Design for Six Sigma
A Roadmap for Product Development
Kai Yang Basem El-Haik
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Contents
Preface xi
Chapter 1. Quality Concepts 1
1.1 What Is Quality? 1 1.2 Quality Assurance and Product/Service Life Cycle 3 1.3 Development of Quality Methods 8 1.4 Business Excellence, Whole Quality, and Other Metrics in Business Operations 17 1.5 Summary 20
Chapter 2. Six Sigma Fundamentals 21 2.1 What Is Six Sigma? 21 2.2 Process : The Basic Unit for the Six Sigma Improvement Project 22 2.3 Process Mapping, Value Stream Mapping, and Process Management 27 2.4 Process Capability and Six Sigma 35 2.5 Overview of Six Sigma Process Improvement 41 2.6 Six Sigma Goes Upstream: Design for Six Sigma (DFSS) 46 2.7 Summary 47
Chapter 3. Design for Six Sigma 49 3.1 Introduction 49 3.2 What is Design for Six Sigma Theory? 50 3.3 Why “Design for Six Sigma?” 52 3.4 Design for Six Sigma (DFSS) Phases 54 3.5 More on Design Process and Design Vulnerabilities 58 3.6 Differences between Six Sigma and DFSS 60 3.7 What Kinds of Problems Can Be Solved by DFSS? 62 3.8 Design for a Six Sigma (DFSS) Company 63 3.9 Features of a Sound DFSS Strategy 64 Appendix: Historical Development in Design 65
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Copyright © 2003 by The McGraw-Hill Companies, Inc. Click here for Terms of Use. vi Contents
Chapter 4. Design for Six Sigma Deployment 69 4.1 Introduction 69 4.2 Black Belt–DFSS Team: Cultural Change 69 4.3 DFSS Deployment Prerequisites 72 4.4 DFSS Deployment Strategy 74 4.5 DFSS Deployment Strategy Goals 77 4.6 Six Sigma Project Financial Management 84 4.7 DFSS Training 85 4.8 Elements Critical to Sustain DFSS Deployment 85 4.9 DFSS Sustainability Factors 86
Chapter 5. Design for Six Sigma Project Algorithm 91 5.1 Introduction 91 5.2 Form a Synergistic Design Team (DFSS Algorithm Step 1) 94 5.3 Determine Customer Expectations (DFSS Algorithm Step 2) 95 5.4 Understand Functional Requirements Evolution (DFSS Algorithm Step 3) 109 5.5 Generate Concepts (DFSS Algorithm Step 4) 110 5.6 Select the Best Concept (DFSS Algorithm Step 5) 114 5.7 Finalize the Physical Structure of the Selected Concept (DFSS Algorithm Step 6) 115 5.8 Initiate Design Scorecards and Transfer Function Development (DFSS Algorithm Step 7) 119 5.9 Assess Risk Using DFMEA/PFMEA (DFSS Algorithm Step 8) 121 5.10 Transfer Function Optimization (DFSS Algorithm Step 9) 129 5.11 Design for X (DFSS Algorithm Step 10) 136 5.12 Tolerance Design and Tolerancing (DFSS Algorithm Step 11) 138 5.13 Pilot and Prototyping Design (DFSS Algorithm Step 12) 5.14 Validate Design (DFSS Algorithm Step 13) 141 5.15 Launch Mass Production (DFSS Algorithm Step 14) 142 5.16 Project Risk Management 143
Chapter 6. DFSS Transfer Function and Scorecards 145 6.1 Introduction 145 6.2 Design Analysis 146 6.3 DFSS Design Synthesis 146 6.4 Design Scorecards and Transfer Function Development 155
Chapter 7. Quality Function Deployment (QFD) 173 7.1 Introduction 173 7.2 History of QFD 175 7.3 QFD Benefits, Assumptions and Realities 175 7.4 QFD Methodology Overview 176 7.5 Kano Model of Quality 184 7.6 The Four Phases of QFD 185 Contents vii
7.7 QFD Analysis 186 7.8 QFD Example 186 7.9 Summary 196
Chapter 8. Axiomatic Design 199 8.1 Introduction 199 8.2 Why Axiomatic Design is Needed 200 8.3 Design Axioms 201 8.4 The Independence Axiom (Axiom 1) 202 8.5 Coupling Measures 213 8.6 The Implication of Axiom 2 224 8.7 Summary 231 Appendix: Historical Development of Axiomatic Design 232
