Design the parts to be symmetrical: If you must use customized unique parts, try to design the parts to be symmetrical, and use a jigless assembly method, if at all possible, i.e., • Avoid internal orientations. • Design an external accentuated locating feature, if it cannot be internally symmetrical. 4. Design the parts to be self-aligned, self-locating, and self-locking, i.e., • Design locating pins and small snap protrusions on mating parts. • Chamfers and tapers. • Use mechanical entrapments and snap-on approach. • Connect necessary wires/harnesses directly and use locking connectors. • Make sure that parts are easy to grip. • Avoid flexible parts — the more rigid the part, the more easily handled and assembled. • Avoid cables, if practical. • Avoid complicated fastening process, if practical. (Special note: If screws must be used, remember these rules: • Shank to head ratio: l greater than or equal to 1.5; l greater than or equal to 1.8 if tube feed • Head design • Thread consideration: Tapped holes? Thread cutting screws? Thread forming screws? • Quality screws) 5. Design for simple or no adjustment at all: • Remember, adjustment is a non-value added operation. Minimum adjustment — if necessary — with one-hand operation should be at most. 6. Modularize sub-assembly design: • Modularize sub-assemblies. Assemble and test them prior to final assembly.

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Manufacturing System Schematic

Input

Activities

Output

Design drawings Specifications, standards Requirements Materials

Manufacturing Controlling Planning Scheduling

Products

Constraints Personnel Policies Quality Control/Assurance Purchasing

FIGURE 5.6 Manufacturing system schematic.

THE MANUFACTURING PROCESS Figure 5.6 shows a schematic of a manufacturing system. There are four categories of manufacturing processes. They are: 1. Fabrication process — which can be further categorized as basic process, secondary process, or finishing process. Typical types are: • Single station • Continuous production flow • Pace production line • Manufacturing cell approach 2. Assembly process — which can be further categorized as manual assembly, mechanical assembly, automatic assembly, or computer-aided assembly. Typical types are: • Continuous transfer • Intermittent transfer • Indexing mechanisms • Operator-paced free-transfer machine 3. Inspection or quality control process • Inspection check point(s) 4. Material handling process • Conveyors • Tractors

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• Fork lifts • Parts/component feeding system: • Vibratory bowl feeder • Reciprocating tube hopper feeder • Centerboard hopper feeder • Reciprocating fork hopper feeder • External gate hopper feeder • Rotary disk feeder • Centrifugal hopper feeder • Revolving hook hopper feeder • Stationary hook hopper feeder • Bladed wheel hopper feeder • Tumbling barrel hopper feeder • Rotary centerboard hopper feeder • Magnetic disk feeder • Elevating hopper feeder • Magnetic elevating hopper feeder Approaches to manufacturing processes include the job shop approach, the assembly line approach, and the one in, one out approach. Details of these processes are as follows: Singled station manufacturing process — job shop approach Definition: Single fixture with one or more operations performed Advantages: • Capital investment — low • Line balance — not needed • Interference with other operations (downtime) — minimum, if any • Flexibility — easy to expand or rearrange • Employment fulfillment — high Disadvantages: • Multiple tooling/fixture investment — high • Material handling — high • Material flow — easy to congest at in/out • Operation cycle time — long • Operator skills — moderate Continuous production flow manufacturing process — assembly line approach Definition: Continuous, sequential motion assembly/manufacturing approach Advantages: • Work-in-process — low • Manufacturing/assembly cycle time — low • Material handling — very low, if not eliminated • Material flow — good • Operator skill/training — only in specialized areas

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Disadvantages: • Capital investment — high • Preventative maintenance and corrective maintenance — absolute necessity (If one part breaks down, the entire line is down.) • Engineering, technician, and flow disciplines — absolute necessity • Flexibility — low • Production changeover — complicated Pace production line — one in, one out Definition: Same cycle time at all work stations, and likely all work pieces transfer at the same time Advantages: • Work-in-process — very low and can be calculated • Material handling — automatic • Material flow — good • Productivity — best Disadvantages: • Capital investment — high • Preventative maintenance and corrective maintenance — absolute necessity (If one part breaks down, the entire line is down.) • Engineering, technician, and flow disciplines — absolute necessity • Flexibility — very low • Production changeover — difficult

MISTAKE PROOFING DEFINITION Mistake proofing by definition is a process improvement system that prevents personal injury, promotes job safety, prevents faulty products, and prevents machine damage. It is also known as the Shingo method, Poka Yoke, error proofing, fail safe design, and by many other names.

THE STRATEGY Establish a team approach to mistake proof systems that will focus on both internal and external customer concerns with the intention of maximizing value. This will include quality indicators such as on-line inspection and probe studies. The strategy involves: • Concentrating on the things that can be changed rather than on the things that are perceived as having to be changed to improve process performance • Developing the training required to prepare team members • Involving all the appropriate people in the mistake proof systems process • Tracking quality improvements using in-plant and external data collection systems (before/after data)

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• Developing a “core team” to administer the mistake proof systems process This core team will be responsible for tracking the status of the mistake proof systems throughout the implementation stages. • Creating a communication system for keeping plant management, local union committee, and the joint quality committee informed of all progress — as applicable • Developing a process for sharing the information with all other departments and/or plants — as applicable • Establishing the mission statement for each team and objectives that will identify the philosophy of mistake proof systems as a means to improve quality A typical mission statement may read: to protect our customers by developing mistake proofing systems that will detect or eliminate defects while continuing to pursue variation reduction within the process. • Developing timing for completion of each phase of the process • Establishing cross-functional team involvement with your customer(s) Typical objectives may be to: • Become more aware of quality issues that affect our customer • Focus our efforts on eliminating these quality issues from the production process • Expose the conditions that cause mistakes • Understand source investigation and recognize its role in preventing defects • Understand the concepts and principles that drive mistake prevention • Recognize the three functional levels of mistake proofing systems • Be knowledgeable of the relationships between mistake proof system devices and defects • Recognize the key mistake proof system devices • Share the mistake proof system knowledge with all other facilities within the organization

DEFECTS Many things can and often do go wrong in our ever-changing and increasingly complex work environment. Opportunities for mistakes are plentiful and often lead to defective products. Defects are not only wasteful but result in customer dissatisfaction if not detected before shipment. The philosophy behind mistake proof systems suggests that if we are going to be competitive and remain competitive in a world market we cannot accept any number of defects as satisfactory. In essence, not even one defect can be tolerated. Mistake proof systems are a simple method for making this philosophy become a daily practice. Simple concepts and methods are used to accomplish this objective.

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Humans tend to be forgetful, and as a result, we make mistakes. In a system where blame is practiced and people are held accountable for their mistakes and mistakes within the process, we discourage the worker and lower morale of the individual, but the problem continues and remains unsolved.

MISTAKE PROOF SYSTEM IS IN THE WORKPLACE

A

TECHNIQUE

FOR

AVOIDING ERRORS

The concept of error proof systems has been in existence for a long time, only we have not attempted to turn it into a formalized process. It has often been referred to as idiot proofing, goof proofing, fool proofing, and so on. These terms often have a negative connotation that appears to attack the intelligence of the individual involved and therefore are not used in today’s work environment. For this reason we have selected the term “mistake proof system.” The idea behind a mistake proof system is to reduce the opportunity for human error by taking over tasks that are repetitive or actions that depend solely upon memory or attention. With this approach, we allow the worker to maintain dignity and self-esteem without the negative connotation that the individual is an idiot, goof, or fool.

TYPES

OF

HUMAN MISTAKES

Forgetfulness There are times when we forget things, especially when we are not fully concentrating or focusing. An example that can result in serious consequences is the failure to lock out a piece of equipment or machine we are working on. To preclude this, precautionary measures can be taken: post lock out instructions at every piece of equipment and/or machine; have an ongoing program to continuously alert operators of the danger. Mistakes of Misunderstanding Jumping to conclusions before we are familiar with the situation often leads to mistakes. For example, visual aids are often prepared by engineers who are thoroughly familiar with the operation or process. Since the aid is completely clear from their perspective, they may make the assumption (and often do) that the operator fully understands as well. This may not be true. To preclude this, we may test this hypothesis before we create an aid; provide training/education; standardize work methods and procedures. Identification Mistakes Situations are often misjudged because we view them too quickly or from too far away to clearly see them. One example of this type of mistake is misreading the identification code on a component of a piece of equipment and replacing that component with the wrong part. To prevent these errors, we might improve legibility

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of the data/information; provide training; improve the environment (lighting); reduce boredom of the job, thus increasing vigilance and attentiveness. Amateur Errors Lack of experience often leads to mistakes. Newly hired workers will not know the sequence of operations to perform their tasks and often, due to inadequate training, will perform those tasks incorrectly. To prevent amateur errors, provide proper training; utilize skill building techniques prior to job assignment; use work standardization. Willful Mistakes Willful errors result when we choose to ignore the rules. One example of this type of error is placing a rack of material outside the lines painted on the floor that clearly designate the proper location. The results can be damage to the vehicle or the material or perhaps an unsafe work condition. To prevent this situation, provide basic education and/or training; require strict adherence to the rules. Inadvertent Mistakes Sometimes we make mistakes without even being aware of them. For example, a wrong part might be installed because the operator was daydreaming. To minimize this, we may standardize the work, through discipline if necessary. Slowness Mistakes When our actions are slowed by delays in judgment, mistakes are often the result. For example, an operator unfamiliar with the operation of a fork lift might pull the wrong lever and drop the load. Methods to prevent this might be: skill building; work standardization. Lack of Standards Mistakes Mistakes will occur when there is a lack of suitable work standards or when workers do not understand instructions. For example, two inspectors performing the same inspection may have different views on what constitutes a reject. To prevent this, develop operation definitions of what the product is expected to be that are clearly understood by all; provide proper training and education. Surprise Mistakes When the function or operation of a piece of equipment suddenly changes without warning, mistakes may occur. For example, power tools that are used to supply specific torque to a fastener will malfunction if an adequate oil supply is not maintained in the reservoir. Errors such as these can often be prevented by work standardization; having a total productive maintenance system in place.

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Intentional Mistakes Mistakes are sometimes made deliberately by some people. These fall in the category of sabotage. Disciplinary measures and basic education are the only deterrents to these types of mistakes. There are many reasons for mistakes to happen. However, almost all of these can be prevented if we diligently expend the time and effort to identify the basic conditions that allow them to occur, such as: • When they happen • Why they happen and then determine what steps are needed to prevent these mistakes from recurring — permanently. The mistake proof system approach and the methods used give you an opportunity to prevent mistakes and errors from occurring.

DEFECTS

AND

ERRORS

Mistakes are generally the cause of defects. Can mistakes be avoided? To answer this question requires us to realize that we have to look at errors from two perspectives: 1. Errors are inevitable: People will always make mistakes. Accepting this premise makes one question the rationale of blaming people when mistakes are committed. Maintaining this “blame” attitude generally results in defects. Also, quite often errors are overlooked when they occur in the production process. To avoid blame, the discovery of defects is postponed until the final inspection, or worse yet, until the product reaches the customer. 2. Errors can be eliminated: If we utilize a system that supports (a) proper training and education and (b) fostering the belief that mistakes can be prevented, then people will make fewer mistakes. This being true, it is then possible that mistakes by people can be eliminated. Sources of mistakes may be any one of the six basic elements of a process: 1. 2. 3. 4. 5. 6.

Measurement Material Method Manpower Machinery Environment

Each of these elements may have an effect on quality as well as productivity. To make quality improvements, each element must be investigated for potential mistakes of operation. To reduce defects, we must recognize that defects are a

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TABLE 5.4 Examples of Mistakes and Defects Mistake

Resulting Defects

Failure to put gasoline in the snow blower Failure to close window of unit being tested Failure to reset clock for daylight savings time Failure to show operator how to properly assemble components Proper weld schedule not maintained on welding equipment Low charged battery placed in griptow

Snow blower will not start Seats and carpet are wet Late for work Defective or warped product Bad welds, rejectable and/or scrap material Griptow will not pull racks resulting in lost production, downtime, etc.

consequence of the interaction of all six elements and the actual work performed in the process. Furthermore, we must recognize that the role of inspection is to audit the process and to identify the defects. It is an appraisal system and it does nothing for prevention. Product quality is changed only by improving the quality of the process. Therefore, the first step toward elimination of defects is to understand the difference between defects and mistakes (errors): Defects are the results. Mistakes are the causes of the results. Therefore, the underlying philosophy behind the total elimination of defects begins with distinguishing between mistakes and defects. Examples of mistakes and defects are shown in Table 5.4.

MISTAKE TYPES

AND

ACCOMPANYING CAUSES

The following categories with the associated potential causes are given as examples, rather than exhaustive lists: Assembly mistakes Inadequate training Symmetry (parts mounted backwards) Too many operations to perform Multiple parts to select from with poor or no identification Misread or unfamiliar with parts/products Tooling broken and/or misaligned New operator Processing mistakes Part of process omitted (inadvertent/deliberate) Fixture inadequate (resulting in parts being set into incorrectly) Symmetrical parts (wrong part can be installed)

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Irregular shaped/sized part (vendor/supplier defect) Tooling damaging part as it is installed Carelessness (wrong part or side installed) Process/product requirements not understood (holes punched in wrong location) Following instructions for wrong process (multiple parts) Using incorrect tooling to complete operations (impact versus torque wrench) Inclusion of wrong part or item Part codes wrong/missing Parts for different products/applications mixing together Similar parts confused Misreading prints/schedules/bar codes etc. Operations mistakes Process elements assigned to too many operators Operator error Consequential results Setup mistakes Improper alignment of equipment Process or instructions for setup not understood or out of date Jigs and fixtures mislocated or loose Fixtures or holding devices will accept mislocated components Assembly omissions — missing parts Special orders (high or low volume parts missing) No inspection capability (hidden parts omitted) Substitutions (unexpected deviations from normal production) Misidentified build parameters (heavy duty versus standard) Measurement or dimensional mistakes Flawed measuring device Operator skill in measuring Inadequate system for measuring Using “best guess” system Processing omissions Operator fatigue (part assembled incorrectly/omitted) Cycle time (incomplete/poor weld) Equipment breakdown (weld omitted) New operator Tooling omitted Automation malfunction Instructions for operation incomplete/missing Job not set up for changeover Operator not trained/improper training Sequence violation Mounting mistakes Symmetry (parts can be installed backwards) Tooling wrong/inadequate

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Operator dependency (parts installed upside down) Fixtures or holding devices accept mispositioned parts Miscellaneous mistakes Inadequate standards Material misidentified No controls on operation Counting system flawed/operating incorrectly Print/specifications incorrect

SIGNALS

THAT

ALERT

Signals that “alert” are conditions present in a process that commonly result in mistakes. Some signals that alert are: • • • • • • • • • • • •

Many parts/mixed parts Multiple steps needed to perform operation Adjustments Tooling changes Critical conditions Lack of or ineffective standards Infrequent production Extremely high volume Part symmetry Asymmetry Rapid repetition Environmental • Housekeeping • Material handing • Poor lighting • Foreign matter and debris • Other

Ten of the most common types of mistakes are: Assembly mistakes Inclusion of wrong part or item Setup mistakes Measurement mistakes Mounting mistakes

APPROACHES

TO

Processing mistakes Operations mistakes Assembly omissions (missing parts) Process omissions Miscellaneous

MISTAKE PROOFING

As we already mentioned, any mistake proofing system is a process that focuses on producing zero defects by eliminating the human element from assembly. There are two approaches to this — see Figure 5.7.

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Operation #1

Operation #2

Ship to Customer

Reactive Systems

Proactive Systems

Focus on defect identification Alerts (signals) operator that failure has occurred Provides immediate feedback to operator Points to area of cause of defect Points to apparent cause (symptom of defect stops production until defective item removed or repaired) Protects customer from receiving defective product Does not prevent mistakes or defects from recurring

Focus on defect prevention Utilizes source inspection to detect when a mistake is about to occur before a defect is produced Halts production before mistake occurs Utilizes ideal Mistake Proofing Methods that eliminate the possibility of mistakes so that defective product cannot be produced Performs 100% inspection without inspection costs Prevents defects and mistakes from occurring

FIGURE 5.7 Approaches to mistake proofing.

1. Reactive systems (defect detection) This approach relies on halting production in order to sort out the good from the bad for repair or scrap. 2. Proactive systems (defect prevention) This approach seeks to eliminate mistakes so that defective products are not produced, production downtime is reduced, costs are lowered, and customer satisfaction is increased. Major Inspection Techniques Figure 5.8 shows major inspection techniques. Source inspection utilizing mistake proofing system devices is the most logical method of defect prevention. Mistake Proof System Devices Mistake proof system “devices” are simple and inexpensive. There are essentially two types of devices used: 1. Detectors (sensors) — to detect mistakes that have occurred or are about to occur 2. Preventers — to prevent mistakes from occurring

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Operation #1

Operation #2

Source Inspection A defect is a result of a mistake. Source inspection looks at the cause(s) for the mistake, rather than the actual defect. By conducting inspection at the source, mistakes can be corrected before they become defects. Inspection utilizing Mistake Proofing System Devices to automatically inspect for mistakes or defective operating conditions is an effective low-cost solution for eliminating defects and resulting defective product. Informative Inspection Looks at the cause(s) of defects and feeds this information back to the appropriate personnel/process so that defects can be reduced/eliminated

217

Ship to Customer Inspect Finished Product Sort “good” from “bad” BAD

Scrap

GOOD

Repair

Judgment Inspection Distinguishes good product from bad. This method prevents defective product from being delivered to the customer but: Does nothing to prevent production of defective products

FIGURE 5.8 Major inspection techniques.

Devices Used as “Detectors of Mistakes” When used as detectors (sensors), these devices: 1. Provide prompt feedback (signals) to the operator that a mistake has occurred or is about to occur 2. Initiate an action or actions to prevent further mistakes from occurring Devices Used as “Preventers of Mistakes” When used to prevent mistakes, these devices prevent mistakes from occurring or initiate an action or actions to prevent mistakes from occurring.

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Operation #1

Operation #2

First Function Eliminates the mistake at the source before it occurs

Ship to customer Third Function Detects a defect that has occurred before it is sent to the next operation or shipped to the customer

Second Function Detects mistakes as they are occurring, but before they result in defects

FIGURE 5.9 Functions of mistake-proofing devices.

EQUATION

FOR

SUCCESS

To be successful with a mistake proofing initiative one must keep in mind the following equation: Source investigation + Mistake proofing = Defect free system However, to reach the state of defect free system, in addition to signals and inspection we must also incorporate appropriate sensors to identify, stop, and/or correct a problem before it goes to the next operation. Sensors are very important in mistake proofing, so let us look at them little closer. A sensor is an electrical device that detects and responds to changes in a given characteristic of a part, assembly, or fixture — see Figure 5.9. A sensor can, for example, verify with a high degree of accuracy the presence and position of a part on an assembly or fixture and can identify damage or wear. Some examples of types of sensors and typical uses are: Welding position indicators: Determine changes in metallic composition, even on joints that are invisible to the surface Fiber sensors: Observe linear interruptions utilizing fiber optic beams Metal passage detectors: Determine if parts have a metal content or mixed metal content, for example in resin materials Beam sensors: Observe linear interruptions using electronic beams Trimetrons: Exclude or detect preset measurement values using a dial gauge (Value limits can be set on plus or minus sides, as well as on nominal values.) Tap sensors: Identify incomplete or missing tap screw machining Color marking sensors: Identify differences in color or colored marking

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Area sensors: Determine random interruptions over a fixed area Double feed sensors: Identify when two products are fed at the same time Positioning sensors: Determine correct/incorrect positioning Vibration sensors: Identify product passage, weld position, broken wires, loose parts, etc. Displacement sensors: Identify thickness, height, warpage, surface irregularities, etc. Typical Error Proofing Devices Some of the most common mistake proofing devices used are: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

Sensors Sequence restrictors Odd part out method Limit or microswitches, proximity detectors Templates Guide rods or pins Stoppers or gates Counters Standardized methods of operation and/or material usage Detect delivery chute Critical condition indicators Probes Mistake proof your mistake proof system

and so on

REFERENCES Boothroyd, G. and Dewhurst, P., Product Design for Assembly, Boothroyd Dewhurst, Inc., Wakefield, RI, 1991. MIL-HDBK-727, Design Guidance for Producibility, U.S. Army Material Command, Washington, DC, 1986. Mitsubishi, Mitsubishi Design Engineering Handbook, Mitsubishi, Kobe, Japan, 1976. Munro, A., S. Munro and Associates, Inc., Design for Manufacture, training manual, 1992.

SELECTED BIBLIOGRAPHY Anon., How To Achieve Error Proof Manufacturing: Poka-Yoke and Beyond: A Technical Video Tutorial, SAE International, undated (may be ordered online for $895 [$25 preview copy]). Anon., 21st Century Manufacturing Enterprise Strategy, An Industry Led View, Volumes 1 and 2, Iacocca Institute, Lehigh University, PA, 1991. Anon., Mistake-Proofing for Operators: The ZQC System, The Productivity Press Development Team, Productivity Press, Portland, OR, 1997.

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Anon., Manufacturing Management Handbook for Program Manager, ABN Fort Belvoir, VA, 1982. Anon., Product Engineering Design Mannual, Litton Industries, Beverly Hills, CA, 1978. Azuma, L. and Tada, M., A case history development of a foolproofing interface documentation system, IEEE Transactions on Software Engineering, 19, 765–773, 1993. Bandyopadhyay, J.K., Poka Yoke systems to ensure zero defect quality manufacturing, International Journal of Management, 10(1), 29–33, 1993. Barkers, R., Motion and Time Study: Design and Measurement of Work, Cot Loge Book Company, Los Angeles, 1970. Barkman, W.E., In-Process Quality Control for Manufacturing, Marcel Dekker, New York, 1989. (Preface and Chapter 3 are of particular interest.) Bayer, P.C., Using Poka Yoke (mistake proofing devices) to ensure quality, IEEE 9th Applied Power Electronics Conference Proceedings, 1, 201–204, 1994. Bodine, W.E., The Trend: 100 Percent Quality Verification, Production, June 1993, pp. 54–55. Bosa, R., Despite fuzzy logic and neural networks, operator control is still a must, CMA, 69, 7, 995. Boothroyd, G. and Murch, P., Automatic Assembly, Marcel Dekker, New York, 1982. Brehmer, B., Variable errors set a limit to adaptation, Ergonomics 33, 1231–1239, 1990. Brall, J., Product Design for Manufacturing, McGraw-Hill, New York, 1986. Casey, S., Set Phasers on Stun and Other True Tales of Design, Technology, and Human Error, Aegean, Santa Barbara, CA, 1993. Chase, R.B., and Stewart, D.M., Make Your Service Fail-safe, Sloan Management Review, Spring 1994, pp. 35–44. Chase, R.B. and Stewart, D.M., Designing Errors Out, Productivity Press, Portland, OR, 1995. Note of interest: Productivity Press has discontinued sales of this book (a very sad outcome). Some copies may be available in local bookstores. It is both more readable and broader in application than Shingo but does not have a catalog of examples as Shingo does. Damian, J., “Agile Manufacturing” Can Revive U.S. Competitiveness, Industry Study Says — A Modest Proposal, Electronics, Feb. 1992, pp. 34, 42–44. Dove, R., Agile and Otherwise — Measuring Agility: The Toll of Turmoil, Production, Jan. 1995, pp. 12–15. Dove, R., Agile and Otherwise — The Challenges of Change, Production, Feb. 1995, pp. 14–16. Gross, N., This Is What the U.S. Must Do To Stay Competitive, Business Week, Dec. 1991, pp. 21–24. Grout, J.R., Mistake-Proofing Production, working paper 75275–0333, Cox School of Business, Southern Methodist University, Dallas, 1995. Grout, J.R. and Downs, B.T., An Economic Analysis of Inspection Costs for Failsafing Attributes, working paper 95–0901, Cox School of Business, Southern Methodist University, Dallas, 1995. Grout, J.R. and Downs. B.T., Fail-safing and Measurement Control Charts, 1995 Proceedings, Decision Sciences Institute Annual Meetings, Boston, MA, 1995. Henricks, M., Make No Mistake, Entrepreneur, Oct. 1996, pp. 86–89. (Last quote should read “average net savings of around $2500 a piece...” not average cost.) Hinckley, C.M. and Barkan, P., The role of variation, mistakes, and complexity in producing nonconformities, Journal of Quality Technology 27(3), 242–249, 1995. Jaikumar, R., Manufacturing a’la Carte Agile Assembly Lines, Faster Development Cycles, 200 Years to CIM, IEEE Spectrum, 76–82, Sept. 1993.

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Kaplan, G., Manufacturing a’la Carte Agile Assembly Lines, Faster Development Cycles, Introduction, IEEE Spectrum, 46–51, Sept. 1993. Kelly, K., Your Job Managing Error is Out of Control, Addison-Wesley, New York, 1994. Kletz, T., Plant Design for Safety: A User-Friendly Approach, Hemisphere Publishing Corp., New York, 1991. Lafferty, J.P., Cpk of 2 Not Good Enough for You? Manufacturing Engineering, Oct. 1992, p. 10. Ligus, R.G., Enterprise Agility: Jazz in the Factory, Industrial Engineering, Nov. 1994, pp. 19–23. Lucas Engineering and Systems Ltd., Design for Manufacture Reference Tables, University of Hull, Hull, England, Lucas Industries, Jan. 1994. Manji, J.F., Sharpen, C., Your Competitive Edge Today and Into the 21st Century, CALS El Journal, Date unknown, pp. 56–61. Marchwinski, C., Ed., Company Cuts the Risk of Defects During Assembly and Maintenance, MfgNet: The Manufacturer’s Internet Newsletter, Productivity, Inc. Norwalk, CT, 1996. Marchwinski, C., Ed., Mistake-proofing, Productivity, 17(3), 1–6, 1995. Marchwinski, C., Ed., (1997). SPC vs. ZQC. Productivity, 18(1), 1–4 1997. (Note: ZQC is another name for mistake proofing. It stands for Zero Quality Control.) McClelland, S., Poka-Yoke and the Art of Motorcycle Maintenance, Sensor Review, 9(2), 63, 1989. Monden, Y., Toyota Production System, Industrial Engineering and Management Press, Norcross, GA, 1983, pp. 10, 137–154. Munro, A., S. Munro and Associates, Inc., Design for Manufacture, training manual, 1994. Munro, A., S. Munro and Associates, Inc., Trainers for Design for Manufacture, analysis, undated. Myers, M., Poka/Yoke-ing Your Way to Success, Network World, Sept. 11, 1995, p. 39. Nakajo, T. and Qume, H., The principles of foolproofing and their application in manufacturing, Reports of Statistical Application Research, Union of Japanese Scientists and Engineers, 32(2), 10–29, 1985. Niebel, C. and Baldwin, J., Designing for Production, Irwin, Homewood, IL, 1963. Nieber, C. and Draper, G., Product Design and Process Engineering, McGraw-Hill, New York, 1974. Noaker, P.M., The Search for Agile Manufacturing, Manufacturing Engineering, Nov. 1994, pp. 57–63. Norman, D.A., The Design of Everyday Things, Doubleday, New York, 1989. O’Connor, L., Agile Manufacturing in a Responsive Factory, Mechanical Engineering, July 1994, pp. 43–46. Otto, K. and Wood, K., Product Design: Techniques in Reverse Engineering and New Product Development, Prentice Hall, Upper Saddle River, NJ, 2001. Port, O., Moving Past the Assembly Line — “Agile” Manufacturing Systems May Bring a U.S. Revival, Business Week/Re-Inventing America, 1992, pp. 17–20. Reason, J., Human Error, Cambridge University Press, New York, 1990. Robinson, A.G. and Schroeder, D.M., The limited role of statistical quality control in a zero defects environment, Production and Inventory Management Journal, 31(3), 60–65, 1990. Robinson, A.G., Ed., Modern Approaches to Manufacturing Improvement: The Shingo System, Productivity Press, Portland, OR, 1991. Shandle, J., Sandia Labs Launches Agile Factory Program, Electronics, Mar. 8, 1993, pp. 48–49.

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Sheridan, J.H., A Vision of Agility, Industry Week, Mar. 21, 1994, pp. 22–24. Shingo, S., Zero Quality Control: Source Inspection and the Poka-Yoke System, Trans. A.P. Dillion, Productivity Press, Portland, OR, 1986. Shingo S., A Study of the Toyota Production System from an Industrial Engineering Viewpoint, Productivity Press, Portland, OR, 1989, online excerpts. Steven, S. and Bowen., H.K., Decoding the DNA of the Toyota Production System, Harvard Business Review, Sept./Oct, 1999, pp. 97–106. Texas Instruments, Design to Cost: An Introduction, Corporate Engineering Council, Texas Instruments, Inc., Dallas, 1977. Trucks, H.E., Designing for Economical Production, SME, Dearborn, MI, 1974. Tsuda, Y., Implications of fool proofing in the manufacturing process, in Quality Through Engineering Design, Kuo, W., Ed., Elsevier, New York, 1993. Vasilash, G.S., Re-engineering, Re-energizing, Objects and Other Issues of Interest, Production, Jan. 1995, pp. 38–41. Vasilash, G.S., On training for mistake-proofing, Production, Mar. 1995, pp. 42–44. Ward, C., What Is Agility? Industrial Engineering, Nov. 1994, pp. 38–44. Warm, J.S., An introduction to vigilance, in Sustained Attention in Human Performance, Warm, J.S., Ed., Wiley, New York, 1984. Weimer, G., Is an American Renaissance at Hand? Industry Week, May 1992, pp. 14–17. Weimer, G., U.S.A. 2006: Industry Leader or Loser, Industry Week, Jan. 20, 1992, pp. 31–34.

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6

Failure Mode and Effect Analysis (FMEA)

This chapter has been developed to assist and instruct design, manufacturing, and assembly engineers in the development and execution of a potential Failure Mode and Effect Analysis (FMEA) for design considerations, manufacturing, assembly processes, and machinery. An FMEA is a methodology that helps identify potential failures and recommends corrective action(s) for fixing these failures before they reach the customer. A concept (system) FMEA is conducted as early as possible to identify serious problems with the potential concept or design. A design FMEA is conducted prior to production and involves the listing of potential failure modes and causes. An FMEA identifies actions required to prevent defects and thus keeps products that may fail or not be fit from reaching the customer. Its purpose is to analyze the product’s design characteristics relative to the planned manufacturing or assembly process to ensure that the resultant product meets customer needs and expectations. When potential failure modes are identified, corrective action can be initiated to eliminate them or continuously reduce their potential occurrence. The FMEA also documents the rationale for the manufacturing or assembly process involved. Changes in customer expectations, regulatory requirements, attitudes of the courts, and the industry’s needs require disciplined use of a technique to identify and prevent potential problems. That disciplined technique is the FMEA. A process FMEA is an analytical technique that identifies potential productrelated process failure modes, assesses the potential customer effects of the failures, identifies the potential manufacturing or assembly process causes, and identifies significant process variables to focus controls for prevention or detection of the failure conditions. (Also, process FMEAs can assist in developing new machine or equipment processes. The methodology is the same; however, the machine or equipment being designed would be considered the product.) A machinery FMEA is a methodology that helps in the identification of possible failure modes and determines the cause for and effect of these failures. The focus of the machinery FMEA is to eliminate any safety issues and to resolve them according to specified procedures between customer and supplier. In addition, the purpose of this particular FMEA is to review both design and process with the intent to reduce risk. All FMEAs utilize occurrence and detection probability in conjunction with severity criteria to develop a Risk Priority Number (RPN) for prioritization of corrective action considerations. This is a major departure in methodology from the Failure Mode and Critical Analysis (FMCA), which focuses primarily on the severity of the failure as a priority characteristic.

