Contents

Cover

Series

Title Page

Preface

Preface to the Second Edition

Preface to the First Edition

Part I: Fundamental Quality Improvement and Statistical Concepts

Chapter 1: Introduction

1.1 QUALITY AND PRODUCTIVITY

1.2 QUALITY COSTS (OR DOES IT?)

1.3 THE NEED FOR STATISTICAL METHODS

1.4 EARLY USE OF STATISTICAL METHODS FOR IMPROVING QUALITY

1.5 INFLUENTIAL QUALITY EXPERTS

1.6 SUMMARY

REFERENCES

Chapter 2: Basic Tools for Improving Quality

2.1 HISTOGRAM

2.2 PARETO CHARTS

2.3 SCATTER PLOTS

2.4 CONTROL CHART

2.5 CHECK SHEET

2.6 CAUSE-AND-EFFECT DIAGRAM

2.7 DEFECT CONCENTRATION DIAGRAM

2.9 SOFTWARE

2.10 SUMMARY

REFERENCES

EXERCISES

Chapter 3: Basic Concepts in Statistics and Probability

3.1 PROBABILITY

3.2 SAMPLE VERSUS POPULATION

3.3 LOCATION

3.4 VARIATION

3.5 DISCRETE DISTRIBUTIONS

3.6 CONTINUOUS DISTRIBUTIONS

3.7 CHOICE OF STATISTICAL DISTRIBUTION

3.8 STATISTICAL INFERENCE

3.9 ENUMERATIVE STUDIES VERSUS ANALYTIC STUDIES

REFERENCES

EXERCISES

Part II: Control Charts and Process Capability

Chapter 4: Control Charts for Measurements With Subgrouping (for One Variable)

4.1 BASIC CONTROL CHART PRINCIPLES

4.2 REAL-TIME CONTROL CHARTING VERSUS ANALYSIS OF PAST DATA

4.3 CONTROL CHARTS: WHEN TO USE, WHERE TO USE, HOW MANY TO USE

4.4 BENEFITS FROM THE USE OF CONTROL CHARTS

4.5 RATIONAL SUBGROUPS

4.6 BASIC STATISTICAL ASPECTS OF CONTROL CHARTS

4.7 ILLUSTRATIVE EXAMPLE

4.8 ILLUSTRATIVE EXAMPLE WITH REAL DATA

4.9 DETERMINING THE POINT OF A PARAMETER CHANGE

4.10 ACCEPTANCE SAMPLING AND ACCEPTANCE CONTROL CHART

4.11 MODIFIED LIMITS

4.12 DIFFERENCE CONTROL CHARTS

4.13 OTHER CHARTS

4.14 AVERAGE RUN LENGTH (ARL)

4.15 DETERMINING THE SUBGROUP SIZE

4.16 OUT-OF-CONTROL ACTION PLANS

4.17 ASSUMPTIONS FOR THE CHARTS IN THIS CHAPTER

4.18 MEASUREMENT ERROR

4.19 SOFTWARE

4.20 SUMMARY

APPENDIX

REFERENCES

EXERCISES

Chapter 5: Control Charts for Measurements Without Subgrouping (for One Variable)