Chapter 9. Theory of Inventive Problem Solving (TRIZ) 235 9.1 Introduction 235 9.2 TRIZ Foundations 239 9.3 TRIZ Problem-Solving Process 252 9.4 Physical Contradiction Resolution/Separation Principles 255 9.5 Technical Contradiction Elimination/Inventive Principles 264 9.6 Functional Improvement Methods/TRIZ Standard Solutions 271 9.7 Complexity Reduction/Trimming 287 9.8 Evolution of Technological Systems 288 9.9 Physical, Chemical, and Geometric Effects Database 293 9.10 Comparisons of Axiomatic Design and TRIZ 293 Appendix: Contradiction Table of Inventive Principles 301
Chapter 10. Design for X 307 10.1 Introduction 307 10.2 Design for Manufacture and Assembly (DFMA) 310 10.3 Design for Reliability (DFR) 319 10.4 Design for Maintainability 321 10.5 Design for Serviceability 322 10.6 Design for Environmentality 331 10.7 Design for Life-Cycle Cost (LCC): Activity-Based Costing with Uncertainty 334 10.8 Summary 339
Chapter 11. Failure Mode–Effect Analysis 341 11.1 Introduction 341 11.2 FMEA Fundamentals 344 11.3 Design FMEA (DFMEA) 350 11.4 Process FMEA (PFMEA) 360 11.5 Quality Systems and Control Plans 364 viii Contents
Chapter 12. Fundamentals of Experimental Design 367 12.1 Introduction to Design of Experiments (DOE) 367 12.2 Factorial Experiment 372 12.3 Two-Level Full Factorial Designs 379 12.4 Fractional Two-Level Factorial Design 391 12.5 Three-Level Full Factorial Design 400 12.6 Summary 402
Chapter 13. Taguchi’s Orthogonal Array Experiment 407 13.1 Taguchi’s Orthogonal Arrays 407 13.2 Taguchi Experimental Design 410 13.3 Special Techniques 414 13.4 Taguchi Experiment Data Analysis 421 13.5 Summary 429 Appendix: Selected Orthogonal Arrays 429
Chapter 14. Design Optimization: Taguchi’s Robust Parameter Design 437 14.1 Introduction 437 14.2 Loss Function and Parameter Design 438 14.3 Loss Function and Signal-to-Noise Ratio 446 14.4 Noise Factors and Inner-Outer Arrays 454 14.5 Parameter Design for Smaller-the Better Characteristics 459 14.6 Parameter Design for Nominal-the-Best Characteristics 463 14.7 Parameter Design for Larger-the-Better Characteristics 466
Chapter 15. Design Optimization: Advanced Taguchi Robust Parameter Design 471 15.1 Introduction 471 15.2 Design Synthesis and Technical Systems 473 15.3. Parameter Design for Dynamic Characteristics 484 15.4 Functional Quality and Dynamic S/N Ratio 503 15.5 Robust Technology Development 506
Chapter 16. Tolerance Design 509 16.1 Introduction 509 16.2 Worst-Case Tolerance 514 16.3 Statistical Tolerance 518 16.4 Cost-Based Optimal Tolerance 525 16.5 Taguchi’s Loss Function and Safety Tolerance Design 530 16.6 Taguchi’s Tolerance Design Experiment 537
Chapter 17. Response Surface Methodology 541 17.1 Introduction 541 17.2 Searching and Identifying the Region that Contains the Optimal Solution 545 Contents ix
17.3 Response Surface Experimental Designs 552 17.4 Response Surface Experimental Data Analysis for Single Response 558 17.5 Response Surface Experimental Data Analysis for Multiple Responses 562
Chapter 18. Design Validation 573 18.1 Introduction 573 18.2 Design Analysis and Testing 578 18.3 Prototypes 590 18.4 Process and Production Validation 597
Acronyms 607
References 611
Index 619 ABOUT THE AUTHORS
KAI YANG, Ph.D., has extensive consulting experience in many aspects of quality and reliability engineering. He is also Associate Professor of Industrial and Manufacturing Engineering at Wayne State University, Detroit.
