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Statistical Process Control » (2nd Edition)

Book cover image of Statistical Process Control by J. R. Thompson

Authors: J. R. Thompson, J. Koronacki
ISBN-13: 9781584882428, ISBN-10: 1584882425
Format: Hardcover
Publisher: Taylor & Francis, Inc.
Date Published: January 2002
Edition: 2nd Edition

Author Biography: J. R. Thompson

Book 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?

Booknews

A textbook for a graduate or advanced undergraduate course; some individual chapters have also been used for short industrial courses in Texas and Poland by Thompson (statistics, Rice U.) and Koronacki (artificial intelligence, Polish Academy of Sciences). To the statistical methods used in industrial process control, they add a mathematical modeling background. Annotation c. Book News, Inc., Portland, OR (booknews.com)

	Preface
1	Statistical Process Control: A Brief Overview	1
1.2	Quality Control: Origins, Misperceptions	3
1.3	A Case Study in Statistical Process Control	6
1.4	If Humans Behaved Like Machines	9
1.5	Pareto's Maxim	10
1.6	Deming's Fourteen Points	14
1.7	QC Misconceptions, East and West	19
1.8	White Balls, Black Balls	21
1.9	The Basic Paradigm of Statistical Process Control	33
1.10	Basic Statistical Procedures in Statistical Process Control	34
1.11	Acceptance Sampling	40
2	Acceptance-Rejection SPC	51
2.2	The Basic Test	53
2.3	The Basic Test with Equal Lot Size	56
2.4	Testing with Unequal Lot Sizes	61
2.5	Testing with Open Ended Count Data	66
3	The Development of Mean and Standard Deviation Control Charts	75
3.2	A Contaminated Production Process	77
3.3	Estimation of Parameters of the "Norm" Process	81
3.4	Robust Estimators for Uncontaminated Process Parameters	90
3.5	A Process with Mean Drift	96
3.6	A Process with Upward Drift in Variance	102
3.7	Charts for Individual Measurements	106
4	Sequential Approaches	127
4.2	The Sequential Likelihood Ratio Test	127
4.3	CUSUM Test for Shift of the Mean	130
4.4	Shewhart CUSUM Chart	134
4.5	Performance of CUSUM Test on Data with Mean Drift	137
4.6	Sequential Tests for Persistent Shift of the Mean	140
4.7	CUSUM Performance on Data with Upward Variance Drift	157
4.8	Acceptance-Rejection CUSUMS	161
5	Exploratory Techniques for Preliminary Analysis	169
5.2	The Schematic Plot	170
5.3	Smoothing by Threes	175
6	Optimization Approaches	189
6.2	A Simplex Algorithm for Optimization	192
6.3	Selection of Objective Function	203
6.4	Motivation for Linear Models	208
6.5	Multivariate Extensions	219
6.6	Least Squares	220
6.7	Model "Enrichment"	200[sic]
6.8	Testing for Model "Enrichment"	227
6.9	2[superscript p]Factorial Designs	233
6.10	Some Rotatable Quadratic Designs	238
6.11	Saturated Designs	245
6.12	A Simulation Based Approach	246
7	Multivariate Approaches	257
7.2	Likelihood Ratio Tests for Location	258
7.3	A Compound Test	267
7.4	A Robust Estimate of "In Control" Location	269
7.5	A Rank Test for Location Slippage	271
7.6	A Rank Test for Change in Scale and/or Location	275
	Appendix A: A Brief Introduction to Linear Algebra	283
A.2	Elementary Arithmetic	286
A.3	Linear Independence of Vectors	289
A.4	Determinants	291
A.5	Inverses	294
A.6	Definiteness of a Matrix	296
A.7	Eigenvalues and Eigenvectors	296
A.8	Matrix Square Root	300
A.9	Gram-Schmidt Orthogonalization	301
	Appendix B: A Brief Introduction to Stochastics	303
B.2	Conditional Probability	309
B.3	Random Variables	311
B.4	Discrete Probability Distributions	316
B.5	More on Random Variables	322
B.6	Continuous Probability Distributions	325
B.7	Laws of Large Numbers	335
B.8	Moment-Generating Functions	337
B.9	Central Limit Theorem	341
B.10	Conditional Density Functions	343
B.11	Random Vectors	344
B.12	Poisson Process	352
B.13	Statistical Inference	354
	Appendix C: Statistical Tables	379
C.1	Table of the Normal Distribution	380
C.2	Table of the X[superscript 2] Distribution	381
C.3	Table of Student's t Distribution	382
C.4	Table of the F(.05) Distribution	383
C.5	Table of the F(.01) Distribution	384
C.6	Table of the F(.002) Distribution	385
C.7	Table of the F(.001) Distribution	386
	Index	387