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### Six+sigma+for+electronics+design+and+manufacturing

1. 1. Six Sigma forElectronics Designand Manufacturing
3. 3. Six Sigma forElectronics Designand Manufacturing Sammy G. Shina University of Massachusetts, Lowell McGraw-Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto
6. 6. To my wife, love, friend, and companion Jackie, and our children and grandchildren
8. 8. viii Contents 2.1.5 Six sigma and the 1.5 ␴ shift 41 2.2 The Cpk Approach Versus Six Sigma 42 2.2.1 Cpk and process average shift 43 2.2.2 Negative Cpk 44 2.2.3 Choosing six sigma or Cpk 45 2.2.4 Setting the process capability index 46 2.3 Calculating Defects Using Normal Distribution 47 2.3.1 Relationship between z and Cpk 54 2.3.2 Example defect calculations and Cpk 54 2.3.3 Attribute processes and reject analysis for 57 six sigma 2.4 Are Manufacturing Processes and Supply Parts 59 Always Normally Distributed? 2.4.1 Quick visual check for normality 59 2.4.2 Checking for normality using chi-square tests 60 2.4.3 Example of ␹2 goodness of fit to normal 62 distribution test 2.4.4 Transformation data into normal distributions 63 2.4.5 The use of statistical software for 65 normality analysis 2.5 Conclusions 65 2.6 References and Bibliography 66Chapter 3. Six Sigma and the Manufacturing Control Systems 69 3.1 Manufacturing Variability Measurement and Control 70 3.2 The Control of Variable Processes and Its 72 Relationship with Six Sigma 3.2.1. Variable control chart limits 74 3.2.2 Control chart limits calculations 74 3.2.3 Control and specifications limits 75 ෆ 3.2.4 X, R variable control chart calculations 76 example 3.2.5 Alternate methods for calculating control 78 limits 3.2.6 Control chart guidelines, out-of-control 78 conditions, and corrective action procedures and examples 3.2.7 Examples of variable control chart 82 calculations and their relationship to six sigma 3.3 Attribute charts and their Relationship with 84 Six Sigma
9. 9. Contents ix 3.3.1 The binomial distribution 85 3.3.2 Examples of using the binomial distribution 86 3.3.3 The Poisson distribution 86 3.3.4 Examples of using the Poisson distribution 87 3.3.5 Attribute control charts limit calculations 88 3.3.6 Examples of attribute control charts 89 calculations and their relationship to six sigma 3.3.7 Use of control charts in factories that are 91 approaching six sigma 3.4 Using TQM Techniques to Maintain Six Sigma 91 Quality in Manufacturing 3.4.1 TQM tools definitions and examples 92 3.5 Conclusions 99 3.6 References and Bibliography 99Chapter 4. The Use of Six Sigma in Determining the 101 Manufacturing Yield and Test Strategy 4.1 Determining Units of Defects 102 4.2 Determining Manufacturing Yield on a Single 104 Operation or a Part with Multiple Similar Operations 4.2.1 Example of calculating yield in a part with 105 multiple operations 4.2.2 Determining assembly yield and PCB and 106 product test levels in electronic products 4.2.3 PCB yield example 107 4.3 Determining Design or Manufacturing Yield on 108 Multiple Parts with Multiple Manufacturing Operations or Design Specifications 4.3.1 Determining first-time yield at the electronic 110 product turn-on level 4.3.2 Example of yield calculations at the PCB 110 assembly level 4.3.3 DPMO methods for standardizing defect 112 measurements 4.3.4 DPMO charts 113 4.3.5 Critique of DMPO methods 115 4.3.6 The use of implied Cpk in product and 116 assembly line manufacturing and planning activities 4.3.7 Example and discussion of implied Cpk in 118 IC assembly line defect projections 4.4 Determining Overall Product Testing Strategy 120
10. 10. x Contents 4.4.1 PCB test strategy 121 4.4.2 PCB test strategy example 123 4.4.3 In-circuit test effectiveness 127 4.4.4 Factors affecting test operation parameters 128 4.4.5 Test coverage 128 4.4.6 Bad and good test effectiveness 129 4.4.7 Future trends in testing 130 4.5 Conclusions 130 4.6 References and Bibliography 131Chapter 5. The Use of Six Sigma With High- and 133 Low-Volume Products and Processes 5.1 Process Average and Standard Deviation 134 Calculations for Samples and Populations 5.1.1 Examples of the use of the t-distribution for 137 sample and population averages 5.1.2 Other statistical tools: Point and interval 138 estimation 5.1.3 Examples of point estimation of the average 139 5.1.4 Confidence interval estimation for the average 140 5.1.5 Standard deviation for samples and 142 populations 5.1.6 Examples of population variance 144 determination 5.2 Determining Process Capability 145 5.2.1 Process capability for large-volume 146 production 5.2.2 Determination of standard deviation ␴ for 148 process capability 5.2.3 Example of methods of calculating ␴ 149 5.2.4 Process capability for low-volume production 150 5.2.5 Moving range (MR) methodologies for low 150 volume: MR control charts 5.2.6 Process capability studies in industry 152 5.3 Determining Gauge Capability 154 5.3.1 GR&R methodology 156 5.3.2 Examples of GR&R calculations 158 5.3.3 GR&R results interpretation 159 5.3.4 GR&R examples 160 5.4 Determining Short- and Long-Term Process 164 Capability
11. 11. Contents xi 5.4.1 Process capability for prototype and early 165 production parts 5.4.2 Corrective action for process capability 168 problems 5.5 Conclusions 168 5.6 References and Bibliography 168Chapter 6. Six Sigma Quality and Manufacturing Costs of 169 Electronics Products 6.1 The Overall Electronic Product Life Cycle Cost Model 170 6.1.1 The use of the quality and cost model to 173 achieve world-class cost and quality 6.1.2 Developing the background information cost 174 estimating of electronic products 6.1.3 Determination of costs and tracking tools 176 for electronic products 6.2 The Quality and Cost Relationship 177 6.2.1 The quality loss function (QLF) 178 6.2.2 Quality loss function example 179 6.2.3 A practical quality and cost approach 181 6.3 Electronic Products Cost Estimating Systems 182 6.3.1 Relating quality data to manufacturing six 184 sigma or Cpk levels 6.3.2 Printed circuit board (PCB) fabrication 185 technologies 6.3.3 Printed circuit board (PCB) design, 187 fabrication cost, and quality issues 6.3.4 PCB fabrication cost and quality alternative 191 example 6.4 PCB Assembly Cost Estimating Systems 192 6.4.1 Material-based PCB assembly cost system 193 6.4.2 The technology cost driver system 193 6.4.3 PCB assembly cost modifiers 197 6.4.4 Quality-based product cost models 201 6.5 Conclusions 203 6.6 References and Bibliography 203Chapter 7. Six Sigma and Design of Experiments (DoE) 205 7.1 DoE Definitions and Expectations 206 7.1.1 DoE objectives and expectations 209 7.2 Design of Experiments (DoE) Techniques 210
