Index

601601

Abbreviations, 595–596Accelerated Life Testing (ALT), 500Access dimension in quality, 102Accuracy in measurement systems analysis,

306–307Active listening, 149–150Actual Factor Value in full factorial designs,

407Advanced process control (APC), 564–565Aesthetics dimension in quality, 101After-tax profits

calculating, 23–24in long term variation costs, 34in tightened specifications costs, 33

AIAG (Automotive Industry Action Group) scale,265

Alarm and recording strategy, 563Algorithm to Solve an Inventive Problem (ARIZ),

217–218Aliasing in fractional factorial design, 413–416Alkaline battery failures, 498AlliedSignal Corporation, 5–6ALT (Accelerated Life Testing), 500Alternative approaches to robust design, 438–443Alternative hypotheses, 344–346. See also

Hypothesis testingAltshuller, Genrich, 19–20, 214–215. See also

TRIZ (Theory of Inventive ProblemSolving) tool

Analysis of Variance (ANOVA)for mean comparisons

from more than two samples, 370, 374one-way, 375–380

for measurement system studies, 315for mixture experiments, 457–458for regression analysis, 387

Analytical data analysis, 343Analytical Physics activity, 489Analyze Factorial Design option, 401–403Analyze phase in DMAIC, 8Analyzing survey results, 179–181Anderson-Darling statistic, 345–346ANOVA. See Analysis of Variance (ANOVA)APC (Advanced process control), 564–565Apparent or Conventional Solution level in TRIZ,

215ARIZ (Algorithm to Solve an Inventive Problem),

217–218As-Is/Can-Be Process Maps, 243–244Assume equal variances option, 360, 369Assumptions in Process Capability Analysis,

336Attribute data

in DFMEA, 260Process Capability Analysis for, 339–340in statistics, 275–276

Attribute Sigma Calculator, 339–340Augmented simplex centroid design, 456

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Automation evolutionary pattern in TRIZ,216–217

Automotive Industry Action Group (AIAG) scale,265

Average moving range, 335Average performance in robust design, 430,

434–436Axial points

in mixture designs, 450–451, 453in response surface designs, 424–428

Bardeen, John, 14Barrentine, Larry B., 310Baseline, 28–30Basic statistics, 275–278Bathtub curves, 494–495

constant failure rates in, 496–498decreasing failure rates in, 495–496distributions for, 507–508increasing failure rates in, 498–499, 510for PDF, 506

Benefits received in product value, 99Berry, Leonard L., 102Best distribution fit

with Crystal Ball, 525, 527–529with Minitab, 523–527

Best products, 274–275BetaMax format vs. VHS, 4Bias in measurement systems, 306–307Big block process maps, 238–239Blackbelt projects, 42–43Blackbelts

in schedule development, 70, 73selecting, 74

Borror, C. M., 439Bossidy, Larry, 5Box-Behnken designs

in response surface design, 426in robust design, 439

Boxplots, 287–292Brattain, Walter, 14Broadcast programming, 14Bullet-point interview objectives, 136–137Burn-in, 496Business case for DFSS, 11

dynamic markets, 15–18product life cycle, 11–15role of DFSS, 18–20

Business plansdeveloping, 75–77Executive Summary section in, 78financial plan, 79Management and Organization section in,

79Marketing Plan and Competitive Analysis

section in, 78Operating Plan section in, 78–79reviewing, 75

Cameras, evolution of, 217Can-Be Process Maps, 243–244Cannibalization, product, 103, 105Categorical data, 275–276Cause and Effects (C&E) Matrix, 247

developing, 248–257link from Process Variables Maps, 241, 243vs. QFD3, 247–248working with, 253

CDF (Cumulative Distribution Function), 506CDOC (Concept, Design, Optimize, and

Capability) implementation, 8–9Censoring in reliability tests, 509Center-cutting approach, 274–275Center point runs, 420Centering data

for multiple regression analysis, 391in Process Capability Analysis, 327–328

Central Composite Designsin response surface designs, 424–426in robust design, 439

Centroid designsaugmented, 454, 456with axial points, 453constraints in, 463–464for three components, 447–448

Chart defects tables, 294Charters, project, 42–46Chi Square statistical analysis, 276Closed-ended questions, 141Coded Factor Value in full factorial designs,

407Coefficients

correlation, 383–384for Crystal Ball, 481in regression analysis, 384–385for three-response optimization, 476

602 Index

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Collection weaknesses in Process Variables Maps,241

Commercialization delay costs, 25–26Commercialization pipeline, 48Common cause variation, 271–272Communication dimension in quality, 102Comparisons

meansto medians, 297–298more than two samples, 370–374one-way ANOVA, 375–380paired, 363–367t-tests for, 360–363to target values, 348–354two samples, 355–363

mediansKruskal-Wallis test, 380–381Mann-Whitney test, 363–364to means, 297–298Wilcoxon Signed Rank test, 354–355

standard deviation, 298more than two samples, 370–374two samples, 355–359

variancesthree variances, 373–375two variances, 360–361

Competence dimension in quality, 102Competitor analysis

in Market Perceived Quality Profile, 122in market segmentation, 89–90

Competitors category for interview questions, 142Components

in mixture designs, 449–450of variation plots, 316

Composite desirability for Response Optimizer,472–473

Concept, Design, Optimize, and Capability(CDOC) implementation, 8–9

Concept Development, 129advantages, 131, 133applications, 131–132process, 129–131

Concept Development tools, 133Concept phase

in Design for Reliability, 487in FMEA, 504

Concept Generation phase in Ideation process,185, 188

Concept Selection Matrixin Pugh concept selection, 190–192rankings in, 192–194

Confidence intervalsfor difference between means, 369–370for means, 367–369for standard deviation, 369

Confirming products, 538–545Conformance dimension in quality, 101Confounding in fractional factorial design,

413–416Constant failure rates in bathtub curve,

496–498Constraints in mixture experiments, 459–462Continuous data in statistics, 275–276Contour plots

for mixture experiments, 459–460for product scale-up, 554in response surface designs, 420, 423for three-response optimization, 475–476

Contradiction Matrix, 217–222, 225Contradictions, technical. See TRIZ (Theory of

Inventive Problem Solving) toolControl charts

creating, 285–287, 289of cycles between failures, 492, 494

Control plans, 555–556advanced process control in, 564alarm and recording strategy in, 563final documentation package, 568–569input variable shifts in, 557, 559–561out-of-control conditions in, 556–558link from Process Variables Maps, 241–242sampling plans for, 564standard operating procedures in, 568summary, 565–568time series analysis for, 557, 561–563