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In its most rigorous form, an FMEA summarizes the engineer’s thoughts while developing a process. This systematic approach parallels and formalizes the mental discipline that an engineer normally uses to develop processing requirements.

DEFINITION OF FMEA FMEA is an engineering “reliability tool” that: 1. Helps to define, identify, prioritize, and eliminate known and/or potential failures of the system, design, or manufacturing process before they reach the customer, with the goal of eliminating the failure modes or reducing their risks 2. Provides structure for a cross-functional critique of a design or a process 3. Facilitates inter-departmental dialog (It is much more than a design review.) 4. Is a mental discipline “great” engineering teams go through, when critiquing what might go wrong with the product or process 5. Is a living document that reflects the latest product and process actions 6. Ultimately helps prevent and not react to problems 7. Identifies potential product- or process-related failure modes before they happen 8. Determines the effect and severity of these failure modes 9. Identifies the causes and probability of occurrence of the failure modes 10. Identifies the controls and their effectiveness 11. Quantifies and prioritizes the risks associated with the failure modes 12. Develops and documents action plans that will occur to reduce risk

TYPES OF FMEAS There are many types of FMEAs (see Figure 6.1). However, the main ones are: • System/Concept — S/CFMEA. These are driven by system functions. A system is an organized set of parts or subsystems to accomplish one or more functions. System FMEAs are typically done very early, before specific hardware has been determined. • Design — DFMEA. A design FMEA is driven by part or component functions. A design/part is a unit of physical hardware that is considered a single replaceable part with respect to repair. Design FMEAs are typically done later in the development process when specific hardware has been determined. • Manufacturing or Process — PFMEA. A process FMEA is driven by process functions and part characteristics. A manufacturing process is a sequence of tasks that is organized to produce a product. A process FMEA can involve fabrication as well as assembly.

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Types of FMEA Design FMEA

Process FMEA

Component Subsystem System

System FMEA

Machinery FMEA Focus: Design changes to lower life cycle costs Objective: Improve the reliability and maintain ability of the machinery and equipment

Focus: Minimize failure effects on the system Objective: Maximize system quality, reliability cost, and maintain ability

Machines Methods Material Manpower Measurement Environment Focus: Minimize production process failure effects on the system Objective: Maximize the system quality, reliability, cost, maintain ability, and productivity

FIGURE 6.1 Types of FMEA.

• Machinery — MFMEA is driven by low volume machinery and equipment where large-scale testing is impractical prior to production and manufacture of the machinery and equipment. The MFMEA focuses on design changes to lower life cycle costs by improving the reliability and maintainability of the machinery and equipment. Note: Service, software, and environmental FMEAs are additional variations. However, in this chapter we will focus only on design, process, and machinery FMEAs. The other FMEAs follow the same rationale as the design and process FMEAs.

IS FMEA NEEDED? If any answer to the following questions is positive, then you need an FMEA: • • • • •

Are Are Are Are Are

customers becoming more quality conscious? reliability problems becoming a big concern? regulatory requirements harder to meet? you doing too much problem solving? you addicted to problem solving?

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“Addiction” to problem solving is a very important consideration in the application of an active FMEA program. When the thrill and excitement of solving problems become dominant, your organization is addicted to problem solving rather than preventing the problem to begin with. A proper FMEA will help break your addiction by: • Reducing the percentage of time devoted to problem solving • Increasing the percentage of time in problem prevention • Increasing the efficiency of resource allocation Note: The emphasis is always on reducing complexity and engineering changes.

BENEFITS OF FMEA When properly conducted, product and process FMEAs should lead to: 1. Confidence that all risks have been identified early and appropriate actions have been taken 2. Priorities and rationale for product and process improvement actions 3. Reduction of scrap, rework, and manufacturing costs 4. Preservation of product and process knowledge 5. Reduction of field failures and warranty cost 6. Documentation of risks and actions for future designs or processes By way of comparison of FMEA benefits and the quality lever, Figure 6.2 may help. In essence, one may argue that the most important benefit of an FMEA is that it helps identify hidden costs, which are quite often greater than visible costs. Some of these costs may be identified through: 1. 2. 3. 4. 5.

Customer dissatisfaction Development inefficiencies Lost repeat business (no brand loyalty) High employee turnover And so on

FMEA HISTORY This type of thinking has been around for hundreds of years. It was first formalized in the aerospace industry during the Apollo program in the 1960s. The initial automotive adoption was in the 1970s in the area of safety issues. FMEA was required by QS-9000 and the advanced product quality planning process in 1994 for all automotive suppliers. It has now been adopted by many other industries.

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Payback: Effort Product design fix 100:1

Process design fix 10:1 Production fix 1:1 Customer fix 1:10

Planning and definition Product design and development Mfg process design and development Product and process validation

Production

FIGURE 6.2 Payback effort.

INITIATION OF THE FMEA Regardless of the type, all FMEAs should be conducted as early as possible. FMEA studies can be carried out at any stage during the development of a product or process. However, the ideal time to start the FMEA is: • When new systems, designs, processes, or machines are being designed, but before they are finalized • When systems design, process, or machine modifications are being contemplated • When new applications are used for the systems, designs, processes, or machines • When quality concerns become visible • When safety issues are of concern Note: Once the FMEA is initiated, it becomes a living document, is updated as necessary, and is never really complete. Therefore: • “FMEA-type thinking” is central to reliability and continual improvement in products and manufacturing processes to remain competitive in our global marketplace. It must be understood that an FMEA conducted after production serves as a reactive tool, and the user has not taken full advantage of the FMEA process.

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• A typical system FMEA should begin even before the program approval stage. The design FMEA should start right after program approval and continue to be updated through prototypes. A process FMEA should begin just before prototypes and continue through pilot build and sometimes into product launching. As for the MFMEA, it should also start at the same time as the design FMEA. It is imperative for a user of an FMEA to understand that sometimes information is not always available. During these situations, users must do the best they can with what they have, recognizing that the document itself is indeed a living document and will change as more information becomes available. • History has shown that a majority of product warranty campaigns and automotive recalls could have been prevented by thorough FMEA studies.

GETTING STARTED Just as with anything else, before the FMEA begins there are some assumptions and preparations that must be taken care of. These are: 1. 2. 3. 4.

Know your customers and their needs. Know the function. Understand the concept of priority. Develop and evaluate conceptual designs/processes based on your customer’s needs and business strategy. 5. Be committed to continual improvement. 6. Create an effective team. 7. Define the FMEA project and scope.

1. UNDERSTAND YOUR CUSTOMERS

AND

THEIR NEEDS

A product or a process may perform functions flawlessly, but if the functions are not aligned with the customer’s needs, you may be wasting your time. Therefore, you must: • Determine all (internal or external) relevant customers. • Understand the customer’s needs better than the customers understand their own needs. • Document the customer’s needs and develop concepts. For example, customers need: • Chewable toothpaste • Smokeless cigarettes • Celery-flavored gum • ???? In FMEA, a customer is anyone/anything that has functions/needs from your product or manufacturing process. An easy way to determine customer needs is to understand the Kano model — see Figure 6.3.

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Satisfied Excitement needs Performance needs

Did not do it at all

Did it very well

Basic needs

Time

Dissatisfied FIGURE 6.3 Kano model.

The model facilitates understanding of all the customer needs, including: Excitement needs: Generally, these are the unspoken “wants” of the customer. Performance needs: Generally, these are the spoken “needs” of the customer. They serve as the neutral requirements of the customer. Basic needs: Generally, these are the unspoken “needs” of the customer. They serve as the very minimum of requirements. It is important to understand that these needs are always in a state of change. They move from basic needs to performance to excitement depending on the product or expectation, as well as value to the customer. For example: SYSTEM customers may be viewed as: other systems, whole product, government regulations, design engineers, and end user. DESIGN customers may be viewed as: higher assembly, whole product, design engineers, manufacturing engineers, government engineers, and end user. PROCESS customers may be viewed as: the next operation, operators, design and manufacturing engineering, government regulations, and end user. MACHINE customers may be viewed as: higher assembly, whole product, design engineers, manufacturing engineers, government regulations, and end user. Another way to understand the FMEA customers is through the FMEA team, which must in no uncertain terms determine: 1. Who the customers are 2. What their needs are 3. Which needs will be addressed in the design/process

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The appropriate and applicable response will help in developing both the function and effects.

2. KNOW

THE

FUNCTION

The dictionary definition of a function is: The natural, proper, or characteristic action of any thing. This is very useful because it implies performance. After all, it is performance that we are focusing in the FMEA. Specifically, a function from an FMEA perspective is the task that a system, part, or manufacturing process performs to satisfy a customer. To understand the function and its significance, the team conducting the FMEA must have a thorough list of functions to evaluate. Once this is done, the rest of the FMEA process is a mechanical task. For machinery, the function may be analyzed through a variety of methodologies including but not limited to: • • • • • • • •

Describing the design intent either through a block diagram or a P-diagram Identifying an iterative process in terms of what can be measured Describing the ideal function — what the machine is supposed to do Identifying relationships in verb–noun statements — function tree analysis Considering environmental and safety conditions Accounting for all R & M parameters Accounting for the machine’s performance conditions Analyzing all other measurable engineering attributes

3. UNDERSTAND

THE

CONCEPT

OF

PRIORITY

One of the outcomes of an FMEA is the prioritization of problems. It is very important for the team to recognize the temptation to address all problems, just because they have been identified. That action, if taken, will diminish the effectiveness of the FMEA. Rather, the team should concentrate on the most important problems, based on performance, cost, quality, or any characteristic identified on an a priori basis through the risk priority number.

4. DEVELOP AND EVALUATE CONCEPTUAL DESIGNS/PROCESSES BASED ON CUSTOMER NEEDS AND BUSINESS STRATEGY There are many methods to assist in developing concepts. Some of the most common are: 1. 2. 3. 4.

Brainstorming Benchmarking TRIZ (the theory of inventive problem solving) Pugh concept selection (an objective way to analyze and select/synthesize alternative concepts)

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Eval. Criteria Stubble length Pain level Mfg. Costs Price/Use Etc Etc

Razor D A T U M

A B

C D

E F

+ S

+ S S S

+ -

-

Totals

+ S

2 1 1

1 3

3 1

+ + S S

S S S +

+ S + -

1 3

1 2 1

4 2 2

G

H

Legend: Evaluation Criteria: These are the criteria that we are comparing the razor with the other approaches. Datum: These are the basic razor characteristics that we are comparing the other concepts to. A: Chemical D: Duct tape G: Straight edge

B: Electric E: Epilady H: ?

C: Electrolysis F: Laser beam

+ : Better than the basic razor requirement - : Worse than the basic razor requirement S : Same as the basic razor requirement

FIGURE 6.4 A Pugh matrix — shaving with a razor.

Figure 6.4 shows what a Pugh matrix may look like for the concept of “shaving” with a base that of a “razor.”

5. BE COMMITTED

TO

CONTINUAL IMPROVEMENT

Everyone in the organization and especially management must be committed to continual improvement. In FMEA, that means that once recommendations have been made to increase effectiveness or to reduce cost, defects, or any other characteristic, a proper corrective action must be developed and implemented, provided it is sound and it complements the business strategy.

6. CREATE

AN

EFFECTIVE FMEA TEAM

Perhaps one of the most important issues in dealing with the FMEA is that an FMEA must be done with a team. An FMEA completed by an individual is only that individual’s opinion and does not meet the requirements or the intent of an FMEA. The elements of an effective FMEA team are: • Expertise in subject (five to seven individuals) • Multi-level/consensus based

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• Representing all relevant stakeholders (those who have ownership) • Possible change in membership as work progresses • Cross-functional and multidisciplinary (One person’s best effort cannot approach the knowledge of an effective cross-functional and multidisciplinary team.) • Appropriate and applicable empowerment The structure of the FMEA team is based on: Core team The experts of the project and the closest to the project. They facilitate honest communication and encourage active participation. Support membership may vary depending on the stage of the project. Champion/sponsor • Provides resources and support • Attends some meetings • Supports team • Promotes team efforts and implements recommendations • Shares authority/power with team • Kicks off team • Higher up in management the better Team leader A team leader is the “watchdog” of the project. Typically, this function falls upon the lead engineer. Some of the ingredients of a good team leader are: • Possesses good leadership skills • Is respected by team members • Leads but does not dominate • Maintains full team participation Recorder Keeps documentation of team’s efforts. The recorder is responsible for coordinating meeting rooms and times as well as distributing meeting minutes and agendas. Facilitator The “watchdog” of the process. The facilitator keeps the team on track and makes sure that everyone participates. In addition, it the facilitator’s responsibility to make sure that team dynamics develop in a positive environment. For the facilitator to be effective, it is imperative for the facilitator to have no stake on the project, possess FMEA process expertise, and communicate assertively. Important considerations for a team include: • Continuity of members • Receptive and open-minded • Committed to success

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• • • • •

233

Empowered by sponsor Cross-functionality Multidiscipline Consensus Positive synergy

Ingredients of a motivated FMEA team include: • • • • • • • • •

Realistic agendas Good facilitator Short meetings Right people present Reach decisions based on consensus Open minded, self initiators, volunteers Incentives offered Ground rules established One individual responsible for coordination and accountability of the FMEA project (Typically for the design, the design engineer is that person and for the process, the manufacturing engineer has that responsibility.)

To make sure the effectiveness of the team is sustained throughout the project, it is imperative that everyone concerned with the project bring useful information to the process. Useful information may be derived due to education, experience, training, or a combination of these. At least two areas that are usually underutilized for useful information are background information and surrogate data. Background information and supporting documents that may be helpful to complete system, design, or process FMEAs are: • • • • • • • • • •

Customer specifications (OEMs) Previous or similar FMEAs Historical information (warranty/recalls etc.) Design reviews and verification reports Product drawings/bill of material Process flow charts/manufacturing routing Test methods Preliminary control and gage plans Maintenance history Process capabilities

Surrogate data are data that are generated from similar projects. They may help in the initial stages of the FMEA. When surrogate data are used, extra caution should be taken. Potential FMEA team members include: • Design engineers • Manufacturing engineers

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• • • • • • • • •

Quality engineers Test engineers Reliability engineers Maintenance personnel Operators (from all shifts) Equipment suppliers Customers Suppliers Anyone who has a direct or indirect interest • In any FMEA team effort the individuals must have interaction with manufacturing and/or process engineering while conducting a design FMEA. This is important to ensure that the process will manufacture per design specification. • On the other hand, interaction with design engineering while conducting a process or assembly FMEA is important to ensure that the design is right. • In either case, group consensus will identify the high-risk areas that must be addressed to ensure that the design and/or process changes are implemented for improved quality and reliability of the product

Obviously, these lists are typical menus to choose an appropriate team for your project. The actual team composition for your organization will depend upon your individual project and resources. Once the team is chosen for the given project, spend 15–20 minutes creating a list of the biggest (however you define “biggest”) concerns for this product or process. This list will be used later to make sure you have a complete list of functions.

7. DEFINE

THE

FMEA PROJECT

AND

SCOPE

Teams must know their assignment. That means that they must know: • • • •

What they are working on (scope) What they are not working on (scope) When they must complete the work Where and how often they will meet

Two excellent tools for such an evaluation are (1) block diagram for system, design, and machinery and (2) process flow diagram for process. In essence, part of the responsibility to define the project and scope has to do with the question “How broad is our focus?” Another way to say this is to answer the question “How detailed do we have to be?” This is much more difficult than it sounds and it needs some heavy discussion from all the members. Obviously, consensus is imperative. As a general rule, the focus is dependent upon the project and the experience or education of the team members. Let us look at an example. It must be recognized that sometimes due to the complexity of the system, it is necessary to narrow the scope of the FMEA. In other words, we must break down the system into smaller pieces — see Figure 6.5.

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Master cylinder Pedals and linkages Hydraulics Brake System

Back plate and hardware

235

Cylinder, fluid bladder, etc Pedal, rubber cover, cotter pins, etc. Rubber hose, metal tubing, proportioning valve, fitting, etc. Back plate, springs, washer, clips, etc.

Caliper system

Pistons, cylinder, casting, plate, etc.

Rotor and studs

Rotor hat, rotor, studs, etc.

Pads and hardware

Friction material, substrate, rivets, clip etc.

OUR FMEA SCOPE

FIGURE 6.5 Scope for DFMEA — braking system.

THE FMEA FORM There are many forms to develop a typical FMEA. However, all of them are basically the same in that they are made up of two parts, whether they are for system, design, process, or machinery. A typical FMEA form consists of the header information and the main body. There is no standard information that belongs in the header of the form, but there are specific requirements for the body of the form. In the header, one may find the following information — see Figure 6.7. However, one must remember that this information may be customized to reflect one’s industry or even the organization: • • • • • • • • • •

Type of FMEA study Subject description Responsible engineer FMEA team leader FMEA core team members Suppliers Appropriate dates (original issue, revision, production start, etc.) FMEA number Assembly/part/detail number Current dates (drawings, specifications, control plan, etc.)

The form may be expanded to include or to be used for such matters as: Safety: Injury is the most serious of all failure effects. As a consequence, safety is handled either with an FMEA or a fault tree analysis (FTA) or critical

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Yes Good? No

No

M

Inpect print

H

Apply paste

L

M

Wash board

Load board

L

L Run, package and ship

Our scope

Dispense paste

H

Set up machine

H

Load screen

L Load sqeegee

L Load tool plate

L Develop program

Legend: L: Low risk M: Medium risk H: High risk Note: Just as in design FMEA, sometimes it is necessary to “narrow the scope” of the process FMEA. FIGURE 6.6 Scope for PFMEA — printed circuit board screen printing process.

FMEA WORKSHEET System FMEA ____Design FMEA ____Process FMEA ____FMEA Number ____ Subject: ______________Team Leader.________________Page ____ of _____ Part/Proc. ID No. __________Date Orig. _____________Date Rev. __________ Key Date. ____________Team Members: ___________________

FIGURE 6.7 Typical FMEA header.

Failure Mode Analysis

Action Plan

Action Results

FIGURE 6.8 Typical FMEA body.

S

Part name or Potential Potential S C Potential O Current D RPN Recommended Target Actual Actions S O D R Remarks L cause of P effect of controls action and finish finish taken process step failure A failure N failure responsibility date date and function mode S mode mode

Description

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Failure Mode and Effect Analysis (FMEA) 237

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analysis (FMCA). In the traditional FTA, the starting point is the list of hazard or undersized events for which the designer must provide some solution. Each hazard becomes a failure mode and thus it requires an analysis. Effect of downtime: The FMEA may incorporate maintenance data to study the effects of downtime. It is an excellent tool to be used in conjunction with total preventive maintenance. Repair planning: An FMEA may provide preventive data to support repair planning as well as predictive maintenance cycles. Access: In the world of recycling and environmental conscience, the FMEA can provide data for tear downs as well as information about how to get at the failed component. It can be used with mistake proofing for some very unexpected positive results. A typical body of an FMEA form may look like Figure 6.8. The details of this form will be discussed in the following pages. We begin with the first part of the form; that is the description in the form of: Part name/process step and function (verb/noun) In this area the actual description is written in concise, exact and simple language.

DEVELOPING

THE

FUNCTION

A fundamental principle in writing functions is the notion that they must be written either in action verb format or as a measurable noun. Remember, a function is a task that a component, subsystem, or product must perform, described in language that everyone understands. Stay away from jargon. To identify appropriate functions, leading questions such as the following may help: • • • •

What does the product/process do? How does the product/process do that? If a product feature or process step is deleted, what functions disappear? If you were this task, what are you supposed to accomplish? Why do you exist?

The priority of asking function questions for a system/part FMEA is: 1. A system view 2. A subsystem view 3. A component view Typical functions are:

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HOW?

239

Primary supporting function

Supporting functions

Primary supporting function

Secondary supporting function

Primary supporting function Task function

Ensure dependability Ensure convenience

WHY?

Secondary enhancing function

Please senses Delight customer

Enhancing functions

Tertiary supporting function Tertiary supporting function

Tertiary enhancing function Tertiary enhancing function Tertiary enhancing function

FIGURE 6.9 Function tree process.

• • • • •

Position Support Seal in, out Retain Lubricate

ORGANIZING PRODUCT FUNCTIONS After the brainstorming is complete, a function tree — see Figure 6.9 — can be used to organize the functions. This is a simple tree structure to document and help organize the functions, as follows: Purposes of the function tree a. To document all the functions b. To improve team communication c. To document complexity and improve team understanding of all the functions Steps a. Brainstorm all the functions. b. Arrange functions into function tree. c. Test for completeness of function (how/why). Building the function tree Ask: What does the product/process do? Which component/process step does that?

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How does it do that? • Primary functions provide a direct answer to this question without conditions or ambiguity. • Secondary functions explain how primary functions are performed. • Continue until the answer to “how” requires using a part name, labor operation, or activity. • Ask “why” in the reverse direction. • Add additional functions as needed. The function tree process can be summarized as follows: 1. Identify the task function. Place on the far left side of a chart pad. 2. Identify the supporting functions. Place on the top half of the pad. 3. Identify enhancing functions. Place on the bottom half of the pad. 4. Build the function tree. Include the secondary/tertiary functions. Place these to the right of the primary functions. 5. Verify the diagram: Ask how and why. For an example of a function tree for a ball point pen (tip), see Figure 6.10.

FAILURE MODE ANALYSIS The second portion of the FMEA body form deals with the failure mode analysis. A typical format is shown in Figure 6.11. Understanding Failure Mode Failure mode (a specific loss of a function) is the inability of a component/subsystem/system/process/part to perform to design intent. In other words, it may potentially fail to perform its function(s). Design failure mode is a technical description of how the system, subsystem, or part may not adequately perform its function. Process failure mode is a technical description of how the manufacturing process may not perform its function, or the reason the part may be rejected. Failure Mode Questions The process of brainstorming failure modes may include the following questions: DFMEA • Considering the conditions in which the product will be used, how can it fail to perform its function? • How have similar products failed in the past?

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241

Super pen makes marks on varied surfaces User grasps barrel and moves pen axially while simultaneously pressing down on the tip at a vector to the 180 degree plane Axial Force Function

Vector Force Function

The inside diameter of the barrel tip end transmits axial force to the tip system housing sheath O.D.

The end of the barrel and the barrel I.D. (tip end) simultaneously apply force to the tip system housing end and sheath.

The tip system housing tip retainer I.D. transmits axial force to the ball housing

The tip assembly housing transmits the vector force to the O.D. of the ball housing

The ball housing I.D. (ball) transmits axial force on the ball

The ball transmits axial force to the marking surface, however, the marking surface is stationary, which causes the ball rotational motion

The ball housing transmits the vector force to the ball, the ball moves up into the ball housing creating a gap between the ball and ball housing The ink flows through the ink tube contacting the ball surface

The ball rotates through the ink supply, picking up a film of ink on the ball surface The ink is transferred from the ball surface to the marking surface The ink remains on the marking surface (3mm width) area, drying in 3 seconds FIGURE 6.10 Example of ballpoint pen.

PFMEA • Considering the conditions in which the process will be used, what could possibly go wrong with the process? • How have similar processes failed in the past? • What might happen that would cause a part to be rejected?

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Potential Failure Mode

Potential Effects of Failure Mode

S E V E R I T Y

C Potential L Causes of A S Failure Mode S

O C C U R R E N C E

Current Controls

D E T E C T I O N

Risk Priority Number (RPN)

Identify the Potential Failure FIGURE 6.11 FMEA body.

Determining Potential Failure Modes Failure modes are when the function is not fulfilled in five major categories. Some of these categories may not apply. As a consequence, use these as “thought provokers” to begin the process and then adjust them as needed: 1. 2. 3. 4. 5. 6.

Absence of function Incomplete, partial, or decayed function Related unwanted “surprise” failure mode Function occurs too soon or too late Excess or too much function Interfacing with other components, subsystems or systems. There are four possibilities of interfacing. They are (a) energy transfer, (b) information transfer, (c) proximity, and (d) material compatibility.

Failure mode examples using the above categories and applied to the pen case include: 1. Absence of function: • DFMEA: Make marks • PFMEA: Inject plastic 2. Incomplete, partial or decayed function: • DFMEA: Make marks • PFMEA: Inject plastic 3. Related unwanted “surprise” failure mode • DFMEA: Make marks • PFMEA: Inject plastic 4. Function occurs too soon or too late • DFMEA: Make marks • PFMEA: Inject plastic 5. Excess or too much function • DFMEA: Make marks • PFMEA: Inject plastic

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243

General examples of failure modes include: Design FMEA: No power Water leaking Open circuit Releases too early Noise Vibration Does not cut

Failed to open Partial insulation Loss of air No spark Insufficient torque Paper jams And so on

Process FMEA: Four categories of process failures: 1. 2. 3. 4.

Fabrication failures Assembly failures Testing failures Inspection failures

Typical examples of these categories are: • • • • • • • • • • • •

Warped Too hot RPM too slow Rough surface Loose part Misaligned Poor inspection Hole too large Leakage Fracture Fatigue And so on

Note: At this stage, you are ready to transfer the failure modes in the FMEA form — see Figure 6.12.

FAILURE MODE EFFECTS A failure mode effect is a description of the consequence/ramification of a system, part, or manufacturing process failure. A typical failure mode may have several effects depending on which customer(s) are considered. Consider the effects/consequences on all the “customers,” as they are applicable, as in the following FMEAs: SFMEA • System • Other systems

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Potential Potential S Failure Effects E V Mode of E Failure R Mode I

C L A S S

Potential Causes of Failure Mode

T Y

O C C U R R E N C E

Current Controls

D E T E C T I O N

Risk Priority Number (RPN)

Does Not Transfer Ink Partial Ink and so on FIGURE 6.12 Transferring the failure modes to the FMEA form.

• Whole product • Government regulations • End user DFMEA • Part • Higher assembly • Whole product • Government regulations • End user PFMEA • Part • Next operation • Equipment • Government regulations • Operators • End user Effects and Severity Rating Effects and severity are very related items. As the effect increases, so does the severity. In essence, two fundamental questions have to be raised and answered: 1. What will happen if this failure mode occurs? 2. How will customers react if these failures happen? • Describe as specifically as possible what the customer(s) might notice once the failure occurs. • What are the effects of the failure mode? • How severe is the effect on the customers? The progression of function, cause, failure mode, effect, and severity can be illustrated by the following series of questions: In function: What is the individual task intended by design? In failure mode: What can go wrong with this function?

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In cause: What is the “root cause” of the failure mode? In effect: What are the consequences of this failure mode? In severity: What is the seriousness of the effect? The following are some examples of DFMEA and PFMEA effects: Customer gets wet System failure Loss of efficiency Reduced life Degraded performance Cannot assemble Violate Gov. Reg. XYZ Damaged equipment

Loss of performance Scrap Rework Becomes loose Hard to load in next operation Operator injury Noise, rattles And so on

Special Note: Please note that the effect remains the same for both DFMEA and PFMEA. Severity Rating (Seriousness of the Effect) Severity is a numerical rating — see Table 6.1 for design and Table 6.2 for process — of the impact on customers. When multiple effects exist for a given failure mode, enter the worst-case severity on the worksheet to calculate risk. (This is the excepted method for the automotive industry and for the SAE J1739 standard. In cases where severity varies depending on timing, use the worst-case scenario. Note: There is nothing special about these guidelines. They may be changed to reflect the industry, the organization, the product/design, or the process. For example, the automotive industry has its own version and one may want to review its guidelines in the AIAG (2001). To modify these guidelines, keep in mind: 1. 2. 3. 4. 5.

List the entire range of possible consequences (effects). Force rank the consequences from high to low. Resolve the extreme values (rating 10 and rating 1). Fill in the other ratings. Use consensus.

At this point the information should be transferred to the FMEA form — see Figure 6.13. The column identifying the “class” is the location for the placement of the special characteristic. The appropriate response is only “Yes” or “No.” A Yes in this column indicates that the characteristic is special, a No indicates that the characteristic is not special. In some industries, special characteristics are of two types: (a) critical and (b) significant. “Critical” refers to characteristics associated with safety and/or government regulations, and “significant” refers to those that affect the integrity of the product. In design, all special characteristics are potential. In process they become critical or significant depending on the numerical values of severity and occurrence combinations.

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TABLE 6.1 DFMEA — Severity Rating Effect

Description

None

No effect noticed by customer; the failure will not have any perceptible effect on the customer Very minor effect, noticed by discriminating customers; the failure will have little perceptible effect on discriminating customers Minor effect, noticed by average customers; the failure will have a minor perceptible effect on average customers Very low effect, noticed by most customers; the failure will have some small perceptible effect on most customers Primary product function operational, however at a reduced level of performance; customer is somewhat dissatisfied Primary product function operational, however secondary functions inoperable; customer is moderately dissatisfied Failure mode greatly affects product operation; product or portion of product is inoperable; customer is very dissatisfied Primary product function is non-operational but safe; customer is very dissatisfied. Failure mode affects safe product operation and/or involves nonconformance with government regulation with warning Failure mode affects safe product operation and/or involves nonconformance with government regulation without warning

Very minor

Minor Very low Low Moderate High Very high Hazard with warning Hazard with no warning

FAILURE CAUSE

AND

Rating 1 2

3 4 5 6 7 8 9 10

OCCURRENCE

The analysis of the cause and occurrence is based on two questions: 1. What design or process choices did we already make that may be responsible for the occurrence of a failure? 2. How likely is the failure mode to occur because of this? For each failure mode, the possible mechanism(s) and cause(s) of failure are listed. This is an important element of the FMEA since it points the way toward preventive/corrective action. It is, after all, a description of the design or process deficiency that results in the failure mode. That is why it is important to focus on the “global” or “root” cause. Root causes should be specific and in the form of a characteristic that may be controlled or corrected. Caution should be exerted not to overuse “operator error” or “equipment failure” as a root cause even though they are both tempting and make it easy to assign “blame.” You must look for causes, not symptoms of the failure. Most failure modes have more than one potential cause. An easy way to probe into the causes is to ask: What design choices, process variables, or circumstances could result in the failure mode(s)?

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TABLE 6.2 PFMEA — Severity Rating Effect

Description

None

No effect noticed by customer; the failure will not have any effect on the customer Very minor disruption to production line; a very small portion of the product may have to be reworked; defect noticed by discriminating customers Minor disruption to production line; a small portion (much 1 in 2

Rating 1 2 3 4 5 6 7 8 9 10

• Experiments — When many causes are suspect or specific cause is unknown • Classical • Taguchi methods Occurrence Rating The occurrence rating is an estimated number of frequencies or cumulative number of failures (based on experience) that will occur in our design concepts for a given cause over the intended life of the design. For example: cause of staples falling out = soft wood. The likelihood of occurrence is a 9 if we pick balsa wood but a 2 if we choose oak. Just as with severity, there are standard tables for occurrence — see Table 6.3 for design and Table 6.4 for process — for each type of FMEA. The ratings on these tables are estimates based on experience or similar products or processes. Nonstandard occurrence tables may also be used, based on specific characteristics. However, reliability expertise is needed to construct occurrence tables. (Typical characteristics may be historical failure frequencies, Cpks, theoretical distributions, and reliability statistics.) At this point the data for causes and their ratings should be transferred to the FMEA form — see Figure 6.14. Current Controls and Detection Ratings Design and process controls are the mechanisms, methods, tests, procedures, or controls that we have in place to prevent the cause of the failure mode or detect the failure mode or cause should it occur. (The controls currently exist.) Design controls prevent or detect the failure mode prior to engineering release. Process controls prevent or detect the failure mode prior to the part or assembly leaving the area.