12. 12. xii Contents 7.2.1 Steps in conducting a successful DoE 211 experiment 7.2.2 Types of DoE experiments using 215 orthogonal arrays 7.2.3 Two-level orthogonal arrays 217 7.2.4 Three-level orthogonal arrays 220 7.2.5 Interaction and linear graphs 221 7.2.6 Multilevel arrangements and combination 225 designs 7.2.7 The Taguchi contribution to DoE 227 7.3 The DoE Analysis Tool Set 227 7.3.1 Orthogonal array L9 saturated design 228 example: Bonding process optimization 7.3.2 Graphical analysis conclusions 231 7.3.3 Analysis of DoE data with interactions: 232 Electrical hipot test L8 partial factorial Resolution IV example 7.3.4 Statistical analysis of DoEs 234 7.3.5 Statistical analysis of the hipot experiment 236 7.4 Variability Reduction Using DoE 238 7.5 Using DoE Methods in Six Sigma Design and 240 Manufacturing Projects 7.6 Conclusions 241 7.7 References and Bibliography 241Chapter 8. Six Sigma and Its Use in the Analysis of Design 243 and Manufacturing for Current and New Products and Processes 8.1 Current Product Six Sigma Strategy 244 8.1.1 Process improvement in current products 246 8.2 Transitioning New Product Development to 250 Six Sigma 8.2.1 Design analysis for six sigma 251 8.2.2 Measuring the capability of current 253 manufacturing processes 8.2.3 Investigating more capable processes for 255 new products 8.2.4 Case studies of process capability 256 investigations for manufacturing: Stencil technology for DoE 8.3 Determining Six Sigma Quality in Different Design 260 Disciplines 8.3.1 Mechanical product design process 260
13. 13. Contents xiii 8.3.2 Mechanical design and tolerance analysis 261 8.3.3 Types of tolerance analysis 262 8.3.4 Statistical tolerance analysis for mechanical 263 design 8.3.5 Tolerance analysis example 263 8.3.6 Statistical analysis of the mechanical design 265 example 8.3.7 Tolerance analysis and CAD 266 8.3.8 Tolerance analysis and manufacturing 266 processes 8.3.9 Mechanical design case study 267 8.3.10 Thermal design six sigma assessment 268 example 8.3.11 Six sigma for electronic circuits with 270 multiple specifications 8.3.12 Special considerations in Cpk for design of 271 electronic products 8.3.13. The use of design quality analysis in systems 272 architecture 8.4 Applying Six Sigma Quality for New Product 272 Introduction 8.4.1 Optimizing new manufacturing processes 273 8.4.2 New process optimization example: 274 Target value manipulations and variability reduction DoE 8.4.3 Trade-offs in new product design disciplines 277 8.4.4 New product design trade-off example— 277 Screening DoE followed by in-depth DoE for defect elimination in thermal printer design 8.4.5 New product test strategy 283 8.4.6 New product test strategy example 283 8.5 Conclusions 284Chapter 9. Six Sigma and the New Product Life Cycle 287 9.1 Background: Concurrent Engineering Successes 288 and New Trends 9.1.1 Changes to the product realization process 291 9.2 Supply Chain Development 294 9.2.1 Outsourcing issues 296 9.2.2 Dependency versus competency 297 9.2.3 Outsourcing strategy 298
14. 14. xiv Contents 9.2.4 Supply chain communications and 300 information control 9.2.5 Quality and supply chain management 303 9.2.6 Supply chain selection process 305 9.3 Product Life Cycle and the Six Sigma Design 307 Quality Issues 9.3.1 Changes in electronic product design 309 9.3.2 Changing traditional design communications 310 and supplier involvement 9.3.3 Design process communications needs 313 9.4 Conclusions 314 9.5 References and Bibliography 315Chapter 10. New Product and Systems Project Management 317 Using Six Sigma Quality 10.1 The Quality System Review and Quality-Based 318 Project Management Methodologies 10.1.1 The quality-based system design process 318 10.1.2 Six sigma quality-based system design 319 process benefits 10.1.3 Historical perspective of project 320 management 10.1.4 Project management of the product 323 development process 10.2 Technical Design Information Flow and Six Sigma 327 System Design 10.2.1 Opportunities in six sigma for system or 328 product design improvements 10.2.2 The system design process 329 10.2.3 The system design steps 329 10.2.4 Composite Cpk 330 10.2.5 Selecting key characteristics for systems 332 design analysis 10.2.6 Standardized procedures in design to 334 determine the composite Cpk 10.2.7 Standardized procedures in manufacturing 335 to determine the composite Cpk 10.3 Conclusions 338Chapter 11. Implementing Six Sigma in Electronics Design 339 and Manufacturing 11.1 Six Sigma Design Project Management Models 340
15. 15. Contents xv 11.1.1 Axioms for creating six sigma within 340 the organization 11.2 Cultural Issues with the Six Sigma Based System 348 Design Process 11.3 Key Processes to Enhance the Concurrent 350 Product Creation Process 11.3.1 Six sigma phased review process 351 11.3.2 Six sigma quality advocacy and the 353 quality systems review 11.3.3 Six sigma manufacturability assessment 353 and tactical plans in production 11.4 Tools to Support Suggested Processes 355Index 357
17. 17. Illustrations and Tables Illustrations Figure 1.1 World-class benchmarks percentage improvements per year. 5 Figure 1.2 QFD product planning matrix. 13 Figure 1.3 Raychem CATV new connector QFD matrix. 14 Figure 1.4 SMT process QFD matrix. 16 Figure 1.5 Use of a DFM scoring system. 18 Figure 1.6 Structured analysis (SA) components. 22 Figure 1.7 Process mapping example. 24 Figure 1.8 Failure mode and effect analysis (FMEA) chart. 28 Figure 2.1 Conceptual view of control charts. 35 Figure 2.2 Specification and tolerance of a typical product. 36 Figure 2.3 Intersection of process capability and specification 37 limits to determine the defect level. Figure 2.4 Conceptual view of control and capability concepts. 38 Figure 2.5 Normal distribution with mean shifted by 2.5 ␴. 40 Figure 2.6 Specification and control limits. 42 Figure 2.7 Cp and Cpk sample calculations. 44 Figure 2.8 Graphical presentation of normal distribution. 48 Figure 2.9 Graphical presentation of normal distribution with parts 53 compliance percentage and multiple ␴ limits. Figure 2.10 z transformation. 55 Figure 2.11 Negative and positive z transformation. 56 Figure 2.12 Quick visual check for normality in Example 2.4.1. 61 Figure 2.13 Normal plot of for data set in Example 2.4.1. 64 Figure 2.14 Plot of observed (dark) versus expected (clear) frequencies. 64 Figure 2.15 Plot of Example 2.4 data set original (top) and transformed 65 by –log ͙x on the bottom. ෆ Figure 3.1 Types of control charts. 71 Figure 3.2 ෆ Control chart example. X 79 Figure 3.3 R control chart example. 80 Figure 3.4 Bonding process control chart example. 81 Figure 3.5 Surface cleanliness control chart example. 94 Figure 3.6 Shipment integrity cause and effect diagram. 96 Figure 3.7 Control chart flow diagram. 96 Figure 3.8 Pareto diagram—% reasons for production downtime 97 Figure 4.1 First-time yield (FTY) IC wire bonding example. 106 Figure 4.2 An example of a multistep manufacturing process line. 108 xviiCopyright 2002 The McGraw-Hill Companies, Inc. Click Here for Terms of Use.
