Index 601 601 Abbreviations, 595–596 Accelerated Life Testing (ALT), 500 Access dimension in quality, 102 Accuracy in measurement systems analysis, 306–307 Active listening, 149–150 Actual Factor Value in full factorial designs, 407 Advanced process control (APC), 564–565 Aesthetics dimension in quality, 101 After-tax profits calculating, 23–24 in long term variation costs, 34 in tightened specifications costs, 33 AIAG (Automotive Industry Action Group) scale, 265 Alarm and recording strategy, 563 Algorithm to Solve an Inventive Problem (ARIZ), 217–218 Aliasing in fractional factorial design, 413–416 Alkaline battery failures, 498 AlliedSignal Corporation, 5–6 ALT (Accelerated Life Testing), 500 Alternative approaches to robust design, 438–443 Alternative hypotheses, 344–346. See also Hypothesis testing Altshuller, Genrich, 19–20, 214–215. See also TRIZ (Theory of Inventive Problem Solving) tool Analysis of Variance (ANOVA) for mean comparisons from more than two samples, 370, 374 one-way, 375–380 for measurement system studies, 315 for mixture experiments, 457–458 for regression analysis, 387 Analytical data analysis, 343 Analytical Physics activity, 489 Analyze Factorial Design option, 401–403 Analyze phase in DMAIC, 8 Analyzing survey results, 179–181 Anderson-Darling statistic, 345–346 ANOVA. See Analysis of Variance (ANOVA) APC (Advanced process control), 564–565 Apparent or Conventional Solution level in TRIZ, 215 ARIZ (Algorithm to Solve an Inventive Problem), 217–218 As-Is/Can-Be Process Maps, 243–244 Assume equal variances option, 360, 369 Assumptions in Process Capability Analysis, 336 Attribute data in DFMEA, 260 Process Capability Analysis for, 339–340 in statistics, 275–276 Attribute Sigma Calculator, 339–340 Augmented simplex centroid design, 456 34205 99 601-624 idx r4 mj.ps 10/4/06 5:44 PM Page 601
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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
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
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,
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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