BASEM S. EL-HAIK, Ph.D. and Doctorate of Engineering, is the Director of Enterprise Excellence, Textron Inc., and is presently spearheading the effort to deploy Design for Six Sigma (DFSS) across the enterprise. In addition to Six Sigma deployment, Dr. El-Haik has worked extensively, consulted, and conducted semi- nars in the field of Six Sigma, lean manufacturing, axiomatic design, TRIZ, reliability, and quality engineering. Dr. El-Haik has received his Ph.D. in Industrial Engineering from Wayne State University and his Doctorate of Engineering in Manufacturing from the University of Michigan—Ann Arbor. Preface
The success of the Six Sigma movement has generated enormous inter- est in business world. By quoting one of our friends, Subir Chowdhury, “people’s power” and “process power ” are among the keys for the suc- cess of Six Sigma. The people’s power means systematic organization support led from the top, and rigorous training for Six Sigma team members. The process power means the rigor of Six Sigma deployment and project management processes, and a wide array of statistically based methods. It is our belief that unlike other quality improvement movements, where the focus is primarily on the quality of the product or service to external customers, Six Sigma is focusing on the whole quality of a business enterprise. The whole quality includes not only the product or service quality to external customers, but also the oper- ation quality of all internal business processes, such as accounting, billing, and so on. The business enterprises that have high levels of whole quality will not only provide high quality product or services, but also they will have much lower cost and high efficiency because all their business processes are optimized. Compared with the “regular” Six Sigma that is featured by “DMAIC” (define-measure-analysis-improve-control), the new wave of Six Sigma is called Design for Six Sigma (DFSS). The regular Six Sigma is also called Six Sigma improvement, that is to improve a process without design or completely redesign the current system. Design for Six Sigma puts a lot of focus on design and it tries to “do things right at the first time.” In our understanding, the ultimate goal of DFSS is to make a process or a product to: (1) Do the right things; and (2) Do things right all the time. Do the right things means achieving absolute excellence in design, be it in designing a product, a manufacturing process, a service process or a business process. Superior product design will deliver superior products that deliver right product functions to generate great cus- tomer excitement. Superior manufacturing process design will gener- ate a process that delivers the product in a most efficient, economic, and flexible manner. Superior service process design will generate a process that fits customer desires and provides service with quality and low cost. Superior business process design will generate the most efficient, effective, and economical business process. Do the right thing all the time means that not only should we have superior design, but the actual product or process that we build according
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Copyright © 2003 by The McGraw-Hill Companies, Inc. Click here for Terms of Use. xii Preface to our design, will always deliver what it is supposed to do. For exam- ple, if a company can develop some very superior products sometimes, but it also develops some poor products, then this company does not do the right thing all the time. If people buy cars from a world-class brand-name, they really expect all the cars from that brand-name to perform well and that these cars will perform consistently during their useful life; that is what we mean by ‘do things right all the time’. Do things right all the time means high consistency and extremely low variation in performance. The term Six Sigma actually means very high consistency and low variation. Nowadays, high consistency is not only necessary for product performance and reputation; it is also a matter of survival. For example, the dispute between Ford and Fire- stone tires only involves an extremely small fraction of tires, but the negative publicity and litigation brought a giant company like Ford into an unpleasant experience. Implementing DFSS, as previously stated, will involve (1) doing the right things and (2) doing things right all the time by using “people’s power” and “process power.” The people’s power involves organizational leadership and support, as well as a tremendous amount of training. The process power involves a sophisticated implementation process and a big collection of methods. Compared to regular Six Sigma (DMAIC), many new methods are introduced in DFSS. Examples are axiomatic design, design for X, and theory of inventive problem solving (TRIZ). Transfer functions and scorecards are really powerful concepts and methods to create superior designs, that is, to do the right things. DFSS also brings another class of powerful methods, Taguchi’s methods, into its tool box. The fundamental objective of the Taguchi methods is to cre- ate a superior product or process that can perform highly consistently despite many external disturbances and uncertainties. In other words, Taguchi methods create a robust product or process, thus achieving do things right all the time. The implementation of DFSS will take more effort and training than that of DMAIC, but it will be more rewarding and provide better results. This book’s main objective is to give a complete picture of DFSS to readers: 1. To provide an in-depth and clear coverage of all the important, philo- sophical, organizational, implementation, and technical aspects of DFSS to readers. 2. To discuss and illustrate very clearly the whole DFSS deployment and execution process. 3. To discuss and illustrate very clearly all major methods used in DFSS. 4. To discuss the theory and background of each method clearly with examples and illustrations. Preface xiii
5. To give the detailed step-by-step implementation process of each DFSS method. 6. To help develop practical skills in applying DFSS in real world implementation.