Cooper, Robert G., 20, 51COPQ (Cost of Poor Quality), 28–30Correlation

analysis of, 381–384in Cause and Effects Matrix, 249, 253–254for control plans, 561–563in regression, 391in reliability modeling, 509for technical interaction, 201in TRIZ, 213–214

Correlation coefficient (r), 383–384

Index 603

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Cost of Poor Quality (COPQ), 28–30Costs

commercialization delays, 25–26in financial value, 108–113long term variation, 33–35

financial sensitivity analysis in, 35–39sigma levels in, 35–36

not killing poor projects, 26–28process variation, 273–274tightened specifications, 32–33warranty, 512–513

Courtesy dimension in quality, 102Cover letters for surveys, 175Cp statistic

adequacy of, 337, 339in short-term process capability analysis,

321–322in sigma level, 326–328

Cpk statistic, 328–331Create Factorial Design option, 399–400Create Mixture Design option, 447–452, 461–462Credibility dimension in quality, 102Critical input variables, 430Critical output parameters, 465Critical Parameter Management

overview, 227–228scorecards in

benefits, 232–233in DFM, 546–549information on, 230–231for minimizing product variation, 230, 232overview, 228–229

Critical path, 71–72Cross correlation, 561–563Crystal Ball program

for best distribution fit, 525, 527–529for Monte Carlo simulation, 38, 115for optimal solutions, 479–485for variation optimization, 533–534

Cubic equationsin mixture experiments, 447, 457for regression analysis, 385

Cumulative Distribution Function (CDF), 506Customer dimension in quality, 102Customer environment in Design for Reliability,

490Customer interviews, 147

active listening in, 149–150

analysis of. See KJ Analysis tooldebriefing, 151etiquette in, 150–151management of, 151–152open mindedness in, 150practice for, 152preparing for, 147–148team roles in, 148

Customer Interviews toolin concept development, 133for market segmentation, 55in Marketing Plan and Competitive Analysis

section, 78Customer needs and requirements

in business case for DFSS, 19in market segmentation, 87–90optimizing variation to, 532–535in concept development, 131, 196–198in QFD, 197, 199–200reliability expectations, 491

Customer Selection Criteria characteristic, 140

Customer Selection Matrix toolin Interview Guides, 137–140in Marketing Plan and Competitive Analysis

section, 78Customer surveys, 578–579Customers category for interview questions,

142

DART (Design Assessment Reliability Testing),488, 502

Data analysis, 343confidence intervals, 367–370correlation analysis, 381–384general methods, 343–344hypothesis testing, 344–346mean comparisons

more than two samples, 370–374one-way ANOVA, 375–380paired, 363–367t-tests for, 360–363to target values, 348–354two samples, 355–363

median comparisonsKruskal-Wallis test, 380–381Mann-Whitney test, 363–364Wilcoxon Signed Rank test, 354–355

604 Index

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regression analysismultiple, 390–396single input variable, 384–390

sample size calculations in, 346–348standard deviations comparisons

more than two samples, 370–374two samples, 355–359

tool summary, 381variances comparisons

three variances, 373–375two variances, 360–361

Data collection weaknesses, 241Data mining

boxplots for, 287–292dotplots for, 292–293

Death spiral, 109, 111–113Debriefing customer interviews, 151Decline stage in product life cycle, 11–12Decreasing failure rates in bathtub curves, 495–496Defects in Attribute Sigma Calculator process,

339Defects per million opportunities (DPMO),

339–340Defects per unit (DPU), 339–340Define, Measure, Analyze, Improve, and Control

(DMAIC)characteristics, 6–7overview, 7–9

Define phase in DMAIC, 7–8DeForest, Lee, 13Degrees of freedom for mean comparisons

one-way ANOVA, 378–379two-sample t-test, 363

Delay costs, 25–26Design Assessment Reliability Testing (DART),

488, 502Design FMEA (DFMEA) tool, 259–260

conducting, 260–261design controls in, 262–264failures in

causes, 262–263effects, 262modes, 261–262

in concept development, 133ratings in, 263–265Risk Priority Number in, 266–267

Design for Manufacturability (DFM) assessment,546–549

Design for Reliability (DfR), 487distribution types in, 507–508FMEA in, 502–505Hazard Function in, 494–499and Kano Model, 490–491mathematical models in, 504–506metrics for, 491–494Minitab for, 508–512reliability requirements in, 489–490reliability tests in, 499–502roadmap for, 487–489warranty costs in, 512–514

Design For Six Sigma (DFSS)defined, 3–4history, 5–7overview, 7–9process, 580–582vs. Operations Six Sigma, 6–7tools, 9

Design Maturity Testing (DMT) plan, 488, 491,502

Design of Experiment (DOE), 397fractional factorial design. See Fractional

factorial designsfull factorial designs. See Full factorial designsresponse surface designs. See Response surface

designsselecting, 426

Desirability function, 468–473Detection (DET) ratings in DFMEA, 264–265Devices under test (DUT), 500DFM (Design for Manufacturability) assessment,

546–549DFMEA. See Design FMEA (DFMEA) toolDfR. See Design for Reliability (DfR)DFSS Tools Checklist, 232–233Difference between means, 369–370Dimensions of quality, 101–103Direction of movement in QFD, 201Direction of steepest ascent, 421, 423–424Disciplined processes, 3Discovery level in TRIZ, 215Discrete data, 275–276Discrimination in measurement systems, 305–306Display Descriptive Statistics option, 297Disruptive technologies, 11–12, 14–15Distribution ID plots, 509Distribution Overview Analysis, 524

Index 605

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Distributionsbathtub curve modeling, 507–508best distribution fit, 523–529F

for mean comparisons, 379for variance comparisons, 360–361

lognormalfor best distribution fit, 523for wear-out mechanisms, 508

normalfor best distribution fit, 523to estimate waste, 319–321overview, 277–278

skewed, 282t. See t-testsWeibull

for best distribution fit, 523–524for reliability, 509–510, 512in statistical tolerancing, 529–530

DMAIC (Define, Measure, Analyze, Improve, andControl)

characteristics, 6–7overview, 7–9

DMT (Design Maturity Testing) plan, 488, 491,502

Documentationfinal documentation package, 568–569Interview Guides, 144–146QFD critical information, 228

DOE (Design of Experiment)fractional factorial design. See Fractional

factorial designsfull factorial designs. See Full factorial designsresponse surface designs. See Response surface

designsselecting, 426

Dotplotsdata mining using, 292–293overview, 279–281

DPMO (defects per million opportunities),339–340

DPU (defects per unit), 339–340Durability dimension in quality, 101DUT (devices under test), 500Dynamic markets, 15–18Dynamic Model, 561Dynamization evolutionary pattern in TRIZ,

