Design for Six Sigma : a roadmap for product development

641 Pages·2003·3.323 MB·English

by  Kai Yang, Basem El-Haik

Design for Six Sigma
ARoadmap for Product Development
Kai Yang
Basem El-Haik
McGraw-Hill
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DOI: 10.1036/0071435999
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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
v
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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 FMEAFundamentals 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