The background required to study this book is some familiarity with simple statistical methods, such as normal distribution, mean, vari- ance, and simple data analysis techniques. Chapter 1 begins with a discussion about “what is quality?” It lists (1) do the right things and (2) do things right all the time as the key tasks to bring superior quality for product and processes. It discusses the relationship between different quality tasks and tools and differ- ent stages of product/process development. Finally, this chapter dis- cusses the Six Sigma quality concept, the whole quality and business excellence. Chapter 2 discusses “What is Six Sigma?” and the differences between regular Six Sigma and DFSS. It also discusses the importance of process management in Six Sigma practice. Chapter 3 provides a high-level description of DFSS, its stages and major tasks, and where and how to use DFSS in a company. Chapter 4 discusses the people aspects of DFSS, such as how to organize DFSS teams, the roles of master black belt, black belt, and green belt, and how to deploy DFSS initiatives in a company along with highlights of financial aspects of DFSS projects. Chapter 5 is a very detailed description of the DFSS project imple- mentation process. We use the term DFSS algorithm to describe this process. The term algorithm is used to emphasize a repeatable and reproducible DFSS project execution. This chapter is very important because it gives a flowchart about how we can turn factors such as product/process development tasks, DFSS teams, and all DFSS methodologies into an executable process. We recommend that the reader revisit this chapter after all methodology chapters. Chapters 6 to 18 are the DFSS methodology chapters. Chapter 6 introduces all aspects of the transfer function and DFSS project score- cards. Transfer functions and scorecards are unique Six Sigma tools. A transfer function includes the clear mathematical relationships between “causes” (which are often design parameters or process variables) and “effects” (which are often product/process performance metrics). By knowing a transfer function relationship, we are able to optimize the design to achieve superior performance. Scorecards are unique Six Sigma design evaluation worksheets where historical data are recorded and project progress on metrics is tracked. Chapter 7 presents the quality function deployment method, a pow- erful method to guide and plan design activities to achieve customer xiv Preface desires. QFD was originally developed in Japan and is now widely used all over the world. Chapter 8 introduces the axiomatic design method. The axiomatic design method is a relatively new method developed at MIT. It gives some very powerful guidelines (axioms) for “what is a good system design” and “what is a weak system design.” Weak designs are often featured by complicated mutual interactions, coupling, nonindepen- dence, and excessive complexity. Good designs are often featured by clear and simple relationship between design parameters and product functions, and elegant simplicity. Axiomatic design principles can help DFSS project to reduce design vulnerabilities and therefore to achieve optimized designs. Chapter 9 presents the theory of inventive problem solving (TRIZ), which was developed in the former Soviet Union. TRIZ is a very pow- erful method that makes innovation a routine activity. It is based on an enormous amount of research worldwide on successful patents and inventions. It has a wide selection of methods and knowledge base to create inventive solutions for difficult design problems. This chapter provides a very detailed description of TRIZ and a large number of examples. TRIZ can help the DFSS team to think “outside of the box” and conceive innovative design solutions. Chapter 10 discusses “Design for X” which includes “design for man- ufacturing and assembly,” “design for reliability,” and many others. Design for X is a collection of very useful methods to make sound design for all purposes. Chapter 11 discusses failure mode and effect analysis (FMEA). FMEA is a very important design review method to eliminate potential failures in the design stage. We discuss all important aspects of FMEA, and also the difference between design FMEA and process FMEA. The objective of FMEA is to mitigate risks to improve the quality of the DFSS project. Chapter 12 gives a very detailed discussion of a powerful and popu- lar statistical method , design of experiment method (DOE). DOE can be used for transfer function detailing and optimization in a DFSS pro- ject. In this chapter, we focus our discussion on the workhorses of DOE, that is, the most frequently used DOE methods, such as full fac- torial design and fractional factorial design. In this chapter, detailed step-by-step instructions and many worked out examples are given. Chapters 13 to 15 discuss the Taguchi method. Chapter 13 discuss Taguchi’s orthogonal array experiment and data analysis. Chapter 14 gives very detailed descriptions on all important aspects of the Taguchi method, such as loss function, signal-to-noise ratio, inner-outer array, control factors, and noise factors. It also gives a detailed description on how to use Taguchi parameter design to achieve robustness in design. Preface xv