216–217

Economic view of product life cycle, 17–18Edison, Thomas, 13Edison Effect, 13Emerging trends, 19Entitlement, 28–30Environmental analysis in market segmentation,

89–90Environmental stress screening (ESS)

for infant mortality failures, 496purpose, 501

Environmental variables, 241Errors

in hypothesis testing, 344measurement. See Measurement systems

analysisESS (environmental stress screening)

for infant mortality failures, 496purpose, 501

Estimated variance in robust design, 443Estimates of long-term variation, 325Estimating wastes, 319–321Etiquette in customer interviews, 150–151Evolution in TRIZ, 216–217Excel Solver tool, 475–478Executive Summary section in business

plans, 78Expectations in Kano model, 17Experimental runs, 397Experiments, design. See Design of Experiment

(DOE)Exponential distribution, 523Extreme Vertices Design, 461–462Extreme Vertices Blend Design, 463

F-Testsfor mean comparisons, 379for variance comparisons, 360–361

Factor regions for 3-component mixtures,445–446

Failure Free Testing, 501Failure Modes and Effects Analysis (FMEA) tool,

259Cause and Effects Matrix for, 253Design FMEA. See Design FMEA (DFMEA)

toolin Design for Reliability, 502–505Market FMEA. See Market FMEA toolin new product development, 259–260

606 Index

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for post-mortem analysis, 580, 583Process Manufacturing, 268–269Product Design, 267–268

Failure ratesin bathtub curve

constant, 496–498decreasing, 495–496increasing, 498–499, 510

in Design for Reliability, 491–494Failures in DFMEA

causes, 262–263effects, 262modes, 261–262

Fast Track projectspipeline for, 48–49in Stage-Gate systems, 62–66

Features dimension in quality, 101Final Financial Value tool, 133Final segmentation strategy, 87–90Financial metrics, 21

commercialization delay costs, 25–26killing poor project costs, 26–28long term variation costs, 33–35

financial sensitivity analysis in, 35–39sigma levels in, 35–36

poor quality costs, 28–30sigma levels, 30–31success, 22–25tightened specifications costs, 32–33

Financial plan, 79Financial results, projects linked to, 43–46Financial sensitivity analysis, 35, 113

Monte Carlo simulation in, 38–39single factor, 36–38, 116–117

Financial value, 107fixed costs in, 108–113market segmentation, 81–86project returns, 113–120project value, 107–108

First Order Dynamic Process, 561First order equations in response surface designs,

423Fitted line plot equations, 385, 387–388Fitted models for three response variables, 474Five-factor interaction effect, 412Fixed costs in financial value, 108–113Fleming, John Ambrose, 13Flowdown process in QFD, 196, 202–208

FMEA. See Design FMEA (DFMEA) tool; FailureModes and Effects Analysis (FMEA)tool; Market FMEA tool

Four-factor interaction effect, 414–416Four in one ANOVA option, 377Fractional factorial designs

available, 416confounding in, 413–416hierarchy of effects in, 416in Minitab, 416–420overview, 411–413purpose, 426

Full factorial designs, 397–398in Minitab

creating, 399–403numerical output, 406–409Pareto plots, 403–406residual plots, 409–411

purpose, 426randomization in, 398–399

Function Within Organization characteristic, 140Functional product requirements, 197, 199–200“Fuzzy front-end” of product development, 129

Gage R&R Study (Crossed) option, 313–314, 317

Galvin, Bob, 5Gaps

in Market Perceived Quality Profile, 124–125QFD process for, 195in worst case analysis, 517–518

“Garbage in, garbage out”in product commercialization, 41with statistical analysis, 303

Garvin, David, 101Gate 3 Review tool, 133Gatekeeper, 51, 53General Electric, Six Sigma at, 6Geography characteristic, 140Glossary, 585–593Goal setting in Cost of Poor Quality, 28–30Goodness-of-fit statistic, 345Goodwill of customers, 514Graphical analysis techniques, 278–279

boxplots, 287–292control charts, 285–287, 289dotplots, 279–281, 292–293histogram plots, 279, 281–282

Index 607

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Graphical analysis techniques (continued )Normal Probability Plots, 283–286Run Charts, 285, 288scatterplots, 294–297summary analysis, 282–283, 299

Greenbelt projects, 42Greenbelts in schedule development

in schedule development, 70, 73selecting, 74

Growth stage in product life cycle, 11–12

Half-fraction factorial design, 413HALT (Highly Accelerated Life Testing), 489,

497, 500HASS (Highly Accelerated Stress Screening),

489, 497, 500HAST (Highly Accelerated Stress Testing),

501Hazard Function, 494–495

constant failure rates in, 496–498decreasing failure rates in, 495–496distributions for, 507–508increasing failure rates in, 498–499, 510for PDF, 506

Hierarchy Location of Person characteristic, 140Hierarchy of effects in fractional factorial design,

416High-end market pricing, 83High Factor Value in full factorial designs, 407Highly Accelerated Life Testing (HALT), 489,

497, 500Highly Accelerated Stress Screening (HASS),

489, 497, 500Highly Accelerated Stress Testing (HAST),

501Histogram plots, 279, 281–282Historical failures for bathtub curve, 507Hopper, project, 46–48Humidity variable, 241Hypothesis testing, 344–346

for correlation analysis, 383for mean comparisons

confidence intervals for, 367–369one-way ANOVA, 378paired, 366–367t-tests for, 362–363to target values, 348–350, 353–354two-sample, 355–357

for mean difference, 369for median comparisons, 363–364for regression analysis

multiple, 393single input variable, 386, 390

sample size for, 347–348for standard deviation comparisons, 355–357for variances, 360–361

I-MR control charts, 357–358Ideation process, 183

example, 186–188problem statement in, 183product concepts in, 185–186solution generation in, 184–185work area in, 183–184

Ideation tool, 78Identify, Design, Optimize, and Validate (IDOV)

implementation, 8–9Images in KJ Analysis, 154–155

defining, 155–156final selection, 160–161in Marketing Plan and Competitive Analysis

section, 78in product development, 133recording, 156–157reducing, 157–160scrubbing, 161–162titling and positioning groups, 162–166YO OH concept, 160–161

Importance parameter for Response Optimizer, 472Improve phase in DMAIC, 8Include center points in the model option, 420Increasing failure rates in bathtub curve, 498–499,