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TABLE 6.4 PFMEA — Occurrence Rating Occurrence

Description

Remote

Failure is very unlikely; no failures associated with similar processes Few failures; isolated failures associated with like processes Occasional failures associated with similar processes, but not in major proportions

Low Moderate

High Very high

Repeated failures; similar processes have often failed Process failure is almost inevitable

Frequency

Cpk

Rating

1.67

1

1 in 150,000 1 in 15,000 1 in 2,000 1 in 400 1 in 80 1 in 20 1 in 8 1 in 3 >1 in 2

1.50 1.33 1.17 1.00 0.83 0.67

2 3 4 5 6 7 8 9 10

0.51 0.33

A good control prevents or detects causes or failure modes. • As early as possible (ideally before production or prototypes) • As early as possible • Using proven methods So, the next step in the FMEA process is to: • Analyze planned controls for your system, part, or manufacturing process • Understand the effectiveness of these controls to detect causes or failure modes Detection Rating Detection rating — see Table 6.5 for design and Table 6.6 for process — is a numerical rating of the probability that a given set of controls will discover a specific cause or failure mode to prevent bad parts from leaving the operation/facility or getting to the ultimate customer. Assuming that the cause of the failure did occur, assess the capabilities of the controls to find the design flaw or prevent the bad part from leaving the operation/facility. In the first case, the DFMEA is at issue. In the second case, the PFMEA is of concern. When multiple controls exist for a given failure mode, record the best (lowest) to calculate risk. In order to evaluate detection, there are appropriate tables for both design and process. Just as before, however, if there is a need to alter them, remember that the change and approval must be made by the FMEA team with consensus. At this point, the data for current controls and their ratings should be transferred to the FMEA form — see Figure 6.15. There should be a current control for every cause. If there is not, that is a good indication that a problem might exist.

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Potential Failure Mode

Potential Effects of Failure Mode

S E V E R I T Y

Does not Pan does 8 transfer ink not work; customer tries and eventually tears paper and scraps the pen Old pen stops 7 Partial ink writing, customer scraps pen Customer 7 has to retrace Writing or drawing looks bad

251

C Potential L Causes of A Failure Mode S S

N

O C C U R R E N C E Ball housing 2 I.D. deform

Current Controls

D E T E C T I O N

Risk Priority Number (RPN)

Ink viscosity 9 too high Debris build- 5 up

N

Inconsistent ball rolling due to deformed housing

2

Ball does not 7 always pick up ink due to ink viscosity Housing I.D. 1 variation due to mfg and so on

and so on

and so on FIGURE 6.14 Transferring causes and occurrences to the FMEA form.

UNDERSTANDING

AND

CALCULATING RISK

Without risk, there is very little progress. Risk is inevitable in any system, design, or manufacturing process. The FMEA process aids in identifying significant risks, then helps to minimize the potential impact of risk. It does that through the risk priority number or as it is commonly known, the RPN index. In the analysis of the RPN, make sure to look at risk patterns rather than just a high RPN. The RPN is the product of severity, occurrence, and detection or:

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TABLE 6.5 DFMEA Detection Table Detection

Description

Almost certain

Design control will almost certainly detect the potential cause of subsequent failure modes Very high chance the design control will detect the potential cause of subsequent failure mode High chance the design control will detect the potential cause of subsequent failure mode Moderately high chance the design control will detect the potential cause of subsequent failure mode Moderate chance the design control will detect the potential cause of subsequent failure mode Low chance the design control will detect the potential cause of subsequent failure mode Very low chance the design control will detect the potential cause of subsequent failure mode Remote chance the design control will detect the potential cause of subsequent failure mode Very remote chance the design control will detect the potential cause of subsequent failure mode There is no design control or control will not or cannot detect the potential cause of subsequent failure mode

Very high High Moderately high Moderate Low Very low Remote Very remote Very uncertain

Rating 1 2 3 4 5 6 7 8 9 10

Risk = RPN = S × O × D Obviously the higher the RPN the more the concern. A good rule-of-thumb analysis to follow is the 95% rule. That means that you will address all failure modes with a 95% confidence. It turns out the magic number is 50, as indicated in this equation: [(S = 10 × O = 10 × D = 10) – (1000 × .95)]. This number of course is only relative to what the total FMEA is all about, and it may change as the risk increases in all categories and in all causes. Special risk priority patterns require special attention, through specific action plans that will reduce or eliminate the high risk factor. They are identified through: 1. High RPN 2. Any RPN with a severity of 9 or 10 and occurrence > 2 3. Area chart The area chart — Figure 6.16 — uses only severity and occurrence and therefore is a more conservative approach than the priority risk pattern mentioned previously. At this stage, let us look at our FMEA project and calculate and enter the RPN — see Figure 6.17. It must be noted here that this is only one approach to evaluating risk. Another possibility is to evaluate the risk based on the degree of severity first,

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TABLE 6.6 PFMEA Detection Table Detection

Description

Almost certain

Process control will almost certainly detect or prevent the potential cause of subsequent failure mode Very high chance process control will detect or prevent the cause of subsequent failure mode High chance the process control will detect or prevent the potential cause of subsequent failure mode. Moderately high chance the process control will detect or prevent the potential cause of subsequent failure mode Moderate chance the process control will detect or prevent the potential cause of subsequent failure mode Low chance the process control will detect or prevent the potential cause of subsequent failure mode Very low chance the process control will detect or prevent the potential cause of subsequent failure mode Remote chance the process control will detect or prevent the potential cause of subsequent failure mode Very remote chance the process control will detect or prevent the potential cause of subsequent failure mode There is no process control or control will not or cannot detect the potential cause of subsequent failure mode

Very high High Moderately high Moderate Low Very low Remote Very remote Very uncertain

Rating 1 2 3 4 5 6 7 8 9 10

in which case the engineer tries to eliminate the failure; evaluate the risk based on a combination of severity (values of 5–8) and occurrence (>3) second, in which case the engineer tries to minimize the occurrence of the failure through a redundant system; and to evaluate the risk through the detection of the RPN third, in which case the engineer tries to control the failure before the customer receives it.

ACTION PLANS AND RESULTS The third portion of the FMEA form deals with the action plans and results analysis. A typical format is shown in Figure 6.18. The idea of this third portion of the FMEA form is to generate a strategy that reduces severity and occurrence and makes the detection effective to reduce the total RPN: Reducing the severity rating (or reducing the severity of the failure mode effect) • Design or manufacturing process changes are necessary. • This approach is much more proactive than reducing the detection rating. Reducing the occurrence rating (or reducing the frequency of the cause) • Design or manufacturing process changes are necessary. • This approach is more proactive than reducing the detection rating.

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Potential Failure Mode

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Potential Effects of Failure Mode

S E V E R I T Y

Does not Pan does 8 transfer ink not work; customer tries and eventually tears paper and scraps the pen Old pen stops 7 Partial ink writing, customer scraps pen Customer has to 7 retrace

Writing or drawing looks bad

C Potential L Causes of A Failure Mode S S

N Ball housing I.D. deform

Ink viscosity 9 Test # X too high

D E T E C T I O N

Risk Priority Number (RPN)

2 10

Debris build- 5 Design review 7 Prototype test # up XY

N Inconsistent ball rolling due to deformed housing

2 Test # X

10

Ball does not 7 None always pick up ink due to ink viscosity

10

Housing I.D. 1 None variation due to mfg

10

and so on

and so on

O Current C Controls C U R R E N C E 2 Life test Test # X

and so on

and so on FIGURE 6.15 Transferring current controls and detection to the FMEA form.

Reducing the detection rating (or increasing the probability of detection) • Improving the detection controls is generally costly, reactive, and does not do much for quality improvement, but it does reduce risk. • Increased frequency of inspection, for example, should only be used as a last resort. It is not a proactive corrective action.

CLASSIFICATION

AND

CHARACTERISTICS

Different industries have different criteria for classification. However, in all cases the following characteristics must be classified according to risk impact:

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Occurrence

Severity

10 9 8 7 6 5 4 3 2 1 1

255

High Priority Medium Priority Low Priority 2

3

4

5

6

7

8

9

10

FIGURE 6.16 Area chart.

• Severity 9, 10: Highest classification (critical) These product- or process-related characteristics: • May affect compliance with government or federal regulations (EPA, OSHA, FDA, FCC, FAA, etc.) • May affect safety of the customer • Require specific actions or controls during manufacturing to ensure 100% compliance • Severity between 5 and 8 and occurrence greater than 3: Secondary classification (significant) These product- or process-related characteristics: • Are non-critical items that are important for customer satisfaction (e.g., fit, finish, durability, appearance) • Should be identified on drawings, specifications, or process instructions to ensure acceptable levels of capability • High RPN: Secondary classification (see Table 6.7) Product Characteristics/“Root Causes” Examples include size, form, location, orientation, or other physical properties such as color, hardness, strength, etc. Process Parameters/“Root Causes” Examples include pressure, temperature, current, torque, speeds, feeds, voltage, nozzle diameter, time, chemical concentrations, cleanliness of incoming part, ambient temperature, etc.

DRIVING

THE

ACTION PLAN

For each recommended action, the FMEA team must:

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Potential Failure Mode

Potential Effects of Failure Mode

S E V E R I T Y

Does not Pan does 8 transfer ink Pan does not work; customer tries and eventually tears paper and scraps the pen Old pen stops writing, Partial ink customer scraps pen

7

Customer 7 has to retrace

Writing or drawing looks bad

C Potential L Causes A of S Failure Mode S

O Current C Controls C U R R E N C E N Ball housing 2 Life test Test # X I.D. deform

Risk Priority Number (RPN)

2

32

Ink viscosity 9 Test # X 10 too high Design review Debris build- 5 Prototype test # 7 up XY

N Inconsistent ball rolling due to deformed housing

2 Test # X

Ball does not 7 None always pick up ink due to ink viscosity 1 None Housing I.D. variation due to mfg and so on

and so on

D E T E C T I O N

and so on

720 280

10

140

10

490

10

70

and so on

and so on FIGURE 6.17 Transferring the RPN to the FMEA form.

• Plan for implementation of recommendations • Make sure that recommendations are followed, demonstrate improvement, and are completed Implementation of action plans requires answering the classic questions… • WHO … (will take the lead) • WHAT… (specifically is to be done)

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Action Plan Recommended Actions and Responsibility

257

Action Results

Target Finish Date

Actual Finish Date

Actions S O D RPN Taken

Remarks

FIGURE 6.18 Action plans and results analysis.

TABLE 6.7 Special Characteristics for Both Design and Process FMEA Type

Classification

Purpose

Criteria

Design

YC

Severity = 9–10

Does not apply

Design

YS

Severity = 5–8 Occurrence = 4–10

Does not apply

Design

Blank

Severity < 5

Does not apply

Process Process

Inverted delta SC

A potential critical characteristic (Initiate PFMEA) A potential significant characteristic (Initiate PFMEA) Not a potential critical or significant characteristic A critical characteristic A significant characteristic

Required Required

Process

HI

High impact

Process Process

OS Blank

Operator safety Not a special characteristic

Severity = 9–10 Severity = 5–8 Occurrence = 4–10 Severity = 5–8 Occurrence = 4–10 Severity = 9–10 Other

• • • •

Control

Emphasis Safety sign-off Does not apply

WHERE … (will the work get done) WHY… (this should be obvious) WHEN… (should the actions be done) HOW… (will we start)

Additional points concerning the action plan include the following: • Accelerate implementation by getting buy-in (ownership). • It is important to draw out and address objections. • When plans address objections in a constructive way, stakeholders feel ownership in plans and actions. Ownership aids in successful implementation.

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• Typical questions that begin a fruitful discussion are: • Why are we…? • Why not this…? • What about this…? • What if…? • Timing and actions must be reviewed on a regular basis to: • Maintain a sense of urgency • Allow for ongoing facilitation • Ensure work is progressing • Drive team members to meet commitments • Surface new facts that may affect plans • Fill in the actions taken. • The “Action Taken” column should not be filled out until the actions are totally complete. • Record final outcomes in the Action Plan and Action Results sections of the FMEA form. Remember, because of the actions you have taken you should expect changes in severity, occurrence, detection, RPN, and new characteristic designations. Of course, these changes may be individual or in combination. The form will look like Figure 6.19.

LINKAGES AMONG DESIGN AND PROCESS FMEAS AND CONTROL PLAN FMEAs are not islands unto themselves. They have continuity, and the information must be flowing throughout the design and process FMEAs as well as to the control plan. A typical linkage is shown in Figure 6.20. In addition to the control plan, the FMEA is also linked with robustness. To appreciate these linkages in FMEA, we must recall that design for six sigma (DFSS) must be a robust process. In fact, to see the linkages of this robustness we may begin with a P diagram (see Volume V) and identify its components. It turns out that the robustness in the FMEA usage is to make sure that the part, subsystem, or system is going to perform its intended function, in spite of problems in both manufacturing and environment. Of particular interest are the error states, control factors, and noise factors. Error states may help in identifying the failures, noise factors may help us in identifying causes, and control factors may help us in identifying the recommendations. The signal and response become the functions or the starting point of the FMEA. The linkages then help generate the inputs and outputs of the FMEA. Typical inputs are: System (concept) inputs P diagram Boundary diagram Interface matrix Potential design verification tests

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Description Failure Action Mode Plan Analysis RPN Recommended Target Action and Finish Responsibility Date 32 No action required 720 DOE - Taguchi 3/22/99

259

Action Results Actual Finish Date

Action Taken

2/15/99 Optimize ink formula

8

DR R Remarks P N 2 2 32 None

8

1 10

80

5 1

40

1 4

28

1 10

70

SO

280 Develop accel. 2/18/99 2/3/99 Test 8 procedure test (thermal revised vibration) 4/3/99 D. Robins 140 Develop new test # ABC

2/2/99 2/2/99

Test implemented 2/2/99

7

5/3/99 4/30/99 Optimized DOE - Taguchi 7 ink optimize formula viscosity on C. Abrams 4/30/99 70 Evaluate TBD machining process and so on and so on 490

7

FIGURE 6.19 Transferring action plans and action results on the FMEA form.

Surrogate data for reliability and robustness considerations Corporate requirements Benchmarking results Customer functionality in terms of engineering specifications Regulatory requirements review Design inputs P diagram Boundary diagram Interface matrix Customer functionality in terms of engineering specifications Regulatory requirements review Process inputs P diagram Process flow diagram Special characteristics from the DFMEA Process characteristics Regulatory requirements review

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Design FMEA Quality Function Deployment

Function Failure Effect Severity Class Cause Controls Rec. Action

System Design Specifications

Sign-Off Report Design Verification Plan and Report Process FMEA Part Characteristic 1 2 3 4 etc.

C Function Failure Effect Controls L Normal A S S

Cause

Controls Reaction Special

Remove the classification symbol

Dynamic Control Plan

Part Drawing (Inverted Delta and Special Characteristics)

FIGURE 6.20 FMEA linkages.

Machinery inputs P diagram Boundary diagram Interface matrix Customer functionality in terms of engineering specifications Regulatory requirements review

GETTING THE MOST FROM FMEA Common team problems that may make it difficult to get the most from FMEA include: • Poor team composition (not cross-functional or multidisciplinary) • Low expertise in FMEA • Not multi-level

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• • • • • • • •

261

• Low experience/expertise in product • One-person FMEA Lack of management support Not enough time Too detailed, could go on forever Arguments between team members (Opinions should be based on facts and data.) Lack of team enthusiasm/motivation Difficulty in getting team to start and stay with the process Proactive vs. reactive (a “before the event” not “after the fact” exercise) Doing it for the wrong reason

Common procedural problems include: • Confusion about, poorly defined or incomplete functions, failure modes, effects, or causes • Subgroup discussion • Using symptoms or superficial causes instead of root causes • Confusion about ratings as estimates and not absolutes (It will take time to be consistent.) • Confusion about the relationship between causes, failure modes, and effects • Using “customer dissatisfied” as failure effect • Shifting design concerns to manufacturing and vice-versa • Doing FMEAs by hand • Dependent on the engineers’ “printing skills” • RPNs or criticality cannot be ranked easily • Hard to update • Much space taken up by complicated FMEAs • Time consuming • Resistance to being the “recorder” when done manually • Inefficient means of storing and retrieving info Note: With FMEA software these are all eliminated. • Working non-systematically on the form (It is suggested that the failure analysis should progress from left to right, with each column being completed before the next is begun.) • Resistance of individuals to taking responsibility for recommended actions • Doing a reactive FMEA as opposed to a proactive FMEA (FMEAs are best applied as a problem prevention tool, not problem solving tool, although one may use them for both. However, the value of a reactive FMEA is much less.) • Not having robust FMEA terminology (A robust communication process is one that delivers its “function” [imparting knowledge and understanding] without being affected by “noise factors” [varying degrees of training]. Simply stated, the process should be as clear as possible with minimum possibility for misunderstanding.)

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Stages of Learning Unconscious incompetence

Stages of FMEA Maturity Never heard of FMEA

Conscious incompetence

We talked about it

Conscious competence

Customer made us do it

Unconscious competence

Some small successes Proper and regular use

FIGURE 6.21 The learning stages.

Institutionalizing FMEA in your company is challenging, and its success is largely dependent upon the culture in the organization as well as the reason it is being utilized. Below are some main considerations: • • • •

Selecting pilot projects (Start small and build successes.) Identifying team participants Developing and promoting FMEA successes Developing templates (databases of failure modes, functions, controls, etc.) • Addressing training needs

Figure 6.21 shows the learning stages (the direction of the arrows indicates the increasing level) in a company that is developing maturity in the use of FMEA.

SYSTEM OR CONCEPT FMEA A concept FMEA is used to analyze concepts at very early stages with new ideas. Concept FMEAs can be design, process, or even machinery oriented. However, in practical terms, most of them are done on a system or subsystem level. The process of the system or concept FMEA is practically the same as that of a design FMEA. In fact, the evaluation guidelines are exactly the same as those of DFMEA. The difference is that in the system FMEA a great effort is made to identify gross failures with high severities. If these problems cannot be overcome, then the project most likely will be killed. If the failures can be fixed through reasonable design changes, then the project moves to a second stage and the design FMEA takes over.

DESIGN FAILURE MODE AND EFFECTS ANALYSIS (DFMEA) The Design Failure Mode and Effects Analysis (Design FMEA) is a method for identifying potential or known failure modes and providing follow-up and corrective actions.

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263

OBJECTIVE The design FMEA is a disciplined analysis of the part design with the intent to identify and correct any known or potential failure modes before the manufacturing stage begins. Once these failure modes are identified and the cause and effects are determined, each failure mode is then systematically ranked so that the most severe failure modes receive priority attention. The completion of the design FMEA is the responsibility of the individual product design engineer. This individual engineer is the most knowledgeable about the product design and can best anticipate the failure modes and their corrective actions.

TIMING The design FMEA is initiated during the early planning stages of the design and is continually updated as the program develops. The design FMEA must be totally completed prior to the first production run.

REQUIREMENTS The requirements for a design FMEA include: 1. Forming a team 2. Completing the design FMEA form 3. FMEA risk ranking guidelines

DISCUSSION The effectiveness of an FMEA is dependent on certain key steps in the analysis process, as follows: Forming the Appropriate Team A typical team for conducting a design FMEA is the following: • • • • • • •

Design engineer(s) Test/development engineer Reliability engineer Materials engineer Field service engineer Manufacturing/process engineer Customer

A design and a manufacturing engineer are required to be team members. Others may participate as needed or as the project calls for their knowledge or experience. The leader for the design FMEA is typically the design engineer.

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Describing the Function of the Design/Product There are three types of functions: 1. Task functions: These functions describe the single most important reason for the existence of the system/product. (Vacuum cleaner? Windshield wiper? Ballpoint pen?) 2. Supporting functions: These are the “sub” functions that are needed in order for the task function to be performed. 3. Enhancing functions: These are functions that enhance the product and improve customer satisfaction but are not needed to perform the task function. After computing the function tree or a block diagram, transfer functions to the FMEA worksheet or some other form of a worksheet to retain. Add the extent of each function (range, target, specification, etc.) to test the measurability of the function. Describing the Failure Mode Anticipated The team must pose the question to itself, “How could this part, system or design fail? Could it break, deform, wear, corrode, bind, leak, short, open, etc.?” The team is trying to anticipate how the design being considered could possibly fail; at this point, it should not make the judgment as to whether it will fail but should concentrate on how it could fail. The purpose of a design FMEA (DFMEA) is to analyze and evaluate a design on its ability to perform its functions. Therefore, the initial assumption is that parts are manufactured and assembled according to plan and in compliance with specifications. Once failure modes are determined under this assumption, then determine other failure modes due to purchased materials, components, manufacturing processes, and services. Describing the Effect of the Failure The team must describe the effect of the failure in terms of customer reaction or in other words, e.g., “What does the customer experience as a result of the failure mode of a shorted wire?” Notice the specificity. This is very important, because this will establish the basis for exploratory analysis of the root cause of the function. Would the shorted wire cause the fuel gage to be inoperative or would it cause the dome light to remain on? Describing the Cause of the Failure The team anticipates the cause of the failure. Would poor wire insulation cause the short? Would a sharp sheet metal edge cut through the insulation and cause the short? The team is analyzing what conditions can bring about the failure mode. The more specific the responses are, the better the outcome of the FMEA.

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The purpose of a design FMEA (DFMEA) is to analyze and/or evaluate a design on its ability to perform its functions (part characteristics). Therefore, the initial assumption in determining causes is that parts are made and assembled according to plan and in compliance with specifications, including purchased materials, components, and services. Then and only then, determine causes due to purchased materials, components, and services. Some cause examples include: Brittle material Weak fastener Corrosion Low hardness Too small of a gap Wrong bend angle Stress concentration Ribs too thin Wrong material selection Poor stitching design High G forces Part interference Tolerance stack-up Vibration Oxidation And so on Estimating the Frequency of Occurrence of Failure The team must estimate the probability that the given failure is going to occur. The team is assessing the likelihood of occurrence, based on its knowledge of the system, using an evaluation scale of 1 to 10. A 1 would indicate a low probability of occurrence whereas a 10 would indicate a near certainty of occurrence. Estimating the Severity of the Failure In estimating the severity of the failure, the team is weighing the consequence of the failure. The team uses the same 1 to 10 evaluation scale. A 1 would indicate a minor nuisance, while a 10 would indicate a severe consequence such as “loss of brakes” or “stuck at wide open throttle” or “loss of life.” Identifying System and Design Controls Generally, these controls consist of tests and analyses that detect failure modes or causes during early planning and system design activities. Good system controls detect faults or weaknesses in system designs. Design controls consist of tests and analyses that detect failure causes or failure modes during design, verification, and validation activities. Good design controls detect faults or weaknesses in component designs.

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Special notes: • Just because there is a current control in place that does not mean that it is effective. Make sure the team reviews all the current controls, especially those that deal with inspection or alarms. • To be effective (proactive), system controls must be applied throughout the pre-prototype phase of the Advanced Product Quality Planning (APQP) process. • To be effective (proactive), design controls must be applied throughout the pre-launch phase of the APQP process. • To be effective (proactive), process controls should be applied during the post-pilot build phase of APQP and continue during the production phase. If they are applied only after production begins, they serve as reactive plans and become very inefficient. Examples of system and design controls include: Engineering analysis • Computer simulation • Mathematical modeling/CAE/FEA • Design reviews, verification, validation • Historical data • Tolerance stack studies • Engineering reviews, etc. System/component level physical testing • Breadboard, analog tests • Alpha and beta tests • Prototype, fleet, accelerated tests • Component testing (thermal, shock, life, etc.) • Life/durability/lab testing • Full scale system testing (thermal, shock, etc) • Taguchi methods • Design reviews Estimating the Detection of the Failure The team is estimating the probability that a potential failure will be detected before it reaches the customer. Again, the 1 to 10 evaluation scale is used. A 1 would indicate a very high probability that a failure would be detected before reaching the customer. A 10 would indicate a very low probability that the failure would be detected, and therefore, be experienced by the customer. For instance, an electrical connection left open preventing engine start might be assigned a detection number of 1. A loose connection causing intermittent no-start might be assigned a detection number of 6, and a connection that corrodes after time causing no start after a period of time might be assigned a detection number of 10.

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Detection is a function of the current controls. The better the controls, the more effective the detection. It is very important to recognize that inspection is not a very effective control because it is a reactive task. Calculating the Risk Priority Number The product of the estimates of occurrence, severity, and detection forms a risk priority number (RPN). This RPN then provides a relative priority of the failure mode. The higher the number, the more serious is the mode of failure considered. From the risk priority numbers, a critical items summary can be developed to highlight the top priority areas where actions must be directed. Recommending Corrective Action The basic purpose of an FMEA is to highlight the potential failure modes so that the responsible engineer can address them after this identification phase. It is imperative that the team provide sound corrective actions or provide impetus for others to take sound corrective actions. The follow-up aspect is critical to the success of this analytical tool. Responsible parties and timing for completion should be designated in all corrective actions. Strategies for Lowering Risk: (System/Design) — High Severity or Occurrence To reduce risk, you may change the product design to: • • • •

Eliminate the failure mode cause or decouple the cause and effect Eliminate or reduce the severity of the effect Make the cause less likely or impossible to occur Eliminate function or eliminate part (functional analysis)

Some “tools” to consider: • • • • •

Quality Function Deployment (QFD) Fault Tree Analysis (FTA) Benchmarking Brainstorming TRIZ, etc.

Evaluate ideas using Pugh concept selection. Some specific examples: • • • •

Change material, increase strength, decrease stress Add redundancy Constrain usage (exclude features) Develop fail safe designs, early warning system

Strategies for Lowering Risk: (System/Design) — High Detection Rating Change the evaluation/verification/tests to:

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• Make failure mode easier to perceive • Detect causes prior to failure Some “tools” to consider: • • • •

Benchmarking Brainstorming Process control (automatic corrective devices) TRIZ, etc.

Evaluate ideas using Pugh concept selection. Some specific examples: • • • •

Change testing and evaluation procedures Increase failure feedback or warning systems Increase sampling in testing or instrumentation Increase redundancy in testing

PROCESS FAILURE MODE AND EFFECTS ANALYSIS (FMEA) The Process Failure Mode and Effects Analysis (process FMEA) is a method for identifying potential or known processing failure modes and providing problem follow-up and corrective actions.

OBJECTIVE The process FMEA is a disciplined analysis of the manufacturing process with the intent to identify and correct any known or potential failure modes before the first production run occurs. Once these failure modes are identified and the cause and effects are determined, each failure mode is then systematically ranked so that the most severe failure modes receive priority attention. The completion of the process FMEA is the responsibility of the individual product process engineer. This individual process engineer is the most knowledgeable about the process structure and can best anticipate the failure modes and their effects and address the corrective actions.

TIMING The process FMEA is initiated during the early planning stages of the process before machines, tooling, facilities, etc., are purchased. The process FMEA is continually updated as the process becomes more clearly defined. The process FMEA must be totally completed prior to the first production run.

REQUIREMENTS The requirements for a process FMEA are as follows:

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1. Form team 2. Complete the process FMEA form 3. FMEA risk ranking guidelines

DISCUSSION The effectiveness of an FMEA on a process is dependent on certain key steps in the analysis, including the following: Forming the Team A typical team for the process/assembly FMEA is the following: • • • • • • • •

Design engineer Manufacturing or process engineer Quality engineer Reliability engineer Tooling engineer Responsible operators from all shifts Supplier Customer

A design engineer, a manufacturing engineer, and representative operators are required to be team members. Others may participate as needed or as the project calls for their knowledge or experience. The leader for the process FMEA is typically the process or manufacturing engineer. Describing the Process Function The team must identify the process or machine and describe its function. The team members should ask of themselves, “What is the purpose of this operation?” State concisely what should be accomplished as a result of the process being performed. Typically, there are three areas of concern. They are: 1. Creating/constructing functions: These are the functions that add value to the product. Examples include cutting, forming, painting, drying, etc. 2. Improving functions: These are the functions that are needed in order to improve the results of the creating function. Examples include deburring, sanding, cleaning, etc. 3. Measurement functions: These are functions that measure the success of the other functions. Examples include SPC, gauging, inspections, etc. Manufacturing Process Functions Just as products have functions, manufacturing processes also have functions. The goal is to concisely list the function(s) for each process operation. The first step in improving any process is to make the current process visible by developing a process

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flow diagram (a sequential flow of operations by people and/or equipment). This helps the team understand, agree, and define the scope. Three important questions exist for any existing process: 1. What do you think is happening? 2. What is actually happening? 3. What should be happening? Special reminder for manufacturing process functions: Remember, if the process flow diagram is too extensive for a “timely” FMEA, a risk assessment may be done on each process operation to narrow the scope. The PFMEA Function Questions Each manufacturing step typically has one or more functions. Determine what functions are associated with each manufacturing process step and then ask: 1. What does the process step do to the part? 2. What are you doing to the part/assembly? 3. What is the goal, purpose, or objective of this process step? For example, consider the pen assembly process (see Figure 6.22), which involves the following steps: 1. 2. 3. 4. 5. 6. 7. 8.

Inject ink into ink tube (0.835 cc) Insert ink tube into tip assembly housing (12 mm) Insert tip assembly into tip assembly housing (full depth until stop) Insert tip assembly housing into barrel (full depth until stop) Insert end cap into barrel (full depth until stop) Insert barrel into cap (full depth until stop) Move to dock (to dock within 8 seconds) Package and ship (12 pens per box)

Note: At the end of this function analysis you are ready to transfer the information to the FMEA form. Remember that another way to reduce the complexity or scope of the FMEA is to prioritize the list of functions and then take only the ones that the team collectively agrees are the biggest concerns. Describing the Failure Mode Anticipated The team must pose the question to itself, “How could this process fail to complete its intended function? Could the resulting workpiece be oversize, undersize, rough, eccentric, misassembled, deformed, cracked, open, shorted, leaking, porous, damaged, omitted, misaligned, out of balance, etc.?” The team members are trying to anticipate how the workpiece might fail to meet engineering requirements; at this point in their analysis they should stress how it could fail and not whether it will fail.

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Ink

Ink tube

Inject ink into ink tube

Insert ink tube into tip assembly

Tip assembly housing

Tip assembly

Insert tip assembly into tip assembly housing Insert tip assembly housing

Barrel

Insert end cap into barrel

End cap

Insert barrel into cap

Cap

Move to dock Package and ship FIGURE 6.22 Pen assembly process.