5.1 INDIVIDUAL OBSERVATIONS CHART

5.2 TRANSFORM THE DATA OR FIT A DISTRIBUTION?

5.3 MOVING AVERAGE CHART

5.4 CONTROLLING VARIABILITY WITH INDIVIDUAL OBSERVATIONS

5.5 SUMMARY

5.6 APPENDIX

REFERENCES

EXERCISES

Chapter 6: Control Charts for Attributes

6.1 CHARTS FOR NONCONFORMING UNITS

EXAMPLE 6.1

6.2 CHARTS FOR NONCONFORMITIES

EXAMPLE 6.2

6.3 SUMMARY

REFERENCES

EXERCISES

Chapter 7: Process Capability

7.1 DATA ACQUISITION FOR CAPABILITY INDICES

7.2 PROCESS CAPABILITY INDICES

7.3 ESTIMATING THE PARAMETERS IN PROCESS CAPABILITY INDICES

7.4 DISTRIBUTIONAL ASSUMPTION FOR CAPABILITY INDICES

7.5 CONFIDENCE INTERVALS FOR PROCESS CAPABILITY INDICES

7.6 ASYMMETRIC BILATERAL TOLERANCES

7.7 CAPABILITY INDICES THAT ARE A FUNCTION OF PERCENT NONCONFORMING

7.8 MODIFIED k INDEX

7.9 OTHER APPROACHES

7.10 PROCESS CAPABILITY PLOTS

7.11 PROCESS CAPABILITY INDICES VERSUS PROCESS PERFORMANCE INDICES

7.12 PROCESS CAPABILITY INDICES WITH AUTOCORRELATED DATA

7.13 SOFTWARE FOR PROCESS CAPABILITY INDICES

7.14 SUMMARY

REFERENCES

EXERCISES

Chapter 8: Alternatives to Shewhart Charts

8.1 INTRODUCTION

8.2 CUMULATIVE SUM PROCEDURES: PRINCIPLES AND HISTORICAL DEVELOPMENT

8.3 CUSUM PROCEDURES FOR CONTROLLING PROCESS VARIABILITY

8.4 APPLICATIONS OF CUSUM PROCEDURES

8.5 GENERALIZED LIKELIHOOD RATIO CHARTS: COMPETITIVE ALTERNATIVE TO CUSUM CHARTS

8.6 CUSUM PROCEDURES FOR NONCONFORMING UNITS

8.7 CUSUM PROCEDURES FOR NONCONFORMITY DATA

8.8 EXPONENTIALLY WEIGHTED MOVING AVERAGE CHARTS

8.9 SOFTWARE

8.10 SUMMARY

REFERENCES

EXERCISES

Chapter 9: Multivariate Control Charts for Measurement and Attribute Data

9.1 HOTELLING’S T2 DISTRIBUTION

9.2 A T2 CONTROL CHART

9.3 MULTIVARIATE CHART VERSUS INDIVIDUAL -CHARTS

9.4 CHARTS FOR DETECTING VARIABILITY AND CORRELATION SHIFTS

9.5 CHARTS CONSTRUCTED USING INDIVIDUAL OBSERVATIONS

9.6 WHEN TO USE EACH CHART

9.7 ACTUAL ALPHA LEVELS FOR MULTIPLE POINTS

9.8 REQUISITE ASSUMPTIONS

9.9 EFFECTS OF PARAMETER ESTIMATION ON ARL

9.10 DIMENSION-REDUCTION AND VARIABLE SELECTION TECHNIQUES

9.11 MULTIVARIATE CUSUM CHARTS

9.12 MULTIVARIATE EWMA CHARTS

9.13 EFFECT OF MEASUREMENT ERROR

9.14 APPLICATIONS OF MULTIVARIATE CHARTS

9.15 MULTIVARIATE PROCESS CAPABILITY INDICES

9.16 SUMMARY

APPENDIX

REFERENCES

EXERCISES

