Statistical Process Control: The Deming Paradigm and Beyond, Second Edition

✍ Scribed by J. Koronacki, J.R. Thompson

• Publisher: Chapman and Hall/CRC
• Year: 2001
• Tongue: English
• Leaves: 448
• Edition: 2nd
• Category: Library

✦ Synopsis

While the common practice of Quality Assurance aims to prevent bad units from being shipped beyond some allowable proportion, statistical process control (SPC) ensures that bad units are not created in the first place. Its philosophy of continuous quality improvement, to a great extent responsible for the success of Japanese manufacturing, is rooted in a paradigm as process-oriented as physics, yet produces a friendly and fulfilling work environment. The first edition of this groundbreaking text showed that the SPC paradigm of W. Edwards Deming was not at all the same as the Quality Control paradigm that has dominated American manufacturing since World War II. Statistical Process Control: The Deming Paradigm and Beyond, Second Edition reveals even more of Deming's philosophy and provides more techniques for use at the managerial level. Explaining that CEOs and service industries need SPC at least as much as production managers, it offers precise methods and guidelines for their use.Using the practical experience of the authors working both in America and Europe, this book shows how SPC can be implemented in a variety of settings, from health care to manufacturing. It also provides you with the necessary technical background through mathematical and statistical appendices. According to the authors, companies with managers who have adopted the philosophy of statistical process control tend to survive. Those with managers who do not are likely to fail. In which group will your company be?

