Statistical Process Control For Quality Improvement- Hardcover Version

by Koronacki; J.

• ISBN13: 9781584882428
• ISBN10: 1584882425
• Edition: 2nd
• Format: Hardcover
• Copyright: 2001-12-26
• Publisher: Chapman & Hall/

Summary

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?

Author Biography

James R. Thompson is a Noah Harding Professor of Statistics at Rice University in Houston, Texas Jacek Koronacki is the Chairman of the Department of Artificial Intelligence at the Polish Academy of Sciences in Warsaw, Poland

Table of Contents

Preface to First Edition	ix
Preface to Second Edition	xix
Statistical Process Control: A Brief Overview	1	(52)
Introduction	1	(2)
Quality Control: Origins, Misperceptions	3	(3)
A Case Study in Statistical Process Control	6	(3)
If Humans Behaved Like Machines	9	(1)
Pareto's Maxim	10	(4)
Deming's Fourteen Points	14	(4)
QC Misconceptions, East and West	18	(2)
White Balls, Black Balls	20	(12)
The Basic Paradigm of Statistical Process Control	32	(1)
Basic Statistical Procedures in Statistical Process Control	33	(6)
Acceptance Sampling	39	(2)
The Case for Understanding Variation	41	(4)
Statistical Coda	45	(8)
References	47	(1)
Problems	48	(5)
Acceptance-Rejection SPC	53	(22)
Introduction	53	(2)
The Basic Test	55	(3)
Basic Test with Equal Lot Size	58	(5)
Testing with Unequal Lot Sizes	63	(4)
Testing with Open-Ended Count Data	67	(8)
Problems	71	(4)
The Development of Mean and Standard Deviation Control Charts	75	(54)
Introduction	75	(2)
A Contaminated Production Process	77	(4)
Estimation of Parameters of the ``Norm'' Process	81	(9)
Robust Estimators for Uncontaminated Process Parameters	90	(5)
A Process with Mean Drift	95	(5)
A Process with Upward Drift in Variance	100	(4)
Charts for Individual Measurements	104	(14)
Process Capability	118	(11)
References	123	(1)
Problems	123	(6)
Sequential Approaches	129	(42)
Introduction	129	(1)
The Sequential Likelihood Ratio Test	129	(3)
CUSUM Test for Shift of the Mean	132	(4)
Shewhart CUSUM Chart	136	(2)
Performance of CUSUM Tests on Data with Mean Drift	138	(3)
Sequential Tests for Persistent Shift of the Mean	141	(17)
CUSUM Performance on Data with Upward Variance Drift	158	(4)
Acceptance-Rejection CUSUMs	162	(9)
References	165	(1)
Problems	166	(5)
Exploratory Techniques for Preliminary Analysis	171	(54)
Introduction	171	(1)
The Schematic Plot	172	(5)
Smoothing by Threes	177	(9)
Bootstrapping	186	(7)
Pareto and Ishikawa Diagrams	193	(4)
A Bayesian Pareto Analysis for System Optimization of the Space Station	197	(9)
The Management and Planning Tools	206	(19)
References	219	(1)
Problems	220	(5)
Optimization Approaches	225	(64)
Introduction	225	(3)
A Simplex Algorithm for Optimization	228	(9)
Selection of Objective Function	237	(5)
Motivation for Linear Models	242	(10)
Multivariate Extensions	252	(1)
Least Squares	253	(5)
Model ``Enrichment''	258	(2)
Testing for Model ``Enrichment''	260	(6)
2p Factorial Designs	266	(4)
Some Rotatable Quadratic Designs	270	(6)
Saturated Designs	276	(2)
A Simulation Based Approach	278	(11)
References	281	(1)
Problems	282	(7)
Multivariate Approaches	289	(32)
Introduction	289	(1)
Likelihood Ratio Tests for Location	290	(12)
Compound and Projection Tests	302	(3)
A Robust Estimate of ``In Control'' Location	305	(3)
A Rank Test for Location Slippage	308	(4)
A Rank Test for Change in Scale and/or Location	312	(9)
References	316	(1)
Problems	317	(4)
Appendix A: A Brief Introduction to Linear Algebra	321	(18)
A.1. Introduction	321	(2)
A.2. Elementary Arithmetic	323	(4)
A.3. Linear Independence of Vectors	327	(1)
A.4. Determinants	328	(3)
A.5. Inverses	331	(2)
A.6. Definiteness of a Matrix	333	(1)
A.7. Eigenvalues and Eigenvectors	333	(4)
A.8. Matrix Square Root	337	(1)
A.9. Gram-Schmidt Orthogonalization	338	(1)
Appendix B: A Brief Introduction to Stochastics	339	(82)
B.1 Introduction	339	(5)
B.2. Conditional Probability	344	(2)
B.3. Random Variables	346	(5)
B.4. Discrete Probability Distributions	351	(5)
B.5. More on Random Variables	356	(4)
B.6. Continuous Probability Distributions	360	(10)
B.7. Laws of Large Numbers	370	(2)
B.8. Moment-Generating Functions	372	(4)
B.9. Central Limit Theorem	376	(1)
B.10. Conditional Density Functions	377	(1)
B.11. Random Vectors	378	(9)
B.12. Poisson Process	387	(1)
B.13. Statistical Inference	388	(23)
B.14. Bayesian Statistics	411	(10)
References	420	(1)
Appendix C: Statistical Tables	421	(6)
C.1 Table of the Normal Distribution	422	(1)
C.2. Table of the Chi-Square Distribution	423	(1)
C.3. Table of Student's t Distribution	424	(1)
C.4. Table of the F Distribution with α = .05	425	(1)
C.5. Table of the F Distribution with α = .01	426	(1)
Index	427