Chapter 10: Miscellaneous Control Chart Topics

10.1 PRE-CONTROL

10.2 SHORT-RUN SPC

10.3 CHARTS FOR AUTOCORRELATED DATA

10.4 CHARTS FOR BATCH PROCESSES

10.5 CHARTS FOR MULTIPLE-STREAM PROCESSES

10.6 NONPARAMETRIC CONTROL CHARTS

10.7 BAYESIAN CONTROL CHART METHODS

10.8 CONTROL CHARTS FOR VARIANCE COMPONENTS

10.9 CONTROL CHARTS FOR HIGHLY CENSORED DATA

10.10 NEURAL NETWORKS

10.11 ECONOMIC DESIGN OF CONTROL CHARTS

10.12 CHARTS WITH VARIABLE SAMPLE SIZE AND/OR VARIABLE SAMPLING INTERVAL

10.13 USERS OF CONTROL CHARTS

10.14 SOFTWARE FOR CONTROL CHARTING

BIBLIOGRAPHY

EXERCISES

Part III: Beyond Control Charts: Graphical and Statistical Methods

Chapter 11: Graphical Methods

11.1 HISTOGRAM

11.2 STEM-AND-LEAF DISPLAY

11.3 DOT DIAGRAMS

11.4 BOXPLOT

11.5 NORMAL PROBABILITY PLOT

11.6 PLOTTING THREE VARIABLES

11.7 DISPLAYING MORE THAN THREE VARIABLES

11.8 PLOTS TO AID IN TRANSFORMING DATA

11.9 SUMMARY

REFERENCES

EXERCISES

Chapter 12: Linear Regression

12.1 SIMPLE LINEAR REGRESSION

12.2 WORTH OF THE PREDICTION EQUATION

12.3 ASSUMPTIONS

12.4 CHECKING ASSUMPTIONS THROUGH RESIDUAL PLOTS

12.5 CONFIDENCE INTERVALS AND HYPOTHESIS TEST

12.6 PREDICTION INTERVAL FOR Y

12.7 REGRESSION CONTROL CHART

12.8 CAUSE-SELECTING CONTROL CHARTS

12.9 LINEAR, NONLINEAR, AND NONPARAMETRIC PROFILES

12.10 INVERSE REGRESSION

12.11 MULTIPLE LINEAR REGRESSION

12.12 ISSUES IN MULTIPLE REGRESSION

12.13 SOFTWARE FOR REGRESSION

12.14 SUMMARY

REFERENCES

EXERCISES

Chapter 13: Design of Experiments

13.1 A SIMPLE EXAMPLE OF EXPERIMENTAL DESIGN PRINCIPLES

13.2 PRINCIPLES OF EXPERIMENTAL DESIGN

13.3 STATISTICAL CONCEPTS IN EXPERIMENTAL DESIGN

13.4 t-TESTS

13.5 ANALYSIS OF VARIANCE FOR ONE FACTOR

13.6 REGRESSION ANALYSIS OF DATA FROM DESIGNED EXPERIMENTS

13.7 ANOVA FOR TWO FACTORS

13.8 THE 23 DESIGN

13.9 ASSESSMENT OF EFFECTS WITHOUT A RESIDUAL TERM

13.10 RESIDUAL PLOT

13.11 SEPARATE ANALYSES USING DESIGN UNITS AND UNCODED UNITS

13.12 TWO-LEVEL DESIGNS WITH MORE THAN THREE FACTORS

13.13 THREE-LEVEL FACTORIAL DESIGNS

13.14 MIXED FACTORIALS

13.15 FRACTIONAL FACTORIALS

13.16 OTHER TOPICS IN EXPERIMENTAL DESIGN AND THEIR APPLICATIONS

13.17 SUMMARY

REFERENCES

EXERCISES

Chapter 14: Contributions of Genichi Taguchi and Alternative Approaches

14.1 “TAGUCHI METHODS”