The purpose of a process FMEA (PFMEA) is to analyze and evaluate a process on its ability to perform its functions. Therefore, the initial assumptions are: 1. The design intent meets all customer requirements. 2. Purchased materials and components comply with specifications. Once failure modes are determined under these assumptions, then determine other failure modes due to: 1. Design flaws that cause or lead to process problems 2. Problems with purchased materials, components, or services Describing the Effect(s) of the Failure The team must describe the effect of the failure on the component or assembly. What will happen as a result of the failure mode described? Will the component or

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assembly be inoperative, intermittently operative, always on, noisy, inefficient, surging, not durable, inaccurate, etc.? After considering the failure mode, the engineer determines how this will manifest itself in terms of the component or assembly function. The open circuit causes an inoperative gage. The rough surface will cause excessive bushing wear. The scratched surface will cause noise. The porous casting will cause external leaks. The cold weld will cause reduced strength, etc. In some cases the process engineer (the leader) must interface with the product design engineer to correctly describe the effect(s) of a potential process failure on the component or total assembly. Describing the Cause(s) of the Failure The engineer anticipates the cause of the failure. The engineer is describing what conditions can bring about the failure mode. Locators are not flat and parallel. The handling system causes scratches on a shaft. Inadequate venting and gaging can cause misruns, porosity, and leaks. Inefficient die cooling causes die hot spots. Undersize condition can be caused by heat treat shrinkage, etc. The purpose of a process FMEA (PFMEA) is to analyze or evaluate a process on its ability to perform its functions (part characteristics). Therefore, the initial assumptions in determining causes are: • The design intent meets all customer requirements. • Purchased materials, components, and services comply with specifications. Then and only then, determine causes due to: • Design flaws that cause or lead to process problems • Problems with purchased materials, components, or services Typical causes associated with process FMEA include: Fatigue Poor surface preparation Improper installation Low torque Improper maintenance Inadequate clamping Misuse High RPM Abuse Inadequate venting Unclear instructions Tool wear Component interactions Overheating And so on

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Estimating the Frequency of Occurrence of Failure The team must estimate the probability that the given failure mode will occur. This team is assessing the likelihood of occurrence, based on their knowledge of the process, using an evaluation scale of 1 to 10. A 1 would indicate a low probability of occurrence, whereas a 10 would indicate a near certainty of occurrence. Estimating the Severity of the Failure In estimating the severity of the failure, the team is weighing the consequence (effect) of the failure. The team uses the same 1 to 10 evaluation scale. A 1 would indicate a minor nuisance, while a 10 would indicate a severe consequence such as “motor inoperative, horn does not blow, engine seizes, no drive, etc.” Identifying Manufacturing Process Controls Manufacturing process controls consist of tests and analyses that detect causes or failure modes during process planning or production. Manufacturing process controls can occur at the specific operation in question or at a subsequent operation. There are three types of process controls, those that: 1. Prevent the cause from happening 2. Detect causes then lead to corrective actions 3. Detect failure modes then lead to corrective actions Manufacturing process controls should be based on process dominance factors. Dominance factors are process elements that generate significant process variation. Dominance factors are the predominant factors that contribute to problems in a process. Most processes have one or two dominant sources of variation. Depending on the source, there are tools that may be used to track these as well as monitor them. Table 6.8 gives a cross reference of the dominance factors and the tools that may be used for tracking them. The following list provides some very common dominance factors: • • • • • • • •

Setup Machine Operator Component or material Tooling Preventive maintenance Fixture/pallet/work holding Environment

Special note: Controls should target the dominant sources of variation. Manufacturing process control examples include:

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TABLE 6.8 Manufacturing Process Control Matrix Dominance Factor

Attribute Data

Variable Date

Setup

Check sheet Checklist p or c chart Check sheet

X-bar/R chart X-MR chart Run chart X-bar/R chart X-MR chart X-bar/R chart X-MR chart Check sheet Supplier information Tool logs Capability study X-MR chart Time to failure chart Supplier information X-MR chart

Machine

Operator Component/material Tool

Preventive maintenance

Fixture/pallet/work holding

Environment

Check sheet Run chart Check sheet Supplier information Tool logs Check sheet p or c chart Time to failure chart Supplier information

Time to failure chart Check sheet p or c chart Check sheet

Time to failure chart X-bar/R chart X-MR chart Run chart X-MR chart

Statistical Process Control (SPC) • X-bar/R control charts (variable data) • Individual X-moving range charts (variable data) • p; n; u; c charts (attribute data) Non-statistical control • Check sheets, checklists, setup procedures, operational definitions/ instruction sheets • Preventive maintenance • Tool usage logs/change programs (PM) • Mistake proofing/error proofing/Poka Yoke • Training and experience • Automated inspection • Visual inspection It is very important to recognize that inspection is not a very effective control because it is a reactive task. Estimating the Detection of the Failure The detection is directly related to the controls available in the process. So the better the controls, the better the detection. The team in essence is estimating the probability

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that a potential failure will be detected before it reaches the customer. The team members use the 1 to 10 evaluation scale. A 1 would indicate a very high probability that a failure would be detected before reaching the customer. A 10 would indicate a very low probability that the failure would be detected, and therefore, be experienced by the customer. For instance, a casting with a large hole would be readily detected and would be assessed as a 1. A casting with a small hole causing leakage between two channels only after prolonged usage would be assigned a 10. The team is assessing the chances of finding a defect, given that the defect exists. Calculating the Risk Priority Number The product of the estimates of occurrence, severity, and detection forms a risk priority number (RPN). This RPN then provides a relative priority of the failure mode. The higher the number, the more serious is the mode of failure considered. From the risk priority numbers, a critical items summary can be developed to highlight the top priority areas where actions must be directed. Recommending Corrective Action The basic purpose of an FMEA is to highlight the potential failure modes so that the engineer can address them after this identification phase. It is imperative that the engineer provide sound corrective actions or provide impetus for others to take sound corrective actions. The follow-up aspect is critical to the success of this analytical tool. Responsible parties and timing for completion should be designated in all corrective actions. Strategies for Lowering Risk: (Manufacturing) — High Severity or Occurrence Change the product or process design to: • Eliminate the failure cause or decouple the cause and effect • Eliminate or reduce the severity of the effect (recommend changes in design) Some “tools” to consider: • • • •

Benchmarking Brainstorming Mistake proofing TRIZ, etc.

Evaluate ideas using Pugh concept selection. Some specific examples: • • • • •

Developing a robust design (insensitive to manufacturing variations) Changing process parameters (time, temperature, etc.) Increasing redundancy, adding process steps Altering process inputs (materials, components, consumables) Using mistake proofing (Poka Yoke), reducing handling

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Strategies for Lowering Risk: (Manufacturing) — High Detection Rating Change the process controls to: • Make failure mode easier to perceive • Detect causes prior to failure mode Some “tools” to consider: • Benchmarking • Brainstorming, etc. Evaluate ideas using Pugh concept selection. Some specific examples: • • • • •

Change testing and inspection procedures/equipment. Improve failure feedback or warning systems. Add sensors/feedback or feed forward systems. Increase sampling and/or redundancy in testing. Alter decision rules for better capture of causes and failures (i.e., more sophisticated tests).

At this stage, now you are ready to enter a brief description of the recommended actions, including the department and individual responsible for implementation, as well as both the target and finish dates, on the FMEA form. If the risk is low and no action is required write “no action needed.” For each entry that has a designated characteristic in the class[ification] column, review the issues that impact cause/occurrence, detection/control, or failure mode. Generate recommended actions to reduce risk. Special RPN patterns suggest that certain characteristics/root causes are important risk factors that need special attention. Guidelines for process control system: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

Select the process. Conduct the FMEA on the process. Conduct gage system analysis. Conduct process potential study. Develop control plan. Train operators in control methods. Implement control plan. Determine long-term process capability. Review the system for continual improvement. Develop audit system. Institute improvement actions.

After FMEA:

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1. Review the FMEA. 2. Highlight the high-risk areas based on the RPN. 3. Identify the critical and major characteristics based on your classification criteria. 4. Ensure that a control plan exists and is being followed. 5. Conduct capability studies. 6. Work on processes that have Cpk of less or equal to 1.33. 7. Work on processes that have Cpk greater than 1.33 to reduce variation and reach a Cpk of greater or equal to 2.0.

MACHINERY FMEA (MFMEA) A machinery FMEA is a systematic approach that applies the traditional tabular method to aid the thought process used by simultaneous engineering teams to identify the machine’s potential failure modes, potential effects, and potential causes of the potential failure modes and to develop corrective action plans that will remove or reduce the impact of the potential failure modes. Generally, the delivery of a MFMEA is the responsibility of the supplier who generates a functional MFMEA for system and subsystem levels. This is in contrast to a DFMEA where the responsibility is still on the supplier but now the focus is to generate transfer mechanisms, spindles, switches, cylinders, exclusive of assembly-level equipment. A typical MFMEA follows a hierarchical model in that it divides the machine into subsystems, assemblies, and lowest replaceable units. For example: Level 1: System level — generic machine Level 2: Subsystem level — electrical, mechanical, controls Level 3: Assembly level — fixtures/tools, material handling, drives Level 4: Component level And so on

IDENTIFY

THE

SCOPE

OF THE

MFMEA

Use the boundary diagram. Once the diagram has been completed, you can focus the MFMEA on the low MTBF and reliability values.

IDENTIFY

THE

FUNCTION

Define the function in terms of an active verb and a noun. Use a functional diagram or the P diagram to find the ideal function. Always focus on the intent of the system, subsystem, or component under investigation.

FAILURE MODE A failure is an event when the equipment/machinery is not capable of producing parts at specific conditions when scheduled or is not capable of producing parts or

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performing scheduled operations to specifications. Machinery failure modes can occur in three ways: • Component defect (hard failure) • Failure observation (potential) • Abnormality of performance (constitutes the equipment as failed)

POTENTIAL EFFECTS The consequence of a failure mode on the subsystem is described in terms of safety and the big seven losses. (The big seven losses may be identified through warranty or historical data.) Describe the potential effects in terms of downtime, scrap, and safety issues. If a functional approach is used, then list the causes first before developing the effects listing. Associated with the potential effects is the severity, which is a rating corresponding to the seriousness of the effect of a potential machinery failure mode. Typical descriptions are: Downtime • Breakdowns: Losses that are a result of a functional loss or function reduction on a piece of machine requiring maintenance intervention. • Setup and adjustment: Losses that are a result of set procedures. Adjustments include the amount of time production is stopped to adjust process or machine to avoid defect and yield losses, requiring operator or job setter intervention. • Startup losses: Losses that occur during the early stages of production after extended shutdowns (weekends, holidays, or between shifts), resulting in decreased yield or increased scrap and defects. • Idling and minor stoppage: Losses that are a result of minor interruptions in the process flow, such as a process part jammed in a chute or a limit switch sticking, etc., requiring only operator or job setter intervention. Idling is a result of process flow blockage (downstream of the focus operation) or starvation (upstream of the focus operation). Idling can only be resolved by looking at the entire line/system. • Reduced cycle: Losses that are a result of differences between the ideal cycle time of a piece of machinery and its actual cycle time. Scrap • Defective parts: Losses that are a result of process part quality defects resulting in rework, repair, or scrap. • Tooling: Losses that are a result of tooling failures/breakage or deterioration/wear (e.g., cutting tools, fixtures, welding tips, punches, etc.). Safety • Safety considerations: Immediate life or limb threatening hazard or minor hazard.

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SEVERITY RATING Severity is comprised of three components: • Safety of the machinery operator (primary concern) • Product scrap • Machinery downtime A rating should be established for each effect listed. Rate the most serious effect. Begin the analysis with the function of the subsystem that will affect safety, government regulations, and downtime of the equipment. A very important point here is the fact that a reduction in severity rating may be accomplished only through a design change. A typical rating is shown in Table 6.9. It should be noted that these guidelines may be modified to reflect specific situations. Also, the basis for the criteria may be changed to reflect the specificity of the machine and its real world usage.

CLASSIFICATION The classification column is not typically used in the MFMEA process but should be addressed if related to safety or noncompliance with government regulations. Address the failure modes with a severity rating of 9 or 10. Failure modes that affect worker safety will require a design change. Enter “OS” in the class column. OS (operator safety) means that this potential effect of failure is critical and needs to be addressed by the equipment supplier. Other notations can be used but should be approved by the equipment user.

POTENTIAL CAUSES The potential causes should be identified as design deficiencies. These could translate as: • Design variations, design margins, environmental, or defective components • Variation during the build/install phases of the equipment that can be corrected or controlled Identify first level causes that will cause the failure mode. Data for the development of the potential causes of failure can be obtained from: • Surrogate MFMEA • Failure logs • Interface matrix (focusing on physical proximity, energy transfer, material, information transfer) • Warranty data • Concern reports (things gone wrong, things gone right) • Test reports • Field service reports

Criteria Severity

Very high severity: affects operator, plant, or maintenance personnel safety and/or effects noncompliance with government regulations without warning Hazardous High severity: affects operator, with warning plant or maintenance personnel safety and/or effects noncompliance with government regulations with warning Very high Downtime of 8+ hours or the production of defective parts for over 2 hours High Downtime of 2–4 hours or the production of defective parts for up to 2 hours

Hazardous without warning

Effect Failure occurs every hour

Failure occurs every shift

Failure occurs every day Failure occurs every week

10

9

8

7

Rank

Probability of Failure

R(t) 37%

7

8

9

10

Rank

1 in 80

1 in 24

1 in 8

1 in 1

Alternate Criteria for Occurrence

Low

Very low

Detection

Machinery control will isolate the cause and failure mode after the failure has occurred, but will not prevent the failure from occurring

Team’s discretion depending on machine and situation

Team’s discretion depending on machine and situation

Present design controls cannot detect potential cause or no design control available

Criteria for Detection

7

8

9

10

Rank

280

R(t) = 20%

R(t) = 5%

R(t) < 1 or some MTBF

Criteria for Occurrence

TABLE 6.9 Machinery Guidelines for Severity, Occurrence, and Detection

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Downtime of 60–120 min or the production of defective parts for up to 60 min Downtime of 30–60 min with no production of defective parts or the production of defective parts for up to 30 min Downtime of 15–30 min with no production of defective parts Downtime up to 15 min with no production of defective parts

Process parameter variability not within specification limits. Adjustments may be done during production; no downtime and no defects produced Process parameter variability within specification limits; adjustments may be performed during normal maintenance

Moderate

Very minor

None

Minor

Very low

Low

Criteria Severity

Effect

1

2

3

4

5

6

Rank R(t) = 60%

Criteria for Occurrence

Failure occurs every 5 years

Failure occurs every 2 years

R(t) = 98%

R(t) = 95%

Failure occurs R(t) = 85% every 6 months Failure occurs R(t) = 90% every year

Failure occurs R(t) = 78% every 3 months

Failure occurs every month

Probability of Failure

TABLE 6.9 Machinery Guidelines for Severity, Occurrence, and Detection

1

2

3

4

5

6

Rank

1 in 25,000

1 in 10,000

1 in 5000

1 in 2500

1 in 1000

1 in 350

Alternate Criteria for Occurrence

Team’s discretion depending on machine and situation Machinery controls will prevent an imminent failure and isolate the cause Team’s discretion depending on machine and situation

Machinery controls will provide an indicator of imminent failure

Team’s discretion depending on machine and situation

Criteria for Detection

Very high Machinery controls not required; design controls will detect a potential cause and subsequent failure almost every time

High

Medium

Detection

1

2

3

4

5

6

Rank

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OCCURRENCE RATINGS Occurrence is the rating corresponding to the likelihood of the failure mode occurring within a certain period of time — see Table 6.8. The following should be considered when developing the occurrence ratings: • Each cause listed requires an occurrence rating. • Controls can be used that will prevent or minimize the likelihood that the failure cause will occur but should not be used to estimate the occurrence rating. Data to establish the occurrence ratings should be obtained from: • • • •

Service data MTBF data Failure logs Maintenance records

SURROGATE MFMEAS Current Controls Current controls are described as being those items that will be able to detect the failure mode or the causes of failure. Controls can be either design controls or process controls. A design control is based on tests or other mechanisms used during the design stage to detect failures. Process controls are those used to alert the plant personnel that a failure has occurred. Current controls are generally described as devices to: • • • •

Prevent the cause/mechanism failure mode from occurring Reduce the rate of occurrence of the failure mode Detect the failure mode Detect the failure mode and implement corrective design action

Detection Rating Detection rating is the method used to rate the effectiveness of the control to detect the potential failure mode or cause. The scale for ranking these methods is based on a 1 to 10 scale — see Table 6.8.

RISK PRIORITY NUMBER (RPN) The RPN is a method used by the MFMEA team to rank the various failure modes of the equipment. This ranking allows the team to attack the highest probability of failure and remove it before the equipment leaves the supplier floor. The RPN typically: • Has no value or meaning (Ratings and RPNs in themselves have no value or meaning. They should be used only to prioritize the machine’s potential

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design weakness [failure mode] for consideration of possible design actions to eliminate the failures or make them maintainable.) • Is used to prioritize potential design weaknesses (root causes) for consideration of possible design actions • Is the product of severity, occurrence and detection (RPN = S × O × D) Special note on risk identification: Whereas it is true that most organizations using FMEA guidelines use the RPN for identifying the risk priority, some do not follow that path. Instead, they use a three path approach based on: Step 1: severity Step 2: criticality Step 3: detection This means that regardless of the RPN, the priority is based on the highest severity first, especially if it is a 9 or a 10, followed by the criticality, which is the product of severity and occurrence, and then the RPN.

RECOMMENDED ACTIONS • Each RPN value should have a recommended action listed. • The actions are designed to reduce severity, occurrence, and detection ratings. • Actions should address in order the following concerns: • Failure modes with a severity of 9 or 10 • Failure mode/cause that has a high severity occurrence rating • Failure mode/cause/design control that has a high RPN rating • When a failure mode/cause has a severity rating of 9 or 10, the design action must be considered before the engineering release to eliminate safety concerns.

DATE, RESPONSIBLE PARTY • Document the person, department, and date for completion of the recommended action. • Always place the responsible party’s name in this area.

ACTIONS TAKEN/REVISED RPN • After each action has been taken, document the action. • Results of an effective MFMEA will reduce or eliminate equipment downtime. • The supplier is responsible for updating the MFMEA. The MFMEA is a living document. It should reflect the latest design level and latest design actions. • Any equipment design changes need to be communicated to the MFMEA team.

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REVISED RPN • Recalculate S, O, and D after the action taken has been completed. Always remember that only a change in design can change the severity. Occurrence may be changed by a design change or a redundant system. Detection may be changed by a design change or better testing or better design control. • MFMEA — A team needs to review the new RPN and determine if additional design actions are necessary.

SUMMARY In summary, the steps in conducting the FMEA are as follows: 1. 2. 3. 4. 5. 6. 7. 8.

Select a project and scope. If DFMEA, construct a block diagram. If PFMEA, construct a process flow diagram. Select an entry point based on the block or process flow diagram. Collect the data. Analyze the data. Calculate results (results must be data driven). Evaluate/confirm/measure the results. • Better off • Worse off • Same as before 9. Do it all over again.

SELECTED BIBLIOGRAPHY Chrysler Corporation, Ford Motor Company, and General Motors Corporation, Potential Failure Mode and Effect Analysis (FMEA) Reference Manual, 2nd ed., distributed by the Automotive Industry Action Group (AIAG), Southfield, MI, 1995. Chrysler Corporation, Ford Motor Company, and General Motors Corporation, Advanced Product Quality Planning and Control Plan, distributed by the Automotive Industry Action Group (A.I.A.G.), Southfield, MI, 1995. Chrysler Corporation, Ford Motor Company, and General Motors Corporation, Potential Failure Mode and Effect Analysis (FMEA) Reference Manual, 32nd ed., Chrysler Corporation, Ford Motor Company, and General Motors Corporation. Distributed by the Automotive Industry Action Group (AIAG), Southfield, MI, 2001. The Engineering Society for Advancing Mobility Land Sea Air and Space, Potential Failure Mode and Effects Analysis in Design FMEA and Potential Failure Mode and Effects Analysis in Manufacturing and Assembly Processes (Process FMEA) Reference Manual, SAE: J1739, The Engineering Society for Advancing Mobility Land Sea Air and Space, Warrendale, PA, 1994.

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The Engineering Society for Advancing Mobility Land Sea Air and Space, Reliability and Maintainability Guideline for Manufacturing Machinery and Equipment, SAE Practice Number M-110, The Engineering Society for Advancing Mobility Land Sea Air and Space, Warrendale, PA, 1999. Ford Motor Company, Failure Mode Effects Analysis: Training Reference Guide, Ford Motor Company — Ford Design Institute. Dearborn, MI, 1998. Kececioglu, D., Reliability Engineering Handbook, Vol. 1–2, Prentice Hall, Englewood Cliffs, NJ, 1991. Stamatis, D.H., Advanced Quality Planning, Quality Resources, New York, 1998. Stamatis, D.H., Failure Mode and Effect Analysis: FMEA from Theory to Execution, Quality Press, Milwaukee, 1995.

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7

Reliability

Reliability n — may be relied on; trustworthiness, authenticity, consistency; infallibility, suggesting the complete absence of error, breakdown, or poor performance. In other words, when we speak of a reliable product, we usually expect such adjectives as dependable and trustworthy to apply. But to measure product reliability, we must have a more exact definition. The definition of reliability then, is: the probability that a product will perform its intended function in a satisfactory manner for a specified period of time when operating under specified conditions. Thus, the reliability of a system expresses the length of failure-free time that can be expected from the equipment. Higher levels of reliability mean less failure of the system and consequently less downtime. To measure reliability it is necessary to: • Relate probability to a precise definition of success or satisfactory performance • Specify the time base or operating cycles over which such performance is to be sustained • Specify the environmental or use conditions that will prevail Note: Theoretically, every product has a designed-in reliability function. This reliability function (or curve) expresses the system reliability at any point in time. As time increases the curve must drop, eventually reaching zero.

PROBABILISTIC NATURE OF RELIABILITY We cannot say exactly when a particular product will fail, but we can say what percentage of the products in use will fail by certain times. This is analogous to the reasoning used by insurance companies in defining mortality. We can state reliability in various ways: • The probability that a product will be performing its intended function at 5000 hours of use is 0.95. • The reliability at 5000 hours is 0.95 or 95%. • If we place 1000 units in use, 950 will still be operating with no failures at 5000 hours. Or to cite another example: • The reliability at 8000 hours is 0.80. • The unreliability at 8000 hours is 0.20.

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From a service point of view, we may be interested in repair frequency and then we say that 20% of the units will have to be repaired by 8000 hours. Or the repair per hundred units (R/100) is 20 at 8000 hours. The important point is that the reliability is a metric expressing the probability of maintaining intended function over time and is measurable as a percentage.

PERFORMING THE INTENDED FUNCTION SATISFACTORILY A product fails when it ceases to function in a way that is satisfactory to the customer. Products rarely fail suddenly in the way that a light bulb does. Rather, they deteriorate over time. This eventually leads to unsatisfactory performance from the customer’s standpoint. Unsatisfactory performance can result from: • • • • • •

Excess vibrations Excess noise Intermittent operation Drift Catastrophic failure And many other possibilities

Unsatisfactory performance must be clearly spelled out. The customer’s perspective must be recognized in this process. There will usually be various levels of failure based on the customer’s perceived level of severity. The levels of severity are frequently grouped into two categories such as: • Major • Minor The severity of the failure to the customer must be documented and recognized in a Failure Definition and Scoring Criterion that precisely delineates how each incident on a system or equipment will be handled in regards to reliability and maintainability calculations. Such documents should be developed early in a design and development program so that all concerned are aware of the consequences of incidents that occur during product testing and in field use. The design team must be able to use the failure definition and scoring criterion to address product trade-offs. If the severity of a failure to the customer can be lowered by design changes, the failure definition and scoring criterion should promote such trade-offs.

SPECIFIED TIME PERIOD Products deteriorate with use and even with age when dormant. Longer lengths of usage imply lower reliability. For design purposes, target usage periods must be identified. Typical usage periods are:

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1. Warranty period(s): A warranty is a contract supplied with the product providing the user with a certain amount of protection against product failure. 2. Expected customer life: Customers have a reasonably consistent belief as to how long a product should last. This belief can be determined through a market survey. 3. Durability life: This is a measure of useful life, defining the number of operating hours (or cycles) until overhaul is required.

SPECIFIED CONDITIONS Different environments promote different failure modes and different failure rates for a product. The environmental factors that the product will encounter must be clearly defined. The levels (and rate of change) at which we want to address these environmental factors must also be defined.

ENVIRONMENTAL CONDITIONS PROFILE The environmental profile must include the level and rate of change for each environmental factor considered. Environmental factors include but are not limited to: • • • • • • • • • • • • • • • • • • • • • • • • •

Temperature Humidity Vibration Shock Corrosive materials Immersion Pressures, vacuum Salt spray Dust Cement floors/basements Ice/snow Lubricants Perfumes Magnetic fields Nuclear radiation Weather Contamination Antifreeze Gasoline fumes Rust inhibitors/under coatings Rain Soda pop/hot coffee Sunlight Electrical discharges And so on

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Not all of these environmental conditions would be appropriate for a particular product. Each product must be considered in its individual operating environment and scenario. Environment must consider the environment induced from operating the product, the environment induced from external factors, and the environment induced by delivering the product to the customer.

RELIABILITY NUMBERS The reliability number attached to a product changes with: • Usage and environmental conditions • Customer’s perception of satisfactory performance At any product age (t) for a population of N products, the reliability at time t denoted by R(t) is R(t) = Number of survivors/N, which is equal to R(t) = 1 – (Number of failures/N) = 1 – Unreliability This is the reliability of this population of products at time t. The real world estimation of reliability is usually much more difficult due to products being sold over time with each having a different usage profile. Calendar time is known but product life on each product is not, while warranty systems monitor and record only failure.

INDICATORS USED

TO

QUANTIFY PRODUCT RELIABILITY

Several metrics are in common use to indicate product reliability. Some of these actually quantify unreliability. Some of the metrics follow: • MTBF — The mean time between failures, also MTTF, MMBF, MCTF. MTBF = 120 hours means that on the average a failure will occur with every 120 hours of operation. • Failure rate — The rate of failures per unit of operating time. λ = 0.05/hour means that one failure will occur with every 20 hours of operation, on the average. • R/100 (or R/1000) — The number of warranty claims per 100 (or 1000) products sold. R/100 = 7 means that there are seven warranty claims for every 100 products sold. • Reliability number — The reliability of the product at some specific time. R = 90% means that 9 out of 10 products work successfully for the specified time.

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RELIABILITY AND QUALITY Customers and product engineers frequently use the terms reliability and quality interchangeably. Ultimately, the customer defines quality. Customers want products that meet or exceed their needs and expectations, at a cost that represents value. This expectation of performance must be met throughout the customer’s expected life for the particular product. Quality is usually recognized as a more encompassing term including reliability. Some quality characteristics are: Psychological • Taste • Beauty, style • Status Technological • Hardness • Vibration • Noise • Materials (bearings, belts, hoses, etc.) Time-oriented • Reliability • Maintainability Contractual • Warranty Ethical • Honesty of repair shop • Experience and integrity of sales force

PRODUCT DEFECTS Quality defects are defined as those that can be located by conventional inspection techniques. (Note: for legal reasons, it is better to identify these defects as nonconformances.) Reliability defects are defined as those that require some stress applied over time to develop into detectable defects. What causes product failure over time? Some possibilities are: • • • • • • • • •

Design Manufacturing Packaging Shipping Storage Sales Installation Maintenance Customer duty cycle

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CUSTOMER SATISFACTION The ultimate goal of a product is to satisfy a customer from all aspects of cost, performance, reliability, and maintainability. The customer trades off these parameters when making a decision to buy a product. Assuming that we are designing a product for a certain market segment, cost is determined within limits. The tradeoffs are as follows: 1. Performance parameters are the designed-in system capabilities such as acceleration, top speed, rate of metal removal, gain, ability to carry a 5ton payload up a 40 degree grade without overheating, and so on. 2. The reliability of equipment expresses the length of failure-free time that can be expected from the equipment. Higher levels of reliability mean less failure of the equipment and consequently less downtime and loss of use. Although we will attach reliability numbers to products, it should be recognized that the customer’s perspective interprets reliability as the ability of a product to perform its intended function for a given period of time without failure. This concept of failure-free operation is becoming more and more fixed in the mind of the customer. This is true whether the customer is purchasing an automobile, a machine tool, a computer system, a refrigerator, or an automatic coffee maker. 3. Maintainability is defined as the probability that a failed system is restored to operable condition in a specified amount of downtime. 4. Availability is the probability that at any time, the system is either operating satisfactorily or is ready to be operated on demand, when used under stated conditions. The availability might also be looked at as the ability of equipment, under combined aspects of its reliability, maintainability, and maintenance support, to perform its required function at a stated instant of time. This availability includes the built-in equipment features as well as the maintenance support function. Availability combines reliability and maintainability into one measure. There are different kinds of availability that are calculated in different ways — see Von Alven (1964) and ANSI/IEEE (1988). The most popular availabilities are achieved availability and inherent availability. a. Achieved availability includes all diagnostic, repair, administrative, and logistic times. This availability is dependent on the maintenance support system. Achieved availability can be calculated as A = Operating Time/(Operating Time + Unscheduled Time) b. Inherent availability only includes operating time and active repair time addressing the built-in capabilities of the equipment. Inherent availability is calculated as

A=

MTBF MTBF + MTTR

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Infant mortality

Failure rate

Normal life

Wear out

Time FIGURE 7.1 Bathtub curve.

where MTTR = mean time-to-repair and the MTTR is for the active repair time. 5. Active repair time is that portion of downtime when the technicians are working on the system to repair the failure situation. It must be understood that the different availabilities are defined for various time-states of the system. 6. Serviceability is the ease with which machinery and equipment can be repaired. Here repair includes diagnosis of the fault, replacement of the necessary parts, tryout, and bringing the equipment back on line. Serviceability is somewhat qualitative and addresses the ease by which the equipment, as designed, can be diagnosed and repaired. It involves factors such as accessibility to test points, ease of removal of the failed components, and ease of bringing the system back on line.

PRODUCT LIFE

AND

FAILURE RATE

Let us assume that we have released a population of products to the marketplace. The failure rate is observed as the products age. The shape of the failure rate is referred to as a bathtub curve (see Figure 7.1). Here we have overemphasized the different parts of the curve for illustration. This bathtub curve has three distinct regions: 1. Infant mortality period: During the infant mortality period the population exhibits a high failure rate, decreasing rapidly as the weaker products fail. Some manufacturers provide a “burn-in” period for their products to help eliminate infant mortality failures. Generally, infant mortality is associated with manufacturing issues. Examples are: • Poor welds • Contamination • Improper installation • And so on 2. Useful life period: During this period the population of products exhibits a relatively low and constant failure rate. It is explained using the stress – strength inference model for reliability. This model identifies the stress

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distribution that represents the combined stressors acting on a system at some point in time. The strength distribution represents the piece-to-piece variability of components in the field. The inference area is indicative of a potential failure when stresses exceed the strength of a component. In other words, any failure in this period is a factor of the designed-in reliability. Examples are: • Low safety factors • Abuse • Misapplication • Product variability • And so on 3. Wear out period: At the onset of wear out, the failure rate starts to increase rapidly. When the failure rate becomes high, replacement or major repair must be performed if the product is to be left in service. Wear out is due to a number of forces such as: • Frictional wear • Chemical change • Maintenance practices • Fatigue • Corrosion or oxidation • And so on In conjunction with the bathtub curve there are two more items of concern. The first one is the hazard rate (or the instantaneous failure rate) and the second, the ROCOF plot. The hazard rate is the probability that the product will fail in the next interval of time (or distance or cycles). It is assumed the product has survived up to that time. For example, there is a one in twenty chance that it will crack, break, bend, or fail to function in the next month. Typically, hazard rate is shown as h(t ) =

f (t ) f (t ) = 1 − F (t ) R(t )

where h(t) = hazard rate; f(t) = probability density function [PDF: f(t) = λe–λt]; F(t) = cumulative distribution function [CDF: F(t) = 1 – e–λt; and R(t) = reliability at time t [R(t) = 1 – F(t) = 1 – (1 – e–λt) = e–λt]. The Rate of Change of Failure or Rate of Change of Occurrence of Failure (ROCOF), on the other hand, is a visual tool that helps the engineer to analyze situations where a lot of data over time has been accumulated. Essentially, its purpose is the same as that of the reliability bathtub curve, that is, to understand the life stages of a product or process and take the appropriate action. A typical ROCOF plot (for warranty item) will display an early (decreasing rate) and useful life (constant rate) performance. If wear out is detected, it should be investigated. Knowing what is happening to a product from one region of the bathtub curve to the next helps the engineer specify what failed hardware to collect and aids with calibrating the severity of development tests.