510Infant-mortality failures

in bathtub curve, 495and Customer goodwill, 514HASS for, 500

Infeasible manufacturing process mode, 98Initial Financial Analysis tool, 55Inner arrays in robust design, 431–435Innovation levels in TRIZ, 215–216Inputs and input variables

in Cause and Effects Matrix, 247–248,252–253

in Crystal Ball, 481in OptQuest, 483

608 Index

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for out-of-control conditions, 556–558for Process Variables Maps, 239–243for three-response optimization, 476

Instability in product development process,272–273

Instrument bias, 306Interaction effects

five-factor, 412four-factor, 414–416in full factorial designs, 403–404, 406, 408two-factor, 411, 414

Interaction plotsin full factorial designs, 404–406in robust design, 436

Intercept in regression analysis, 384–385Internal Rate of Return (IRR)

in financial plans, 79in financial sensitivity analysis, 37, 113in Monte Carlo simulation, 115as success measure, 25

Interpretation in fractional factorial design, 413Interview Guides, 135

bullet-point objectives, 136–137Customer Selection Matrix in, 137–140documenting, 144–146finalizing, 144Purpose Statements in, 135–136for questions, 139

areas to be explored, 139, 141developing, 141–143

Interviews, customer, 147active listening in, 149–150analysis of. See KJ Analysis tooldebriefing, 151etiquette in, 150–151management of, 151–152open mindedness in, 150practice for, 152preparing for, 147–148team roles in, 148

Introduction stage in product life cycle, 11–12Invention Outside Technology level in TRIZ, 215iPod, 4–5IRR (Internal Rate of Return)

in financial plans, 79in financial sensitivity analysis, 37, 113in Monte Carlo simulation, 115as success measure, 25

Jobs, Steve, 4

Kano Model, 15–16economic view of product life cycle, 17–18implications, 16–17reliability expectations in, 490–491

Kawakita, Jiro, 153Key assumptions in Process Capability Analysis,

336Key inventive principles in TRIZ, 217–220,

222Key Items area for voice translation,

168–169Key Process Input Variables (KPIVs)

in APC, 565in control plans, 555, 557, 559–561, 567in DFM, 546–547in DOE, 397in FMEA, 259in optimal solutions, 479–481in PFMEA, 269process maps for, 237for Process Variables Maps, 239–241in product scale-up, 552–553in QFD, 205

Key Process Output Variables (KPOVs)in APC, 565in control plans, 555–556, 558, 567in DFM, 547in DOE, 397for Process Variables Maps, 239, 241in product scale-up, 552

Key requirements in Market Perceived QualityProfile, 122

Killing projects, costs of not, 26–28KJ Analysis, 153

images, 154–155defining, 155–156final selection, 160–161in Marketing Plan and Competitive

Analysis section, 78in concept development, 133recording, 156–157reducing, 157–160scrubbing, 161–162titling and positioning groups, 162–166YO OH, 160–161

overview, 154

Index 609

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KJ Analysis (continued )in concept confirmation, 540in concept development, 131requirement, 164

in concept development, 133conclusion, 172, 174final selection round, 170–171identifying, 172–173in Marketing Plan and Competitive

Analysis section, 78reducing, 170scrubbing, 171theme, 164, 169–170titling and positioning groups, 171voice recording, 166–167voice reduction, 166–167voice translating, 167–169

in Stage-Gate systems, 55Knowledge gaps, QFD process for, 195KPIVs. See Key Process Input Variables (KPIVs)KPOVs. See Key Process Output Variables

(KPOVs)Kruskal-Wallis Test, 380–381

“Ladder of abstraction” in customer interviews, 149Lapsed Customers, 139Launch plans

product, 573–579in Stage-Gate systems, 60–61

Law of Ideality in TRIZ, 216LCLs (Lower Control Limits) for control charts,

286–287Least squares in regression analysis, 385Level of variation in performance, 325–326Levels of innovation in TRIZ, 215–216Levene’s Test, 360–361Lidstone, John, 91Life cycles, product

in business case for DFSS, 11–15economic view of, 17–18vacuum tube example, 13–15

Life hazards in Design for Reliability, 487Linear relationships, correlation analysis for,

381–384Linearity problems in measurement systems, 318Linking

customer needs to product development,196–198

Process Variables Maps to downstream DFSStools, 241, 243

projectsto financial results, 43–46to strategy, 41–42

Listening, active, 149–150Lognormal distributions

for best distribution fit, 523for wear-out mechanisms, 508

Long-term measurement system assessments, 318Long-term process capability analysis, 322–325,

331–332Long-term reliability, 490Long term variation

costs, 33–35financial sensitivity analysis in, 35–39sigma levels in, 35–36statistical tools for, 274–275

standard deviation, 319–320Low-end market pricing, 83Low Factor Value in full factorial designs, 407Lower Control Limits (LCLs) for control charts,

286–287Lower Specification Limit (LSL) values

in Cp, 327in Cpk, 328, 330–331in long-term process capability analysis,

322–324in measurement system error, 305in short-term process capability analysis,

321–322in sigma levels, 30–32

Maclennan, Janice, 91Main effects plots, 434–437Management and Organization section in

business plans, 79Mann-Whitney test, 363–364Manufacturing processes in QFD

identifying, 205in Stage-Gate systems, 58–59

MapsProcess Variables Maps, 237–238

big block process maps, 238–239input variables for, 239–243output variables for, 239

product positioning, 127–128value chain, 103–106

610 Index

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Market analysis in Stage-Gate systems, 55Market FMEA tool, 96, 259

developing, 96–98in Marketing Plan and Competitive Analysis

section, 78results, 98in Stage-Gate systems, 55

Market opportunities, 91Market FMEA, 96–98SWOT analysis. See SWOT Analysis tool

Market Perceived Quality Profile tool, 105,121–123

competitive position and market share analysisin, 122

gap in, 124–125key requirements in, 122in Marketing Plan and Competitive Analysis

section, 78output interpretation in, 124–126price and quality sensitivity in, 122in production scale-up, 540

Market segmentation, 81customer interviews for, 55financial value of, 81–86ratings by, 93–95strategy for, 86–90value chain mapping in, 105–106

Market Segmentation tool, 78Market share analysis, 122Marketing Plan and Competitive Analysis section

in business plans, 78Mathematical models in reliability, 504–506Maturity stage in product life cycle, 11–12Mean Cycles Between Failure (MCBF), 492, 494Mean shift variation, 336Mean square of residuals (MS) error, 385Mean Squared Deviation (MSD), 430Mean squares for mean comparisons, 378Mean Time Between Failures (MTBF), 492, 494Means

comparingto medians, 297–298more than two samples, 370–374one-way ANOVA, 375–380paired, 363–367t-tests for, 360–363to target values, 348–354two samples, 355–363

confidence intervals for, 367–369for control charts, 287difference between, 369–370for normal distributions, 277–278