18. 18. xviii Illustrations and TablesFigure 4.3 DPMO chart example. 115Figure 4.4 IC assembly line Cpk example. 118Figure 4.5 PCB test alternatives. 121Figure 5.1 t distribution with standard normal distribution. 135Figure 5.2 t distribution with significance ␣. 166Figure 5.3 Confidence interval around the mean ␮ and ␴ is known. 141Figure 5.4 ␹2 distribution with significance ␣. 143Figure 5.5 Obtaining confidence limits from ␹2 distribution with 144 confidence (1 – ␣)%.Figure 5.6 Sources of process variation and error. 155Figure 5.7 Accuracy and precision target example. 156Figure 5.8 Summation of averages and standard deviations. 157Figure 5.9 Distributions of prototype and early production of parts. 166Figure 6.1 Product life cycle stages. 171Figure 6.2 Typical cost distribution of an electronic product. 176Figure 6.3 Cost history of an electronic product based on the 177 concept stage.Figure 6.4 Volume sensitivity of the cost of an electronic product. 178Figure 6.5 Electronic design implementation in PCBs. 183Figure 6.6 PCB fabrication steps. 185Figure 6.7 A typical approach to printed circuit board (PCB) assembly. 194Figure 7.1 Basic elements of DoE. 206Figure 7.2 Possible effects of different factors. 209Figure 7.3 The use of an L8 as full factorial versus saturated design. 219Figure 7.4 The plot of interactions of the example in Table 7.6. 222Figure 7.5 Linear graphs for the interactions of L8 shown in Table 7.7. 223Figure 7.6 Bonding process DoE graphical analysis. 231Figure 7.7 Hipot design DoE graphical analysis. 234Figure 7.8 Visualizing the error of the hipot experiment. 238Figure 8.1 Progression of quality tools for existing products. 245Figure 8.2 Cause and effect diagram for mixed technology PCBs. 248Figure 8.3 Graphical analysis of DoE for mixed technology soldering of 248 PCBs.Figure 8.4 Histogram of solder defects distribution 6 months before 250 and after DoE.Figure 8.5 Overall new product quality, including design and 251 manufacturing.Figure 8.6 Design six sigma example—bandpass filter. 252Figure 8.7 Tolerance analysis example, three square parts. 264Figure 8.8 Mechanical design of a typical vibrating angioplasty probe. 267Figure 8.9 S/N analysis for fine pitch SMT processing variability. 276Figure 8.10 Printer qaulity DoE test pattern. 278Figure 8.11 Prodcut test strategy. 284Figure 9.1 Concurrent engineering culture. 288Figure 9.2 Life cycle models for different products. 292Figure 9.3 Traditional versus concurrent engineering project 294 communications.Figure 9.4 Core competencies chart and outsource matrix. 299
19. 19. Illustrations and Tables xixFigure 9.5 Supplier management models. 302Figure 9.6 Mature sales volume for personal computer family. 308Figure 10.1 Typical electronic product development cycle. 322Figure 10.2 Development project time line: phases and milestones. 326Figure 10.3 Project communications monthly meeting example. 327Figure 10.4 Cpk tree. 331Figure 11.1 Spider web diagram of six sigma project goals. 351TablesTable 1.1 Criteria rating (CR) to select a solder system for PCB assembly 12Table 1.2 HP 34401A multimeter DFM results 19Table 2.1 Defect rates in PPM for different quality levels and 41 distribution shiftsTable 2.2 Cpk and process average shift 44Table 2.3 Standard normal distribution 49Table 2.4 Examples of calculating defect rates, Cp, and Cpk 56Table 2.5 ␹2 goodness of fit test using case study 63Table 3.1 Control chart factors 75Table 3.2 Control chart limit calculations example 77Table 3.3 Probabilities for out-of-control conditions 82Table 3.4 TQM tool usage 92Table 4.1 Yield calculation in a three-step production line 109Table 4.2 Yield calculation in a line with n parts in a three-step 110 production lineTable 4.3 DPMO grouping of defects and opportunities for PCB 112 assembliesTable 4.4 DPMO chart data 114Table 4.5 PCB test methods comparison 123Table 4.6 PCB test methods scenario 1 (two strategies) 124Table 4.7 PCB test methods scenario 2 (four sigma company) 125Table 4.8 PCB test methods scenario 3 (six sigma company), 126 three strategiesTable 4.9 Factors that affect test effectiveness 128Table 5.1 Selected values of t␣,␯ of student’s t distribution 137Table 5.2 Error of the t␣,v of student’s t distribution 139Table 5.3 Selected values of ␹2 distribution 143Table 5.4 Amount of data required for process capability studies 146Table 5.5 Example of process capability studies for PCB assembly line 154Table 5.6 ෆ estimator of ␴ for GR&R R 158Table 5.7 GR&R example 162Table 6.1 Product development life cycle stages attributes 172Table 6.2 Complexity-based process DPUs from a typical PCB 189 fabrication shopTable 6.3 Design-related causes of PCB defects 191Table 6.4 Classifications for different types of PCB assemblies 193Table 6.5 Material-based cost model, NRE and test costs 195
20. 20. xx Illustrations and TablesTable 6.6 Cost rate calculations for machine-loaded TH components 197Table 6.7 Technology cost model with modifiers for PCB assembly 198Table 6.8 Cost model drivers example for sheet metal fabrication 200Table 6.9 PCB quality-based technology cost models 201Table 7.1 “One factor at a time” experiments 216Table 7.2 XOR logic table for interaction level determinations 216Table 7.3 L8 orthogonal array 217Table 7.4 L16 orthogonal array 220Table 7.5 L9 orthogonal array 221Table 7.6 Interaction example using L4 orthogonal array 221Table 7.7 Interaction scenarios for L8 with Resolution IV design 223Table 7.8 Interaction scenarios for L16 with confounding 224Table 7.9 Plackett and Burman L12 orthogonal array 225Table 7.10 L18 orthogonal array 225Table 7.11 Multilevel designs with L8 orthogonal arrays 226Table 7.12 Bonding process DoE 230Table 7.13 Hipot DoE experiment 233Table 7.14 F table value for 95% confidence or 0.05 confidence 236Table 7.15 Hipot design ANOVA statistical analysis 237Table 7.16 Hipot design ANOVA statistical analysis with pooled error 237Table 8.1 Design and analysis of DoE for mixed technology PCBs 247Table 8.2 Specification for bandpass filter example 252Table 8.3 Simulation results for Cpk analysis of a bandpass filter 253Table 8.4 Quality data for PCB assembly manufacturing processes 254Table 8.5 Quality analysis of a two-sided PCB with TH, SMT, and 255 mechanical assembly and multiple components and leadsTable 8.6 Quality drivers for printed circuit board (PCB) assembly 256Table 8.7 DoE stencil technology experiment factor and level selection 258Table 8.8 Stencil technology DoE L16 design 259Table 8.9 Stencil technology percent contribution analysis of average 259 solder deposition areaTable 8.10 Stencil technology quality loss function (QLF) formula 260Table 8.11 Tolerance analysis for three-part example, worst-case 264 analysisTable 8.12 Tolerance analysis for three-part example, six sigma analysis. 265 Case 3: statistical toleranceTable 8.13 Statistical design analysis of angioplasty probe 268Table 8.14 Thermal design six sigma assessment 268Table 8.15 Composite Cpk design analysis of an RF amplifier 270Table 8.16 Fine pitch SMT processing parameters DoE 275Table 8.17 Defect classifications for printer DoE 278Table 8.18 Printer quality screening DoE L8 design 279Table 8.19 Printer quality screening DoE defect results 280Table 8.20 Printer quality screening DoE results analysis 280Table 8.21 Printer quality second DoE design 281Table 8.22 Printer quality screening DoE defect results 282Table 8.23 Printer in-depth DoE analysis and final recommnedations 282Table 9.1 Status of companies outsourcing hardware design and 290 manufacturing capabilities
21. 21. Illustrations and Tables xxiTable 9.2 Common issues in selecting outsourced products and 296 competenciesTable 9.3 Supply chain communications 301Table 9.4 Weighted criteria for supplier selection matrix 306Table 9.5 Weighted quality criteria for supplier selection matrix 306Table 9.6 Comparison of PCB assembly costs 307Table 9.7 Attributes and metrics of success for each design phase 311Table 9.8 Changes from traditional engineering to new methodologies 314Table 9.9 Communications summary for design phases 315Table 10-1 Total product development process concept-to-development 322 criteriaTable 10.2 Cpk design quality matrix selection for systems 334 specifications and modesTable 10.3 Example of a machining center Cpk status 337Table 11.1 Factors that affect test effectiveness 354
23. 23. Abbreviations AQAP Advance product quality planning and control plan ANOVA Analysis of variance AV Appraiser variation BIST Built in self-test BOM Bill of materials CAD Computer-aided design CAE Computer-aided engineering CAM Computer-aided manufacturing CEM Contract electronic manufacturers CLT Central limit theory CIM Computer-integrated manufacturing CPI Continuous process improvement Cp Capability of the process Cpk Capability of the process, with average shift CR Criteria rating DA Decision analysis DFD Data flow diagrams DFM Design for manufacture DFT Design for testability DoE Design of experiments DOF Degrees of freedom DPMO Defect per million opportunities DPU Defects per unit ECO Engineering change orders ERP Enterprise requirements planning ESI Early supplier involvement EV Equipment variation IPC Institute for Interconnecting and Packaging of Electronic Circuits FMEA Failure mode effect analysis FT Functional test FTY First-time yield GMP Good manufacturing practices GR&R Gauge repeatability and reproducibility Hipot High potential IC Integrated circuit ICT In-circuit test JIT Just in time MR Moving range MTBF Mean time between failure NIH Not invented here xxiiiCopyright 2002 The McGraw-Hill Companies, Inc. Click Here for Terms of Use.