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If the number of failures is small, the ROCOF plot approach may be difficult to interpret. When that happens, it is recommended that a “smoothing” approach be taken. The typical smoothing methodology is to use log paper for the plotting. Obviously, many more ways and more advanced techniques exist. It must be noted here that most statistical software provides this smoothing as an option for the data under consideration. See Volume III for more details on smoothing.

PRODUCT DESIGN AND DEVELOPMENT CYCLE Developing a product that can be manufactured economically and consistently to be delivered to the marketplace in quantity and that will work satisfactorily for the customer takes a well established and precisely controlled design and development cycle. Events must be scheduled to occur at precise times to phase the product into the marketplace. To develop a new internal combustion engine for an automobile takes about a three-year design cycle (down recently from five years), while a new minicomputer takes about 18 months. Although the timing may be different for different companies, the activities comprising a design and development cycle are similar. The following is representative of the activities in a product development cycle: • Market research • Forecast need. • Forecast sales. • Understand who the customer is and how the product will be used. • Set broad performance objectives. • Establish program cost objectives. • Establish technical feasibility. • Establish manufacturing capacity. • Establish reliability and maintainability (R&M) requirements. • Understand governmental regulations. • Understand corporate objectives. • Concept phase • Formulate project team. • Formulate design requirements. • Establish real world customer usage profile. • Develop and consider alternatives. • Rank alternatives considering R&M requirements. • Review quality and reliability history on past products. • Assess feasibility of R&M requirements. • Design phase • Prepare preliminary design. • Perform design calculations. • Prepare rough drawings. • Compare alternatives to pursue. • Evaluate manufacturing feasibility of design approach (design for manufacturability and assembly).

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• • •

• Complete detailed design. • Perform a design failure mode and effect analysis (FMEA). • Complete detailed design package. • Update FMEA to reflect current design and details. • Develop design verification plan. • Develop R&M model for product. • Estimate product R&M using current design approach. Prototype program • Build components and prototypes. • Write test plan. • Perform component/subsystem tests. • Perform system test. • Eliminate design weaknesses. • Estimate reliability using growth techniques. Manufacturing engineering • Process planning • Assembly planning • Capability analyses • Process FMEA Finalized design • Consider test results. • Consider manufacturing engineering inputs (design for manufacturability/assembly). • Make design changes. Freeze design Release to manufacturing Engineering changes • Manufacturing experience • Field experience

RELIABILITY

IN

DESIGN

The cost of unreliability is: • • • •

High warranty costs Field campaigns Loss of future sales Cost of added field service support

It has been demonstrated in the marketplace that highly reliable products (failure free) gain market share. A very classic example of this is the American automotive market. In the early 1960s, American manufacturers were practically the only game in town with GM capturing some 60% of the market. Since then, progressively and on a yearly basis the market has shifted to the point where Flint (2001) reports that now GM has a shade over 25% without trucks and Saab, Ford 14.7% without Volvo and Jaguar, and Chrysler about 5%. The projections for the 2002 model year are

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not any better with GM capturing only 25%, Ford 15%, and Chrysler 6%. The sad part of the automotive scene is that GM, Ford, and DaimlerChrysler have lost market share, and sales are continually nudging down with no end in sight. That is, as Flint (2001, p. 21) points out, “they are not going to recover that market share, not in the short term, not in the next five to ten years.” The evidence suggests that the mission of a reliability program is to estimate, track, and report the reliability of hardware before it is produced. The reliability of the equipment must be reported at every phase of design and development in a consistent and easy-to-understand format. Warranty cost is an expensive situation resulting from poor manufacturing quality and inadequate reliability. For example, the chairman and chief executive of Ford Motor Company, Jacques Nasser, in the 1st quarter of 2001 leadership cascading meeting made the statement that in 1999, there were 2.1 times as many vehicles recalled as were sold. In 2000, there were six times as many. By way of comparison: In 1994, according to an article in USA Today, the cost of warranty for a Chrysler automobile was as high as $850 per vehicle. From the same article, one could deduce that the cost per vehicle for General Motors was about $350 and for Ford $650. This would be to cover the 36,000 mile warranty in effect at that time. In 2000, the warranty cost for Chrysler was about $1,300, GM about $1,200, and Ford about $850 (Mayne et al., 2001). For each car sold, the manufacturer must collect and retain this expense in a warranty account.

COST

OF

ENGINEERING CHANGES

AND

PRODUCT LIFE CYCLE

The stage of product development/manufacturing and the cost of an engineering change have been estimated many times by many different industries and various trade magazines as a cost that grows by a factor of five to ten as one moves from early design to manufacturing. Typical figures for this high cost are • Prototype stage: $100,000 Therefore, reliability can play an important role in designing products that will satisfy the customer and will prove durable in the real world usage application. The focus of reliability is to design, identify, and detect early potential concerns at a point where it is really cost effective to do so. Reliability must be valued by the organization and should be a primary consideration in all decision making. Reliability techniques and disciplines are integrated into system and component planning, design, development, manufacturing, supply, delivery, and service processes. The reliability process is tailored to fit individual business unit requirements and is based on common concepts that are focused on producing reliable products and systems, not just components.

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Any organization committed to satisfy the customer’s expectations for reliability (and value) throughout the useful life of its product must be concerned with reliability. For without it, the organization is doomed to fail. The total reliability process includes robustness concepts and methods that are integrated into the organization’s timing schedule and overall business system. Cross-functional teams and empowered individuals are key to the successful implementation of any reliability program. Reliability concepts and methods are generally thought of as a proprietary domain of only the product development department or community. That is not completely true. Reliability may be used anywhere there is a need for design and development work, such as manufacturing and tooling. However, it does not address actions specifically targeted at manufacturing and assembly. This is the reason why under Design for Six Sigma (DFSS), reliability becomes very important from the “get go.” To be sure, reliability currently does not include all the elements of the Advanced Product Quality Plan (APQP), but it is compatible with APQP. It outlines the three quality and reliability phases that all program teams and supporting organizations should go through in the product development process to achieve a more reliable and robust product. The three phases stress useful life reliability, focusing specifically on the deployment of customer-driven requirements, designing in robustness, and verifying that the designs meet the requirements.

RELIABILITY

IN THE

TECHNOLOGY DEPLOYMENT PROCESS

Technology is ever changing on all fronts. Customers expect increased reliability and better quality for a reasonable cost. Reliability may indeed play a major role in bringing technology, customer satisfaction, and lower cost into reality. Let us then try to understand the process of support and the cascading of requirements throughout the Technology Deployment Process (TDP). Understanding the TDP begins with the recognition that this process has three phases and each phase has specific requirements. The three phases are pre deployment process, core engineering process, and quality support. In the pre deployment process, there are three stages with very specific inputs and outputs. In core engineering, the development of generic requirements begins, and in quality support, the “best” reliability practices are developed. 1. Pre-Deployment Process Three stages are involved here. They are: 1. Identify/select new technologies: The main function of this stage is to identify and select technology for reliable and robust products that meet future customer needs or wants. In essence, here we are to develop and understand: • Customer wants process • Competitive analysis • Technology strategy/roadmap

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2. Develop/optimize technology to achieve concept readiness: The main function of this stage is to sufficiently develop and prove through analytical and/or surrogate testing that the technology meets the functional and reliability requirements for customer wants or needs under real world usage conditions. In essence, here we are to generate, understand, and develop readiness through: • Reviewing quality history of similar systems/concepts • Understanding real world usage profile • Defining functional requirements of system • Planning for robustness • Reviewing quality/reliability/durability reports or worksheets 3. Develop/optimize technology to achieve implementation readiness: The main function of this stage is to optimize the technology to meet functional and/or reliability requirements. Additionally, the aim is to demonstrate that the technology is robust and reliable under real world usage conditions. In essence, here we are to further understand the requirements by: • Refining design requirements • Designing for robustness • Verifying the design • Reviewing quality/reliability/durability reports or worksheets 2. Core Engineering Process Develop generic requirements for forward models by providing product lines with generic information on system robust design, such as case studies, system P-diagrams, measurement of ideal functions, etc. In this stage, we also conduct competitive technical information analysis to our potential product lines through test-thebest and reliability benchmarking. Some of the specific tools we may use are: • • • • • • •

System design specification guidelines Real world usage demographics Failure mode and effect analysis Key life testing Fault tree analysis Design verification process And so on

The idea here is to be able to develop common-cause problem resolution, that is, to be able to identify common-cause problems/root causes across the product line(s) and champion corrective action by following reliability disciplines. In essence then, core engineering should: • Prioritize concerns • Identify root causes • Determine/incorporate corrective action

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• Validate improvements • Champion implementation across product line(s) 3. Quality Support Identify best reliability practices and lead the process standardization and simplification. Develop a toolbox and provide reliability consultation.

RELIABILITY MEASURES — TESTING The purpose of performing a reliability test is to answer the question, “Does the item meet or exceed the specified minimum reliability requirement?” Reliability testing is used to: • Determine whether the system conforms to the specified, quantitative reliability requirements • Evaluate the system’s expected performance in the warranty period and its compliance to the useful life targets as defined by corporate policy • Compare performance of the system to the goal that was established earlier • Monitor and validate reliability growth • Determine design actions based on the outcomes of the test In addition to their other uses, the outcomes of reliability testing are used as a basis for design qualification and acceptance. Reliability testing should be a natural extension of the analytical reliability models, so that test results will clarify and verify the predicted results, in the customer’s environment.

WHAT IS

A

RELIABILITY TEST?

A reliability test is effectively a “sampling” test in that it involves a sample of objects selected from a “population.” From the sample data, some statement(s) are made about the population parameter(s). In reliability testing, as in any sampling test: • The sample is assumed to be representative of the population. • The characteristics of the sample (e.g., sample mean) are assumed to be an estimate of the true value of the population characteristics (e.g., population mean). A key factor in reliability test planning is choosing the proper sample size. Most of the activity in determining sample size is involved with either: 1. Achieving the desired confidence that the test results give the correct information 2. Reducing the risk that the test results will give the wrong information

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WHEN DOES RELIABILITY TESTING OCCUR? Prior to the time that hardware is available, simulation and analysis should be used to find design weaknesses. Reliability testing should begin as soon as hardware is available for testing. Ideally, much of the reliability testing will occur “on the bench” with the testing of individual components. There is good reason for this: The effect of failure on schedule and cost increases progressively with the program timeline. The later in the process that the failure and corrective action are found, the more it costs to correct and the less time there is to make the correction. Some key points to remember regarding test planning: • Develop the reliability test plan early in the design phase. • Update the plan as requirements are added. • Run the formal reliability testing according to the predetermined procedure. This is to ensure that results are not contaminated by development testing or procedural issues. • Develop the test plan in order to get the maximum information with the fewest resources possible. • Increase test efficiency by understanding stress/strength and acceleration factor relationships. This may require accelerated testing, such as AST (Accelerated Stress Test), which will increase the information gained from a test program. • Make sure your test plan shows the relationship between development testing and reliability testing. While all data contribute to the overall knowledge about a system, other functional development testing is an opportunity to gain insight into the reliability performance of your product. Note: A “control sample” should be maintained as a reference throughout the reliability testing process. Control samples should not be subjected to any stresses other than the normal parametric and functional testing.

RELIABILITY TESTING OBJECTIVES When preparing the test plan, keep these objectives in mind: • Test with regard to production intent. Make sure the sample that is tested is representative of the system that the customer will receive. This means that the test unit is representative of the final product in all areas including materials (metals, fasteners, weight), processes (machining, casting, heat treat), and procedures (assembly, service, repair). Of course, consider that these elements may change or that they may not be known. However, use the same production intent to the extent known at the time of the test plan. • Determine performance parameters before testing is started. It is often more important in reliability evaluations to monitor the percentage change in a parameter rather than the performance to specification.

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• Duplicate/simulate the full range of the customer stresses and environments. This includes testing to the 95th percentile customer. (For most organizations this percentile is the default. Make sure you identify what is the exact percentile for your organization.) • Quantify failures as they relate to the system being tested. A failure results when a system does not perform to customer expectations, even if there is no actual broken part. Remember, • Customer requirements include the specifications and requirements of internal customers and regulatory agencies as well as the ultimate purchaser. • You should structure testing to identify hardware interface issues as they relate to the system being tested. Sudden-Death Testing Sudden-death testing allows you to obtain test data quickly and reduces the number of test fixtures required. It can be used on a sample as large as 40 or more or as small as 15. Sudden-death testing reduces testing time in cases where the lower quartile (lower 25%) of a life distribution is considerably lower than the upper quartile (upper 25%). The philosophy involved in sudden-death testing is to test small groups of samples to a first failure only and use the data to determine the Weibull distribution of the component. The method is as follows: 1. Choose a sample size that can be divided into three or more groups with the same number of items in each group. Divide the sample into three or more groups of equal size and treat each group as if it were an individual assembly. 2. Test all items in each group concurrently until there is a first failure in that group. Testing is then stopped on the remaining units in that group as soon as the first unit fails, hence the name “sudden death.” 3. Record the time to first failure in each group. 4. Rank the times to failure in ascending order. 5. Assign median ranks to each failure based on the sample size equal to the number of groups. Median rank charts are used for this purpose. 6. Plot the times to failure vs. median ranks on Weibull paper. 7. Draw the best fit line. (Eye the line or use the regression model.) This line represents the sudden-death line. 8. Determine the life at which 50% of the first failures are likely to occur (B50 life) by drawing a horizontal line from the 50% level to the suddendeath line. Drop a vertical line from this point down. 9. Find the median rank for the first failure when the sample size is equal to the number of items in each subgroup. Again, refer to the median rank charts. Draw a horizontal line from this point until it intersects the vertical line drawn in the previous step.

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TABLE 7.1 Failure Rates with Median Ranks Failure Order Number

Life Hours

Median Ranks, %

1 2 3 4 5

65 120 155 200 300

12.95 31.38 50.00 68.62 87.06

10. Draw a line parallel to the sudden-death line passing through the intersection point from step 9. This line is called the population line and represents the Weibull distribution of the population. Sudden-death testing is a good method to use to determine the failure distribution of the component. (Note: Only common failure mechanisms can be used for each Weibull distribution. Care must be taken to determine the true root cause of all failures. Failure must be related to the stresses applied during the test.) EXAMPLE Assume you have a sample of 40 parts from the same production run available for testing purposes. The parts are divided into five groups of eight parts as shown below: Group Group Group Group Group

l 2 3 4 5

12345678 12345678 12345678 12345678 12345678

All parts in each group are put on test simultaneously. The test proceeds until any one part in each group fails. At that time, testing stops on all parts in that group. In the test, we experience the following first failures in each group: Group Group Group Group Group

1 2 3 4 5

Part Part Part Part Part

#3 #4 #1 #5 #7

fails fails fails fails fails

at at at at at

120 hours 65 hours 155 hours 300 hours 200 hours

Failure data are arranged in ascending hours to failure, and their median ranks are determined based on a sample size of N = 5. (There are five failures, one in each of five groups.) The chart in Table 7.1 illustrates the data. The median rank percentage for each failure is derived from the median rank (Table 7.2) for five samples. If the life hours and median ranks of the five failures are plotted on Weibull paper, the resulting line is called the sudden-death line. The sudden-death line represents

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TABLE 7.2 Median Ranks Rank Order 1 2 3 4 5 6 7 8 9 10 Rank Order 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Sample size 1

2

3

4

5

6

7

8

9

10

50.0

29.3 70.7

20.6 50.0 7 9.4

15.9 38.6 61.4 84.1

12.9 31.4 50.0 68.6 87.1

10.9 26.4 42.1 57.9 73.9 89.1

9.4 22.8 36.4 50.0 63.6 77.2 90.6

8.3 20.1 3G.1 44:0 56.0 67.9 79.9 91.7

7.4 18.0 Z8.6 39.3 50.0 60.7 71.4 82.0 92.6

6.7 16.2 25.9 35.5 45.2 54.8 64.5 74.1 83.8 93.3

11

12

13

14

15

16

17

18

19

20

6.1 14.8 23.6 32.4 41.2 50.0 58.8 67.6 76.4 85.2 93.9

5.6 13.6 21.7 29.8 37.9 46.0 54.0 62.1 70.2 78.3 86.4 94.4

5.2 12.6 20.0 27.5 35.0 42.5 50.0 57.5 65.0 72.5 80.0 87.4 94.8

4.8 1 1.7 18.6 25.6 32.6 39.5 46.5 53.5 60.5 67.4 74.4 81.4 88.3 95.2

4.5 10.9 17.4 23.9 30.4 37.0 43.5 50.0 56.5 63.0 69.5 76.1 82.6 89.1 95.5

4.2 10.3 16.4 22.5 28.6 34.7 40.8 46.9 53.1 59.2 65.3 71.4 77.5 83.6 89.7 95.8

4.0 9.7 15.4 21.2 26.9 32.7 38.5 44.2 50.0 55.8 61.5 67.3 73.1 78.8 84.6 90.3 96.0

3.8 9.2 14.6 20.0 25.5 30.9 36.4 41.8 47.3 52.7 58.2 63.6 69.1 74.5 80.0 85.4 90.8 96.2

3.6 8.7 13.8 19.0 24.2 29.3 34.5 39.7 44.8 50.0 55.2 60.3 65.5 70.7 75.8 81.0 86.2 91.3 96.4

3.4 8.3 13.1 18.1 23.0 27.9 32.8 37.7 42.6 47.5 52.5 57.4 62.3 67.2 72.1 77.0 81.9 86.9 91.7 96.6

Sample Size

the cumulative distribution that would result if five assemblies failed, but it actually represents five measures of the first failure in eight of the population. The median life point on the sudden-death line (point at which 50% of the failures occur) will correspond to the median rank for the first failure in a sample of eight, which is 8.30%. The population line is drawn parallel to the sudden-death line through a point plotted at 8.30% and at the median life to first failure as determined above. This estimate of the population’s minimum life is just as reliable as the one that would have been obtained if all 40 parts were tested to failure.

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Accelerated Testing Accelerated testing is another approach that may be used to reduce the total test time required. Accelerated testing requires stressing the product to levels that are more severe than normal. The results that are obtained at the accelerated stress levels are compared to those at the design stress or normal operating conditions. We will look at examples of this comparison during this section. We use accelerated testing to: • Generate failures, especially in components that have long life under normal conditions • Obtain information that relates to life under normal conditions • Determine design/technology limits of the hardware Accelerated testing is accomplished by reducing the cycle time, such as by: • Compressing cycle time by reducing or eliminating idle time in the normal operating cycle • Overstressing There are some pitfalls in using accelerated testing: • Accelerated testing can cause failure modes that are not representative. • If there is little correlation to “real” use, such as aging, thermal cycling, and corrosion, then it will be difficult to determine how accelerated testing affects these types of failure modes.

ACCELERATED TEST METHODS There are many test methods that can be used for accelerated testing. This section covers: • • • •

Constant-stress testing Step-stress testing Progressive-stress testing AST/PASS testing

Before we discuss the methods, keep in mind that any product may be subjected to multiple stresses and combinations of stresses. The stresses and combinations are identified very early in the design phase. When accelerated tests are run, ensure that all the stresses are represented in the test environment and that the product is exposed to every stress.

CONSTANT-STRESS TESTING In constant-stress testing, each test unit is run at constant high stress until it fails or its performance degrades. Several different constant stress conditions are usually

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employed, and a number of test units are tested at each condition. Some products run at constant stress, and this type of test represents actual use for those products. Constant stress will usually provide greater accuracy in estimating time to failure. Also, constant-stress testing is most helpful for simple components. In systems and assemblies, acceleration factors often differ for different types of components.

STEP-STRESS TESTING In step-stress testing, the item is tested initially at a normal, constant stress for a specified period of time. Then the stress is increased to a higher level for a specified period of time. Increases continue in a stepped fashion. The main advantage of step-stress testing is that it quickly yields failure, because increasing stress ensures that failures occur. A disadvantage is that failure modes that occur at high stress may differ from those at normal use conditions. Quick failures do not guarantee more accurate estimates of life or reliability. A constantstress test with a few failures usually yields greater accuracy in estimating the actual time to failure than a shorter step-stress test; however, we may need to do both to correlate the results so that the results of the shorter test can be used to predict the life. (Always remember that failures must be related to the stress conditions to be valid. Other test discrepancies should be noted and repaired and the testing continued.)

PROGRESSIVE-STRESS TESTING Progressive-stress testing is step-stress testing carried to the extreme. In this test, the stress on a test unit is continuously increased, rather than being increased in steps. Usually, the accelerating variable is increased linearly with time. Several different rates of increase are used, and a number of test units are tested at each rate of increase. Under a low rate of increase of stress, specimens tend to live longer and to fail at lower stress because of the natural aging effects or cumulative effects of the stress on the component. Progressive-stress testing has some of the same advantages and disadvantages as step-stress testing.

ACCELERATED-TEST MODELS The data from accelerated tests are interpreted and analyzed using different models. The model that is used depends upon the: • Product • Testing method • Accelerating variables The models give the product life or performance as a function of the accelerating stress. Keep these two points in mind as you analyze accelerated test data: 1. Units run at a constant high stress tend to have shorter life than units run at a constant low stress.

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2. Distribution plots show the cumulative percentage of the samples that fails as a function of time. In fact, over time the smoothing of the curve in the shape of an “S” is indeed the estimate of the actual cumulative percentage failing as a function of time. Two common models — although appropriate for component level testing — that deal specifically with accelerated tests are: 1. Inverse Power Law Model 2. Arrhenius Model Inverse Power Law Model The inverse power law model applies to many failure mechanisms as well as to many systems and components. This model assumes that at any stress, the time to failure is Weibull distributed. Thus: • The Weibull shape parameter β has the same value for all the stress levels. • The Weibull scale parameter θ is an inverse power function of the stress. The model assumes that the life at rated stress divided by the life at accelerated stress is equal to the quantity, accelerated stress divided by rated stress, raised to the power n, where: n = acceleration factor determined from the slope of the S-N diagram on the log-log scale. Using the above information, we can say that: θu = θa[Accelerated stress/Rated stress]n where θu = life at the rated (usage) stress level; θa = life at the accelerated stress level; and n = acceleration factor determined from the slope of the S-N diagram on the log-log scale. EXAMPLE Let us assume we tested 15 incandescent lamps at 36 volts until all items in the sample failed. A second sample of 15 lamps was tested at 20 volts. Using these data, we will determine the characteristic life at each test voltage and use this information to determine the characteristic life of the device when operated at 5 volts. From the accelerated test data:

θ20 volts = 11.7 hours θ36 volts = 2.3 hours Since we know these two factors, we can determine the acceleration factor, n. We have the following relationship:

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[Life at rated stress/life at accelerated stress] = [Accelerated stress/rated stress]n This relationship becomes

[θ20 volts/θ36 volts] = [36 volts/20 volts]n Substituting the values for theta 20 v and theta 36 v we have

11.7hrs  36v  = 2.3hrs  20 v 

n

Therefore,

n = 2.767 Now we can use the following equation to determine the characteristic life at 5 volts:

θu = θa [Accelerated stress/Rated stress]n  36  θ5 v = θ36 v [Accelerated stress/Rated stress] = 2.3    5 n

2.767

= 542 hours

The characteristic life at 5 volts is 542 hours.

The reader must be very careful here because not all electronic parts or assemblies will follow the inverse power law model. Therefore, its applicability must usually be verified experimentally before use. Arrhenius Model The Arrhenius relationship for reaction rate is often used to account for the effect of temperature on electrical/electronic components. The Arrhenius relationship is as follows:  − Ea  Reaction rate = A exp    K BT 

where: A = normalizing constant; KB = Boltzman’s constant (8.63 × 10–5 ev/degrees K); T = ambient temperature in degrees Kelvin; and Ea = activation energy type constant (unique for each failure mechanism). In those situations where it can be shown that the failure mechanism rate follows the Arrhenius rate with temperature, the following Acceleration Factor (AF) can be developed:

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 − Ea  Rateuse = A exp    K BTuse    − Ea Rateaccelerated = A exp    K BTaccelerated 

 − Ea  A exp    K BTa  Acceleration Factor = AF = Ratea/Rateu =  − Ea  A exp    K BTu   −E  1 E  1 1  1  AF = exp  a  −   = exp  a  −    K B  Ta Tu    K B  Tu Ta   where Ta = acceleration test temperature in degrees Kelvin and Tu = actual use temperature in degrees Kelvin. EXAMPLE Assume we have a device that has an activation energy of 0.5 and a characteristic life of 2750 hours at an accelerated operating temperature of 150°C. We want to find the characteristic life at an expected use temperature of 85°C. (Remember that the conversion factor for Celsius to Kelvin is: °K = °C + 273 — You may want to review Volume II.) Therefore:

Ta = 150 + 273 = 423°K and Tu = 85 + 273 = 358°K The Ea = 0.5. Our calculations would look like:

  1 .5 1  − AF = exp    −5  8.63x10  358 423   AF = exp [2.49] = 12. Therefore, the acceleration factor is 12. To determine life at 85°C, multiply the acceleration factor times the characteristic life at the accelerated test level of 150°C.

Characteristic life at 85°C = (12) (2750 hours) = 33,000 hours

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AST/PASS HALT (Highly Accelerated Life Test) and HASS (Highly Accelerated Stress Screens) are two types of accelerated test processes used to simulate aging in manufactured products. The HALT/HASS process was invented by Dr. Gregg Hobbs in the early 1980s. It has since been used with much success in various military and commercial applications. The HALT/HASS methods and tools are still in the development phase and will continue to evolve as more companies embrace the concept of accelerated testing. Many companies use this type of testing, which they call AST (Accelerated Stress Test) and PASS (Production Accelerated Stress Screen). The goal of accelerated testing is to simulate aging. If the stress-strength relationships are plotted, the design strength and field stress are distributed around means. Let us assume the stress and strength distributions are overlapped (the right tail of the stress curve is overlapped with the left tail of the strength curve). When that happens, there is an opportunity for the product to fail in the field. This area of overlap is called interference. Many products, including some electronic products, have a tendency to grow weaker with age. This is reflected in a greater overlap of the curves, thus increasing the interference area. Accelerated testing attempts to simulate the aging process so that the limits of design strength are identified quickly and the necessary design modifications can be implemented.

PURPOSE

OF

AST

AST is a highly accelerated test designed to fail the target component or module. The goal of this process is to cause failure, discover the root cause, fix it, and retest it. This process continues until the “limit of technology” is reached and all the components of one technology (i.e., capacitors, diodes, resistors) fail. Once a design reaches its limit of technology, the tails of the stress-strength distribution should have minimal overlap. The AST method uses step-stress techniques to discover the operating and destruct limits of the component or module design. This method should be used in the pre-prototype and/or pre-bookshelf phase of the product development cycle or as soon as the first parts are available. Let us look at an example: We want to discover the operating and destruct limits of a component/module design for minimum temperature. The unit is placed in a test chamber, stabilized at –40°C, then powered up to verify the operation. The unit is then unpowered, the temperature lowered to –45°C and the unit allowed to stabilize at that temperature. It is then powered on and verified. This process is repeated as the temperature is lowered by 5° increments. At –70°C, the unit fails. The unit is warmed to –65°C to see if it recovers. Normally, it will recover. The temperature of –65°C is said to be its operational limit. The test continues to determine the destruct limit. The limit is lowered to –75°C, stabilized, powered to see if it operates, then returned it to –65°C to see if it recovers. If when this unit is taken down to –95°C and returned to –65°C, it does not recover, the minimum temperature destruct limit for this module is determined

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to be –95°C. The failed module is then analyzed to determine the root cause of the failure. The team must then determine if the failure mode is the limit of technology or if it is a design problem that can be fixed. Experience has shown that 80% of the failures are design problems accelerated to failure using the AST or similar accelerated stress test methods.

AST PRE-TEST REQUIREMENTS Before AST is run on a product, the product development team should verify that: • The component/module meets specification requirements at minimum and maximum temperature. • The vibration evaluation test (sine-sweep) is complete. • Data are available for review by the reliability engineer. • A copy of all schematics is available for review. The product development team will provide the component/module monitoring equipment used during AST and will work with the reliability engineer to define what constitutes a “failure” during the test.

OBJECTIVE

AND

BENEFITS

OF

AST

The objective of AST is to discover the operational and destruct limits of a design and to verify how close these limits are to the technological limits of the components and materials used in the design. It also verifies that the component/module is strong enough to meet the requirements of the customer and product application. These requirements must be balanced with reasonable cost considerations. The benefits of AST include: • Easier system and subsystem validation due to: • Elimination of component- /module-related failures • Verification of worst-case stress analysis and derating requirements • A list of failure modes and corrections to be shared with the design team and incorporated into future designs • Products that allow the manufacturing team to use PASS and to eliminate the in-process “build and check” types of tests The failure modes from the AST and PASS are used by the manufacturing team to ensure that they do not see any of these problems in their products.

PURPOSE

OF

PASS

PASS is incorporated into a process after the design has been first subjected to AST. The purpose of PASS is to take the process flaws created in the component/module from latent (invisible) to patent (visible). This is accomplished by severely stressing a component enough to make the flaws “visible” to the monitoring equipment. These

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flaws are called outliers, and they result from process variation, process changes, and different supplier sources. The goal of PASS is to find the outliers, which will assist in the determination of the root cause and the correction of the problem before the component reaches the customer. This process offers the opportunity for the organization to eliminate module conditioning and burn-in. PASS development is an iterative process that starts when the pre-pilot units become available in the pre-pilot phase of the product development cycle. The initial PASS screening test limits are the AST operational limits and will be adjusted accordingly as the components/modules fail and the root cause determinations indicate whether the failures are limits of technology or process problems. The PASS also incorporates findings from process failure mode and effect analysis (PFMEA) regarding possible “significant” process failure modes that must be detected if present. When PASS development is complete, a strength-of-PASS test is performed to verify that the PASS has not removed too much useful life from the product. A sample of 12 to 24 components is run through 10 to 20 PASS cycles. These samples are then tested using the design verification life test. If the samples fail this test, the screen is too strong. The PASS will be adjusted based on the root cause analysis, and the strength-of-PASS will be rerun.

OBJECTIVE

AND

BENEFITS

OF

PASS

The objective of PASS is to precipitate all manufacturing defects in the component/module at the manufacturing facility, while still leaving the product with substantially more fatigue life after screening than is required for survival in the normal customer environment. The benefits of PASS include: • Accelerated manufacturing screens • Reduced facility requirements • Improved rate of return on tester costs

CHARACTERISTICS OF A RELIABILITY DEMONSTRATION TEST Eight characteristics are important in reliability demonstration testing. These are: 1. Specified reliability, Rs: This value is sometimes known as the “customer reliability.” Traditionally, this value is represented as the probability of success (i.e., 0.98); however, other measures may be used, such as a specified MTBF. 2. Confidence level of the demonstration test: While customers desire a certain reliability, they want the demonstration test to prove the reliability at a given confidence level. A demonstration test with a 90% confidence level is said to “demonstrate with 90% confidence that the specified reliability requirement is achieved.”