✦ Table of Contents

Appendix B: A Brief Introduction to Stochastics......Page 0
STATISTICAL PROCESS CONTROL: The Deming Paradigm and Beyond, SECOND EDITION......Page 2
Contents......Page 5
Preface to the First Edition......Page 9
Preface to the Second Edition......Page 19
1.1 Introduction......Page 23
1.2 Quality Control: Origins, Misperceptions......Page 25
1.3 A Case Study in Statistical Process Control......Page 28
1.4 If Humans Behaved Like Machines......Page 31
1.5 Pareto’s Maxim......Page 32
1.6 Deming’s Fourteen Points......Page 36
1.7 QC Misconceptions, East and West......Page 40
1.8 White Balls, Black Balls......Page 42
1.9 The Basic Paradigm of Statistical Process Control......Page 54
1.10 Basic Statistical Procedures in Statistical Process Control......Page 55
1.11 Acceptance Sampling......Page 61
1.12 The Case for Understanding Variation......Page 63
1.13 Statistical Coda......Page 67
References......Page 69
Problems......Page 70
2.1 Introduction......Page 75
2.2 The Basic Test......Page 77
2.3 Basic Test with Equal Lot Size......Page 80
2.4 Testing with Unequal Lot Sizes......Page 85
2.5 Testing with Open-Ended Count Data......Page 89
Problems......Page 93
3.1 Introduction......Page 97
3.2 A Contaminated Production Process......Page 99
3.3 Estimation of Parameters of the “Norm” Process......Page 103
3.4 Robust Estimators for Uncontaminated Process Parameters......Page 112
3.5 A Process with Mean Drift......Page 117
3.6 A Process with Upward Drift in Variance......Page 122
3.7 Charts for Individual Measurements......Page 126
3.8 Process Capability......Page 140
Problems......Page 145
4.2 The Sequential Likelihood Ratio Test......Page 151
4.3 CUSUM Test for Shift of the Mean......Page 154
4.4 Shewhart CUSUM Chart......Page 158
4.5 Performance of CUSUM Tests on Data with Mean Drift......Page 160
4.6 Sequential Tests for Persistent Shift of the Mean......Page 163
4.7 CUSUM Performance on Data with Upward Variance Drift......Page 180
4.8 Acceptance-Rejection CUSUMs......Page 184
References......Page 187
Problems......Page 188
5.1 Introduction......Page 193
5.2 The Schematic Plot......Page 194
5.3 Smoothing by Threes......Page 199
5.4 Bootstrapping......Page 208
5.5 Pareto and Ishikawa Diagrams......Page 215
5.6 A Bayesian Pareto Analysis for System Optimization of the Space Station......Page 219
5.6.1 Hierarchical Structure......Page 220
5.6.3 A Bayesian Pareto Model......Page 221
5.6.4 An Example......Page 223
5.6.5 Allowing for the Effect of Elimination of a Problem......Page 225
5.7 The Management and Planning Tools......Page 228
References......Page 241
Problems......Page 242
6.1 Introduction......Page 247
6.2 A Simplex Algorithm for Optimization......Page 250
6.3 Selection of Objective Function......Page 259
6.4 Motivation for Linear Models......Page 264
6.5 Multivariate Extensions......Page 274
6.6 Least Squares......Page 275
6.7 Model “Enrichment”......Page 280
6.8 Testing for Model “Enrichment”......Page 282
6.9 2p Factorial Designs......Page 288
6.10 Some Rotatable Quadratic Designs......Page 292
6.11 Saturated Designs......Page 298
6.12 A Simulation Based Approach......Page 300
References......Page 303
Problems......Page 304
7.1 Introduction......Page 311
7.2 Likelihood Ratio Tests for Location......Page 312
7.3 Compound and Projection Tests......Page 324
7.4 A Robust Estimate of “In Control” Location......Page 327
Rank Test for Location Shift......Page 330
7.6 A Rank Test for Change in Scale and/or Location......Page 334
References......Page 338
Problems......Page 339
A.1 Introduction......Page 343
A.2 Elementary Arithmetic......Page 345
A.3 Linear Independence of Vectors......Page 349
A.4 Determinants......Page 350
A.5 Inverses......Page 353
A.7 Eigenvalues and Eigenvectors......Page 355
A.8 Matrix Square Root......Page 359
A.9 Gram-Schmidt Orthogonalization......Page 360
B.1 Introduction......Page 361
B.2 Conditional Probability......Page 366
B.3 Random Variables......Page 368
B.4.1 Hypergeometric Distribution......Page 373
B.4.2 Binomial and Geometric Distributions......Page 374
B.4.3 Poisson Distribution......Page 377
B.5 More on Random Variables......Page 378
B.6.1 Normal and Related Distributions......Page 382
B.6.2 Gamma Distributions......Page 385
B.6.3 t and F Distributions......Page 389
B.6.4 Weibull Distribution......Page 391
B.7 Laws of Large Numbers......Page 392
B.8 Moment-Generating Functions......Page 394
B.9 Central Limit Theorem......Page 398
B.10 Conditional Density Functions......Page 399
B.11.1 Introduction......Page 400
B.11.2 Moment Generating Functions......Page 402
B.11.4 Normal Distribution......Page 405
B.11.5 Quadratic Forms of Normal Vectors......Page 407
B.11.6 Central Limit Theorem......Page 408
B.12 Poisson Process......Page 409
B.13.1 Motivation......Page 410
B.13.2 Maximum Likelihood......Page 412
B.13.3 Confidence Intervals......Page 414
B.13.4 Testing Hypotheses......Page 417
B.13.5 Goodness-of-Fit Test......Page 422
B.13.6 Empirical Distribution Functions......Page 424
B.13.7 Nonstandard Estimators of Mean and Variance......Page 426
B.14.1 Bayes Theorem......Page 433
B.14.2 A Diagnostic Example......Page 435
B.14.3 Prior and Posterior Density Functions......Page 436
B.14.4 Example: Priors for Failure Rate of a Poisson Process......Page 438
References......Page 442
Appendix C: Statistical Tables......Page 443
C.1. Table of the Normal Distribution......Page 444
C.2. Table of the Chi-Square Distribution......Page 445
C.3. Table of Student’s t Distribution......Page 446
C.4. Table of the F Distribution with alpha = .05......Page 447
C.5. Table of the F Distribution with alpha = .01......Page 448