24. 24. xxiv AbbreviationsNS Normal (probability) scoreNTF No trouble foundOA Orthogonal arraysOEM Original equipment manufacturersPCB Printed circuit boardPPM Parts per millionPTF Polymer thick filmQA Quality assuranceQFD Quality function deploymentQLF Quality loss functionRFI Radio frequency interferenceROI Return on investmentRPN Risk priority numberRSS Root sum of the squaresSA Structure analysisSMT Surface mount technologySOW Same old waySL Specification limitsSS Sum of the squaresTH Through-hole (technology)TQC Total quality controlTQM Total quality management
25. 25. Preface Six sigma is becoming more important as companies compete in a worldwide market for high-quality, low-cost products. Successful im- plementations of six sigma in different companies, large and small, do not follow identical scripts. The tools and methodologies of six sigma are fused with the company’s culture to create a unique and success- ful blend in each instance. This book is intended to introduce and familiarize design, produc- tion, quality, and process engineers and their managers with many of the issues regarding the use of six sigma quality in design and manu- facturing of electronic products, and how to resolve them. It is based on my experience in practicing, consulting, and teaching six sigma and its techniques over the last 15 years. During that time, I confront- ed many engineers’ natural reservation about six sigma: its assump- tions are too arbitrary, it is too difficult to achieve, it works only for large companies, it is too expensive to implement, it works only for manufacturing, not for design, and so on. They continuously chal- lenged me to apply it in their own areas of interest, presenting me with many difficult design and manufacturing six sigma application problems to solve. At the same time, I was involved with many compa- nies and organizations whose engineers and managers were using original and ingenious applications of six sigma in traditional design and manufacturing. Out of these experiences came many of the exam- ples and case studies in this book. I observed and helped train many engineers in companies using tools and methodologies of six sigma. The companies vary in size, scope, product type, and strategy, yet they are similar in their ap- proach to successfully implementing six sigma through an interdisci- plinary team environment and using the tools and methods men- tioned in this book effectively by altering them to meet their particular needs. I believe the most important impact of six sigma is its use in the de- sign of new products, starting with making it one of the goals of the new product creation process. It makes the design engineers extreme- ly cognizant of the importance of designing and specifying products that can be manufactured with six sigma quality at low cost. Too many times, a company introduces six sigma by having manufactur- xxvCopyright 2002 The McGraw-Hill Companies, Inc. Click Here for Terms of Use.
26. 26. xxvi Prefaceing adopt it as its goal, a very daunting task, especially if currentproducts were not designed with six sigma in mind. The approach I use in this book is not to be rigid about six sigma. Ihave attempted to present many of the options available to measureand implement six sigma, and not to specifically recommend a courseof action in each instance. Engineers are very creative people, andthey will always try to meld new concepts into ones familiar to them.Many will put their own stamp on its methodology or add their ownway of doing things to the six sigma techniques. The one sure way tomake them resist a new concept is to force it down their throats. I be-lieve these individual engineers’ efforts should be encouraged, as longas they do not detract from the overall goal of achieving six sigma. I hope that this book will be of value to the neophyte as well as theexperienced practitioners of Six Sigma. In particular, it will benefitthe small to medium size companies that do not have the support staffand the resources necessary to try out some of the six sigma ideas andtechniques and meld them into the company culture. The experiencesdocumented here should be helpful to encourage many companies toventure out and develop new world-class products through six sigmathat can help them grow and prosper for the future.AcknowledgmentsThe principals of six sigma discussed in this book were learned, col-lected and practiced through 14 years on the faculty of the Universityof Massachusetts, Lowell, where working as a teacher, researcher,and consultant to different companies increased my personal knowl-edge and experience in the fields of design, manufacturing, quality,and six sigma. I am indebted to several organizations for supporting and encourag-ing me during the lengthy time needed to collect my materials, writethe chapters, and edit the book. I thank The University of Massachu-setts, Lowell for its continuing support for product design and manu-facturing, especially Chancellor Bill Hogan; the Dean of the James B.Francis College of Engineering, Krishna Vedula; and the chairman ofthe Department of Mechanical Engineering, John McKelliget. TheReed Exhibition Companies and SMTA, through their NEPCON andSMTI conferences in Anaheim and Chicago, encourage and nurturethe design and manufacturing of electronic products. In addition, I offer my thanks to Mr. Steve Chapman of McGraw-Hill, Inc., who was my editor for this book, as well as my previous twobooks on concurrent engineering. He always believed in me and en-couraged and guided me through three books, and for that I am verygrateful.
27. 27. Preface xxvii Finally, many thanks to my family for emotional support during thewriting, editing, and production of the book, including my wife Jackieand our children, Mike, Gail, Nancy, and Jon, as well as my grand-children, who brought me great joy between the many days of writingand editing. I also thank the many attendees of my seminars on sixsigma and quality methods, including the in-company presentations,who kept alive my interest and faith in six sigma. I wish them successin implementing six sigma tools and methods in their companies.Sammy G. ShinaJanuary, 2002
29. 29. Chapter The Nature of Six Sigma 1 and Its Connectivity to Other Quality Tools 1.1 Historical Perspective The modern attention to the use of statistical tools for the manufac- ture of products and processes originated prior to and during World War II, when the United States of America geared up to a massive buildup of machinery and arms to successfully conclude the war. The need to manage the myriad of complex weapon systems and their var- ied and distributed defense contractors led to the evolution of the sys- tem of Statistical Quality Control (SQC), a set of tools that culminat- ed in the military standards for subcontracting, such as MIL-Std 105. The term “government inspector” became synonymous with those in- dividuals who were trained to use the tables that controlled the amount of sampling inspection between the different suppliers of parts used by the main weapons manufacturers. The basis of the SQC process was the use of 3 sigma limits, which yields a rate of 2700 de- fective parts per million (PPM). Prior to that period, large U.S. companies established a quality strategy of vertical integration. In order to maintain and manage quality, companies had to control all of the resources used in the prod- uct. Thus, the Ford Motor Company in the early part of the 20th cen- tury purchased coal and iron mines for making steel for car bodies and forests in Brazil to ensure a quality supply of tires. This strategy was shelved during the rapid buildup for the war because of the use of coproducers as well as subcontractors. 1Copyright 2002 The McGraw-Hill Companies, Inc. Click Here for Terms of Use.
