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: 447
• Edition: 2nd
• Category: Library

✦ Synopsis

Variation is inherent in many processes common in companies and societies. Edwards Deming, one of the architects of Japan's industrial success, regarded the understanding of variation as one of the most important managerial skills. Unfortunately only few books on management treat that subject at all, and these that do, most frequently give it only a very superficial coverage. The reviewed book is the only one I know that gives a full explanation of both the practice and the theory of statistical process control (SPC) - the way to understand variation. As a manager and TQM coach I fully recommend the book to managers and management students, as a mathematician I recommend it to students and researchers in statistics. Readers without mathematical background should not be scared by many mathematical formulas in the book that explain why SPC works. They may very well skip the reading of equations without any loss in understanding of how SPC works and how to use it in practice. In my opinion the book of James R. Thompson and Jacek Koronacki should be regarded as a must for any business school library and the bookshelf of any manager.

Andrzej Blikle Professor in mathematics and computer science Member of Academia Europaea President of "A.Blikle Ltd."

✦ Table of Contents

Contents......Page 4
1.1 Introduction......Page 22
1.2 Quality Control: Origins, Misperceptions......Page 24
1.3 A Case Study in Statistical Process Control......Page 27
1.4 If Humans Behaved Like Machines......Page 30
1.5 Pareto’s Maxim......Page 31
1.6 Deming’s Fourteen Points......Page 35
1.7 QC Misconceptions, East and West......Page 39
1.8 White Balls, Black Balls......Page 41
1.9 The Basic Paradigm of Statistical Process Control......Page 53
1.10 Basic Statistical Procedures in Statistical Process Control......Page 54
1.11 Acceptance Sampling......Page 60
1.12 The Case for Understanding Variation......Page 62
1.13 Statistical Coda......Page 66
References......Page 68
Problems......Page 69
Appendix B: A Brief Introduction to Stochastics......Page 0
2.1 Introduction......Page 74
2.2 The Basic Test......Page 76
2.3 Basic Test with Equal Lot Size......Page 79
2.4 Testing with Unequal Lot Sizes......Page 84
2.5 Testing with Open-Ended Count Data......Page 88
Problems......Page 92
3.1 Introduction......Page 96
3.2 A Contaminated Production Process......Page 98
3.3 Estimation of Parameters of the “Norm” Process......Page 102
3.4 Robust Estimators for Uncontaminated Process Parameters......Page 111
3.5 A Process with Mean Drift......Page 116
3.6 A Process with Upward Drift in Variance......Page 121
3.7 Charts for Individual Measurements......Page 125
3.8 Process Capability......Page 139
Problems......Page 144
4.2 The Sequential Likelihood Ratio Test......Page 150
4.3 CUSUM Test for Shift of the Mean......Page 153
4.4 Shewhart CUSUM Chart......Page 157
4.5 Performance of CUSUM Tests on Data with Mean Drift......Page 159
4.6 Sequential Tests for Persistent Shift of the Mean......Page 162
4.7 CUSUM Performance on Data with Upward Variance Drift......Page 179
4.8 Acceptance-Rejection CUSUMs......Page 183
References......Page 186
Problems......Page 187
5.1 Introduction......Page 192
5.2 The Schematic Plot......Page 193
5.3 Smoothing by Threes......Page 198
5.4 Bootstrapping......Page 207
5.5 Pareto and Ishikawa Diagrams......Page 214
5.6 A Bayesian Pareto Analysis for System Optimization of the Space Station......Page 218
5.6.1 Hierarchical Structure......Page 219
5.6.3 A Bayesian Pareto Model......Page 220
5.6.4 An Example......Page 222
5.6.5 Allowing for the Effect of Elimination of a Problem......Page 224
5.7 The Management and Planning Tools......Page 227
References......Page 240
Problems......Page 241
6.1 Introduction......Page 246
6.2 A Simplex Algorithm for Optimization......Page 249
6.3 Selection of Objective Function......Page 258
6.4 Motivation for Linear Models......Page 263
6.5 Multivariate Extensions......Page 273
6.6 Least Squares......Page 274
6.7 Model “Enrichment”......Page 279
6.8 Testing for Model “Enrichment”......Page 281
6.9 2p Factorial Designs......Page 287
6.10 Some Rotatable Quadratic Designs......Page 291
6.11 Saturated Designs......Page 297
6.12 A Simulation Based Approach......Page 299
References......Page 302
Problems......Page 303
7.1 Introduction......Page 310
7.2 Likelihood Ratio Tests for Location......Page 311
7.3 Compound and Projection Tests......Page 323
7.4 A Robust Estimate of “In Control” Location......Page 326
Rank Test for Location Shift......Page 329
7.6 A Rank Test for Change in Scale and/or Location......Page 333
References......Page 337
Problems......Page 338
A.1 Introduction......Page 342
A.2 Elementary Arithmetic......Page 344
A.3 Linear Independence of Vectors......Page 348
A.4 Determinants......Page 349
A.5 Inverses......Page 352
A.7 Eigenvalues and Eigenvectors......Page 354
A.8 Matrix Square Root......Page 358
A.9 Gram-Schmidt Orthogonalization......Page 359
B.1 Introduction......Page 360
B.2 Conditional Probability......Page 365
B.3 Random Variables......Page 367
B.4.1 Hypergeometric Distribution......Page 372
B.4.2 Binomial and Geometric Distributions......Page 373
B.4.3 Poisson Distribution......Page 376
B.5 More on Random Variables......Page 377
B.6.1 Normal and Related Distributions......Page 381
B.6.2 Gamma Distributions......Page 384
B.6.3 t and F Distributions......Page 388
B.6.4 Weibull Distribution......Page 390
B.7 Laws of Large Numbers......Page 391
B.8 Moment-Generating Functions......Page 393
B.9 Central Limit Theorem......Page 397
B.10 Conditional Density Functions......Page 398
B.11.1 Introduction......Page 399
B.11.2 Moment Generating Functions......Page 401
B.11.4 Normal Distribution......Page 404
B.11.5 Quadratic Forms of Normal Vectors......Page 406
B.11.6 Central Limit Theorem......Page 407
B.12 Poisson Process......Page 408
B.13.1 Motivation......Page 409
B.13.2 Maximum Likelihood......Page 411
B.13.3 Confidence Intervals......Page 413
B.13.4 Testing Hypotheses......Page 416
B.13.5 Goodness-of-Fit Test......Page 421
B.13.6 Empirical Distribution Functions......Page 423
B.13.7 Nonstandard Estimators of Mean and Variance......Page 425
B.14.1 Bayes Theorem......Page 432
B.14.2 A Diagnostic Example......Page 434
B.14.3 Prior and Posterior Density Functions......Page 435
B.14.4 Example: Priors for Failure Rate of a Poisson Process......Page 437
References......Page 441
Appendix C: Statistical Tables......Page 442
C.1. Table of the Normal Distribution......Page 443
C.2. Table of the Chi-Square Distribution......Page 444
C.3. Table of Student’s t Distribution......Page 445
C.4. Table of the F Distribution with alpha = .05......Page 446
C.5. Table of the F Distribution with alpha = .01......Page 447