14.2 QUALITY ENGINEERING

14.3 LOSS FUNCTIONS

14.4 DISTRIBUTION NOT CENTERED AT THE TARGET

14.5 LOSS FUNCTIONS AND SPECIFICATION LIMITS

14.6 ASYMMETRIC LOSS FUNCTIONS

14.7 SIGNAL-TO-NOISE RATIOS AND ALTERNATIVES

14.8 EXPERIMENTAL DESIGNS FOR STAGE ONE

14.9 TAGUCHI METHODS OF DESIGN

14.10 DETERMINING OPTIMUM CONDITIONS

14.11 SUMMARY

REFERENCES

EXERCISES

Chapter 15: Evolutionary Operation

15.1 EVOP ILLUSTRATIONS

15.2 THREE VARIABLES

15.3 SIMPLEX EVOP

15.4 OTHER EVOP PROCEDURES

15.5 MISCELLANEOUS USES OF EVOP

15.6 SUMMARY

APPENDIX

EXERCISES

REFERENCES

Chapter 16: Analysis of Means

16.1 ANOM FOR ONE-WAY CLASSIFICATIONS

16.2 ANOM FOR ATTRIBUTE DATA

16.3 ANOM WHEN STANDARDS ARE GIVEN

16.4 ANOM FOR FACTORIAL DESIGNS

16.5 ANOM WHEN AT LEAST ONE FACTOR HAS MORE THAN TWO LEVELS

16.6 USE OF ANOM WITH OTHER DESIGNS

16.7 NONPARAMETRIC ANOM

16.8 SUMMARY

APPENDIX

REFERENCES

EXERCISES

Chapter 17: Using Combinations of Quality Improvement Tools

17.1 CONTROL CHARTS AND DESIGN OF EXPERIMENTS

17.2 CONTROL CHARTS AND CALIBRATION EXPERIMENTS

17.3 SIX SIGMA PROGRAMS

17.4 STATISTICAL PROCESS CONTROL AND ENGINEERING PROCESS CONTROL

REFERENCES

Appendix: Statistical Tables

Author Index

Subject Index

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Preface

The field of statistical methods applied to quality improvement continues to evolve and there has been an attempt to parallel this development with the editions of this book.

For the control chart chapters, Chapter 9 on multivariate control charts has grown considerably, with several new sections and many additional references.

There is also a major addition to Chapter 12. Within the past 10 years there has been considerable research on the monitoring of linear profiles and other types of profiles. Section 12.9, a moderately long section, was added to cover this new material.

A major addition to the chapter on attribute control charts (Chapter 6) has been the sections on how to use software such as MINITAB® to obtain probability limits for attribute charts, with this addition motivated by reader feedback. That chapter also contains 15 new references.

Two sections were added to the chapter on process capability indices, Chapter 7, in addition to 16 new references.

Chapter 8, on alternatives to Shewhart charts, has been expanded considerably to include sections on the effects of parameter estimation on the properties of CUSUM and EWMA procedures, in addition to information on certain freeware that can be used to aid in the design of CUSUM procedures. Following the recommendation of a colleague, a section on generalized likelihood ratio charts (Section 8.5) has also been added, in addition to 28 new chapter references.

An important, although brief, section on conditional effects was added to Chapter 13, along with a section on fraction of design space plots and 31 new references. Chapter 14 has one new section and four additional references. More material on Six Sigma programs and training has been added to Chapter 17, and there is a new section on Lean Six Sigma, in addition to eight new references.

There has been a moderate increase in the number of chapter exercises, including nine new exercises in Chapter 3, five in Chapter 4, a total of eleven in Chapters 5–8, and five in Chapter 13.

For a one-semester college course, Chapters 4–10 could form the basis for a course that covers control charts and process capability. Instructors who wish to cover only basic concepts might use Chapters 1, 2, as much of 3 as is necessary, 4, 5, and 6, and selectively choose from Chapters 7, 8, and 10.

The book might also be used in a special topics design of experiments course, with emphasis on Chapters 13 and 14, with Chapter 16 also covered and perhaps Chapter 15. For reader convenience, the book's data sets can be found online at:

`ftp://ftp.wiley.com/public/sci_tech_med/quality_improvement`

I am indebted to the researchers who have made many important contributions since the publication of the previous edition, and I am pleased to present their work in addition to my own work. I am also grateful for the feedback from instructors who have taught from the first two editions and also appreciate the support of my editor at Wiley, Susanne Steitz-Filler, and the work of the production people, especially Rosalyn Farkas.

Thomas P. Ryan

December 2010

Preface to the Second Edition

There have been many developments in statistical process control (SPC) during the past ten years, and many of those developments have been incorporated into this edition.

In particular, major changes were made to the chapters on process capability and multivariate control charts as much material has been added with the result that these chapters are now considerably longer.

