A
Acceptance sampling, 361–369
Analysis of means, application to nested designs, 663–670
Analysis of variance, 323–331
Attribute plan, 155–160
Average run length, 519–532, 639–642, 671–676, 677–680
B
Batch-correlated data, adaptation of exponentially weighted moving average control charts for, 545–556
Bayesian framework, 649–662
Bivariate empirical loss function, 37–47
Block effects, 311–322
Bootstrap confidence limits, 643–648
C
Capability indices, 427–439
Central limit theorem, 361–369
Chain sampling plan, 155–160
Chi-square goodness of fit tests, 371–382
Classes arising from a continuum, classification strategies for, 113–126
Classification, when classes arise from a continuum, 113–126
Clearance, 197–207
Color control, in the automotive industry, 161–170
Complex networks, 593–608
Component reliability, 593–608
Composition and property constraints, 91–111
Concise experimental designs, 403–406
Confidence intervals, for a discrimination ratio in a gauge R&R study, 383–389
Confidence regions, 419–426
Constrained optimization, 419–426
Continuous measurements, versus pass/fail data, 253–258
Control Charts
exponentially weighted moving average, adaptation for batch-correlated data, 545–556
Hotelling's χ2, 671–676
multivariate, 275–280, 639–642
multivariate CUSUM, computation of the percentage points of the run-length distributions of, 299–310
Shewhart, 519–532
synthetic, 677–680
tolerance interval control limits for, 471–487
two stage short run, 441–448, 609–638
Cumulative count control chart, 587–591
Cumulative plot, 411–417
Customer satisfaction, 147–153
D
Define-measure-analyze-improve-control method, 127–145, 259–274
Design of experiments, 161–170, 197–207, 311–322, 403–406, 533–543, 581–585, 663–670
Deterministic predictor variables, 371–382
Distribution of studentized variance, 441–448, 609–638
Draper-Lin small-composite design, 581–585
Dual response model, 419–426
Dynamic systems, 489–505
E
Electricity
environmental management system for production of, 49–65
supply industry, 449–462
Entropy method, 75–89
Environmental management system, 49–65
Error modeling, in sampling inspection, 67–74
Excel, 333–340
Experimental design, 347–350
Exponential data, 677–680
Exponentially weighted moving average, 519–532
control charts, adaptation for batch-correlated data, 545–556
F
Factor relationship diagram, 533–543
Fiber strength, 311–322
First-order mixture experiment model, 91–111
Fractional factorial designs, 311–322, 403–406
Fuzzy logic, 649–662
Fuzzy set concepts, 1–8
Fuzzy sets, 75–89
G
Gauge R&R study, 323–331, 383–389
Gear blank casting process, 351–359
Grease, 407–409
Green Belt, 127–145, 259–274
Grey relational analysis, in multiple attribute decision making problems, 209–217
Group of moving averages plans, 519–532
H
Hotelling's χ2 control chart, multivariate, 671–676
Hotelling's T 2, 275–280
House of quality, 9–21, 23–35
Human factors, 67–74
I
Importance rating, 209–217
Inference space, 533–543
Inspection systems, 557–563
Inspector error, 557–563
L
Language processing method, use in NASA's microgravity flight development process, 219–231
Layered experimental design, 91–111
Least-squares regression, 275–280
Lower confidence bound, 253–258
M
Markov chains, 183–195
Meaningfulness, 9–21
Measurement study, 383–389
Measurement system analysis, 243–251, 293–298
Measurement theory, 9–21
Metal blanking process, 197–207
Microgravity flight development process, NASA's use of the language processing method in, 219–231
Mixed effects models, 323–331
Mixed sampling plan, design and selection of, 155–160
Mixture experiment, 91–111
Monte Carlo simulation, 333–340
Multi-criterion, 1–8
Multinomial distribution, 361–369
Multiple attribute decision making problems, use of grey relational analysis in, 209–217
Multiple inspection, 557–563
Multiple quality attributes, a hybrid weight assessment system for, 75–89
