Applying Design for Six Sigma to Software and Hardware Systems

The Practical, Example-Rich Guide to Building Better Systems, Software, and Hardware with DFSS   Design for Six Sigma (DFSS) offers engineers powerful opportunities to develop more successful systems, software, hardware, and processes. In Applying Design for Six Sigma to Software and Hardware Systems, two leading experts offer a realistic, step-by-step process for succeeding with DFSS. Their clear, start-to-finish roadmap is designed for successfully developing complex high-technology products and systems that require both software and hardware development.   Drawing on their unsurpassed experience leading Six Sigma at Motorola, the authors cover the entire project lifecycle, from business case through scheduling, customer-driven requirements gathering through execution. They provide real-world examples for applying their techniques to software alone, hardware alone, and systems composed of both. Product developers will find proven job aids and specific guidance about what teams and team members need to do at every stage.   Using this book’s integrated, systems approach, marketers, software professionals, and hardware developers can converge all their efforts on what really matters: addressing the customer’s true needs.   Learn how to Ensure that your entire team shares a solid understanding of customer needs Define measurable critical parameters that reflect customer requirements Thoroughly assess business case risk and opportunity in the context of product roadmaps and portfolios Prioritize development decisions and scheduling in the face of resource constraints Flow critical parameters down to quantifiable, verifiable requirements for every sub-process, subsystem, and component Use predictive engineering and advanced optimization to build products that robustly handle variations in manufacturing and usage Verify system capabilities and reliability based on pilots or early production samples Master new statistical techniques for ensuring that supply chains deliver on time, with minimal inventory Choose the right DFSS tools, using the authors’ step-by-step flowchart If you’re an engineer involved in developing any new technology solution, this book will help you reflect the real Voice of the Customer, achieve better results faster, and eliminate fingerpointing.   About the Web Site The accompanying Web site, sigmaexperts.com/dfss, provides an interactive DFSS flowchart, templates, exercises, examples, and tools.

Table of Contents:
Foreword      xvii Preface      xxi Acknowledgments      xxvii About the Authors      xxix Chapter 1: Introduction: History and Overview of DFSS      1 A Brief Historical Perspective on Six Sigma and Design for Six Sigma (DFSS) 1 Historical Perspective on Design for Six Sigma 8 DFSS Example 14 Summary 27 Chapter 2: DFSS Deployment      29 Ideal Scenario for DFSS Deployment 29 Steps Involved in a Successful DFSS Deployment 30 DFSS Deployment: Single Project 45 Minimum Set of Tools, and the “One Tool Syndrome” 47 Goals for DFSS 48 “The DFSS Project was a Success, But . . .” 50 Summary 50 Chapter 3: Governance, Success Metrics, Risks, and Certification      53 DFSS Governance 53 Success Metrics 57 Product Development Risks 58 DFSS Certification 62 Summary 64 Chapter 4: Overview of DFSS Phases      65 DFSS for Projects, Including Software and Hardware 65 DFSS Process Nomenclatures 69 Requirements Phase 73 Architecture Phase 75 Architecture Phase for the Software Aspects 78 Design Phase 78 Integrate Phase 78 Optimize Phase 78 Verify Phase 80 Summary 82 Chapter 5: Portfolio Decision Making and Business Case Risk      83 Position within DFSS Flow 83 Portfolio Decision Making as an Optimization Process 84 Financial Metric 85 Portfolio Decisions and Resource Constraints 89 Goals, Constraints, Considerations, and Distractions 91 Adjusting Portfolio Decisions Based on Existing Commitments and the Organization’s Strategic Direction 92 Summary: Addressing Business Case Risk 94 Chapter 6: Project Schedule Risk      95 Position within DFSS Flow 95 Project Schedule Model 95 The “Fuzzy Front End” and Delays Caused by Changing Requirements 97 Time for First Pass: Critical Path versus Critical Chain 98 Critical Chain/Theory of Constraints Project Management Behaviors 103 Iterations, Qualification, and Release to Product 105 Summary: Addressing Schedule Risk 106 Chapter 7: Gathering Voice of the Customer