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3. Consumer’s risk, β: Any demonstration test runs the risk of accepting bad product or rejecting good product. From the consumer’s point of view, the risk is greatest if bad product is accepted. Therefore, the consumer wants to minimize that risk. The consumer’s risk is the risk that a test can accept a product that actually fails to meet the reliability requirement. Consumer’s risk can be expressed as: β = 1 – confidence level 4. Probability distribution: This is the distribution that is used for the number of failures or for time to failure. These are generally expressed as normal, exponential, or Weibull. 5. Sampling scheme 6. Number of test failures to allow 7. Producer’s risk, α : From the producer’s standpoint, the risk is greatest if the test rejects good product. Producer’s risk is the risk that the test will reject a product that actually meets the reliability requirement. 8. Design reliability, Ra: This is the reliability that is required in order to meet the producer’s risk, α, requirement at the particular sample size chosen for the test. Small test sample sizes will require a high design reliability in order to meet the producer’s risk objective. As the sample size increases, the design reliability requirement will become smaller in order to meet the producer’s risk objective.

THE OPERATING CHARACTERISTIC CURVE The relationship between the probability of acceptance and the population reliability can be shown with an operating characteristic (OC) curve. An operating characteristic curve can also be used to show the relationship between the probability of acceptance and MTBF or failure rate. Given then an OC curve, one may calculate the: 1. Producer’s risk, α 2. Consumer’s risk, β 3. Probability of acceptance at any other population reliability or MTBF or failure rate Obviously, a specific OC curve will apply for each test situation and will depend on the number of pieces tested and the number of failures allowed.

ATTRIBUTES TESTS If the components being tested are merely being classified as acceptable or unacceptable, the demonstration test is an attributes test. Attributes tests: • May be performed even if a probability distribution of the time to failure is not known • May be performed if a probability distribution such as normal, exponential, or Weibull is assumed by dichotomizing the life distribution into acceptable and unacceptable time to failure

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• Are usually simpler and cheaper to perform than variables tests • Usually require larger sample sizes to achieve the same confidence or risks as variables tests

VARIABLES TESTS Variables tests are used when more information is required than whether the unit passed or failed, for example, “What was the time to failure?” The test is a variables test if the life of the items under test is: • Recorded in time units • Assumed to have a specific probability distribution such as normal, exponential, or Weibull

FIXED-SAMPLE TESTS When the required reliability and the test confidence/risk are known, statistical theory will dictate the precise number of items that must be tested if a fixed sample size is desired.

SEQUENTIAL TESTS A sequential test may be used when the units are tested one at a time and the conclusion to accept or reject is reached after an indeterminate number of observations. In a sequential test: 1. The “average” number of samples required to reach a conclusion will usually be lower than in a fixed-sample test. This is especially true if the population reliability is very good or very poor. 2. The required sample size is unknown at the beginning of the test and can be substantially larger than that in the fixed-sample test in certain cases. 3. The test time required is much longer because samples are tested one at a time (in series) rather than all at the same time (in parallel), as in fixedsample tests. Now that you are familiar with the four test types, let us look at the test methods. Note that the four test types are not mutually exclusive. We can have fixed-sample or sequential-attributes tests as well as fixed-sample or sequential-variables tests.

RELIABILITY DEMONSTRATION TEST METHODS Attributes tests can be used when: • The accept/reject criterion is a go/no-go situation. • The probability distribution of times to failure is unknown. • Variables tests are found to be too expensive.

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SMALL POPULATIONS — FIXED-SAMPLE TEST USING THE HYPERGEOMETRIC DISTRIBUTION When items from a small population are tested and the accept/reject decision is based on attributes, the hypergeometric distribution is applicable for test planning. The definition of successfully passing the test will be that an item survives the test. The parameter to be evaluated is the population reliability. The estimation of the parameter is based on a fixed sample size and testing without repair. The method to use is described below: 1. 2. 3. 4.

Define the criteria for success/failure. Define the acceptance reliability, RS. Specify the confidence level or the corresponding consumer’s risk, β. Specify, if desired, producer’s risk, α. (Producer’s risk can be used to calculate the design reliability target, Rd, needed in order to meet the α requirements.)

The process consists of a trial-and-error solution of the hypergeometric equation until the conditions for the probability of acceptance are met. The equation that is used is: f

Pr(x ≤ f) =

∑ x =0

 N (1 − R)  NR   x   n − x    N  n  

where Pr(x < –f) = probability of acceptance; f = maximum number of failures to be allowed; x = observed failures in sample; R = reliability of population; N = population size; and n = sample size.  N N!  n = n N −n! !  

(

)

LARGE POPULATION — FIXED-SAMPLE TEST USING THE BINOMIAL DISTRIBUTION When parts from a large population are tested and the accept/reject decision is based on attributes, the binomial distribution can be used. Note that for a large N (one in which the sample size will be less than 10% of the population), the binomial distribution is a good approximation for the hypergeometric distribution. The binomial attribute demonstration test is probably the most versatile for use on product components for several reasons: 1. The population is large. 2. The time-to-failure distribution for the parts is probably unknown. 3. Pass/fail criteria are usually appropriate.

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As with the hypergeometric distribution, the procedure begins by identification of: 1. Specified reliability, Rs 2. Confidence level or consumer’s risk, β 3. Producer’s risk, α (if desired) The process consists of a trial-and-error solution of the binomial equation until the conditions for the probability of acceptance are met. The equation that is used is: f

Pr(x ≤ f) =

 n

∑  x (1 − R) ( R) x

n− x

x =0

where Pr(x < f) = probability of acceptance; f = maximum number of failures to be allowed; x = observed failures in sample; R = reliability of population; and n = sample size.

LARGE POPULATION — FIXED-SAMPLE TEST USING THE POISSON DISTRIBUTION The Poisson distribution can be used as an approximation of both the hypergeometric and the binomial distributions if: The population, N, is large compared to the sample size, n. The fractional defective in the population is small (Rpopulation > 0–9). The process consists of a trial-and-error solution using the following equation or Poisson tables, Rs, Rd, and various sample sizes until the conditions of α and β are satisfied. f

Pr(x ≤ f) =

∑ x =0

λxpoie

− λ poi

x!

where Pr(x ≤ f) = probability of acceptance; f = maximum number of failures to be allowed; x = observed failures in sample; λpoi = (n) (1 – R) (The reader should note that the λpoi is the Poisson density and does not relate to failure rate); r = reliability of population; and n = sample size.

SUCCESS TESTING Success testing is a special case of binomial attributes testing for large populations where no failures are allowed. Success testing is the simplest method for demonstrating a required reliability level at a specified confidence level. In this test case, n items are subjected to a test for the specified time of interest, and the specified

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reliability and confidence levels are demonstrated if no failures occur. The method uses the following relationship: R = (1 – C)1/n = (β)1/n where R = reliability required; n = number of units tested; C = confidence level; and β = consumer’s risk. The necessary sample size to demonstrate the required reliability at a given confidence level is: n=

SEQUENTIAL TEST PLAN

FOR THE

ln(1 − C ) ln R

BINOMIAL DISTRIBUTION

The sequential test is a hypothesis testing method in which a decision is made after each sample is tested. When sufficient information is gathered, the testing is discontinued. In this type of testing, sample size is not fixed in advance but depends upon the observations. Sequential tests should not be used when the exact time or cost of the test must be known beforehand or is specified. This type of test plan may be useful when the: 1. Accept/reject criterion for the parts on test is based on attributes 2. Exact test time available and sample size to be used are not known or specified The test procedure consists of testing parts one at a time and classifying the tested parts as good or defective. After each part is tested, calculations are made based on the test data generated to that point. The decision is made as to whether the test has been passed or failed or if another observation should be made. A sequential test will result in a smaller average number of parts tested when the population tested has a reliability close to either the specified or design reliability. The method to use is described below: Determine Rs, Rd, α.β Calculate the accept/reject decision points using: β 1−β and 1−α α As each part is tested, classify it as either a failure or success. Evaluate the following expression for the binomial distribution, f

 1 − Rs   Rs  L =     1 − Rd   Rd 

s

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where F = total number of failures and S = total number of successes.

If

If L >

1−β , the test is failed. α

If L


1−β , the test is failed. α

If L
0); and θ = characteristic life (θ ≥ δ). For a given reliability

1

 1 β t = θ + (θ – δ) × ln( )  R  and the B10 life is

1

 1 β B10 = θ + (θ – δ) × ln( )  0.90 

DESIGN OF EXPERIMENTS IN RELIABILITY APPLICATIONS Certainly we can use DOE in passive observation of the covariates in the tested components. We can also use DOE in directed experimentation as part of our reliability improvement. Covariates are usually called factors in the experimentation framework. Two main technical problems arise in the reliability area, however, when standard methods of experimental design are employed. 1. Failure time data are rarely normally distributed, so standard analysis tools that rely on symmetry, e.g., normal plots, do not work too well. 2. Censoring. The first problem can be overcome by considering a transformation of the fail times to make them approximately normal — the log transformation is usually a good choice. The exact form of the fail time distribution is not important because we are looking for effects that improve reliability, rather than exact predictions of the reliability itself. The second problem of censoring is a little bit trickier but can be dealt with by iteration as follows: 1. Choose a basic model to fit to the data. 2. Fit the model to the data, treating the censor times as failure times. 3. Using this model, make a conditional prediction for the unobserved fail times for each censored observation. The prediction is conditional because the actual failure time must be consistent with the censoring mechanism. 4. Replace censor times with the fail time predictions from step 3. 5. Go back to step 2.

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Eventually this process will converge, i.e., the predictions for the fail times of the censorings will stop changing from one iteration to the next. If necessary, the process can be tried with several model choices for step 1. In fact, the algorithm of the five steps leads to the same results as maximum likelihood estimation.

RELIABILITY IMPROVEMENT THROUGH PARAMETER DESIGN Two special categories of covariates in any parameter design are design parameters (or control factors) and error variables (or noise factors). The terms in parenthesis are the equivalent terms within the context of robustness, which we already have discussed in Volume V of this series. The achievement of higher reliability can also be viewed as an improvement to robustness. Robustness is defined as reduced sensitivity to noise factors. In most industries, noise factors have five main categories: 1. 2. 3. 4. 5.

Piece to piece variation Changes to component characteristics over time Customer duty cycle Environmental conditions Interfacing (environment created by neighboring components in the system)

Typically, noises in categories 3, 4, and 5 can induce noises in category 2. If the function of the component can be made robust to noises in category 2, then the component will, by definition, be more reliable. Often, noise category 1 contributes to infant mortality, category 2 to degradation, and categories 3, 4, and 5 to useful life problems. Recognizing this pattern of noises, we can relate them to the bathtub curve (see Figure 7.1) for the hazard function. Often, knowing the type of failure rate that is acting on our component can give a clue as to the offending noise factor and hence lead to a root cause analysis of the failure mechanism. Components can be made robust to noises by experimenting with control factors. The idea (as in robustness generally) is to look for interactions between control and noise factors. The reliability connection is made if there is a “time lag” between the extremes of the noise space, denoted N– and N+, say — see Figure 7.6. Note that the functional measure is not failure time, but some ideal function of the system. C1 and C2 represent two settings of a control factor. A design with C2 is more robust to noise than one with C1 and is therefore more reliable. Note: A closely related area to robustness in reliability studies is Accelerated Degradation Testing (ADT), which is closely associated with Accelerated Life Testing (ALT). A parameter design layout in reliability applications follows the pattern for parameter design studies, as in the example shown in Figure 7.7.

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Functional measure C2

C1 N-

N+ Time

FIGURE 7.6 Control factors and noise interactions.

Control Factors Configuration

A

B

C ...

G

2 3 4 5 6 7 8

+ + + +

+ + + +

+ + + +

+ + + +

Noise Factors time N(new) ylY2Y3Y4YsY6Y7Y8-

N+ (old) Y1+ Y2+ Y3+ Y4+ Ys+ Y6+ Y7+ Y8+

FIGURE 7.7 An example of a parameter design in reliability usage.

The idea of experimental layouts of this type is to look for interactions between control factors and noise factors, which lead to configurations with minimum difference between the y values.

DEPARTMENT OF DEFENSE RELIABILITY AND MAINTAINABILITY — STANDARDS AND DATA ITEMS Table 7.5 provides very useful information about reliability and maintainability (R&M) standards and data items used in reliability.

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TABLE 7.5 Department of Defense Reliability and Maintainability — Standards and Data Items Standard General Design Standards MIL-STD-454M MIL-HDBK-727 MIL-STD-810E MIL-STD-1629A MIL-STD-1686A MIL-E-4158E-(USAF) MIL-E-5400T MIL-HDBK-251 MIL-HDBK-263A MIL-HDBK-338A Reliability Standards MIL-STD-721C MIL-STD-756B MIL-STD-781 D

Explanation

Standard General Requirements for Electronic Equipment Design Guidance for Producibility Environmental Test Methods & Engineering Guidelines Procedures for Performing a Failure Mode Effects & Criticality Analysis Electrostatic Discharge Control Program for Protection of Electrical & Electronic Parts, Assemblies & Equipment General Specification for Ground Electronic Equipment General Specification for Aerospace Electronic Equipment Reliability/Design Thermal Applications Electrostatic Discharge Handbook for Protection of Electrical & Electronic Parts, Assemblies & Equipment Electronic Reliability Design Handbook

DoD-HDBK-344-(USAF)

Definitions of Terms for Reliability & Maintainability Reliability Modeling & Prediction Reliability Testing for Engineering Development Qualification & Production Reliability Program Systems & Equipment Development & Production Reliability Program Requirements for Space & Launch Vehicles Failure Reporting Analysis & Corrective Action System Environmental Stress Screening Process for Electronic Equipment Quality Program Requirements Reliability Growth Management Reliability Prediction of Electronic Equipment Reliability Test Methods, Plans & Environments for Engineering Development, Qualification & Production Environmental Stress Screening of Electronic Equipment

Maintainability Standards MIL-STD-470B MIL-STD-471A MIL-STD-2084-(AS) MIL-STD-2165 MIL-HDBK-472

Maintainability Program for Systems & Equipment Maintainability Demonstration General Requirements for Maintainability Testability Program for Electronic Systems & Equipment Maintainability Prediction

Major Parts Standards MIL-STD-198E MIL-STD-199E MIL-STD-202E MIL-STD-701N MIL-STD-750C

Selection & Use of Capacitors Selection & Use of Resistors Test Methods for Electronic & Electrical Component Parts Lists of Standard Semiconductor Devices Test Methods for Semiconductor Devices

MIL-STD-785B MIL-STD-1543B-(USAF) MIL-STD-2155-(AS) MIL-STD-2164-(EC) MIL-0–9858A MIL-HDBK-189 MIL-HDBK-217F MIL-HDBK-781

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TABLE 7.5 (continued) Department of Defense Reliability and Maintainability — Standards and Data Items MIL-STD-790E MIL-STD-883D MIL-STD-965A MIL-STD-983 MIL-STD-1546A-(USAF) MIL-STD-1547A-(USAF) MIL-STD-1556B MIL-STD-1562W MIL-STD-1772B MIL-S-19500H + OPL MIL-M-38510J + QPL MIL-H-38534A + QML MIL-1–38535A + QML MIL-HDBK-339-(USAF) MIL-HDBK-780A MIL-BUL-103J

Reliability Assurance Program for Electronic Part Specifications Test Methods & Procedures for Microelectronics Parts Control Program Substitution List for Microcircuits Parts, Materials & Processes Control Program for Space & Launch Vehicles Electronic Parts, Materials & Processes for Space & Launch Vehicles Government/Industry Data Exchange Program (GIDEP) Contractor Participation Requirements Lists of Standard Microcircuits Certification Requirements for Hybrid Microcircuit Facility & Lines General Specification for Semiconductor Devices General Specification for Microcircuits General Specification for Hybrid Microcircuits General Specification for Integrated Circuits (Microcircuits) Manufacturing Custom LSI Circuit Development & Acquisition for Space Vehicles Standardized Military Drawings List of Standardized Military Drawings (SMDs)

Reliability Analysis Center Publications DSR Discrete Semiconductor Device Reliability FMD Failure Mode/Mechanism Distributions FTA Fault Tree Analysis MFAT-1 Microelectronics Failure Analysis Techniques — A Procedural Guide MFAT-2 GaAs Characterization & Failure Analysis Techniques NONOP-1 Nonoperating Reliability Data NPRD Nonelectronic Parts Reliability Data NPS-1 Analysis Techniques for Mechanical Reliability PRIM A Primer for DoD Reliability, Maintainability, Safety and Logistics Standards RDSC-1 Reliability Sourcebook RMST Reliability and Maintainability Software Tools SOAR-2 Practical Statistical Analysis for the Reliability Engineer SOAR-4 Confidence Bounds for System Reliability SOAR-6 ESD Control in the Manufacturing Environment SOAR-7 A Guide for Implementing Total Quality Management SOAR-8 Process Action Team (PAT) Handbook VZAP Electrostatic Discharge Susceptibility Data Computer Formats NPRD-P NRPS VZAP-P

Nonelectronic Parts Reliability Data (IBM PC database) Nonoperating Reliability Prediction Software (Includes NONOP-1) VZAP Data (IBM PC database)

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TABLE 7.5 (continued) Department of Defense Reliability and Maintainability — Standards and Data Items Rome Laboratory Technical Reports Rome Laboratory (formerly Rome Air Development Center) has published hundreds of useful R&M technical reports that are available from the Defense Technical Information Center and the National Technical Information Service. Call RAC for a list. [Address at publication time: Reliability Analysis Center * 201 Mill Street * Rome, NY. 13440–6916 * Telephone: 315.337.0900] Data Item Descriptions MIL-STD-756 Reliability Modeling and Prediction DI-R-7081 B Mathematical Model(s) B Predictions Report(s) DI-R-7082 B Block Diagrams & Math. Models Report DI-R-7094 B Predict. & Doc. of Support. Material DI-R-7095 B Report for Explor. Advanced Develop. DI-R-7100 MIL-STD-781 Reliability Test Methods, Plans, and Environments for engineering development, Qualification and Production DI-RELI-80247 Thermal Survey Report DI-RELI-80248 Vibration Survey Report DI-RELI-80249 ESS Report DI-RELI-80250 B Test Plan DI-RELI-80251 B Test Procedures DI-RELI-80252 B Test Report DI-RELI-80253 Failed Item Analysis Report DI-RELI-80254 Corrective Action Plan DI-RELI-80255 Failure Summary and Analysis Report MIL-STD-785 Reliability Program for Systems and Equipment Development and Production and MIL-STD-1543 Reliability Program Requirements for Space and Launch Vehicles DI-R-7079 R Program Plan DI-R-7084 Elect. Parts/Circuits Tol. Analysis Report DI-R-7086 FMECA Plan DI-A-7088 Conference Agenda DI-A-7089 Conference Agenda DI-OCIC-80125 ALERT/SAFE ALERT DI-OCIC-80126 Response to ALERT/SAFE ALERT DI-RELI-80249 ESS Report DI-RELI-80250 Test Plan DI-RELI-80251 Test and Demo. Procedures DI-RELI-80252 Test Reports DI-RELI-80253 Failed Item Analysis Report DI-RELI-80255 Report, Failure Summary and Analysis DI-RELI-80685 Critical Item List DI-RELI-80686 Allocat., Assess. & Analysis Report DI-RELI-80687 Report, FMECA

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TABLE 7.5 (continued) Department of Defense Reliability and Maintainability — Standards and Data Items MIL-STD-2155 FRACA System DI-E-2178 Computer Software Trouble Report DI-R-21597 FRACA System Plan DI-R-21598 Failure Report DI-R-21599 Develop. & Product. Failure Summary Report MIL-STD-2164 ESS Process for Electronic Equipment DI-ENVR-80249 Environmental Stress Screening Report DOD-HDBK-344 ESS of Electronic Equipment DI-ENVR-80249 Environmental Stress Screening Report MIL-STD-810 Environmental Test Methods and Engineering Guidelines DI-ENVR-80859 Environmental Management Plan DI-ENVR-80860 Life Cycle Environmental Profile DI-ENVR-80861 Environmental Design Test Plan DI-ENVR-80862 Operational Environment Verif. Plan DI-ENVR-80863 Environmental Test Report MIL-STD-1629 Procedures for Performing a FMECA DI-R-7085 FMECA Report DI-R-7086 FMECA Plan MIL-STD-1686 ESD Control Program for Protection of Electrical and Electronic Parts, Assemblies and Equipment DI-RELI-80669 ESD Control Program Plan DI-RELI-80670 Reporting Results of ESD Sensitivity Tests of Electrical & Electronic Parts DI-RELI-80671 Handling Procedure for ESD Sensitive Items MIL-STD-1546 Parts, Materials, and Processes Control Program for Space and Launch Vehicles DI-A-7088 Conference Agenda DI-A-7089 Conference Minutes DI-MI SC-80526 Parts Control Program Plan DI-MISC-80072 Program Parts Selection List (PPSL) DI-MISC-80071 Part Approval Requests MIL-STD-1556 GIDEP Contractor Participation Requirements DI-QCIC-80125 ALERT/SAFE-ALERT DI-QCIC-80126 Response to an ALERT/SAFE-ALERT DI-QCIC-80127 GIDEP Annual Progress Report

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TABLE 7.5 (continued) Department of Defense Reliability and Maintainability — Standards and Data Items MIL-STD-470 Maintainability Program for Systems and Equipment DI-R-2129 M Demo. Plan (MIL-STD-470A, Task 301 only) DI-R-7085 FMECA Report DI-MNTY-80822 Program Plan DI-MNTY-80823 M Status Report DI-MNTY-80824 Data Collect., Anal. & Correct. Action System DI-MNTY-80825 M Modeling Report DI-MNTY-80826 M Allocations Report M Predictions Report DI-MNTY-80827 M Analysis Report DI-MNTY-80828 M Design Criteria Plan DI-MNTY-80829 Inputs to the Detailed Maintenance Plan & LSA DI-MNTY-80830 M Testability Demo. Test Plan DI-MNTY-80831 M Testability Demo. Test Report DI-MNTY-80832 MIL-STD-471 Maintainability Demonstration DI-R-2129 M Demonstration Plan DI-MNTY-80831 M Testability Demo. Test Plan DI-MNTY-80832 M Testability Demonstration Report DI-MNTY-81188 Verif., Demo., Assess. & Evaluation Plan DI- QCIC-81187 Quality Assessment Report MIL-STD-2165 Testability Program for Electronic Systems and Equipments DI-E-5423 Design Review Data Package DI-T-7198 Testability Program Plan DI-T-7199 Testability Analysis Report DI-MNTY-80824 Data Collect., Anal. & Correct. Act. System Plan DI-MNTY-80831 M/Testability Demo. Test Plan DI-MNTY-80832 M/Testability Demo. Report MIL-HDBK-472 Maintainability Prediction DI-MNTY-80827 M Predictions Report Note: Only data items specified in the Contract Data Requirements List (CDRL) are deliverable.

REFERENCES Anon., Warranty Cost Issue Hurts Chrysler, USA Today, Oct. 24, 1994, p. 3B. ANSI/IEEE Standard 100–1988, 4th ed., IEEE Standard Dictionary of Electrical and Electronic Terms, The Institute of Electrical and Electronic Engineers, Inc., New York, 1988. Flint, J., It Is Time To Get Realistic, WARD’S AUTOWORLD, Oct. 2001, p. 21. Mayne, E. et al., Quality Crunch, Ward’s AUTOWORLD, July 2001, pp. 14–18. VonAlven, W.H., Ed., Reliability Engineering, Prentice Hall, Inc., Englewood Cliffs, NJ, 1964.

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SELECTED BIBLIOGRAPHY Aitken, M., A note on the regression analysis of censored data, Technometrics, 23, 161–163, 1981. Box, G.E.P. and Meyer, R.D., Finding the active factors in fractionated screening experiments, Journal of Quality Technology, 25, 94–105, 1993. Cox, D.R. and Oakes, D., Analysis of Survival Data, Chapman Hall, London, 1984. Grove, D.M. and Davis, T.P., Engineering, Quality, and Experimental Design, Longman, Harlow, England, 1992. Hamada, M. and Wu, C.F.J., Analysis of censored data from highly fractionated experiments. Technometrics, 33, 25–3, 1991. Hamada, M. and Wu, C.F.J., Analysis of designed experiments with complex aliasing, Journal of Quality Technology, 23, 130–137, 1992. Kalbfleisch, J.D. and Prentice, R.L., The Statistical Analysis of Failure Time Data, Wiley, New York, 1980. Kapur, K.C. and Lamberson, L.R., Reliability in Engineering Design, Wiley, New York, 1977. Kececioglu, D., Reliability Engineering Handbook, Vols. 1 and 2, Prentice Hall, Englewood Cliffs, NJ, 1991. Lawless, J. F., Statistical Models and Methods for Lifetime Data, Wiley, New York, 1982. McCormick, N.J., Reliability and Risk Analysis, Academic Press, New York, 1981. Nelson, W., Theory and applications of hazard plotting for censored failure data, Technometrics, 14, 945–966, 1972. Schmee, J. and Hahn, G., A simple method of regression analysis with censored data. Technometrics, 21, 417–432, 1979. Smith, R.L., Weibull regression models for reliability data, Reliability Engineering and System Safety, 34, 55–57, 1991.

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As the world moves towards building more competitive products, it is important to put additional emphasis on reliability and maintainability (R&M), which support reduction of inventories and “build to schedule” targets. The Quality Systems Requirements, Tooling & Equipment (TE) Supplement to QS-9000 was developed by Chrysler, Ford, General Motors, and Riviera Die & Tool to enhance quality systems while eliminating redundant requirements, facilitating consistent terminology, and reducing costs. It is important that everyone involved in the design or purchase of machinery be aware of this supplement and their responsibilities as outlined in the QS-9000 process. It is also important that everyone understand that the TE supplement defines machinery as tooling and equipment combined. Machinery is a generic term for all hardware, including necessary operational software, which performs a manufacturing process. The TE goal is to improve the quality, reliability, maintainability, and durability of products through development and implementation of a fundamental quality management system. The supplement communicates additional common system requirements unique to the manufacturers of tooling and equipment as applied to the QS-9000 requirements. This particular chapter will emphasize the reliability and maintainability areas. Quality operating systems (QOS) and durability are equally important subjects but are beyond the scope of this work. The reader is encouraged to review Volume IV — the material on machine acceptance.

WHY DO RELIABILITY AND MAINTAINABILITY? Due to a lack of confidence in the performance of our equipment, we have traditionally purchased excessive facilities and tooling in order to meet production objectives. It is estimated that approximately 73% of the total cost in a program development through launching, in the automotive industry for example, is in this area. Additionally, capital spent on “insurance-type” spare tooling hidden for unplanned breakdowns shows a lack of confidence in production equipment. Operational effects of production shortfall and the inability to predict downtime are countless. They include unplanned overtime, unplanned and increasing maintenance requirements and costs, and excessive work in process around constraint operations. The R&M process builds confidence in predicting performance of machinery, and, through this process, we can improve the expected and demonstrated levels of machinery performance. Properly predicting and improving performance contributes to lower total cost and improved profits for the organization.

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The R&M process consists of five phases that form a continuous loop. The five phases are: (1) concept; (2) design and development; (3) machinery build and installation; (4) machinery operation, continuous improvement, performance analysis; and (5) conversion concept of next cycle. As the loop continues, each generation of machinery improves. In this chapter we will concentrate on the first three phases of the loop, not because they are more important, but because they are the major focus of this planning effort of the design for six sigma (DFSS) campaign. The last two phases should be well documented in each organization for they are facility dependent.

OBJECTIVES The emphasis of all R & M is focused on three objectives: Reliability — The probability that machinery and equipment can perform continuously, without failure, for a specified interval of time (when operating under stated conditions) Maintainability — A characteristic of design, installation, and operation, usually expressed as the probability that a machine can be retained in, or restored to, specified operable conditions within a specified interval of time (when maintenance is performed in accordance with prescribed procedures) Durability — Ability to perform intended function over a specified period (under normal use with specified maintenance) without significant deterioration

MAKING RELIABILITY AND MAINTAINABILITY WORK Machinery reliability and maintainability should be considered an integral part of all facilities and tooling (F&T) purchases. However, the appropriate degree of time and effort dedicated to R&M engineering must be individually applied for each unique application and purchase situation. Each project engineering manager should consider the value proposition of applying varying degrees of R&M engineering for the unique circumstances surrounding each equipment purchase. For example, we may choose to apply a large amount of R&M engineering resources to a project that includes a large quantity of single design machines. The value proposition would show that investing up-front resources on a single design that can be leveraged beyond a single application would offer a large payoff. We would also consider applying high-level R&M engineering to equipment critical to a continuous operation. On the other hand, we may choose to apply a minimal level of R&M engineering on a purchase of equipment that has a mature design and minimally demonstrated field problems. Some of the issues to consider when determining appropriate levels of R&M engineering for a project include:

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1. Review the availability of existing machines in the organization that may be idle. This is a good opportunity for reusability. 2. How many units are we ordering with identical or leverageable design? 3. What is the condition of the existing machinery that will be rehabilitated? 4. What is the status of the operating conditions? Are they extremely demanding? 5. What is the cycle plan for the machinery? Does it require continuous or intermittent duty? For how many years is the equipment expected to produce? 6. Where is the machinery in the manufacturing process? Is it a constraint (bottleneck) operation? 7. How well documented and complete is the root cause analysis for the design? Will it decrease up-front work? 8. How much data exist to support known design problems?

WHO’S RESPONSIBLE? Full realization of R&M benefits requires consistent application of the process. Simultaneous engineering (SE) teams, together with the plants and the supply base, must align their efforts and objectives to provide quality machinery designed for R&M. Reliability and maintainability engineering is the responsibility of everyone involved in machinery design, as much as the collection and maintenance of operational data are the responsibility of those operating and maintaining the equipment day to day. The R&M process places responsibility on the groups possessing the skills or knowledge necessary to efficiently and accurately complete a given set of tasks. It turns out that much of the expertise is in the supply base, and as such, the suppliers must take the lead role and responsibility in R&M efforts. The R&M process encourages the organization and suppliers to lock into budget costs based on Life Cycle Costing (LCC) analysis of options and cost targets. Warranty issues should be considered in the LCC analysis so that design helps decrease excessive warranty costs after installation. The focus places responsibility for correcting design defects on the machinery designers. Facility and tooling producers who practice R&M will ultimately reduce the cost (such as warranty) of their product and will become more competitive over time. Further, suppliers that practice R&M will qualify as QS-9000 certified, preferred, global sourcing partners. Engineers and program managers who practice and encourage R&M will reduce operational costs over time. In doing so, they will meet manufacturing and cost objectives for their projects or programs.

TOOLS There are many R&M tools. The ones mentioned here are required in the Design and Development Planning (4.4.2) section of the TE Supplement. Many others beyond the few that are addressed here are available and can improve reliability.