Chapter 10 has also been considerably expanded and now includes sections on short-run control charts, pre-control, autocorrelated data, nonparametric control charts, and various other topics that were not covered in the first edition.

Chapter 13 on the design of experiments is noticeably longer, in part because of the addition of material on robust design considerations. Chapter 14 on Taguchi methods and alternatives while retaining the material from the first edition now includes considerable discussion and illustration of combined arrays and product arrays.

Chapter 17 is a new chapter on using SPC tools together as is done in Six Sigma programs. These programs are also discussed in the chapter.

Other significant additions include material on probability-type limits for attribute charts and cause-selecting (regression-type) control charts.

In addition to new material, retained material from the first edition has been extensively reorganized. In particular, cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) methods are now in a separate chapter, and are covered in considerable detail.

The first edition has been used in college courses as well as in short courses. Chapters 4–10 of the second edition could form the basis for a course that covers control charts and process capability. Instructors who wish to cover only basic concepts might cover Chapters 1, 2, as much of 3 as is necessary, 4, 5, and 6, and selectively choose from 7, 8 and 10.

The book might also be used in a course on design of experiments, especially a special topics course. There are some topics in Chapters 13 and 14 that have not been covered in experimental design texts, and evolutionary operation and analysis of means (Chapters 15 and 16, respectively) are not covered to any extent in design texts. So an atypical design course could be put together using Chapters 13–16 as a basis.

I am indebted to the researchers who have made many important contributions during the past 10 years, and I am pleased to present their work, in addition to my own work.

Many people have commented on the strengths and weaknesses of the first edition and their comments were considered for the second edition.

I am also indebted to Bill Woodall and Dennis Lin who made many helpful suggestions when the second edition was in manuscript form, and Rich Charnigo's proofreading assistance is gratefully acknowledged. I also appreciate the support of my editor at Wiley, Steve Quigley, and the work of the production people, especially Rosalyn Farkas.

Thomas P. Ryan

July 1999

Preface to the First Edition

A moderate number of books have been written on the subject of statistical quality control, which in recent years has also been referred to as statistical process control (SPC). These range from books that contain only the basic control charts to books that also contain material on acceptance sampling and selected statistical methods such as regression and analysis of variance.

Statistical Methods for Quality Improvement was written in recognition of the fact that quality improvement requires the use of more than just control charts. In particular, it would be difficult to keep a particular process characteristic “in control” without some knowledge of the factors affecting that characteristic. Consequently, Chapters 13–16 were written to provide insight into statistically designed experiments and related topics.

The first two chapters provide an overview of the use of statistics in quality improvement in the United States and Japan. Chapter 3 presents statistical distributions that are needed for the rest of the book, and also reviews basic concepts in probability and statistics. Basic control chart principles are discussed in Chapter 4, and Chapters 5, 6, 8, and 9 contain the material on the various control charts. This material has several unique features. In particular, there is some emphasis on cumulative sum (CUSUM) procedures, and an entire chapter (Chapter 9) is devoted to multivariate charts. Chapter 7 discusses the commonly used process capability indices and compares them. The bibliography of control chart applications at the end of Chapter 10 is another unique feature of the book.

Quality improvement practitioners are beginning to recognize what can be accomplished using statistical design of experiments, but progress has been slow. With this in mind, Chapter 13 was written to show what can be accomplished using experimental design principles.

In recent years there has been much interest and discussion regarding a set of statistical and nonstatistical tools referred to as Taguchi methods. These are critically examined in Chapter 14. Evolutionary Operation is presented in Chapter 15; Chapter 16 is an updated treatment of Analysis of Means. The latter is a valuable tool that allows nonstatisticians, in particular, to analyze data from designed experiments.

In general, there has been a conscious attempt to bring the reader up to date in regard to the various topics that are presented in each chapter. There was also a concerted effort to use simple heuristics and intuitive reasoning, rather than relying heavily upon mathematical and statistical formalism and symbolism. The control chart material, in particular, has also been written under the assumption that a sizable percentage of readers will have access to a computer for control charting.