to Prioritize Technical Requirements      107 Importance and Position within DFSS Flow 107 VOC Purpose and Objectives 110 The VOC Gathering (Interviewing) Team 110 Customer Selection 111 Voices and Images 112 Customer Interview Guide 113 Planning Customer Visits and Interviews 115 Customer Interviews 116 KJ Analysis: Grouping, Structuring and Filtering the VOC 117 Identifying Challenging Customer Requirements (NUDs) 120 Kano Analysis 122 Validation and Prioritization of Customer Requirements 124 Translating Customer Requirements to System Requirements: The System-Level House of Quality 124 Constructing a House of Quality 128 Summary: VOC Gathering—Tying It All Together 134 Chapter 8: Concept Generation and Selection      137 Position within DFSS Flow 137 Concept Generation Approaches 137 Brainstorming and Mind-Mapping 140 TRIZ 141 Alternative Architecture Generation: Hardware and Software 143 Generation of Robust Design Concepts 146 Consideration of Existing Solutions 147 Feasibility Screening 148 Developing Feasible Concepts to Consistent Levels 148 Concept Selection 149 Summary 152 Appendix: Kansei Engineering 152 Chapter 9: Identification of Critical Parameters and FMEA      153 Position within DFSS Flow 153 Definition of a Critical Parameter 153 Considerations from VOB and Constraints 155 Prioritization and Selection of Critical Parameters 157 FMEA 160 Software FMEA Process (Software Systems, Software Subsystems, and Software Components FMEA) 164 Software FMEA Implementation Case Study 169 Considerations of Reliability and Availability 172 Examples of Critical Parameters 174 Summary 176 Appendix: Software FMEA Process Documentation 176 Chapter 10: Requirements Flow-Down      187 Position within DFSS Flow 187 Flow-Down for Hardware and Software Systems 190 Anticipation of Potential Problems: P-Diagrams and DFMEA 193 Target and Spec Limits 197 Measurement System Analysis 198 Capability Analysis 202 Flow-Down or Decomposition 203 Flow-Down Examples 206 Initial Tolerance Allocation 208 Summary 210 Chapter 11: Software DFSS and Agile      211 Measuring the Agile Design 218 Summary 221 Chapter 12: Software Architecture Decisions      223 Software Architecture Decision-Making Process 224 Using Design Heuristics to Make Decisions 227 Using Architecture Tactics to Make Decisions 228 Using DFSS Design Trade-Off Analysis to Make Decisions 230 Using Design Patterns, Simulation, Modeling, and Prototyping for Decisions 234 Summary 235 Chapter 13: Predictive Engineering: Continuous and Discrete Transfer Functions      237 Discrete versus Continuous Critical Parameters 238 Methods for Deriving a Transfer Function for a Discrete Critical Parameter 241 Logistic Regression for Discrete Parameters 242 Methods for Deriving a Transfer Function for a Continuous or Ordinal Critical Parameter 244 Existing or Derived Equation (First Principles Modeling) 245 Modeling within a Spreadsheet, Mathematical Modeling Software, or Simulation Software 246 Empirical Modeling using Historical Data: Regression Analysis and General Linear Model 247 Empirical Modeling using Design of Experiments 251 Empirical Modeling using Response Surface Methods 256 DOE with Simulators: Design and Analysis of Computer Experiments (DACE) 259 Summary 261 Chapter 14: Predictive Engineering: Optimization and Critical Parameter Flow-Up      263 Critical Parameter Flow-Up: Monte Carlo Simulation 266 Critical Parameter Flow-Up: Generation of System Moments (Root Sum of Squares) 267 Critical Parameter Scorecard 269 Selecting Critical Parameters for Optimization 270 Optimization: Mean and/or Variance 271 Optimization: Robustness through Variance Reduction 273 Multiple Response Optimization 280 Cooptimization of Cpk’s 282 Yield Surface Modeling 283 Case Study: Integrated Alternator Regulator (IAR) IC for Automotive 288 Summary 290 Chapter 15: Predictive Engineering: Software Optimization      293 Multiple Response Optimization in Software 293 Use Case Modeling in Optimization 294 Evaluate the Model 298 Software Mistake Proofing 299 Software Stability 303 Summary 305 Chapter 16: Verification of Design Capability: Hardware      307 Position within DFSS Flow 307 Measurement System Analysis (MSA) 307 Improvements for Inadequate Measurement Systems 310 The Risk of Failures Despite Verification: Test Escapes 313 Determine the Capability 315 Summary 316 Chapter 17: Verification of Reliability and Availability      319 Customer Perspective 319 