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Mean Time Between Failure (MTBF) is defined as the average time between failure occurrences. It is simply the sum of the operating time of a machine divided by the total number of failures. For example, if a machine runs for 100 hours and breaks down four times, the MTBF is 100 divided by 4 or 25 hours. As changes are made to the machine or process, we can measure the success by comparing the new MTBF with the old MTBF and quantify the action that has been taken. Mean Time to Repair (MTTR) is defined as the average time to restore machinery or equipment to its specified conditions. This is accomplished by dividing the total repair time by the number of failures. It is important to note that the MTTR calculation is based on repairing one failure and one failure only. The length of time it takes to repair each failure directly affects up-time, up-time %, and capacity. For example, if a machine runs 100 hours and has eight failures recorded with a total repair time of four hours, the MTTR for this machine would be four hours divided by eight failures or .5 hours. This is the mean time it takes to repair each failure. Fault Tree Analysis (FTA) is an effect-and-cause diagram. It is a method used to identify the root causes of a failure mode using symbols developed in the defense industry. The FTA is a great prescriptive method for determining the root causes associated with failures and can be used as an alternative to the Ishikawa Fish Bone Diagram. It compliments the Machinery Failure Mode and Effects Analysis (MFMEA) by representing the relationship of each root cause to other failure-mode root causes. Some feel the FTA is better suited than the FMEA to providing an understanding of the layers and relationships of causes. An FTA also aids in establishing a troubleshooting guide for maintenance procedures. It is a top down approach. Life Cycle Costs (LCC) are the total costs of ownership of the equipment or machinery during its operational life. A purchased system must be supported during its total life cycle. The importance of life cycle costs related to R&M is based on the fact that up to 95% of the total life cycle costs are determined during the early stages of the design and development of the equipment. The first three phases of the equipment’s life cycle are typically identified as non-recurring costs. The remaining two phases are associated with the equipment’s support costs.

SEQUENCE AND TIMING The R&M process is a generic model of logically sequenced events that guides the simultaneous engineering team through the main drivers of good design for R&M engineering. The amount of time budgeted for each activity or task should vary depending on the circumstances surrounding the equipment or processes in design. However, regardless of the unique conditions, all of the steps in the R&M process need to be considered in their logical sequence and applied as needed. In Table 8.1, we identify different activities that you may consider in the first three phases of the R&M process. These phases are divided into main areas for consideration; then, various activities are listed for each area. This list is not complete, but it focuses the reader on the type of activities that should occur during each time period. This list also helps identify the sequence in which these activities may be completed, depending on the project.

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TABLE 8.1 Activities in the First Three Phases of the R&M Process Concept

Design/Development

Build and Installation

Bookshelf data manufacturing process selection R&M and production needs analysis

R&M planning Process design for R&M machinery FMEA — design review

Equipment run-off Operation of machinery

To determine timing for the R&M process, you may use the following procedure: 1. Determine deadline dates to meet production requirements. 2. Check relevance of R&M activities with regard to achieving program/project targets. 3. Plan relevant R&M activities by working backwards from deadline dates, estimating time required for completion of each activity. 4. Set appropriate start dates for each activity/stage based on requirements and timing. 5. Determine and assign responsibility for stage-based deliverables. 6. Continually track progress of your plan, within and at the conclusion of each stage.

CONCEPT BOOKSHELF DATA Activities associated with the bookshelf data stage include: 1. 2. 3. 4. 5. 6. 7. 8. 9.

Identify good design practices. Collect machinery things gone right/things gone wrong (TGR/TGW). Document successful machinery R&M features. Collect similar machinery history of mean time between failures (MTBF). Collect similar standardized component history of mean time between failures (MTBF). Collect similar machinery history of mean time to repair (MTTR). Collect similar machinery history of overall equipment effectiveness (OEE). Collect similar machinery history of reliability growth. Collect similar machinery history of root cause analyses.

At this point it is important to ask and answer this question: Have we collected all of the relevant historical data from similar operations or designs and documented them for use during the process selection and design stages?

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MANUFACTURING PROCESS SELECTION Activities associated with the manufacturing process selection stage include: 1. Identify general life cycle costs to drive the manufacturing process selection. 2. Establish OEE targets including availability, quality, and performance efficiency numbers that drive the manufacturing process selection. 3. Establish broad R&M target ranges that drive the manufacturing process selection. 4. Establish manufacturing assumptions based on cycle plan, including volumes and dollar targets. 5. Identify simultaneous engineering (SE) partners for project. 6. Select manufacturing process based on demonstrated performance and expected ability to meet established targets. 7. Search for other surplus equipment to be considered for reuse. 8. If surplus machinery has not been identified for reuse, identify a supplier, based on manufacturing process selection (evaluate R&M capability). 9. Generate detailed life cycle costing analysis on selected manufacturing process. At this point it is important to ask and answer these questions: Have broad, high level R&M targets been set to drive detailed process trade-off decisions? Is the life cycle cost analysis complete for the selected manufacturing process? Do the projections support the budget per the affordable business structure?

R&M

AND

PREVENTIVE MAINTENANCE (PM) NEEDS ANALYSIS

Activities associated with the R&M and PM needs analysis stage include: 1. Establish a clear definition of failure by using all known operating conditions and unique circumstances surrounding the process. 2. Establish R&M requirements for the unique operating conditions surrounding the chosen manufacturing process. 3. Establish/issue R&M engineering requirements for the project to the designers of the machinery. 4. Identify PM requirements for maintainability. At this point it is important to ask and answer this question: Have specific R&M targets been set to support the unique operating conditions and PM program objectives?

DEVELOPMENT AND DESIGN R&M PLANNING Activities associated with the R&M planning stage include:

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1. Conduct process concept review. 2. Identify design effects for other related equipment (automation, integration, processing, etc.). 3. Standardize fault diagnostics (controls, software, interfaces, level of diagnosis, etc.). 4. Develop R&M/PM plan (process/machinery FMEA, mechanical/electrical derating, materials compatibility, thermal analyses, finite element analysis to support machine condition signature analysis, R&M predictions, R&M simulations, design for maintainability, etc.). 5. Establish R&M/PM testing requirements (burn-in testing, voltage cycling, probability ratio sequential testing, design of experiments for process optimization, environmental stress screening, life testing, test-analyze-fix, etc.). At this point it is important to ask and answer these questions: Does the R&M plan address each project target? Is the R&M plan sufficient to meet project targets?

PROCESS DESIGN

FOR

R&M

Activities associated with the process design for R&M stage include: 1. 2. 3. 4.

Conduct process design review. Develop process flow chart. Develop process simulation model. Conduct process design simulation for multiple scenarios by analyzing operational effects of various R&M design trade-offs. 5. Develop life cycle costing analysis on process-related equipment. 6. Review process FMEA. 7. Complete final process review and simultaneous engineering team input. At this point it is important to ask and answer this question: Is the process FMEA complete, and have causes of potentially common failure modes been addressed and redesigned?

MACHINERY FMEA DEVELOPMENT Activities associated with the machinery FMEA development stage include: 1. Develop plant floor computer data collection system (activity tracking, downtime, reliability growth curves). 2. Establish machinery data feedback plan (crisis maintenance, MTBF, MTTR, tool lives, OEE, production report, etc.). 3. Verify completion of machinery FMEA on all critical machinery. Confirm design actions, maintenance burdens, things gone wrong, root cause analyses, etc. 4. Develop fault diagnostic strategy (built in test equipment, rapid problem diagnosis, control measures).

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5. Review equipment and material handling layouts (panels, hydro, coolant systems). At this point it is important to ask and answer these questions: Is the machinery FMEA complete, and have causes of potentially common failure modes been addressed and redesigned? Is the data collection plan complete?

DESIGN REVIEW Activities associated with the design review stage include: 1. Conduct machinery design review (field history, machinery FMEA, test or build problems, R&M simulation and reliability predictions, maintainability, thermal/mechanical/electrical analyses, etc.). 2. Provide R&M requirements to tier two suppliers (levels, root cause analyses, standardized component applications, testing, etc.). At this point it is important to ask and answer this question: Have the R&M plan requirements been incorporated in the machinery design?

BUILD AND INSTALL EQUIPMENT RUN-OFF Activities associated with the equipment run-off stage include: 1. Conduct machinery run-off (perform root cause analysis, Failure, Reporting Analysis, and Corrective Action System [FRACAS], complete testing, verify R&M and TPM requirements, validate diagnostic logic and data collection). 2. Complete preventative maintenance/predictive maintenance manuals and review maintenance burden. At this point it is important to ask and answer this question: Has the plant maintenance department devised a maintenance plan based on expected machine performance?

OPERATION

OF

MACHINERY

Activities associated with the operation of machinery stage include: 1. 2. 3. 4. 5.

Implement and utilize machinery data feedback plan. Implement and utilize FRACAS. Evaluate PM program. Update FMEA and reliability predictions. Conduct reliability growth curve development and analysis.

At this point it is important to ask and answer this question: Have design practices been documented for use by the next generation design teams? (Also note that as

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the machinery begins to operate, the continuous improvement cycle phases begin to lead the R&M effort in phases four and five.)

OPERATIONS AND SUPPORT After the equipment has been installed and the run-off has been performed, the Durability phase of the cycle begins. The PM program now begins to utilize the R&M team member more as a team leader than a participant. Durability, as defined in the TE supplement, is the “ability to perform intended function over a specified period under normal use (with specified maintenance, without significant deterioration).” As the machinery begins to acquire additional operation hours, PM personnel identify issues and take corrective action. These issues and corrections are fed back to FMEA personnel and R&M planners as lessons learned for the next generation of machinery. Whether these corrections involve the design of the machinery or the maintenance schedule/tasks, each must be incorporated into the continuous improvement loop.

CONVERSION/DECOMMISSION Conversion is one of the key elements of the investment efficiency loop. The R&M process for reuse of equipment is very similar to the purchase of new equipment except that you have more limitations on the concept of the new process. The data are collected and phase one is repeated, often, with more specific direction as the current equipment may limit some of the other concepts. While decommission may be the process of equipment disposal, it is necessary to verify and record R&M data from this equipment to help identify the best design practices. It is also important to make note of those design practices that did not work as well as planned. As plans for decommission become firm, it is important to generate forecasts for equipment availability. These forecasts should then be entered into a database for future forecasted and available machinery and equipment. Maintenance data, including condition, operation description, and reason for availability should be included. This will assist engineers evaluating surplus machinery and equipment for reuse in their programs.

TYPICAL R&M MEASURES R&M MATRIX Perhaps the most important document in the R&M process is the R&M matrix. This matrix identifies the requirements of the customer on a per phase basis. Three major categories of tasks are usually identified. They are: R&M programmatic tasks Engineering tasks R&M continuous improvement

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RELIABILITY POINT MEASUREMENT This may be expressed by:

R(t ) = e

 −t     MTBF 

where R(t) = reliability point estimate during a constant failure rate period; e = natural logarithm which is 2.718281828…; t = schedule time or mission time of the equipment or machinery; and MTBF = mean time between failure. Special note: This calculation may be performed only when the machine has reached the bottom of the bathtub curve. EXAMPLE A water pump is scheduled (mission time) to operate for 100 hours. The MTBF for this pump is also rated at 100 hours and the MTTR is 2 hours. The probability that the pump will not fail during the mission is:

R(t ) = e

 −t     MTBF 

= R(t ) = e

 −100     100 

= .37 or 37%.

This means that the pump will have a 37% chance of not breaking down during the 100-hour mission time. Conversely, the unreliability of the pump can be calculated as:

R = 1 – R = 1 – .37 = .63 or 63%. This means that the pump has a 63% chance of failing during the 100 hour mission.

MTBE Mean time between event can be calculated as: MTBE = Total Operating Time/N where Total Operating Time = the total scheduled production time when machinery or equipment is powered and producing parts and N = the total number of downtime events, scheduled and unscheduled. EXAMPLE The total operating time for a machine is 550 hours. In addition, the machine experiences 2 failures, 2 tool changes, 2 quality checks, 1 preventive maintenance meeting, and 5 lunch breaks. What is the MTBE?

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MTBE = Total Operating Time/N = 550/12 = 45.8 hours

MTBF Mean time between failure is the average time between failure occurrences and is calculated as: MTBF = Operating Time/N where Operating Time = scheduled production time and N = total number of failures observed during the operating period. EXAMPLE If machinery is operating for 400 hours and there are eight failures, what is the MTBF? MTBF = Operating Time/N = 400/8 = 50 hours. (Special note: Sometimes C (cycles) is substituted for T. In that case, we calculate the MCBF. The steps are identical to those of the MTBF calculation.)

FAILURE RATE Failure rate estimates the number of failures in a given unit of time, events, cycles, or number of parts. It is the probability of failure within a unit of time. It is calculated as: Failure rate = 1/MTBF EXAMPLE The failure rate of a pump that experiences one failure within an operating time period of 2000 hours is:

Failure rate = 1/MTBF = 1/2000 = .0005 failures per hour. This means that there is a .0005 probability that a failure will occur with every hour of operation.

MTTR Mean time to repair is a calculation based on one failure and one failure only. The longer each failure takes to repair, the more the equipment’s cost of ownership goes up. Additionally, MTTR directly effects uptime, uptime percent, and capacity. It is calculated as:

MTTR =

∑t N

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where

∑ t = total repair time and N = total number of repairs.

EXAMPLE A pump operates for 300 hours. During that period there were four failure events recorded. The total repair time was 5 hours. What is the MTTR?

MTTR =

∑t N

= 5/4 = 1.25 hours

AVAILABILITY Availability is the measure of the degree to which machinery or equipment is in an operable and committable state at any point in time. Availability is dependent upon (a) breakdown loss, (b) setup and adjustment loss, and (c) other factors that may prevent machinery from being available for operation when needed. When calculating this metric, it is assumed that maintenance starts as soon as the failure is reported. (Special note: Think of the measurement of R&M in terms of availability. That is, MTBF is reliability and MTTR is maintainability.) Availability is calculated as: Availability = MTBF/(MTBF + MTTR) EXAMPLE What is the availability for a system that has an MTBF of 50 hours and an MTTR of 1 hour?

Availability = MTBF/(MTBF + MTTR) = 50/(50 + 1) = .98 or 98%

OVERALL EQUIPMENT EFFECTIVENESS (OEE) Overall equipment effectiveness (OEE) is a measure of three variables. They are: 1. Availability = percent of time a machine is available to produce 2. Performance efficiency = actual speed of the machine as related to the design speed of the machine 3. Quality rate = percent of resulting parts that are within specifications A good OEE is considered to be 85% or higher.

LIFE CYCLE COSTING (LCC) Life cycle costing (LCC) is the total cost over the life of the machine or equipment. It is calculated based on the following: LCC = Acquisition costs (A) + Operating costs (O) + Maintenance costs (M) ± Conversion and or decommission costs (c)

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TABLE 8.2 Cost Comparison of Two Machines Costs Acquisition costs (A) Operating costs (O) Maintenance costs (M) Conversion and/or decommission costs (C) Total LCC

Machine A

Machine B

$2,000.00 $9,360.00 $7,656.00

$1,520.00 $10,870.00 $9,942.00

$19,016.00

$22,332.00

EXAMPLE What is the LCC for the two machines shown in Table 8.2 and which one is a better deal? The reader should notice that before the decision is made all costs should be evaluated. In this case, machine A has a higher acquisition cost than machine B, but it turns out that machine A has a lower LCC than machine B. Therefore, machine A is the better deal.

TOP 10 PROBLEMS

AND

RESOLUTIONS

This list allows the designer to see the major sources of downtime associated with the current equipment. Once the list items are identified, a root cause analysis or problem resolution should be conducted on each of the failures. If the design is known, the designer can then modify the design to reflect the changes. (Sometimes the top ten problems are based on historical data and must be adjusted to reflect current design considerations.)

THERMAL ANALYSIS This analysis is conducted to help the designer to develop the appropriate and applicable heat transfer (Table 8.3). The actual analysis is conducted by following these six steps: 1. 2. 3. 4. 5.

Develop a list of all electrical components in the enclosure. Identify the wattage rating for each component located in the enclosure. Sum the total wattage for the enclosure. Add in any external heat generating sources. Calculate the surface area of the enclosure that will be available for cooling. 6. Calculate the thermal rise above ambient. EXAMPLE The electrical enclosure is 5 ft. tall by 4 ft. deep. The surface area for this enclosure is calculated as follows:

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TABLE 8.3 Thermal Calculation Values Thermal Calculation Values Component Name

Quantity

Individual Wattage Maximum

Total Wattage

Internal Relay A18 contactor A25 contactor PS27 power supply Monochrome monitor

4 1 2 1 1

2.5 1.7 2 71 85 Subtotal wattage

10.0 1.7 4.0 71.0 85.0 171.7

External Servo transformer

1

450 Subtotal wattage Total enclosure wattage

63.0 63.0 234.7

Note: The servo transformer is mounted externally and next to the enclosure. Therefore, only 14% of the total wattage is estimated to radiate into the enclosure

Front and Back = 5 ft. × 4ft. × 2 = 40 sq. ft. Sides = 2 ft. × 5 ft. × 2 = 20 sq. ft. Enclosure top = 2ft. × 4ft. = 8 sq. ft Bottom is ignored due to the fact that heat rises.

Total surface area = 40 + 20 + 8 = 68 sq. ft. To calculate the thermal rise (∆T) we use the following formula:

Thermal rise (∆T) = Thermal resistance (θCA) cabinet to ambient × Power (W) θCA = 1/(Thermal conductivity × Cooling area) The thermal conductivity value is found in the catalog of the National Electrical Manufacturing Association (NEMA).

θCA = 1/(.25 W/degree F) × (square footage) θCA = 1/.25 × 68 = .0588 Thus, .25 W/degree F is the thermal conductivity value for a NEMA 12 enclosure. If the equipment inside the enclosure generates 234.7 watts, then the thermal rise is

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∆T = θCA × wattage = .0588 × 234.7 = 13.8°F. If the ambient temperature is 100°F, then the enclosure temperature will reach 113.8°F. If the enclosure temperature is specified as 104°F, then the design exceeds the specification by approximately 9.8°F. The enclosure must be increased in size, the load must be reduced, or active cooling techniques need to be applied. (Special note: Remember that a 10% rise in temperature decreases the reliability by about 50%. Also the method just mentioned in this example is not valid for enclosures that have other means of heat dissipation such as fans, or for those made of heavier metal or if the material were changed. This specific calculation assumes that the heat is being radiated through convection to the outside air.)

ELECTRICAL DESIGN MARGINS Design margins in electrical engineering of the equipment are referred to as derating. On the other hand, mechanical design margins are referred to as safety margins. A rule of thumb for derating is about 20% for electrical components. However, the actual calculation is % derating = 1 −

IT IS

where IT = total circuit current draw and IS = total supply current. EXAMPLE During a design review, the question arose as to whether the 24 V power supply for a motor was adequately derated. The power supply takes 480 VAC three phase with a 2 A circuit breaker and has a rated output of 10 A. An examination of the system reveals that 24 V power is delivered to the load through three circuit breakers (A = .477 A, B = .73 A, and C = 5.53 A. The total for the three circuits is therefore 6.737 A.) When these circuit breakers are combined, 11 A of current flow to the load. This situation may not happen, but further investigation is required.

% derating = 1 −

IT 6.737 = 1− = 32.63% IS 10.0

This means that in this case the power supply will not be overloaded and the circuit breakers are generously oversized. In other words, the circuit breakers should not be tripped due to false triggers.

SAFETY MARGINS (SM) For mechanical components, SM are generally defined as the amount of strength of a mechanical component relating to the applied stress. A rule of thumb for SM with a normally distributed stress load relationship is that the safety margin should always be greater or equal to three. However, the actual calculation for the MS is

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SM =

U STRENGTH − U STRESS Sv 2 + Lv 2

Where SM = safety margin; USTRENGTH = mean strength; ULOAD = mean load; Lv2 = load variance; and Sv2 = strength variance. EXAMPLE A robot’s arm has a mean strength of 80 kg. The maximum allowable stress applied by the end of arm tooling is 50 kg. The strength variance is 8 kg and the stress variance is 7 kg. What is the SM?

SM =

U STRENGTH − U STRESS Sv + Lv 2

2

=

80 − 50 82 + 72

= 2.822

(A low SM may indicate the need to assign another size robot or redesign the tooling material.)

INTERFERENCE Once the SM is calculated, it can be used to calculate the interference and reliability of the components under investigation. Interference may be thought of as the overlap between the stress and the strength distributions. In more formal terms, it is the probability that a random observation from the load distribution exceeds a random observation from the strength distribution. To calculate interference, we use the SM equation and substitute the z for the SM distribution:

Z=

U STRENGTH − U STRESS Sv 2 + Lv 2

EXAMPLE If we use the answer from the previous example (z = 2.822), we can use the z table (in this case the area under the z = 2.822 is .0024). This means that there exists a .0024 or .24% probability of failure. Reliability, on the other hand, may be calculated as

R = 1 – interference or R = 1 – α R = 1 – .0024 = .9976 or 99.76%. This means that even though the strength and the load have a very low (.24%) probability of failure, the reliability of the system is very high with a 99.76%.

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TABLE 8.4 Guidelines for the Duane Model β

Recommended Actions

0 to .2

No priority is given to reliability improvement; failure data not analyzed; corrective action taken for important failure modes, but with low priority Routine attention to reliability improvement; corrective action taken for important failure modes Priority attention to reliability improvement; normal (typical stresses) environment utilization; well-managed analysis and corrective action for important failure modes Eliminating failures takes top priority; immediate analysis and corrective action for all failures

.2 to .3 .3 to .4 .4 to .6

CONVERSION

OF

MTBF

TO

FAILURE RATE

AND

VICE VERSA

The relationship between these two metrics is

MTBF =

1 1 and FR = FR MTBF

RELIABILITY GROWTH PLOTS This plot is an effective method to track continual improvement for R&M as well as to predict reliability growth of machinery from one machine to the other. The steps to generate this plot are: Step 1. Collect data on the machine and calculate the cumulative MTBF value for the machine. Step 2. Plot the data on log–log paper. (An increasing slope indicates a reliability growth flatness, which indicates that the machine has achieved its inherent level of MTBF and cannot get any better) Step 3. Calculate the slope, using regression analysis or best fit line. Once the slope (the beta value) is calculated, we can apply the Duane model interpretation. The guidelines (Table 8.4) for the interpretation are

MACHINERY FMEA Machinery FMEA is a systematic approach that applies the tabular method to aid the thought process used by simultaneous engineering teams to identify the machine’s potential failure modes, potential effects, and potential causes and to develop corrective action plans that will remove or reduce the impact of the failure modes. Perhaps the most important use of the machinery FMEA is to identify and correct all safety issues. A more detailed discussion will be given in Chapter 6.

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KEY DEFINITIONS IN R&M The following terms are commonly encountered in R&M: Accelerated life testing — Verification of machine and equipment design relationship much sooner than if operated typically. Intended especially for new technology, design changes, and ongoing development. Derating — The practice of limiting stresses that may be applied to a component to levels below the specified maxima in order to enhance reliability. Derating values of electrical stress are expressed as ratios of applied stress to rated maximum stress. The applied stress is taken as the maximum likely to be applied during worst-case operating conditions. Thermal derating is expressed as a temperature value. Design of experiments (DOE) — A technique that focuses on identifying factors that affect the level or magnitude of a product/process response, examining the response surface, and forming the mathematical prediction model. Design review — A review providing in-depth detail relative to the evolving design supported by drawings, process flow descriptions, engineering analyses, reliability design features, and maintainability design considerations. Dry run — The rehearsal or cycling of machinery, normally with the intent of not processing the work piece, to verify function, clearances, and construction stability. Durability — Ability to perform intended function over a specified period under normal use with specified maintenance, without significant deterioration. Equipment — The portion of process machinery that is not specific to a component or sub assembly. Failure — An event when machinery/equipment is not available to produce parts under specified conditions when scheduled or is not capable of producing parts or performing scheduled operations to specifications. For every failure, an action is required. Failure mode and effects analysis (FMEA) — A technique to identify each potential failure mode and its effect on machinery performance. Failure reporting, analysis, and corrective action system (fracas) — An orderly system of recording and transmitting failure data from the supplier’s plant to the end users fits into a unitary database. The database allows identification of pattern failures and rapid resolution of problems through rigorous failure analysis. Fault tree analysis (FTA) — A top down approach to failure analysis starting with an undesirable event and determining all the ways it can happen. Feasibility — A determination that a process, design, procedure, or plan can be successfully accomplished in the required time frame. Finite element analysis (FEA) — A computational structure analysis technique that quantifies a structure’s response to applied loading conditions. Total productive maintenance (TPM) — Natural cross-functional groups working together in an optimal balance to improve the overall effectiveness

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of their equipment and processes within their work areas. TPM implementation vigorously benchmarks, measures, and corrects all losses resulting from inefficiencies. Life cycle — The sequence through which machinery and equipment pass from conception through decommission. Life cycle costs (LCC) — The sum of all cost factors incurred during the expected life of machinery. Machine condition signature analysis (MCSA) — An application that applies mechanical signature (vibration) analysis techniques to characterize machinery and equipment on a systems level to significantly improve reliability and maintainability. Machinery — Tooling and equipment combined. A generic term for all hardware (including necessary operational software) that performs a manufacturing process. Maintainability — A characteristic of design, installation, and operation, usually expressed as the probability that a machine can be retained in, or restored to, specified operable condition within a specified interval of time when maintenance is performed in accordance with prescribed procedures. Mean time between failures (MTBF) — The average time between failure occurrences. The sum of the operating time of a machine divided by the total number of failures. Predominantly used for repairable equipment. Mean time to failure (MTTF) — The average time to failure for a specific equipment design. Used predominantly for non-repairable equipment. Mean time to repair (MTTR) — The average time to restore machinery or equipment to specified conditions. Overall equipment effectiveness (OEE) — Percentage of the time the machinery is available (Availability) × how fast the machinery is running relative to its design cycle (Performance efficiency) × percentage of the resulting product within quality specifications (Yield). Perishable tooling — Tooling which is consumed over time during a manufacturing operation. Plant floor information system (PFIS) — An information gathering system used on the plant floor to gather data relating to plant operations including maintenance activities. Predictive maintenance (PdM) — A portion of scheduled maintenance dedicated to inspection for the purpose of detecting incipient failures. Preventative maintenance (PM) — A portion of scheduled maintenance dedicated to taking planned actions for the purpose of reducing the frequency or severity of future failures, including lubrication, filter changes, and part replacement dictated by analytical techniques and predictive maintenance procedures. Probability ratio sequential testing (PRST) — A reliability qualification test to demonstrate if the machinery/equipment satisfies a specified MTBF requirement and is not lower than an acceptable MTBF (MIL-STD-781). Process — Any operation or sequence of operations that contributes to the transformation of raw material into a finished part or assembly.

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Product — In relation to tooling and equipment suppliers, the term “product” refers to the end item produced (e.g., machine, tool, die, etc.). Production — In relation to tooling and equipment suppliers, the term “production” refers to the process required to produce the product. R&M plan — A reliability and maintainability (R&M) plan shall establish a clear implementation strategy for design assurance techniques, reliability testing and assessment, and R&M continuous improvement activities during the machinery/equipment life cycle. R&M targets — The range of values that MTBF and MTTR are expected to fall between plus an improvement factor that leads to MTBF and MTTR requirements. Reliability — The probability that machinery and equipment can perform continuously, without failure, for a specified interval of time when operating under stated conditions. Reliability growth — Machine reliability improvement as a result of identifying and eliminating machinery or equipment failure causes during machine testing and operations. Root cause analysis (RCA) — A logical, systematic approach to identifying the basic reasons (causes, mechanisms, etc.) for a problem, failure, nonconformance, process error, etc. The result of root cause analysis should always be the identification of the basic mechanism by which the problem occurs and a recommendation for corrective action. Simultaneous engineering (SE) — Product engineering that optimizes the final product by the proper integration of requirements, including product function, manufacturing and assembly processing, service engineering, and disposal. Things gone right/things gone wrong (TGR/TGW) — An evolving program-level compilation of lessons learned that capture successful and unsuccessful manufacturing engineering activity and equipment/performance for feedback to an organization and its suppliers for continuous improvement. Tooling — The portion of the process machinery that is specific to a component of sub assembly.

DFSS AND R&M R&M’s goal is to make sure that the machinery/tool delivered to the customer meets or exceeds its requirements. DFSS, on the other hand, is the methodology that controls the process for satisfying the customer’s expectations early on in the product development cycle. This is very important since in R&M the reliability matrix actually attempts to quantify the initial product vision with the customer’s requirements. Having said that, we must also recognize that quite often in product development we do not have all the answers. In fact, quite often we are on a fuzzy front end. This is where DFSS offers its greatest contribution. That is, with the process knowledge of DFSS, the engineer not only will be aware but also will make sure that the appropriate design fits within both the customer’s and the organization’s goals.

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DFSS may be applied in an original design, which involves elaborating original solutions for a given task; adaptive design, which involves adapting a known system to a changed task or evolving a significant subsystem of a current product; variant design, which involves varying parameters of certain aspects of a product to develop a new or more robust design; and redesign, which implies any of the items just mentioned. A redesign is not a variant design, rather it implies that a product already exists that is perceived to fall short in some criteria, and a new solution is needed. The new solution can be developed through any of the above approaches. In fact, it is often difficult to argue against the maxim that all design is redesign (Otto and Wood, 2001).

REFERENCES Otto, K. and Wood, K., Product Design, Prentice Hall, Upper Saddle River, NJ, 2001.

SELECTED BIBLIOGRAPHY Anon., Reliability and Maintainability Guideline for Manufacturing Machinery and Equipment, M-110.2, 2nd ed., Society of Automotive Engineers, Inc., Warrendale, PA and National Center for Manufacturing Sciences, Inc., Ann Arbor, MI, 1999. Anon., ISO/TS16949. International Automotive Task Force. 2nd ed. AIAG. Southfield, MI, 2002. Automotive Industry Action Group, Potential Failure Mode and Effect Analysis, 3rd ed., Chrysler Corp., Ford Motor Co., and General Motors. Distributed by AIAG, Southfield, MI, 2001. Blenchard, B.S., Logistics Engineering and Management, 3rd ed., Prentice Hall, Englewood Cliffs, NJ, 1986. Chrysler, Ford, and GM, Quality System Requirements: QS-9000, distributed by Automotive Industry Action Group, Southfield, MI, 1995. Chrysler, Ford, and GM, Quality System Requirements: Tooling and Equipment Supplement, distributed by Automotive Industry Action Group, Southfield, MI, 1996. Creveling, C.M., Tolerance Design: A Handbook for Developing Optimal Specifications, Addison Wesley Longman, Reading, MA, 1997. Hollins, B. and Pugh, S., Successful Product Design, Butterworth Scientific. London, 1990. Kapur, K.C. and Lamberson, L.R., Reliability in Engineering Design, Wiley, New York, 1977. Nelson, W., Graphical analysis of system repair data, Journal of Quality Technology, 20, 24–35, 1988. Stamatis, D.H., Implementing the TE Supplement to QS-9000, Quality Resources, New York, 1998.

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9

Design of Experiments SETTING THE STAGE FOR DOE

Design of Experiments (DOE) is a way to efficiently plan and structure an investigatory testing program. Although DOE is often perceived to be a problem-solving tool, its greatest benefit can come as a problem avoidance tool. In fact, it is this avoidance that we emphasize in design for six sigma (DFSS). This chapter is organized into nine sections. The user who is looking for a basic DOE introduction in order to participate with some understanding in a problemsolving group is urged to study and understand the first two sections or go back and review Volume V of this series. The remaining sections discuss more complex topics including problem avoidance in product and process design, more advanced experimental layouts, and understanding the analysis in more detail.