30. 30. 2 Six Sigma for Electronics Design and Manufacturing The war was won and U.S. companies returned to their originalstrategy while the defeated countries were rebuilding their industries.In order to revive the Japanese economy, General McArthur, who wasthe governor general of Japan at that time, imported some of the U.S.pioneers of SQC to help train their counterparts in Japan. These effortswere largely successful in transforming Japanese industry from a low-technology producer of low-quality, low-cost products such as toys tothe other side of the spectrum. By the 1970s and 1980s Japanese prod-ucts were renowned for their quality and durability. Consumers andcompanies flocked to buy Japanese electronics, cars, and computerchips, willing to pay a premium for their high quality. In recognition ofthis effort, Japan established the Deming prize for quality, which waslater emulated in the United States, with the Baldrige award. U.S. companies’ response to their loss of market share to Japanesecompanies was to investigate the Japanese companies’ secrets of suc-cess. Many U.S. companies organized trips in the 1980s to Japanesecompanies or branches of U.S. companies in Japan. Initial findingswere mostly unsuccessful. Japanese concepts such as “quality circles”or “zero defects” did not translate well into the U.S. companies’ cul-ture. Quality circles, which were mostly ad hoc committees of engi-neers, workers, and their managers, were created to investigate qual-ity problems. In many cases, they were not well organized, and aftermany months of meetings and discussions, resulted in frivolous solu-tions. It was also difficult to implement quality circles in unionizedshops. The term zero defects was also ambiguous, because it was hardto define: Does the fact that a production line produces a million partsand only one is found to be defective constitute a failure to reach thezero defects goal? The industrial and business press in the 1980s was filled with arti-cles comparing Japanese and U.S. quality. The pressures mounted toclose the quality gap. U.S. Companies slowly realized that quality im-provements depended on the realization of two major elements—theyhave to be quantifiable and measurable, and all elements that makethe company successful must be implemented: superior pricing, deliv-ery, performance, reliability, and customer satisfaction. All of thecompany’s elements, not just manufacturing, have to participate inthis effort, including management, marketing, design, and external(subcontractors) as well as internal suppliers (in-house manufactur-ing). The six sigma concept satisfies these two key requirements,which has led to its wide use in U.S. industry today. The Motorola Company pioneered the use of six sigma. Bill Smith,Motorola Vice President and Senior Quality Assurance Manager, iswidely regarded as the father of six sigma. He wrote in the Journal ofMachine Design issue of February 12, 1993:
31. 31. The Nature of Six Sigma and Its Connectivity to Other Quality Tools 3 For a company aiming to design products with the lowest possible num- ber of defects, traditional three-sigma designs are completely inade- quate. Accordingly in 1987, Motorola engineers were required to create all new designs with plus or minus six sigma tolerance limits, given that the sigma is that of a world-class part or process in the first place. This marked the start of Motorola’s Six Sigma process and its adoption of robust design as one capable of withstanding twice the normal varia- tion of a process. Early in 1987, Bob Galvin, the CEO of Motorola and head of itsOperating/Policy Committee, committed the corporation to a planthat would determine quality goals of 10 times improvement by1989, 100 times improvement by 1991, and six sigma capability by1992. At that time, no one in the company knew how to achieve thesix sigma goal, but, in their drive for quality, they committed thecompany to reach the six sigma defect rate of just 3.4 defective partsper million (PPM) in each step of their processes. By 1992, they metthese goals for the most part. At several Motorola facilities, theyeven exceeded six sigma capability in some products and processes.On average, however, their manufacturing operations by 1992 wereat about 5.4 sigma capability, or 40 defective PPM—somewhat shortof their original goal. The six sigma effort at Motorola has led to a reduction of in-processdefects in manufacturing by 150 times from 1987 to 1992. Thisamounts to total savings of \$2.2 billion since the beginning of the sixsigma program. Richard Buetow, Motorola’s Director of Quality, com-mented that six sigma reduced defects by 99.7% and had saved thecompany \$11 billion for the nine-year period from 1987 to 1996. Today, Motorola has reached its goal of six sigma. The complexity ofnew technology has resulted in a continued pressure to maintain thishigh level of quality. As product complexity continues to increase—such as semiconductor chips with billions of devices and trillions of in-structions per second—it will be essential that Motorola master theprocess of producing quality at a parts-per-billion level. That is quitea challenge. One part per billion is equivalent to one second in 31years! Therefore, Motorola expanded the six sigma program in 1992 andbeyond to achieve the following:1. Continue their efforts to achieve six sigma results, and beyond, in everything they do2. Change metrics from parts per million to parts per billion (PPB)3. Go forward with a goal of 10 times reduction in defects every 2 years
33. 33. The Nature of Six Sigma and Its Connectivity to Other Quality Tools 5million people in March 1999 to 304 million in March 2000, an in-crease of 78%. In quality, similar improvements have been made, as shown by someof the numbers quoted above. These improvements have led to an in-crease in customer expectations of quality. Companies have respondedto this increase by continuously measuring themselves and their com-petition in several areas of capabilities and performance. This concept,also known as benchmarking, is a favorite tool of managers to set goalsfor the enterprise that are commensurate with their competition. Theycan also gauge the progress of enterprises toward achieving their goalsin quality, as well as cost, responsiveness, flexibility, and inventoryturnover. Figure 1.1 is a spider diagram of U.S. versus world classbenchmarks outlining annual improvements generated by Motorola in1988, showing the range of capabilities and their annual percentageimprovements over a 4 year average period. At that time, it was esti-mated that the average business in the United States is somewhatprofitable, with market prices declining and new competitors enteringthe marketplace. These companies were spending 10–25% of sales dol-lars on reworking defects. Concurrently, 5–10% of their customerswere dissatisfied and would not recommend that others purchase theirproducts. These companies believed that typical six sigma quality isneither realistic nor achievable, and were unaware that the “best inclass” companies are 100 times better in quality. The inner closed segment in Figure 1.1 represents an average U.S.company in 1988, profiled above. The middle segment represents aworld class company, and the outer segment represents the best inclass companies. World class is the level of improvements that is Cost Reduction % Inventory Quality Turnover % Yield Increase % Best in Class 1000 800 600 400 200 World Class Flexibility Responsiveness Model # # Lead Time Decrease Figure 1.1 World-class benchmarks, percentage improvements per year.