Availability and Reliability Flow Down 321 Bathtub Curve and Weibull Model 322 Software Reliability 325 Early Life Failures/Infant Mortality 326 Useful Life/Constant Failure Rate 326 Wear Out 327 Detailed Flowchart for Reliability Optimization and Verification 327 Accelerated Life Testing 328 WeiBayes: Zero Failures Obtained from ALT 330 Risk of Failures Despite Verification: Reliability Test Escapes 331 Methods to Improve Reliability and Availability 332 Summary 333 Appendix: Case Studies—Software Reliability, and System Availability (Hardware and Software Availability) 333 Chapter 18: Verification: Software Testing Combined with DFSS Techniques      347 Software Verification Test Strategy Using Six Sigma 350 Controlling Software Test Case Development through Design Patterns 354 Improving Software Verification Testing Using Combinatorial Design Methods 356 Summary 358 Bibliography 359 Glossary of Common Software Testing Terms 359 Chapter 19: Verification of Supply Chain Readiness      363 Position within DFSS Flow 363 Verification that Tolerance Expectations Will Be Met 366 Confidence in Robust Product Assembly (DFMA) 366 Verification of Appropriate and Acceptable Interface Flows 369 Confidence in the Product Launch Schedule 369 Confidence in Meeting On-Time Delivery and Lead-Time Commitments 370 Case Study: Optoelectronic Multichip Module 380 Summary 382 Chapter 20: Summary and Future Directions      385 Future Directions 386 Index       391

About the Author :
Eric Maass has thirty years of experience with Motorola, ranging from research and development through manufacturing, to director of operations for a $160 million business and director of design and systems engineering for Motorola’s RF Products Division. Dr.Maass was a cofounder of the Six Sigma methods at Motorola, and was a key advocate for the focus on variance reduction; his article on a “Strategy to Reduce Variance” was published in 1987, the year that Motorola announced Six Sigma. He codeveloped a patented method for multiple response optimization that has resulted in over 60 first-pass successful new products, and most recently has been the lead Master Black Belt for Design for Six Sigma at Motorola. He coauthored the Handbook of Fiber Optic Data Communication and a variety of chapters in books and articles ranging from concept selection to augmentation of design of experiments to multiple response optimization to advanced decision-making methods. Dr. Maass’s other accomplishments include driving the turnaround of the Logic Division from “virtual chapter 11” to second-most profitable division (of 22 divisions) in two years, and he also won the contract for Freescale Semiconductor’s largest customer, Qualcomm. Dr. Maass has a rather diverse educational background, with a B.A. in biological sciences, an M.S. in chemical and biomedical engineering, a Ph.D. in industrial engineering, and nearly thirty years’ experience in electrical engineering. Dr. Maass is currently consulting with and advising several companies and institutions including Motorola, Arizona State University, Oracle, and Eaton.   Patricia McNair is the director of Motorola’s software Design for Six Sigma program and a Certified Six Sigma Master Black Belt. She served as cochair of the Software Development Consortium and program director of the Motorola Six Sigma Software Academy. She travels internationally to various countries including France, England, China, Singapore, India, Malaysia, Brazil, and many others for consulting and training of Motorola engineers.   She spent more than twenty-five years in software and systems engineering roles including systems engineering manager, design engineer manager, architect and requirements lead, senior process manager, certified SEI instructor for the introduction to CMMI, certified Six Sigma black belt, and authorized SEI CBA IPI lead assessor for various companies such as Motorola, GE Healthcare, and IBM Federal Systems, where she worked through and managed all phases of a software development life cycle, from requirement gathering, design, development, and implementation, to production and support.   She has served as an adjunct professor at De Paul University in Chicago, the State University of New York at Binghamton, and at the University of Phoenix.   She holds an M.S. in computer science from the State University of New York at Binghamton and an MBA from the Lake Forest Graduate School of Management.