Measure phase in DMAIC, 8Measurement System Assessment, 253Measurement systems analysis, 303

accuracy in, 306–307discrimination in, 305–306errors in, 303–305long-term, 318precision in, 307–308in Process Capability Analysis, 337, 339samples in, 311studies in, 312–318variation quantification in, 309–311

Mediansin boxplots, 290comparing

Kruskal-Wallis test, 380–381Mann-Whitney test, 363–364to means, 297–298Wilcoxon Signed Rank test, 354–355

confidence intervals for, 367for normal distributions, 278

Metricsfinancial. See Financial metricsfor reliability, 491–494

Mid-range market pricing, 83Milestones, 70–71Minitab program, 278

best distribution fit, 523–527boxplots, 287–292confidence intervals

means, 367–369standard deviation, 369

control charts, 285–287, 289control plans, 561–562correlation analysis, 383–384descriptive statistics, 298–300dotplots, 279–281, 292–293fractional factorial design, 414–420full factorial designs

creating, 399–403numerical output, 406–409Pareto plots, 403–406residual plots, 409–411

histogram plots, 279, 281–282

Index 611

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Minitab program (continued )hypothesis tests, 346long-term process capability analysis, 322–324mean comparisons

one-way ANOVA, 378–380paired, 364–367sample size, 356to target values, 349–350two-sample, 360–363

measurement system studies, 312–318medians

comparing, 363–364testing, 354–355

mixture designsanalyzing, 451–454creating, 447–451

multiple response optimization process,466–473, 475

Normal Probability Plots, 283–286out-of-control conditions, 556Process Capability Analysis, 333–337product scale-up, 553–554regression analysis

multiple, 391–396single variable, 385

reliability, 508–512robust design, 434, 439, 441–443Run Charts, 285, 288sample size, 347–348scatterplots, 294–297standard deviation

comparisons, 356confidence intervals, 369

variance comparisonsthree variances, 373–375two variances, 360–361

Mistake proofing opportunities, 568Mixture experiments, 445

constraints in, 459–462mixture designs, 447

analyzing, 451–454choosing, 462–464in Minitab, 447–451

mixture equations, 445–447response surface study for, 454–462

Monitoring inputs and outputs for out-of-controlconditions, 556–558

Monte Carlo Risk Analysis tool, 133

Monte Carlo simulation, 113, 117–120for best distribution fit, 524in financial sensitivity analysis, 38–39in optimal solutions, 477–485in statistical tolerancing, 529–532, 535

Montgomery, D. C., 439Mood’s median test, 380Motorola, Six Sigma at, 5–6MS (mean square of residuals) error, 385MSD (Mean Squared Deviation), 430MTBF (Mean Time Between Failures), 492, 494Multi-State Picking Method, 157–158, 166Multiple regression analysis, 390–396Multiple response optimization process, 466

Minitab for, 475reduced model for, 466–467Response Optimizer for, 467–468

composite desirability for, 472–473desirability function, 468–472setup for, 468, 470

Multivalued responses in customer interviews,150

Murphy’s Analysis, 580Myers, R. H., 439Mystery shoppers, 579

Net Present Value (NPV)in commercialization delay costs, 26in Cost of Poor Quality, 29–30in financial plans, 79in financial sensitivity analysis, 36–38, 113in long term variation costs, 34–35in Monte Carlo simulation, 38, 115in pipeline management, 46–48as success measure, 24–25in tightened specifications costs, 32–33

New product concept finalized stage, 55–57New product design stage, 58–59Nominal gap values, 517–518Non-normal data, statistical analysis tools for,

277Non-Value-Added step, 244Non-Value-Added but Necessary step, 244Nonparametric median tests, 354Normal distributions

for best distribution fit, 523overview, 277–278for waste estimates, 319–321

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Normal probability plotscreating, 283–286in hypothesis testing, 345

Normalityin mean comparisons, 351, 370–372in standard deviation comparisons, 357, 359,

370–372statistical analysis tools for, 277

NPV. See Net Present Value (NPV)Null hypotheses, 344–346. See also Hypothesis

testingNumerical descriptive statistics, 297–300

Objectives section in Interview Guides, 145–146Observer role for customer interviews, 148Occur (OCC) ratings in DFMEA, 2641-sample t-tests, 351–3541-sample Wilcoxon tests, 354–355One-sided 2-sample t-tests, 362One-sided significance tests, 350One-way ANOVA

for mean comparisons, 375–380for regression analysis, 387

Open-ended questions, 141Open mindedness in customer interviews, 150Operating Plan section in business plans, 78–79Operations Six Sigma, 6–7Operator bias, 306Operator-sample interaction, 316, 318Opportunities, 91

Market FMEA, 96–98in Stage-Gate systems, 53–55SWOT analysis. See SWOT Analysis tool

Optimal solutions, 465–466in control plans, 565Monte Carlo simulation in, 477–485multiple response optimization process,

466–473three-response optimization, 473–478

Optimize phasein Design for Reliability, 488in FMEA, 504

Optimizing variation, 532–535OptQuest program

for variation optimization, 533–534for variation requirements, 480–484

Out-of-control conditions, 556–558Outer arrays in robust design, 431–435

Outputs and output variablesin Cause and Effects Matrix, 247–251in Market Perceived Quality Profile,

124–126for out-of-control conditions, 556–558for Process Variables Maps, 239

Overlay Contour Plots, 554

P/T ratio (Precision to Tolerance Ratio), 309–310p-values

for correlation analysis, 383–384in hypothesis tests, 345–346for means

one-way ANOVA, 378paired comparisons, 366testing, 354two-sample t-test, 363

for medians, 355for regression analysis, 386–387

multiple, 393–395single input variable, 390

in response surface designs, 420Paired mean comparisons, 363–367Parameter management. See Critical Parameter

ManagementPareto analysis for TRIZ, 222Pareto plots

for dotplots, 292, 294in fractional factorial design, 418in full factorial designs, 403–406in robust design, 436, 438

Partnering relationships, 87–88Patent analysis in TRIZ, 216Path of steepest ascent, 421, 423–424PDF (Probability Density Function), 504–506Perceived Quality dimension, 101Percent Repeatability and Reproducibility (% R&R)

value, 310–311, 313–314, 316–317Performance

Process Capability Analysis for, 325–326in quality, 101in robust design, 429–431, 434–436