34. 34. 6 Six Sigma for Electronics Design and Manufacturingneeded to compete globally. Best in class represents the best achiev-able annual improvements recorded anywhere, and not necessarily inthe business segment that the company competes in. It is the bench-mark of what is achievable in any measure of performance. It is apparent that an accurate method for developing and improv-ing quality systems in design and manufacturing as well as customersatisfaction is needed to achieve these high quality and capability re-sults, and to compete with products that can be designed, manufac-tured, and sold anywhere. Six sigma is an excellent tool to achieveworld class status as well as best in class results in quality, especiallygiven the increased complexity of designs and products. At the same time, the requirements for developing new products inhigh-technology industries have followed these increases in complexi-ty and improvements in quality, necessitating faster product develop-ment processes and shorter product lifecycles. Many of the leadingtechnology companies have created “virtual enterprises,” aligningthemselves with design and manufacturing outsourcing partners tocarry out services that can be performed more efficiently outside theboundaries of the organization. These partnerships enabled a compa-ny to focus on its core competencies, its own product brand, its cus-tomers, and its particular competency in design or manufacturing. These newly formed outsourcing companies are providing cost-effective and timely services. In manufacturing, they provide multi-disciplinary production; test and support services, including printedcircuit board (PCB) assembly and testing and packaging technologysuch as sheet metal and plastic injection molding; and software con-figuration and support services such as repair depot and warranty ex-changes. They also offer lower cost, higher flexibility, and excellentquality, eliminating the need to spend money on capital equipmentfor internal capacity. This new outsourcing model allows all links inthe supply chain to focus on their own core competencies while stillreducing overall cycle times. In design outsourcing, the supply chain offers the flexibility of sin-gle or multiple competencies, including specialized engineering analy-sis and design validation, testing, and conformance to design stan-dards for multiple countries or codes. In addition, suppliers can offertheir own supply chain of strategic alliances in tooling and manufac-turing services worldwide. Most of these outsourcing companies offerdesign feedback in terms of design for manufacture (DFM) throughearly supplier involvement (ESI). These design service providers havereduced the need for high-technology companies to purchase or main-tain expensive engineering and design competencies, such as specificdesign analysis, some of which are used infrequently in project designcycles.
35. 35. The Nature of Six Sigma and Its Connectivity to Other Quality Tools 7 Several industries, especially the auto industry, have worked tostandardize their relationship with their suppliers. They created theAdvance Product Quality Planning (APQP) Task Force. Its purposewas to standardize the manuals, procedures, reporting format, andtechnical nomenclature used by Daimler-Chrysler, Ford, and GeneralMotors in their respective supplier quality systems for their designand manufacturing. The APQP also issued a reference manual devel-oped by the Measurement Systems Analysis (MSA) Group for insur-ing supplier compliance with their standards, especially QS9000.These standards contain many of the principles of six sigma and asso-ciated quality tools, such as Cpk requirements. These manuals werepublished in the mid-1990s and are available from the Automotive In-dustry Action Group (AIAG) in Southfield Michigan. Six sigma can be used as a standard for design and manufacturing,as well as a communication method between design and manufactur-ing groups, especially when part of the design or manufacturing isoutsourced. This is important for companies in meeting shorter prod-uct lifecycles and speeding up product development through faster ac-cess to design and manufacturing information and the use of globalsupply chains.1.3 Defending Six SigmaSix sigma, like many new trends or initiatives, is not without its crit-ics and detractors. The author has run into several issues brought upby engineers and managers struggling with six sigma concepts, andhas attempted to address these concerns by writing this book. Some ofthe most frequent critiques of six sigma, and the author’s approach toaddressing these problems are listed below. 1. The goal of six sigma defects, at 3.4 PPM, and some of its princi-ples, such as the ±1.5 sigma shift of the average manufactured partfrom specification nominal, sound arbitrary. In addition, there is nosolid evidence as to why these numbers have been chosen. These are reasonable assumptions that were made to implement sixsigma. There are other comparable systems, such as Cpk targets usedin the auto industry, that could substitute for some of these assump-tions. Discussions of these concepts are in Chapters 2 and 3. 2. The cost of achieving six sigma might result in a negative returnon investment. Conventional wisdom once held that higher qualitycosts more, or that there is an optimum point at which cost and quali-ty balance each other, and any further investment in quality will re-sult in negative returns (see the discussion of the quality loss functionin Chapter 6). These beliefs are based on the misconceptions that more tests and
36. 36. 8 Six Sigma for Electronics Design and Manufacturinginspections are needed in the factory prior to delivery to the customer,in order to deliver higher quality. Six sigma advocates the identifica-tion of these costs during the design stage, prior to the manufacturingrelease of the product, so that these costs are well understood. In ad-dition, it has been demonstrated in six sigma programs that the costof changing the product in the design stage to achieve higher quality,whether through design changes, different specifications, better man-ufacturing methods, or alternate suppliers, are much lower than sub-sequent testing and inspection in manufacturing. These issues arediscussed in the chapters on product testing (Chapter 4) and cost(Chapter 6). 3. Many companies feel that the six sigma programs only work wellfor large-volume, well-established, and consumer-oriented companiessuch as Motorola and GE, but do not work for other industries such asaerospace, defense, or medical, since their volumes are small or theyare more focused on maximizing the performance of products or re-ducing the time of development projects. There are many statistical methods that can be used to supplantthe sampling and analysis required for six sigma, allowing smallercompanies the full benefits of six sigma in product design and manu-facturing. Six sigma methods can be used successfully to introducenew low-volume products as well as quantifying marginal designs.These methods will be discussed in the chapters on high and low vol-ume (Chapter 5) and six sigma current and new products (Chapter 8). 4. Many engineers feel that six sigma is for manufacturing only,not for product design, and that it is very difficult to accomplish andcannot be achieved in a timely manner. In this book, there will be many examples of using six sigma and itsassociated tools, such as design of experiments (DoE), in product de-sign. These methods can help in realizing the six sigma goals and tar-gets in a timely and organized manner in design and manufacturing.In addition, there are many examples where design engineers weresurprised to find out that they are already achieving six sigma in cur-rent designs. Six sigma can also be used to flush out “gold plated” de-signs: designs that are overly robust, beyond the six sigma limits, andtherefore costing more than required. These issues are discussed inChapter 7 on DoE and Chapter 8 on designing current and new prod-ucts.1.4 The Definitions of Six SigmaSix sigma integrates well with all of the quality programs and trendsof the last few decades. The purpose of this section is to outline con-ceptually where the six sigma program connects in the quality hierar-
37. 37. The Nature of Six Sigma and Its Connectivity to Other Quality Tools 9chy and some of the quality tools that are in common use today. Spe-cific mathematical background and formulations are discussed in de-tail in later chapters. Six sigma is a condition of the generalized formula for process capa-bility, which is defined as the ability of a process to turn out a goodproduct. It is a relationship of product specifications to manufacturingvariability, measured in terms of Cp or Cpk, or expressed as a numer-ical index. Six sigma is equivalent to Cp = 2 or Cpk = 1.5 (more onthat in the next chapter). The classical definition of the capability ofthe process or Cp is: specification width (or design tolerance) Cp = ᎏᎏᎏᎏᎏᎏ (1.1) process capability (or total process variation)Specifically, USL – LSL Cp = ᎏᎏᎏᎏᎏ (1.2) 6␴ (total process range from –3␴ to +3␴)This formula can be expressed conceptually as product specifications Cp = ᎏᎏᎏᎏ (1.3) manufacturing variability Six sigma is achieved when the product specifications are at ±6␴ (␴is the symbol for standard deviation) of the manufacturing processcorresponding to Cp = 2 (or Cpk = 1.5, discussed in Chapter 2) Six sigma or Cp is an excellent indicator of the capability of aprocess, which can be expressed numerically. This numerical expres-sion can be translated into a defect level using normal distributionstatistical assumptions. It is a useful tool for manufacturing processcomparisons, as well as a common language of design and manufac-turing personnel during the development phase of a product. The de-sign project team and their managers can use it to set new productquality goals. It can be used to assess the quality of internal manufac-turing plants anywhere in the world or to measure the capability of asupplier. Companies can use it to communicate a particular contrac-tual level of quality for their supply chain.1.5 Increasing the Cp Level to Reach Six SigmaThe quality tools in wide use today can easily be integrated within thesix sigma definitions. The object of six sigma is to steadily increasethe process capability index until it reaches the desired level: thespecification limits of the design are equal to six sigma of the manu-facturing variability.