Multiple stream processes, 183–195
Multistage processes, process capability improvement for, 281–292
Multivariate control chart, 275–280
Multivariate CUSUM control charts, 299–310
N
NASA. See National Aeronautics and Space Administration
National Aeronautics and Space Administration (NASA), 219–231
Nested designs, 663–670
Net weight, 407–409
Non-homogeneous Poisson process, 411–417
Nonlinear optimizations, 581–585
Non-normal process, 643–648
Non-normality, 463–469
Normalizing transformations, 371–382
Nuclear waste, 91–111
O
Objective-oriented rating system, of vendor quality performance, 147–153
Observation process monitoring charts, for systems with response lags, 489–505
Optimization, 37–47, 351–359, 419–426
of process capability, 233–242
Optimum operating conditions, 463–469
Outlier-resistant estimators, 463–469
P
Performance measure, 113–126
Power industry, 449–462
Precision-to-tolerance ratio, 243–251
Prediction intervals, 323–331
Prequential likelihood ratio, 411–417
Probability
of conformance, 253–258
content, 471–487
Process analytical measurements variation, 391–402
Process capability, 243–251
improvement for multistage processes, 281–292
optimization of, 233–242
Process capability indices, 643–648
Process dispersion, 639–642
Process monitoring, 161–170
Process target, use of regression analysis for, 37–47
Process variability, 407–409
Product development, 23–35, 649–662
Profit, 407–409
Property–composition model, 91–111
Proportion nonconforming, 427–439
Q
QFD. See Quality Function Deployment
Quality control, 67–74
Quality Function Deployment (QFD), 565–579
concept and method review, 23–35
scores, 9–21
Quality management, 23–35
Quality quandaries, 177–182, 347–350, 513–517, 687–692
R
Random effects models, 323–331
Randomization restrictions, 533–543
Regression, 113–126
Regression analysis, use for optimum process target for quality characteristics, 37–47
Reliability
prediction
during product development process, 649–662
for software, 411–417
software tool for estimation of, 593–608
Repeatability, 383–389
Reproducibility, 383–389
Response surface, 1–8, 197–207
Response surface designed experiment, for door closing effort, 581–585
Response surface experiment optimization, 419–426
Robust design, 351–359, 463–469
Robust parameter design, 419–426
Run-length distributions, of multivariate CUSUM control charts, 299–310
Runs rules, 671–676
S
Sampling
adaptive fractional, 183–195
chain, 155–160
Sampling plan
error modeling in, 67–74
three-level acceptance, 361–369
Scales, 9–21
Sensitivity analysis, 233–242
Sheet thickness, 197–207
Shewhart chart, 677–680
Shewhart control charts, 519–532
Six Sigma, 127–145, 177–182, 259–274, 587–591
Skewness, 427–439
Software reliability prediction, 411–417
Special-cause chart, 371–382
Specification limits, 253–258
Spectrocolorimeter, 161–170
SREMS, for system reliability, 333–340
Standards column, 171–176, 341–346, 507–512, 681–685
Statistical process control, 311–322, 371–382, 441–448, 449–462, 587–591
short run, 609–638
Steel industry, 391–402
Suh's axiomatic design, 565–579
Supplier quality, 243–251
Supply chain, 147–153
Synthetic control chart, 677–680
System reliability, 593–608
T
Taguchi
robust design process, for optimization of gear blank casting process, 351–359
technique, 449–462
Technical measures, 209–217
Thermal power plant, 49–65
Time series models, 489–505
Tolerance, 687–692
Tolerance interval control limits, 471–487
Tool wear, 197–207
Total quality management, 449–462
Two-stage control charting, 441–448, 609–638
U
Unimodel distributions, 427–439
V
Validation, 663–670
Variance components, 293–298, 383–389
Variation reduction, 391–402
Vendor performance rating, 147–153
Virtual integrated design method, 565–579
Visual basic, 333–340
Visual inspection, 67–74
V-process, 1–8
W
Weibull distribution, 649–662
Weight assessment, for multiple quality attributes, 75–89