Statistical Methods for Quality Improvement E-Book

Thomas P. Ryan

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Wiley Series in Probability and Statistics
• Sprache: Englisch

Seitenzahl: 1191

Veröffentlichungsjahr: 2011

Beschreibung

Praise for the Second Edition "As a comprehensive statistics reference book for quality improvement, it certainly is one of the best books available." --Technometrics This new edition continues to provide the most current, proven statistical methods for quality control and quality improvement The use of quantitative methods offers numerous benefits in the fields of industry and business, both through identifying existing trouble spots and alerting management and technical personnel to potential problems. Statistical Methods for Quality Improvement, Third Edition guides readers through a broad range of tools and techniques that make it possible to quickly identify and resolve both current and potential trouble spots within almost any manufacturing or nonmanufacturing process. The book provides detailed coverage of the application of control charts, while also exploring critical topics such as regression, design of experiments, and Taguchi methods. In this new edition, the author continues to explain how to combine the many statistical methods explored in the book in order to optimize quality control and improvement. The book has been thoroughly revised and updated to reflect the latest research and practices in statistical methods and quality control, and new features include: * Updated coverage of control charts, with newly added tools * The latest research on the monitoring of linear profiles and other types of profiles * Sections on generalized likelihood ratio charts and the effects of parameter estimation on the properties of CUSUM and EWMA procedures * New discussions on design of experiments that include conditional effects and fraction of design space plots * New material on Lean Six Sigma and Six Sigma programs and training Incorporating the latest software applications, the author has added coverage on how to use Minitab software to obtain probability limits for attribute charts. new exercises have been added throughout the book, allowing readers to put the latest statistical methods into practice. Updated references are also provided, shedding light on the current literature and providing resources for further study of the topic. Statistical Methods for Quality Improvement, Third Edition is an excellent book for courses on quality control and design of experiments at the upper-undergraduate and graduate levels. the book also serves as a valuable reference for practicing statisticians, engineers, and physical scientists interested in statistical quality improvement.

Leseprobe

Contents

Cover

Series

Title Page

Copyright

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

2.8 THE SEVEN NEWER TOOLS

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

Answers to Selected Exercises

Appendix: Statistical Tables

Author Index

Subject Index

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Library of Congress Cataloging-in-Publication Data

Ryan, Thomas P., 1945- Statistical methods for quality improvement / Thomas P. Ryan. – 3rd ed. p. cm. – (Wiley series in probability and statistics ; 840) Includes bibliographical references and index. ISBN 978-0-470-59074-4 (hardback) 1. Quality control–Statistical methods. 2. Process control–Statistical methods. I. Title TS156.R9 2011 658.5′62–dc23

2011022715

oBook ISBN: 978-1-118-05811-4 ePDF ISBN: 978-1-118-05809-1 ePub ISBN: 978-1-118-05810-7

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