PFMEA (Process FMEA), 259–260, 268–269Photography, camera evolution in, 217Physical reactions in customer interviews, 148PID (Proportional-Integral-Derivative) control

calculations, 564Pipeline management, 46–50

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Plansbusiness

developing, 75–77Executive Summary section in, 78financial plan, 79Management and Organization section in, 79Marketing Plan and Competitive Analysis

section in, 78Operating Plan section in, 78–79reviewing, 75

control, 555–556advanced process control in, 564alarm and recording strategy in, 563final documentation package, 568–569input variable shifts in, 557, 559–561out-of-control conditions in, 556–558for Process Variables Maps, 241–242sampling plans for, 564standard operating procedures in, 568summary, 565–568time series analysis for, 557, 561–563

Pooled standard deviationfor mean comparisons, 362in Process Capability Analysis, 336

Portfolio scorecards, 48, 50Position in Value Chain characteristic, 140Positioning

in KJ Analysisimage groups, 162–166requirements, 171

product, 121maps, 127–128Market Perceived Quality Profile,

121–126Post-mortem analysis, 579–583Power of hypothesis tests, 344Pp statistic, 331–332Ppk statistic, 331–332Practical data analysis, 343Precision in measurement systems analysis,

307–308Precision to Tolerance Ratio (P/T ratio), 309–310Predictive process control approach, 562Price

in financial sensitivity analysis, 38in Market Perceived Quality Profile, 122in segmentation, 81–86

Probability Density Function (PDF), 504–506

Probing in customer interviews, 149–150Problem statement in Ideation process, 183Process, 3

in QFD, 205sigma levels of, 30–31

Process Capability Analysis, 319for attribute data, 339–340capability index interpretations in, 332–333Cause and Effects Matrix for, 253Cpk statistic for, 328–331importance of, 340–341long-term, 322–325, 331–332measurement system adequacy in, 337, 339Minitab tools for, 333–337normal distribution curves for waste estimates,

319–321short-term, 321–322for Six Sigma performance, 325–326

Process Control Plans, 253Process Design FMEA, 259Process design in Stage-Gate systems, 58, 60Process Design Package, 549–554Process FMEA (PFMEA), 259–260, 268–269Process Hazards Analysis reviews, 551Process maps, 237

As-Is/Can-Be Process Maps, 243–244final thoughts, 245Process Variables Maps. See Process Variables

MapsProcess optimization in control plans, 565Process stability in product development,

272–273Process variables in mixture designs, 450Process Variables Maps, 237–238

big block process maps, 238–239input variables for, 239–243output variables for, 239

Product cannibalization, 103, 105Product Design FMEA, 267–268Product Development

in Design for Reliability, 488in FMEA, 504linking customer needs to, 196–198measurement error impact in, 304–305in product life cycle, 11–12risk, 4–5

Product development cycle time, 195–197Product Positioning Maps tool, 78

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Product scale-up, 537Design for Manufacturability assessment,

546–549Process Design Package, 549–554product confirmation in, 538–545

Product value, 99quality in, 100–103tools for, 105value chain mapping in, 103–106value concept, 99–100

Products, 3confirming, 538–545developing. See Developmentin Ideation process, 185–186life cycle

in business case for DFSS, 11–15economic view of, 17–18vacuum tube example, 13–15

positioning, 121maps, 127–128Market Perceived Quality Profile, 121–126

in QFD, 197, 199–201in Stage-Gate systems, 55, 60–61variation in. See Variation

Profit, after-taxcalculating, 23–24in long term variation costs, 34in tightened specifications costs, 33

Project Construction phase, 552Project cycle time, 195–197Project Detailed Engineering and Procurement

phase, 552Project management, 67

customer interviews, 151–152roadmaps, 67–69schedules

developing, 69–73managing, 73–74overview, 74

Project Plans, 549, 551Project returns

financial sensitivity analysis, 113, 116–117Monte Carlo simulation, 113, 117–120

Project Scope Definition phase, 551Project value, 107–108Projects

charter, 42–46death spiral, 109, 111–113

hopper, 46–48killing, 26–28launch plans, 573–579linking

to financial results, 43–46to strategy, 41–42

loser, 41post-mortem analysis, 579–583risk

development, 4–5quantifying, 25

Proportional-Integral-Derivative (PID) controlcalculations, 564

Pugh, Stuart, 190Pugh Concept Selection tool, 189

in concept development, 133example, 194in FMEA, 504follow-up, 194in Marketing Plan and Competitive Analysis

section, 78process, 190–194in Stage-Gate systems, 55

Purpose Statements in Interview Guides, 135–136,145

QFD (Quality Function Deployment) process, 195

critical information documentation, 228

in DFM, 546executing, 197–202flowdown in, 202–208parameters from. See Critical Parameter

Managementroof, 213–214summary, 211–212across value chain, 205, 209–211value of, 195–198

QFD1 toolin concept development, 133in Operating Plan section, 78overview, 202–203in Stage-Gate systems, 55

QFD1.5 toolin concept development, 133in Operating Plan section, 78overview, 203–204

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QFD2 toolin Operating Plan section, 79overview, 204–206

QFD2.5 toolin Operating Plan section, 79overview, 205, 207

QFD3 toolvs. Cause and Effects Matrix, 247–248, 253in Operating Plan section, 79overview, 205, 208for Process Variables Maps, 241

Quadratic equations, 385, 389–390Quadratic model output, 456Quality

dimensions of, 101–103in Market Perceived Quality Profile, 122in product value, 100–103for services, 102–103

Quality Function Deployment. See QFD (QualityFunction Deployment) process

Quality system failures in bathtub curve, 495Quantifying

measurement system variation, 309–311performance in robust design, 429–431

Questions for Interview Guides, 139areas to be explored, 139, 141developing, 141–143

R Chart by Operator graphs, 316R-Sq value, 387RACI (Resource, Accountable, Consulted, and