WHY DOE (DESIGN

OF

EXPERIMENTS) IS

A

VALUABLE TOOL

DOE is a valuable tool because: 1. DOE helps the responsible group plan, conduct, and analyze test programs more efficiently. 2. DOE is an effective way to reduce cost. Usually the term DOE brings to mind only the analysis of experimental data. The application of DOE necessitates a much broader approach that encompasses the total process involved in testing. The skills required to conduct an effective test program fall into three main categories: 1. Planning/organizational 2. Technical 3. Analytical/statistical The planning of the experiment is a critical phase. If the groundwork laid in the planning phase is faulty, even the best analytic techniques will not salvage the disaster. The tendency to run off and conduct tests as soon as a problem is found, without planning the outcome, should be resisted. The benefits from up-front planning almost always outweigh the small investment of time and effort. Too often, time and resources are wasted running down blind alleys that could have been avoided. Section 2 of this chapter contains a more detailed discussion of planning and the techniques used to ensure a well-planned experiment. 367

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TABLE 9.1 One Factor at a Time The group tests configurations containing the following combinations of the factors: Level of Factor (1 and 2 Indicate the Different Levels)

Results

Test Number

A

B

C

D

E

F

G

a

b

1 2 3 4 5 6 7 8

1 2 1 1 1 1 1 1

1 1 2 2 2 2 2 2

1 1 1 2 1 1 1 1

1 1 1 1 2 2 2 2

1 1 1 1 1 2 1 1

1 1 1 1 1 1 2 1

1 1 1 1 1 1 1 2

271.4 215.0 275.3 235.2 296.6 305.2 278.8 251.9

266.3 211.2 271.1 231.5 301.6 301.1 275.3 254.3

DOE can be a powerful tool in situations where the effect on a measured output of several factors, each at two or more levels, must be determined. In the traditional “one factor at a time” approach, each test result is used in a small number of comparisons. In DOE, each test is used in every comparison. A simplified example follows. EXAMPLE A problem-solving brainstorming group suspects 7 factors (named A, B, C, D, E, F, and G), each at two levels (level 1 and level 2), of influencing a critical, measurable function of the design. The group wants to determine the best settings of these factors to maximize the measured test results — see Table 9.1. Two evaluations (a and b) are run at each test configuration rather than a single evaluation in order to attain a higher confidence in the difference between factor levels (this assumes no need for a “tie breaker”). The group makes comparisons as shown in Table 9.2. Sixteen total tests are run, and four tests are used to determine the difference between levels for each factor. The best combination of factors is (1, 2, 1, 2, 2, 1, 1) for factors A through G. However, using DOE the group runs test configurations as shown in Table 9.3. The group makes comparisons as shown in Table 9.4. Eight total tests are run, and eight tests are used to determine the difference between levels for each factor. This can be done because each level of every factor equally impacts the determination of the average response at all levels of all of the other factors (i.e., of the four tests run at A = 1, two were run at B = 1 and two were run at B = 2; this is also true of the four tests run at A = 2). This relationship is called orthogonality. This concept is very important, and the reader should work through the relationships between the levels of at least two other factors to better understand the use of orthogonality in this testing matrix. The best level is [1, (1 or 2), 1, 2, (1 or 2), 1, 1] for A through G. Factors B and E are not significant and may be set to the least expensive level.

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TABLE 9.2 Test Numbers for Comparison Test Numbers Used to Determine: Factor A B C D E F G

Level 1 1a, 1a, 3a, 3a, 5a, 6a, 6a,

Difference Level 1 – Level 2

Level 2

1b 1b 3b 3b 5b 6b 6b

2a, 3a, 4a, 5a, 6a, 7a, 8a,

2b 3b 4b 5b 6b 7b 8b

55.8 –4.4 39.9 –25.7 –4.3 26.1 50.1

TABLE 9.3 The Group Runs Using DOE Configurations Level of Factor (1 and 2 Indicate the Different Levels)

Test Number

A

B

C

D

E

F

G

Result

1 2 3 4 5 6 7 8

1 1 1 1 2 2 2 2

1 1 2 2 1 1 2 2

1 1 2 2 2 2 1 1

1 2 1 2 1 2 1 2

1 2 1 2 2 1 2 1

1 2 2 1 1 2 2 1

1 2 2 1 2 1 1 2

270.7 223.8 158.2 263.1 129.3 175.1 195.4 194.6

TABLE 9.4 Comparisons Using DOE Test Numbers Used to Determine Factor A B C D E F G

Level 1

Level 2

1, 1, 1, 1, 1, 1, 1,

5, 3, 3, 2, 2, 2, 2,

2, 2, 2, 3, 3, 4, 4,

3, 5, 7, 5, 6, 5, 6,

4 6 8 7 8 8 7

6, 4, 4, 4, 4, 3, 3,

7, 7, 5, 6, 5, 5, 5,

8 8 6 8 7 8 8

Difference Level 1 – Level 2 55.4 –3.1 39.7 –25.8 –3.3 26.3 49.6

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TABLE 9.5 Comparison of the Two Means Number of Tests

Estimate at the Best Levels

Confidence Interval at 90% Confidence

16 8

301.1 299.6

± 3.7 ± 3.3

One factor at a time DOE

For a comparison of the two methods, see Table 9.5. Half as many tests are required using a DOE approach and the estimate at each level is better (four tests per factor level versus two). This is almost like getting something for nothing. The only thing that is required is that the group plan out what is to be learned before running any of the tests. The savings in time and testing resources can be significant. Direct benefits include reduced product development time, improved problem correction response, and more satisfied customers. And that is exactly what DFSS should be aiming at.

This approach to DOE is also very flexible and can accommodate known or suspected interactions and factors with more than two levels. A properly structured experiment will give the maximum amount of information possible. An experiment that is less well designed will be an inefficient use of scarce resources.

TAGUCHI’S APPROACH Here it is appropriate to summarize Dr. Taguchi’s approach, which is to minimize the total cost to society. He uses the “Loss Function” (Section 4) to evaluate the total cost impact of alternative quality improvement actions. In Dr. Taguchi’s view, we all have an important societal responsibility to minimize the sum of the internal cost of producing a product and the external cost the customer incurs in using the product. The customer’s cost includes the cost of dissatisfaction. This responsibility should be in harmony with every company’s objectives when the long-term view of survival and customer satisfaction is considered. Profits may be maximized in the short run by deceiving today’s customers or trading away the future. Traditionally, the next quarter’s or next year’s “bottom line” has been the driving force in most corporations. Times have changed, however. Worldwide competition has grown, and customers have become more concerned with the total product cost. In this environment, survival becomes a real issue, and customer satisfaction must be a part of the cost equation that drives the decision process. Dr. Taguchi uses the signal-to-noise (S/N) ratio as the operational way of incorporating the loss function into experimental design. Experiment S/N is analogous to the S/N measurement developed in the audio/electronics industry. S/N is used to ensure that designs and processes give desired responses over different conditions of uncontrollable “noise” factors. S/N is introduced in Section 4 and developed in examples in later sections.

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There are three basic types of product design activity in Dr. Taguchi’s approach: 1. System design 2. Parameter design 3. Tolerance design System design involves basic research to understand nature. System design involves scientific principles, their extension to unknown situations, and the development of highly structured basic relationships. Parameter and tolerance design involves optimizing the system design using empirical methods. Taguchi’s methods are most useful in parameter and tolerance designs. The rest of this chapter will discuss these applications. Parameter design optimizes the product or process design to reach the target value with minimum possible variability with the cheapest available components. Note the emphasis on striving to satisfy the requirements in the least costly manner. Parameter design is discussed in Section 8. Tolerance design only occurs if the variability achieved with the least costly components is too large to meet product goals. In tolerance design, the sensitivity of the design to changes in component tolerances is investigated. The goal is to determine which components should be more tightly controlled and which are not as crucial. Again, the driving force is cost. Tolerance design is discussed in Section 9. Problem resolution might appear to be another type of product design. If targets are set correctly, however, and parameter and tolerance design occur, there will be little need for problem resolution. When problems do arise, they are attacked using elements of both parameter and tolerance design, as the situation warrants.

MISCELLANEOUS THOUGHTS A tremendous opportunity exists when the basic relationships between components are defined in equation form in the system design phase. This occurs in electrical circuit design, finite element analysis, and other situations. In these cases, once the equations are known, testing can be simulated on a computer and the “best” component values and appropriate tolerances obtained. It might be argued that the true best values would not be located using this technique; only the local maxima would be obtained. The equations involved are generally too complex to solve to the true best values using calculus. Determining the local best values in the region that the experienced design engineer considers most promising is generally the best available approach. It definitely has merit over choosing several values and solving for the remaining ones. The cost involved is computation time, and the benefit is a robust design using the widest possible tolerances. Those readers who have some experience in classical statistics may wonder about the differences between the classical and Taguchi approaches. Although there are some operational differences, the biggest difference is in philosophical emphasis — see Volume V of this series. Classical statistics emphasizes the producer’s risk. This means a factor’s effect must be shown to be significantly different

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from zero at a high confidence level to warrant a choice between levels. Taguchi uses percent contribution as a way to evaluate test results from a consumer’s risk standpoint. The reasoning is that if a factor has a high percent contribution, more often than not it is worth pursuing. In this respect, the Taguchi approach is less conservative than the classical approach. Dr. Taguchi uses orthogonal arrays extensively in his approach and has formulated them into a “cookbook” approach that is relatively easy to learn and apply. Classical statistics has several different ways of designing experiments including orthogonal arrays. In some cases, another approach may be more efficient than the orthogonal array. However, the application of these methods may be complex and is usually left to statisticians. Dr. Taguchi also approaches uncontrollable “noise” differently. He emphasizes developing a design that is robust over the levels of noise factors. This means that the design will perform at or near target regardless of what is happening with the uncontrollable factors. Classical statistics seeks to remove the noise factors from consideration by “blocking” the noise factors. In certain cases, the approaches Taguchi recommends may be more complicated than other statistical approaches or may be questioned by classical statisticians. In these cases, alternative approaches are presented as supplemental information at the end of the appropriate section. Additional analysis techniques are also presented in section supplements. The reader is encouraged to thoroughly analyze the data using all appropriate tools. Incomplete analysis can result in incorrect conclusions.

PLANNING THE EXPERIMENT The purpose of this section is to: 1. Impress upon the reader the importance of planning the experiment as a prerequisite to achieving successful results 2. Present some tools to use and points to consider during the planning phase 3. Demonstrate DOE applications via simple examples

BRAINSTORMING The first steps in planning a DOE are to define the situation to be addressed, identify the participants, and determine the scope and the goal of the investigation. This information should be written down in terms that are as specific as possible so that everyone involved can agree on and share a common understanding and purpose. The experts involved should pool their understanding of the subject. In a brainstorming session, each participant is encouraged to offer an opinion of which factors cause the effect. All ideas are recorded without question or discussion at this stage. To aid in the organization of the proposed factors, a branching (fishbone) format is often used, where each main branch is a main aspect of the effect under investigation (e.g., material, methods, machine, people, measurement, environment). The construction of a cause-and-effect (fishbone or Ishikawa) diagram in a brainstorming session provides a structured, efficient way to ensure that pertinent ideas are collected

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373

Engine Control Hardware Calibration Spark Scatter Poor Ground Injectors F/A Stuck Too Great Ratio Broken Spark Advance Contaminated Range Internal Harness & Too Small to Veh Connectors Intermittents EMI Fuel Improper Air Ports Flow Connector CBs Fit Piston Ring Scuff/Power Bolt Torque Loss Bore Fit Distortion Compression Grinding Height Piston Piston Rings Design Timing Suppliers Bore Buffs Camshaft Finish Finish Compression Ratio Assembly Engine Manufacturing Hardware

FIGURE 9.1 An example of a partially completed fishbone diagram.

and considered and that the discussion stays on track. An example of a partially completed cause-and-effect diagram is shown in Figure 9.1. After the participants have expressed their ideas on possible causes, the factors are discussed and prioritized for investigation. Usually, a three-level (high, moderate, and low) rating system is used to indicate the group consensus on the level of suspected contribution. Quite often, the rating will be determined by a simple vote of the participants. In situations where several different areas of contributing expertise are represented, participants’ votes outside of their areas of expertise may not have the importance of the expert’s vote. Handling this situation becomes a management challenge for the group leader and is beyond the scope of this document — the reader may need to review Volume II of this series. During the brainstorming and prioritization process, the participants should consider the following: 1. The situation — What is the present state of affairs and why are we dissatisfied? 2. The goal — When will we be satisfied (at least in the short term)? 3. The constraints — How much time and resources can we use in the investigation? 4. The approach — Is DOE appropriate right now or should we do other research first? 5. The measurement technique and response — What measurement technique will be used and what response will be measured?

CHOICE

OF

RESPONSE

The choice of measurement technique and response is an important point that is sometimes not given much thought. The obvious response is not always the best.

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Factor 2 = Low

Response Factor 2 = High

Low Level

High Level Factor 1

FIGURE 9.2 An example of interaction.

As an example, consider the gap between two vehicle body panels. At first thought, that gap could be used as the response in a DOE aimed at achieving a target gap. However, the gap can be a symptom of more basic problems with the: • Width of the panels • Location holes in the panels • Location of the attachment points on the body frame All of these must be right for the gap to be as intended. If the goal of the experiment is to identify which of these has the biggest impact on the gap, the choice of the gap as a response is appropriate. If the purpose is to minimize the deviation from the target gap, the gap may not be the right response. A more basic investigation of the factors that contribute to the underlying cause is required. Do not confuse the symptom with the underlying causes. This thought process is very similar to the thought process used in SPC and failure mode and effect analysis (FMEA) and draws heavily upon the experience of experts to frame the right question. In DOE, the choice of an improper response could result in an inconclusive experiment or in a solution that might not work as things change due to interactions between the factors. An interaction occurs when the change in the response due to a change in the level of a factor is different for the different levels of a second factor. An example is shown in Figure 9.2. The choice of the proper response characteristic will usually result in few interactions being significant. Since there is a limitation as to how much information can be extracted from a given number of experiments, choosing the right response will allow the investigation of the maximum number of factors in the minimum number of tests without interactions between factors blurring the factor main effect. Interactions will be discussed in more detail in Section 3. The proper setup of an

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experiment is not only a statistical task. Statistics serve to focus the technical expertise of the participating experts into the most efficient approach. In summary, the response should: 1. Relate to the underlying causes and not be a symptom 2. Be measurable (if possible, a continuous response should be chosen) 3. Be repeatable for the test procedure The prioritization process continues until the most critical factors that can be addressed within the resources of the test program are identified. The next step is to determine: 1. Are the factors controllable or are some of them “noise” beyond our control? 2. Do the factors interact? 3. What levels of each factor should be considered? 4. How do these levels relate to production limits or specs? 5. Who will supply the parts, machines, and testing facilities, and when will they be available? 6. Does everyone agree on the statement of the problem, goal, approach, and allocation of roles? 7. What kind of test procedure will be used? When all of these questions have been answered, the person who is acting as the statistical resource for the group can translate the answers into a hypothesis and experimental setup to test the hypothesis. The following example illustrates how the process can work: EXAMPLE A particular bracket has started to fail in the field with a higher than expected frequency. Timothy, the design engineer, and Christine, the process engineer, are alerted to the problem and agree to form a problem-solving team to investigate the situation. Timothy reviews the design FMEA, while Christine reviews the process FMEA. The information relating to the previously anticipated potential causes of this failure and SPC charts for the appropriate critical characteristics are brought to the first meeting. The team consists of Timothy, Christine, Cary (the machine operator), Stephen (the metallurgist), and Eric (another manufacturing engineer who has taken a DOE course and has agreed to help the group set up the DOE). In the first meeting, the group discussed the applicable areas from the FMEAs, reviewed the SPC charts, and began a cause-and-effect listing for the observed failure mode. At the conclusion of the meeting, Timothy was assigned to determine if the loads on the bracket had changed due to changes in the components attached to it; Christine was asked to investigate if there had been any change to the incoming material; Stephen was asked to consider the testing procedure that should be used to duplicate field failure modes and the response that should be measured, and all

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Machine

Operator/Machine Interface C8

Material C1

C4

C9

C5

C6

C2

C3

C7 Bracket Breaks

C16

C12

C14

C17

C15

Process

C10 C13 C11 Design

FIGURE 9.3 Example of cause-and-effect diagram.

TABLE 9.6 The Test Matrix for the Seven Factors Test Number 1 2 3 4 5 6 7 8

Levels for Each Suspected Factor for Each of Eight Tests C1

C2

C7

C11

C13

C15

C16

1 1 1 1 2 2 2 2

1 1 2 2 1 1 2 2

1 1 2 2 2 2 1 1

1 2 1 2 1 2 1 2

1 2 1 2 2 1 2 1

1 2 2 1 1 2 2 1

1 2 2 1 2 1 1 2

of the group members were asked to consider additions to the cause-and-effect list. At the second meeting, the participants reported on their assignments and continued constructing the cause-and-effect (C & E) diagram. Their cause-and-effect diagram is shown in Figure 9.3 with the specific causes shown as “C1, C2, …” rather than the actual descriptions that would appear on a real C & E diagram. The group easily reached the consensus that seven of the potential causes were suspected of contributing to the field problem. Eric agreed to set up the experiment assuming two levels for each factor, and the others determined what those levels should be to relate the experiment to the production reality. Eric returned to the group and announced that he was able to use an L8 orthogonal array to set up the experiment and that eight tests were all that were needed at this time. The test matrix for the seven suspected factors is shown in Table 9.6. Eric explained that this matrix would allow the group to determine if a difference in test responses existed for the two levels of each factor and would prioritize the

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TABLE 9.7 Test Results

10 13 15 17 14 16 19 21

C2







1 2 LEVEL

C16

1 2 LEVEL





– – – – – – –





1 2 LEVEL

C15

– – – – – – 1 2 LEVEL

– – – – – –



– – – – – –







1 2 LEVEL

C13 18 17 16 15 14 13

C11

C7

– – – – – –



1 2 3 4 5 6 7 8

– – – – – –

– – – – – – 1 2 LEVEL

R E S P O N S E

Result

C1 18 17 16 15 14 13



R E S P O N S E

Test Number

1 2 LEVEL

FIGURE 9.4 Plots of averages (higher responses are better). within-factor differences. Since the two levels of each factor represented an actual situation that existed in production during the time the failed parts were produced, this information could be used to correct the problem. By now, Stephen had identified a test procedure and response that seemed to fit the requirements outlined in this section.

Two weeks were required to gather all the material and parts for the experiment and to run the experiment. The test results are shown in Table 9.7. While Eric entered the data into the computer for analysis, Timothy and Christine plotted the data to see if anything was readily apparent. The factor level plots are shown in Figure 9.4.

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Part 1 5 factors

Part 2 2 factors

Part 3 5 factors

Part 4 3 factors

Part 5 6 factors

FIGURE 9.5 A linear example of a process with several factors. When Eric finished with the computer, he reported that of all the variability observed in the data, 53.65% was due to the change in factor C2; 33.38% was due to the change in factor C1; and 11.92% was due to the change in factor C11. The remaining 1.04% was due to the other factors and experimental error. The large percentage variability contribution, coupled with the fact that the differences between the levels of the three factors are significant from an engineering standpoint, indicate that these three factors may indeed be the culprits. The computer analysis indicated that the best estimate for a test run at C1 = 2, C2 = 2, and C11 = 2 is 21.4. One of the eight tests in the experiment was run at this condition and the result was 21. Two confirmatory tests were run and the results were 11 and 20. The group then moved into a second phase of the investigation to identify what the specs limits should be on C1, C2, and C11. In the second round of testing, eight tests were required to investigate three levels for each of the three factors. The setup for the second round of testing involved an advanced procedure (idle column method) that will be presented later in this chapter, so the example will be concluded for now.

In summary, the group in the example took the following actions: 1. 2. 3. 4. 5. 6. 7. 8.

Gathered appropriate backup data Called together the right experts Made a list of the possible causes for the problem Prioritized the possible causes Determined the proper test procedure and response to be measured Reached agreement prior to running any tests Approached the investigation in a structured manner Asked and addressed one question at a time

Obviously, there are many ways to approach a particular DOE. In a situation where testing or material is very expensive, the most efficient experimental layout must be used. In the following sections, techniques are introduced that help the experimenter optimize the experimental design. Additional opportunities to optimize the experiment should be examined. Consider the situation where there is a five-part process. A brainstorming group has constructed a cause-and-effect diagram for a particular process problem. The number of suspected factors for each part of the process is shown in Figure 9.5. The obvious approach would be to set up the experiment with 21 factors. An alternative approach would be to consider only seven factors for the first round of testing. These would be the six factors within part 5 plus one factor for the best and worst input to part 5. If the difference in input to part 5 is significant, then the investigation is expanded upstream. The decision to approach a problem in this manner is dependent upon the beliefs of the experts. If the experts have a strong

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TABLE 9.8 An Example of Contrasts Factor C1 C2 C7 C11 C13 C15 C16

Average at Level One

Average at Level Two

13.75 13.25 15.18 14.50 15.50 15.50 15.50

17.50 18.00 15.50 16.75 15.75 15.75 15.75

Contrast (Level 2 Avg. – Level 1 Avg.) 3.75 4.75 –0.25 2.25 0.25 0.25 0.25

prior belief that a factor in part 1, for instance, is significant, then a different approach should be used. This approach is also dependent upon the structure of the situation. The above example is presented to illustrate the point that the experimenter should be alert for ways to test more efficiently and effectively.

MISCELLANEOUS THOUGHTS An additional useful method of looking at the data is to plot the contrasts on normal probability paper. For a two-level factor, the contrast is the average of all the tests run at one level subtracted from the average of the tests run at the other level. For the example in this section, the contrasts are shown in Table 9.8. These contrasts are plotted on normal probability paper versus median ranks. The values for median ranks are available in many statistics and reliability books and are used in Weibull reliability plotting. For this example, the normal contrast plot is shown in Figure 9.6. To plot the contrasts on normal paper, the contrasts are ranked in numerical order, here from –0.25 (C7) to 4.75 (C2). The contrasts are then plotted against the median ranks or, in this case, against the rank number shown on the left margin of the plot. Factors that are significant have contrasts that are relatively far from zero and do not lie on a line roughly defined by the rest of the factors. These factors can lie off the line on the right side (level 2 higher) or on the left side (level 1 higher). In the example, two separate lines seem to be defined by the contrasts. This could be due to either of these situations: • C1, C2, and C11 are significant and the others are not. • There may be one or more bad data points that occur when C1, C2, and C11 are at one level and the other factors are set at the other level. In this example, C1, C2, C11, and C16 were at level 2 and the other factors were set at level 1 for run number eight. Depending upon the situation, it would be worthwhile to either rerun that test or to investigate the circumstances that accompanied that the test (e.g., was the test hard to run because of the factor settings or

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C2

Numerical Rank Corresponding To Median Rank Probability

7

6

C1

C11 5

C16 4

3

C13 2

C15 C7 1 0

-1

1

2

3

4

5

Contrast

FIGURE 9.6 Contrasts shown in a graphical presentation.

did something else change that was not in the experiment?). In the example, this combination of factors represented the best observed outcome, and the confirmation runs supported the results of the original test. Plotting contrasts is a way of better understanding the data. It helps the experimenter visualize what is happening with the data. Sometimes, information that might be lost in a table of data will be crystal clear on a plot.

SETTING UP THE EXPERIMENT This section discusses: 1. 2. 3. 4.

The choice of the number of levels for each factor Fitting a linear graph to the experiment Special applications to reduce the number of tests How to handle noise factors in an experiment

CHOICE

OF THE

NUMBER

OF

FACTOR LEVELS

To review: A factor is a unique component or characteristic about which a decision will be made.

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A factor level is one of the choices of the factor to be evaluated (e.g., if the screw speed of a machine is the factor to be investigated, two factor levels might be 1200 and 1400 rpm). Investigating a larger number of levels for a factor requires more tests than investigating a smaller number of levels. There is usually a trade-off required concerning the amount of information needed from the experiment to be very confident of the results and the time and resources available. If testing and material are cheap and time is available, evaluate many levels for each factor. Usually, this is not the case, and two or three levels for each factor are recommended. An exception to this occurs when the factor is non-continuous, and several levels are of interest. Examples of this type of factor include the evaluation of a number of suppliers, machines, or types of material. This situation will be discussed later in this section. The first round of testing is usually designed to screen a large number of factors. To accomplish this in a small number of tests, two levels per factor are usually tested. The choice of the levels depends upon the question to be addressed. If the question is “Have we specified the right spec limits?” or “What happens to the response in the clear worst possible situation?” then the choice of what the levels should be clear. A more complicated question to address is “How will the distribution in production affect the response?” As suppliers become capable of maintaining low variability about a target value, testing at the spec limits will not give a good answer to this question. There are at least two approaches that can be used: 1. Test at the production limits, as a worst case. 2. Test at other points that put less emphasis on the tails of the distribution where few parts are produced and more emphasis on the bulk of the distribution. It is a difficult choice to pick two points to represent an entire distribution. If this approach is being used, a rule of thumb is to choose a level that encompasses approximately 70% of that distribution (mean ± 1 standard deviation). The main point of this discussion is that the choice of levels is an integral part of the experimental definition and should be carefully considered by the group setting up the experiment. The second and subsequent rounds of testing are usually designed to investigate particular factors in more detail. Generally, three levels per factor are recommended. Using two levels allows the estimation of a linear trend between the points tested. The testing of three levels gives an indication of non-linearity of the response across the levels tested. This non-linearity can be used in determining specification limits to optimize the response. Although this concept will be explored in more detail in a later section on tolerance design, its application can be illustrated as follows: First round of testing — Level B of factor 1 gives a response that is more desirable than that given by level A. See Figure 9.7. Second round of testing — Level B gives a response that is more desirable than those given by either C or D. However, the differences are not great. Spec limits are set at C and D with B as the nominal. See Figure 9.8.

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Response

Levels of Factor 1

B

A

FIGURE 9.7 First round testing.

Response

Levels of Factor 1

C

B

D

FIGURE 9.8 Second round testing.

In a manner similar to the two-level-per-factor situation, the choice of the specific three levels to be tested depends upon the question under investigation. Testing at three levels can be used by the experimenter to focus on a particular area of the possible factor settings to optimize the response over as large a range as possible. If three levels of a factor are used to gain understanding for an entire distribution, a rule of thumb is to choose the levels at the mean and mean ± 1.225 standard deviations that encompass approximately 78% of the distribution. These rules of thumb will be used in tolerance design.

LINEAR GRAPHS After the number of levels has been determined for each factor, the next step is to decide which experimental setup to use. Dr. Taguchi uses a tool called “linear graphs” to aid the experimenter in this process. Linear graphs are provided in the Appendix of Volume V for several situations. Typical designs, however, are: 1. All factors at two levels (L4, L8, L12, L16, L32) 2. All factors at three levels (L9, L27) 3. A mix of two- and three-level factors (L18, L36)

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DEGREES

OF

383

FREEDOM

In the orthogonal array designation, the number following the L indicates how many testing setups are involved. This number is also one more than the degrees of freedom available in the setup. Degrees of freedom are the number of pair-wise comparisons that can be made. In comparing the levels of a two-level factor, one comparison is made and one degree of freedom is expended. For a three-level factor, two comparisons are made as follows: first, compare A and B, then compare whichever is “best” with C to determine which of the three is “best.” Two degrees of freedom are expended in this comparison. Once the number of levels for each factor is determined, the degrees of freedom required for each factor are summed. This sum plus one becomes the bottom limit to the orthogonal array choice. The degrees of freedom for an interaction are determined by multiplying the degrees of freedom for the factors involved in the interaction. A two-level factor interacting with a two-level factor requires one degree of freedom (df) (1 × 1 = 1). A three-level factor interacting with a three-level factor requires 4 df (2 × 2 = 4). A three-level factor interacting with a two-level factor requires 2 df (2 × 1 = 2). Although the test response should be chosen to minimize the occurrence of interactions, there will be times when the experts know or strongly suspect that interactions occur. In these cases, linear graphs allow the interaction to be readily included in the experiment. If more than one test is run for each test setup, the total df is the total number of tests run minus one. The dfs used for assigning factors remain the same as without the repetition. The other dfs are used to estimate the non-repeatability of the experiment.

USING ORTHOGONAL ARRAYS

AND

LINEAR GRAPHS

In an orthogonal array, the number of rows corresponds to the number of tests to be run and, in fact, each row describes a test setup. The factors to be investigated are each assigned to a column of the array. The value that appears in that column for a particular test (row) tells to what level that factor should be set for that test. As an example, consider an L4 test setup — Table 9.9. If factor A was assigned to column 1 and factor B was assigned to column 2, then test number 3 would be set up with A at level 2 and B at level 1. The sum of the degrees of freedom required for each column (a two-level column requires 1 df; a three-level column requires 2 df) equals the sum of the available dfs in the setup. Another property of the arrays is that orthogonally is maintained among the columns. Orthogonally, mentioned earlier, is the property that allows each level of every factor to equally impact the average response at each level of all other factors. Using the L4 as an example, for the test where column 1 (factor A) is at level one, column 2 (factor B) is tested at the low level and at the high level an equal number of times. This is also column 1 at level 2. In fact, orthogonality is maintained for all three columns. The reader is invited to study the L4 and verify this statement.

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Six Sigma and Beyond

TABLE 9.9 L4 Setup Column

1

Row

1

2

3

1 2 3 4

1 1 2 2

1 2 1 2

1 2 2 1

3

2

FIGURE 9.9 Linear graph for L4.

Generally, near the orthogonal array are line-and-dot figures that look a little like “stick” drawings. These are linear graphs. The dots represent the factors that can be assigned to the orthogonal array, and the lines represent the possible interaction of the two dots joined by the line. The numbers next to the dots and lines correspond to the column numbers in the orthogonal array. For example, the linear graph for the L4 is shown in Figure 9.9. The interpretation of this linear graph is that if a factor is assigned to column 1 and a factor is assigned to column 2, column 3 can be used to evaluate their interaction. If the interaction is not suspected of influencing the response, another factor can be assigned to column 3. If no other factor remains, column 3 is left unassigned and becomes an estimator of experimental error or non-repeatability. This will be explained in more detail later in this chapter. The interrelationships between the columns are such that there are many ways of writing the linear graphs.

COLUMN INTERACTION (TRIANGULAR) TABLE Also shown in Volume V near the orthogonal array is the column interaction table for that particular array. This table shows in which column(s) the interaction would be located for every combination of two columns. The linear graphs have been constructed using this information. The L8 column interaction table is shown in Table 9.10. The interaction between two factors can be assigned by finding the intersection in the column interaction table of the orthogonal array columns to which those factors have been assigned. As an example, suppose that a factor was assigned to column 3 and another factor was assigned to column 5. If the brainstorming group suspects that the interaction of these two factors is a significant influence and includes that interaction in the analysis, that interaction must be assigned to column 6 in the orthogonal array. (Note that the interaction of two two-level factors [one degree of freedom each] can be assigned to one column which as one degree of freedom [1 × 1 = 1]).

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Design of Experiments

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TABLE 9.10 The L8 Interaction Table Column Column 1 2 3 4 5 6

FACTORS

WITH

1

2

3

4

5

6

7

3

2 1

5 6 7

4 7 6 1

7 4 5 2 3

6 5 4 3 2 1

THREE LEVELS

The orthogonal arrays, linear graphs, and column interaction tables for factors with three levels are similar to the two-level situation. Since a three-level factor requires two degrees of freedom, the three-level orthogonal array columns use two of the available dfs. The interaction of two three-level factors requires 4 dfs (2 × 2). In the linear graphs and column interaction