Chapter 15 discusses some recent development in Taguchi methods, such as ideal functions, dynamic signal-to-noise ratio, functional qual- ity, and robust technology development. Chapter 16 is a very comprehensive chapter on tolerance design or specification design. It gives all important working details on all major tolerance design methods, such as worst case tolerance design, statisti- cal tolerance design, cost based optimal tolerance design, and Taguchi tolerance design. Many examples are included. Chapter 17 discusses the response surface method (RSM), which can be used as a very useful method to develop transfer functions and con- duct transfer function optimization. We provide fairly complete and comprehensive coverage on RSM. Chapter 18 is a chapter discussing design validation. We introduce the process of three important validation activities: design validation, process validation, and production validation. In design validation, we discuss in detail the roles of design analysis, such as computer simu- lation and design review, validation testing in design validation, the guideline to plan design validation activities, and the roles of proto- types in validation. We also discuss many important aspects of process validation, such as process capability validation. This book’s main distinguishing feature is its completeness and com- prehensiveness. All important topics in DFSS are discussed clearly and in depth. The organizational, implementation, theoretical, practi- cal aspects of both DFSS process and DFSS methods are all covered very carefully in complete detail. Many of the books in this area usu- ally only give superficial description of DFSS without any details. This is the only book so far to discuss all important DFSS methods, such as transfer functions, axiomatic design, TRIZ, and Taguchi methods in great detail. This book can be used ideally either as a complete refer- ence book on DFSS or a complete training guide for DFSS teams. In preparing this book we received advice and encouragement from several people. For this we express our thanks to Dr. G. Taguchi, Dr. Nam P. Suh, Dr. K. Murty, Mr. Shin Taguchi, and Dr. O. Mejabi. We are appreciative of the help of many individuals. We are very thankful for the efforts of Kenneth McCombs, Michelle Brandel, David Fogarty, and Pamela A. Pelton at McGraw-Hill. We want to acknowledge and express our gratitude to Dave Roy, Master Black Belt of Textron, Inc. for his con- tribution to Chapters 7 and 11. We want to acknowledge to Mr. Hongwei Zhang for his contribution to Chapter 9. We are very thankful to Invention Machine Inc. and Mr. Josh Veshia, for their permission to use many excellent graphs of TRIZ examples in Chapter 9. We want to acknowledge Miss T. M. Kendall for her editorial support of our draft. We want to acknowledge the departmental secretary of the Industrial xvi Preface and Manufacturing Engineering Department of Wayne State Univer- sity, Margaret Easley, for her help in preparing the manuscript. Readers’ comments and suggestions would be greatly appreciated. We will give serious consideration to your suggestions for future edi- tions. Also, we are conducting public and in-house Six Sigma and DFSS workshops and provide consulting services.
Kai Yang [email protected]
Basem El-Haik [email protected] Chapter 1 Quality Concepts
Profitability is one of the most important factors for any successful business enterprise. High profitability is determined by strong sales and overall low cost in the whole enterprise operation. Healthy sales are to a great extent determined by high quality and reasonable price; as a result, improving quality and reducing cost are among the most important tasks for any business enterprise. Six Sigma is a new wave of enterprise excellence initiative which would effectively improve quality and reduce cost and thus has received much attention in the business world. However, quality is a more intriguing concept than it appears to be. To master quality improvement, it is very important to understand exactly “what is quality.”
1.1 What Is Quality? “Quality: an inherent or distinguishing characteristic, a degree or grade of excellence.” (American Heritage Dictionary, 1996)
“Quality: The totality of characteristics of an entity that bear on its ability to satisfy stated and implied needs” (ISO 8402)
“Quality: Do the right thing, and do things right all the time.”
When the word quality is used, we usually think in terms of an excel- lent product or service that fulfills or exceeds our expectations. These expectations are based on the intended use and the selling price. For example, the performance that a customer expects from a roadside motel is different from that of a five-star hotel because the prices and
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Copyright © 2003 by The McGraw-Hill Companies, Inc. Click here for Terms of Use. 2 Chapter One expected service levels are different. When a product or service sur- passes our expectations, we consider that its quality is good. Thus, quality is related to perception. Mathematically, quality can be quan- tified as follows: P Q (1.1) E where Q quality P performance E expectations
The perceived “performance” is actually “what this product can do for me” in the eyes of customers. The American Society for Quality (ASQ) defines quality as “A subjective term for which each person has his or her own definition. In technical usage, quality can have two meanings: 1. the characteristics of a product or service that bear on its ability to satisfy stated or implied needs. 2. a product or service free of deficiencies.” By examining the ASQ’s quality definition, we can find that “on its ability to satisfy stated or implied needs” means that the product or service should be able to deliver potential customers’ needs; we call it “doing the right things,” and “free of deficiencies” means that the prod- uct or service can deliver customer’s needs consistently. We can call this “Doing things right all the time.” However, when we try to further define “what is quality” in detail, we would easily find that quality is also an intangible, complicated concept. For different products or services, or different aspects there- of—for different people, such as producers, designers, management, and customers, even for different quality gurus—the perceived con- cepts of quality are quite different. According to David A. Garvin (1988), quality has nine dimensions. Table 1.1 shows these nine dimensions of quality with their meanings and explanations in terms of a slide projector. There are also many other comments about quality (ASQ Website: www.asq.org):