Informed) approach, 71Randomization in full factorial designs,

398–399Ranges for normal distributions, 278Rankings

in Concept Selection Matrix, 192–194in QFD, 197

competing products, 197functional product requirements, 200

Wilcoxon Signed Rank tests, 354–355Ratings

in Cause and Effects Matrix, 249, 255in DFMEA, 263–265by market segment, 93–95

Reaction plans, 568Recording

in control plans, 563

KJ images, 156–157voices, 166–167

Reduced models, 466–467Reducing

in KJ Analysisimages, 157–160requirements, 170voices, 164, 166–167

Reduction targets, variation, 274Regression analysis, 276

multiple, 390–396single input variable, 384–390

Regulatory category for interview selection, 142Relationship between variables

regression analysis for, 276multiple, 390–396single input variable, 384–390

scatterplots for, 294–297Relationship characteristic, 140Relative Importance Surveys, 175

analyzing, 179–181in concept development, 131, 133designing and conducting, 175–178in Marketing Plan and Competitive Analysis

section, 78in production scale-up, 540for requirements identification, 181

Relative value, 100Reliability, 101–102. See also Design for

Reliability (DfR)Reliability Monitoring Program, 489Reliability Verification activity, 489Repeatability (Rpt) in measurement systems

analysis, 307–308, 315Replacement of Human evolutionary pattern in

TRIZ, 216–217Reproducibility (Rpd) in measurement systems

analysis, 307–308, 315Requirements

KJ Analysis, 164in concept development, 133conclusion, 172, 174final selection round, 170–171identifying, 172–173in Marketing Plan and Competitive

Analysis section, 78reducing, 170scrubbing, 171

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themes, 164, 169–170titling and positioning groups, 171voice recording, 166–167voice reduction, 166–167voice translating, 167–169

in QFD, 197, 199–201Relative Importance Surveys for, 181

Requirements Management system. See CriticalParameter Management

Residual plotsin fractional factorial design, 418–419in full factorial designs, 409–411for mean comparisons, 377for mixture experiments, 452, 455, 457for regression analysis

multiple, 394–395single input variable, 387–388, 390

in response surface designs, 420, 422Residuals checks for mean comparisons, 379–380Resource, Accountable, Consulted, and Informed

(RACI) approach, 71Resource assignment in schedule development, 71Response Optimizer option, 467–468

composite desirability for, 472–473desirability function, 468–472setup for, 468, 470

Response surface designs, 420–422axial points in, 424–428for mixture experiments, 454–462path of steepest ascent in, 421, 423–424purpose, 426in robust design, 439–443

Response variables in full factorial designs, 397Responsiveness dimension in quality, 102Rewards and recognition, 582Risk

development, 4BetaMax vs. VHS, 4iPod, 4–5minimizing, 5

quantifying, 25Risk Priority Number (RPN), 266–267Roadmap for Reliability, 487–489Roadmaps, project, 67–69Robust design, 429

alternative approaches to, 438–443example, 434–438quantifying performance in, 429–431

response surface techniques in, 439–443Taguchi approach to, 431–434variation analysis in, 436–438

Robust tests, 354Roof, QFD, 213–214Root Sum of Squares (RSS) analysis, 517,

519–521RPN (Risk Priority Number), 266–267Run Charts, 285, 288Runs, experimental, 397

S-curves, spending, 27Sales volume after launch, 573–579Sample statistics, 277Samples and sample size

for control plans, 564in data analysis, 346–348in mean comparisons, 349–350, 355–359for measurement system studies, 311in standard deviation comparisons, 355–359

Scatterplots, 294–297for correlation analysis, 381–382for regression analysis, 387–388

Schedulesdeveloping, 69–70

critical paths in, 71–72milestones in, 70–71resource assigning in, 71task definitions in, 70

managing, 73–74Scorecards

in Critical Parameter Managementbenefits, 232–233in DFM, 546–549information on, 230–231for minimizing product variation, 230,

232overview, 228–229

portfolio, 48, 50Scoring guidelines in Stage-Gate systems, 53Screening failures in bathtub curve, 495Screening studies in fractional factorial design,

418Scribe role for customer interviews, 148Scrubbing

KJ Analysis images, 161–162KJ Analysis requirements, 171

Security dimension in quality, 102

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Segmentation, market, 81customer interviews for, 55financial value of, 81–86ratings by, 93–95strategy for, 86–90value chain mapping in, 105–106

Serviceability dimension in quality, 101Services

defined, 3quality characteristics for, 102–103

Severity (SEV) ratings in DFMEA, 264Shifts in long-term process variation, 322–325Shockley, William, 14Short-term process capability analysis, 321–322Short-term variation standard deviation,

319–320Siadat, Barry, 6Sigma levels

Cp index in, 326–328in long term variation costs, 35–36of processes, 30–31

Sigma shift in long-term process variation,322–325

Significance levels in hypothesis tests, 348Significance tests, 348–349Simple histogram option, 279, 281Simple Set of Numbers option, 312Simplex centroid designs

augmented, 454, 456with axial points, 453constraints in, 463–464for three components, 447–448

Simplex Design Plot option, 450, 462Single factor financial sensitivity, 36–38,

116–117Single input variable, regression analysis for,

384–390Skewed distributions, 282Slope coefficients, 384–385Small Invention Inside Paradigm invention level

in TRIZ, 215Smith, Bill, 5Solution generation in Ideation process, 184–185Solver tool, 475–478SOPs (standard operating procedures) in control

plans, 568Sorting Cause and Effects Matrix input variables,

253, 257

Special cause variationin Process Capability Analysis, 336in product development, 271–272

Special cubic equations, 447, 457Specifications, tightened, 32–33Spending S-curves, 27SS (sum of squares), 378, 385, 387SST (Step-Stress Testing), 500–502Stability

in data comparisons, 350–351, 357–358, 370,373–374

in product development process, 272–273Stability of object composition in TRIZ example,

225Stage-Gate systems, 20, 51

in business plans, 75–76managing, 62–66market analysis and product definition in, 55monitoring points for, 46new product concept finalized in, 55–57new product design and supporting

manufacturing process in, 58–59opportunity assessment in, 53–55product launch plan in, 60–61structure in, 51–53validate product and process design in, 58, 60

Standard deviationscomparing, 298

more than two samples, 370–374two samples, 355–359

confidence intervals for, 369for control charts, 287in hypothesis tests, 348long-term and short-term, 319–320for normal distributions, 277–278in OptQuest, 483–484pooled, 336, 362in Process Capability Analysis, 335–336in stability, 350–351in tolerance analysis, 521–522

Standard operating procedures (SOPs) in controlplans, 568

Standard order in mixture designs, 450Star points, 424–428Start-up, product, 537Statistical analysis tools, 271, 275–276

graphical analysis techniques. See Graphicalanalysis techniques

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measurement systems analysis. SeeMeasurement systems analysis

for non-normal data, 277for normality, 277numerical descriptive statistics, 297–300sample statistics for, 277selecting, 276–277for variation, 271–275

Statistical Process Control techniques, 269Statistical tolerancing, 515

analysis, 521best distribution fit

with Crystal Ball, 525, 527–529with Minitab, 523–527

Monte Carlo Simulation in, 529–532, 535optimizing variation in, 532–535Root Sum of Squares analysis, 517,