38. 38. 10 Six Sigma for Electronics Design and Manufacturing Design engineers normally set the product specifications, whereasmanufacturing engineers are responsible for production variability.The object of increasing the process capability to six sigma or Cp = 2 istwofold: increase the product specifications, either by widening themor reducing the manufacturing variability. Either effort can have apositive effect on reaching six sigma. The design specifications for any part or process are related to thetop published product specifications. Ultimately, it is the customerthat determines the relative importance of each specification and thedesired level of performance. Good market research and project man-agement for new products can determine the best level of specifica-tion. This level can be set to balance the wishes of the customer, tem-pered by what the competition is offering and considering inputs fromdesign and manufacturing engineers as to the difficulty of meetingthat specification level. The quality of supplied parts and the efforts of the manufacturingengineers in production solely determine the denominator of the sixsigma equation, or the manufacturing variability. Implementing thetraditional quality tools of manufacturing, such as statistical qualitycontrol (SQC) and associated quality tools, can reduce the manufac-turing variability. The tools of SQC and their relationship to processcapability are discussed in Chapter 3. The Cp formula can then be rewritten as specifications design engineering customerCp = ᎏᎏ = ᎏᎏᎏᎏ = ᎏᎏ (1.4) variability manufacturing engineering supplier1.6 Definitions of Major Quality Tools and HowThey Affect Six SigmaBefore a six sigma effort is launched, it is mandatory to have a well-defined and successfully managed total quality management (TQM)program. The tools of TQM encourage the use of well-establishedmethodologies for quantifying, analyzing, and resolving quality prob-lems. A brief description of the TQM tools and examples of each willbe given in Chapter 2.1.7 Mandatory Quality ToolsIt is widely recognized that TQM tools and techniques should be infull utilization before the launch of any six sigma program. SQCshould also be well implemented in the organization, with wide use ofcontrol charts in manufacturing and the supply chain. Both of thesetools will be discussed in Chapter 3 regarding process control.
39. 39. The Nature of Six Sigma and Its Connectivity to Other Quality Tools 11 The major tools of quality can be arrayed as to their use in achiev-ing six sigma. Some tools can effect the numerator, denominator, orboth elements in Equation 1.4. However, a definition of each majortool is given below, in order to examine its relationship with six sig-ma.1.8 Quality Function Deployment (QFD)QFD is a structured process that provides a means for identifying andcarrying the customer’s voice through each stage of product develop-ment and implementation. QFD is achieved by cross-functional teamsthat collect, interpret, document, and prioritize customer require-ments to identify bottlenecks and breakthrough opportunities. QFD is a market-driven design and development process resultingin products and services that meet or exceed customer needs and ex-pectations. It is achieved by hearing the voice of the customer, direct-ly stated in their own words, as well as analyzing the competitive po-sition of the company’s products and services. Usually, a QFD team isformed, consisting of marketing, design, and manufacturing engi-neers, to help in designing new products, using customer inputs andcurrent product capabilities as well as competitive analysis of themarketplace. QFD can be used alternately for new product design aswell as focusing the efforts of the QFD team on improving existingproducts and processes. QFD combines tools from many traditionaldisciplines, including engineering, management, and marketing.1.8.1 EngineeringTools such as structured analysis or process mapping, which is a top-down division of requirements into multiple elements in severalcharts, each related to a requirement in the higher chart, are em-ployed. An example of two tiers of structured analysis is given in Fig-ures 1.6 and 1.7 and will be discussed later in this chapter.1.8.2 ManagementTools such as decision analysis (DA) or criteria rating (CR) are em-ployed. This technique consists of breaking a complex decision intodistinct criteria, ranking each alternative decision versus each criteri-on, then adding the total weighted criteria to determine the most ef-fective overall decision. An example of criteria rating is the decisionon a soldering material for PCB assembly given in Table 1.1. Thereare four alternatives being considered by the selection team, and thecriteria for the decision are listed on the left side of Table 1.1, each
40. 40. 12 Six Sigma for Electronics Design and Manufacturing Table 1.1 Criteria rating (CR) to select a solder system for PCB assemblyCriterion Weight A B C DResistance 10 70/7 50/5 70/7 60/6Quality 10 10/1 80/8 10/1 80/8Foaming 3 30/10 21/7 21/7 30/10TLV 4 40/10 32/8 24/6 32/8History 4 40/10 24/6 40/10 40/10Supplier 3 30/10 30/10 30/10 12/4Total 220 237 195 254Rank 3 2 4 1with its own weight or rank of importance. The selection team decideson the criteria topics and their relative weight based on team discus-sions and members’ individual experiences. Each alternative solder-ing material is then rated against each criterion, and a relative scoreis given. In this example, both the criteria weight and the alternativescore were recorded with a maximum value of 10. This choice is arbi-trary and smaller numbers can be used for the maximum, such as 3,5, or 9. Each alternative score is multiplied by the criteria weight andrecorded in the table. The total weighted score for each alternative isthen calculated, and the final decision is selected based on the highestscore. In the case shown in Table 1.1, alternative D has the highestscore and should be selected.1.8.3 MarketingTools such as customer surveys and competitive analyses are em-ployed. These are traditional elements used by marketing to deter-mine customer needs and perceptions about the company’s productsversus their competition. In its simplest form, QFD could be used as a relationship matrixwhose input is the customer requirements or needs, and outputs arethe product specifications. The QFD process is an interaction betweenthe customer needs and the product characteristics, tempered by acompetitive analysis and a ranking of the importance of the differentcustomer needs. The QFD matrix is commonly known as the “house ofquality,” or QFD chart. A simplified approach to the general QFDchart is shown in Figure 1.2. The “hat” on top of the matrix is used toindicate the presence, if any, of interaction(s) between the variousproduct design characteristics. This interaction should be consideredwhen setting the final product specifications. For example, in a discdesign, changing the disc characteristic or storage capacity might in-fluence other characteristics such as the data access time for the disc.