519–521variation levels in, 522worst case analysis, 516–518

Steady State Model, 561Steady-state portion of bathtub curve, 507Steepest ascent, 421, 423–424Step-Stress Testing (SST), 500–502Straight line equations, 385, 387Strategy

linking projects to, 41–42for market segmentation, 86–90

Stress conditionsin Design Maturity Testing, 488in Step Stress tests, 500

Structured methodology, 3, 20Subgroup analysis, 335–336Substantial Invention Inside Technology level in

TRIZ, 215Substitutes in product life cycle, 17–18Success measures

after-tax profit, 23–24Internal Rate of Return, 25money, 22–23Net Present Value, 24–25

Successes in post-mortem analysis, 582Sum of squares (SS), 378, 385, 387Supply-demand system in product life cycle,

17–18Supporting manufacturing process in Stage-Gate

systems, 58–59“Surprised and delighted” attribute, 16

Surveys. See Relative Importance SurveysSurvival Function for reliability, 510SWOT Analysis tool, 91

in Marketing Plan and Competitive Analysissection, 78

opportunities and threats, 92ratings by market segment, 93–95results of, 95–96in Stage-Gate systems, 55strengths and weaknesses, 92–93

System evolution in TRIZ, 216–217

t-testsfor mean comparisons

one-sample, 351–354paired, 365–367two-sample, 356, 360–363

Taguchi, Genichi, 431, 434Taguchi approach to robust design, 431–434“Taken for granted” attribute, 15–16Tangibles dimension in quality, 102Tape recorders in customer interviews, 148Target ranges in QFD, 202Target values, comparing means to, 348–354Tasks in schedule development, 70Team roles in customer interviews, 148Teaming relationships in market segmentation,

87–88Technical contradictions

description, 214TRIZ for. See TRIZ (Theory of Inventive

Problem Solving) toolTechnical interactions in QFD, 201Technical support, 578Technical system evolution in TRIZ, 216–217Technology

in Customer Selection Matrix, 140in market segmentation, 89–90

Technology category for interview questions, 142Technology Platform Projects, 48Terminology, glossary for, 585–593Test for Equal Variances option, 374–375Testimonials, 579Tests, hypothesis. See Hypothesis testing“The more the better” attribute, 16Theme

for Requirements KJ, 169–170for voice reduction, 164, 166

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Theory of Inventive Problem Solving. See TRIZ(Theory of Inventive Problem Solving)tool

Three-response optimization, 473–478Tightened specifications, 32–33Time Lagged Cross Correlation, 561Time series analysis, 557, 561–563Titling

KJ Analysis image groups, 162–166KJ Analysis requirements, 171

Tolerance, statistical. See Statistical tolerancingTotal Sum of Squares value

for mean comparisons, 378in Process Capability Analysis, 336

Trace plots, 459–460Trade-off analyses, 214Training, product, 578Transactional customers, 87–88Transforming Properties principle in TRIZ, 225Transistors, 14–15Transitioning from Macro to Micro Level using

Energy Fields evolutionary pattern inTRIZ, 216–217

Translating voices, 167–169TRIZ (Theory of Inventive Problem Solving) tool,

214–215in business case for DFSS, 19–20Contradiction Matrix in, 217–222, 225example, 223–225final thoughts, 225key inventive principles in, 217–220, 222Law of Ideality in, 216levels of innovation in, 215–216for QFD, 201technical system evolution in, 216–217

Two-factor interaction effects, 411, 4142-level Fractional Factorial Design, 416–417,

4262-sample t-tests, 356, 360–363Two-sided confidence intervals, 367Type I errors, 344, 346Type II errors, 344Type of Customer characteristic, 140

UCL (Upper Control Limits) for control charts,286–287

Uncontrolled variables for Process VariablesMaps, 241

Unit variable cost projections, 107, 109Units in Attribute Sigma Calculator process, 339Unstable product development processes,

272–273Upper Control Limits (UCL) for control charts,

286–287Upper Specification Limit (USL) values

in Cp, 327in Cpk, 328, 330–331in long-term process capability analysis,

322–324in measurement system error, 305in short-term process capability analysis,

321–322in sigma levels, 30–32

Vacuum tubes, 13–15Validating product in Stage-Gate systems, 58, 60Value, product, 99

quality in, 100–103tools for, 105value chain mapping in, 103–106value concept, 99–100

Value-Added step for As-Is Process Maps, 244Value Chain Mapping tool, 78Value chains

mapping, 103–106QFDs across, 205, 209–211

Value Proposition Identification tool, 78Variable cost projections, 107, 109Variable relationships

regression analysis for, 276multiple, 390–396single input variable, 384–390

scatterplots for, 294–297Variables

in Cause and Effects Matrix, 247–253descriptive statistics for, 298–299for out-of-control conditions, 556–558Process Variables Maps, 237–238

big block process maps, 238–239input variables, 239–243output variables, 239

VariancesANOVA. See Analysis of Variance (ANOVA)comparing

three variances, 373–375two variances, 360–361

620 Index

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in mixture experiments, 457–458in robust design, 443

Variationmeasurement. See Measurement systems

analysisoptimization, 532–535in OptQuest, 480–484, 533–534performance, 325–326, 430–431in product development, 271–275in robust design, 430–431, 436–438scorecards for, 230, 232in statistical tolerancing, 522worst case, 519

VCRs, BetaMax vs. VHS, 4Verification phase

in Design for Reliability, 489in FMEA, 504

Vertex points in mixture designs, 450VHS format vs. BetaMax, 4Voice of the Customer (VOC)

in Design for Reliability, 489–490in post-mortem analysis, 580in product confirmation, 540for variation, 274

Voices in KJ Analysis, 154recording, 166–167reducing, 164, 166–167theme for, 164, 166translating, 167–169

Volume in financial sensitivity analysis, 38Volume of Use characteristic, 140

Warranty costs, 512–514Waste estimates, 319–321Wear-out area in bathtub curve, 498–499, 507Weibull distribution

for best distribution fit, 523–524for reliability, 509–510, 512in statistical tolerancing, 529–530

Weibull power law, 494–495Weibull shape factor, 533–534Weight parameter for desirability function, 471–472Welch, Jack, 6White, T. K., 310Wilcoxon Signed Rank tests, 354–355Within Group Sum of Squares value, 336Work area in Ideation process, 183–184Worst case analysis, 516–518Worst case gap values, 517–518Worst case variation, 519“Wow” factor, 16

Xbar Chart by Operator graph, 318Xbar values for control charts, 286–287

YO OH, 160–161

Zero Failure Testing, 501

Index 621

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