41. 41. The Nature of Six Sigma and Its Connectivity to Other Quality Tools 13 Matrix Correlation Strong Relationship = 5 Medium Relationship = 3 Product Weak Relationship = 1 Characteristics I M P O Customer Relationship R Competitive Needs Matrix T Analysis A N C E Product Specifications Figure 1.2 QFD product planning matrix. The relation between the customer needs and the product charac-teristics can be considered by the QFD team as having one of fourstates: strong, medium, weak, or none. Each customer need is givenan importance ranking, and that ranking is multiplied by the rela-tionship to generate a total score for each of the product design char-acteristics. In some cases, the importance ranking could further bemodified by the marketing emphasis on that customer need. For ex-ample, lighter weight of a product might be considered an importantcustomer need, and customer feedback indicated it should be rankedas medium in importance, for a value of 5. Marketing managers mightdecide that the new product could compete better if they could empha-size this attribute as a sales point. The importance level could be mul-tiplied by a factor of two, increasing its value to 10. In this manner,the design of the product is forced to be of lighter weight than wouldotherwise be indicated by the customer’s wishes. A high characteristic total score indicates that the design character-istic is important and the related specification should be enhanced, ei-ther in the positive or negative direction, depending on the directionof “goodness” of the specification. A low score indicates that the speci-fication of the current product design is adequate, and should be leftalone or even widened or decreased in value. In this manner, QFD
42. 42. 14 Six Sigma for Electronics Design and Manufacturingacts as a guide to the design team on what areas of design or specifi-cations to improve, and which others could be left alone. QFD could be used as a design tool to generate appropriate specifi-cations for new product designs based on customer expectation andcompetitive analysis as well as marketing inputs. An example is givenby Figure 1.3, a modified QFD matrix for the design of a new cable TVconnector by Raychem Corporation. This case study was authored byMarylin Liner and published in a book edited by the author (Shina,1994). Only a portion of the matrix is shown for brevity. Several cus-tomer needs obtained from a survey of cable installers are shown,with each having an importance rating (not shown). The relationshipsare outlined in the top left-hand part of the matrix, with a strong rela-tionship given a value of 9 instead of the commonly used 5, to empha-size the strong customer input contribution in the design of the prod-uct. Some of the product characteristics are shown at the top of thematrix. The QFD relationship matrix output is the target value of thedesign characteristics, where symbolic numbers are shown. The ar-rows at the bottom show the direction of the enhanced specifications.One of the product characteristics, the number of installation modes,scored the highest total for weighted requirements. This indicated aneed for the most important specification change, to a smaller numberindicated by the arrow. Hence, the target value or specification for thenumber of installation modes was assigned a 1. Another product char-acteristic, the force on equipment panel, which also scored high onweighted requirements, should have its target value or specification TargetRelationships Values ... 1 mode x lbs xx dB n steps˾ Strong = 9 pts Product ... Installation Force on RF Installation . . .b Medium = 3 pts Characteristics ... modes Equipment Shielding steps ...̆ Weak = 1 pt Panel ImportanceCustomer NeedsClear picture ... b ̆Easy to tell when installed ... ˾ ˾ ˾Long lifetime ... ˾ ˾ ̆Simple to install ... ˾ b b ˾Weighted Requirements ... 327 314 322 234 ...Enhanced Specifications Direction ȇ Ȇ Ȇ ȇ Figure 1.3 Raychem CATV new connector QFD matrix.
43. 43. The Nature of Six Sigma and Its Connectivity to Other Quality Tools 15raised to a higher value, noted symbolically in the figure in units ofweight (lbs.). A QFD example for improving the quality of a manufacturingprocess is shown in Figure 1.4. In this case, the PCB assembly, con-sisting of surface mount technology (SMT) solder processes, was an-alyzed. The QFD team used the QFD process to identify customerneeds for quality and delivery of PCBs and rank their importance, aswell as the process characteristics of various elements in SMT man-ufacturing, such as process steps and suppliers of PCBs. The cus-tomers of PCB assembly were the personnel in the next stage of pro-duction: final product assembly and test technicians. The output ofthe QFD chart indicates which process element was the most impor-tant in meeting customer needs. This is the element that the teamshould focus on to reduce process defects or manufacturing variabil-ity. In the example given in Figure 1.4, the relationship matrix andtheir calculations for the weighted requirements are outlined. Itshows that the team should work most effectively on improving thequality of the screening process before all others, to increase internalcustomer satisfaction. Indeed, the team decided to run a DoE to op-timize the process, similar to the DoE example 8.2.4, given in Chap-ter 8. The customer needs were identified in a survey of the appropriatecustomers that use the PCBs, which are the output of the manufac-turing process, divided into primary and secondary needs. The cus-tomers also indicated their ratings of importance for each need. Thisrating is qualitative and is ranked by the team using a scale of 1 to 5,with the larger number being the most important. The process engi-neers also identified the PCB assembly process characteristics. Theteam then generated the relationship matrix by matching the cus-tomer needs to the process characteristics, in terms of four levels(strong, medium, weak, and none). There should at least one matchfor each item in the matrix. If an item from the customer needs is notmatched by at least one item in the quality characteristics, then theteam has to reevaluate the QFD analysis. This is true of the oppositecase of a process characteristic not matched by a least one customerneed. The results of the analysis, or the weighted requirements, are de-termined by multiplying the importance factor by the relationshipstrength. The screening operation achieved the highest score, indicat-ing that customer needs are best satisfied when that process is im-proved before the others. This chart represents the analysis by theQFD team at that moment in time, and their collective findings; itdoes not necessarily reflect a universal solution to improving an SMTprocess.
44. 44. 16 I m p o r t a n c e No solder shorts No opens No loading errors Paste height Low downtime Consistent output Figure 1.4 SMT process QFD matrix.
45. 45. The Nature of Six Sigma and Its Connectivity to Other Quality Tools 17 The competitive analysis portion of the QFD chart is used mostlyfor product design. It outlines the team’s evaluation of the position ofthe company’s current products against the competition as perceivedby the customers. The team could decide to counteract a particular de-ficiency of the current design in meeting one of the customer needs,and therefore add a multiplier to the importance factor. This multipli-er forces the design team to focus on reversing this deficiency in thenew product. This occurs when the deficient customer need generatesa higher score when multiplied by the importance factor. As was shown by both design and manufacturing examples, QFDcan be an excellent tool to improve the design quality and to attain sixsigma levels through focusing on customer needs. In the design exam-ple, it can be used to show which specifications should be widened andwhich can be left alone or even reduced. Widened specifications wouldaffect the numerator of the six sigma equation, making the goal of sixsigma easier to achieve. In the manufacturing example, it was used asa defect reduction tool by the manufacturing quality team to identifywhich process should be investigated to reduce defects and hencemanufacturing variability. Such processes could undergo a design ofexperiments (DoE) project to reduce variability, which is the denomi-nator of the six sigma equation. It is important to note that QFD is a process designed to solicit cus-tomer needs from experienced users of established products orprocesses. In both examples, those directly involved in the use of theproduct, such as cable installers or the recipients of PCBs, were partof the customer needs assessment. Products and processes using newtechnology would benefit less from QFD. For example, it would not bebeneficial for slide rule users to quantify their experience into cus-tomer needs for calculators. In this case, more traditional marketingresearch methods could substitute for QFD.1.9 Design for Manufacture (DFM)The principles of design for manufacture and design for electronic as-sembly have been widely been used in industry through design guide-lines and DFM systems for effectively measuring the efficiency of de-signs for manufacture and cost. The most important guidelines forDFM design for parts are:1. Use minimum parts types2. Use standard components3. Use parts that fit or snap together with no fasteners4. Tools are not required for product assembly
46. 46. 18 Six Sigma for Electronics Design and Manufacturing DFM analysis results in reduced production time and need for oper-ator skills. The DFM design guidelines, such as the ones mentionedabove, are based on common lessons learned while developing elec-tronic products. Prior to formal DFM systems, checklists were beingused by major electronic companies as a repository for the collectivewisdom of their successful design engineers. DFM design guidelines emphasize the design of electronic productsusing self-locating and self-aligning parts, built on a suitable basepart. The number of parts should be minimized by using standardparts and integrating functionality and utility. Several cost savingtechniques should be used, such as standard and automatic labeling,self-diagnosis capability at the lowest level, and using symmetricaland tangle-free part designs. In the formal methodology of DFM, a scoring system is used tomeasure the design efficiency, based on the performance objectiveand the manufacturing capability. Several alternate designs can becreated using the principles of DFM, and the best design can then bechosen based on the scoring system. A conceptual view of a DFMscoring system is shown in Figure 1.5. A typical output of well-designed DFM products is shown in Table 1.2, which compares thedesign of a new product to older non-DFM designs. Such a product isthe Hewlett Packard (now Agilent) 34401A Multi-meter. This casestudy was authored by Robert Williams and published in a book ed-ited by the author (Shina, 1994). The product was designed using sixsigma and QFD. It can be seen that the number of parts and assem-blies have been reduced significantly over previous generations ofmulti-meters through the application of DFM as well as QFD princi- Figure 1.5 Use of a DFM scoring system.