Applied Data Analysis for Process Improvement

James L. Lamprecht

Description

At last, a book that offers the reader a practical approach to process improvement using examples of common problems faced by data analysts! Author James L. Lamprecht, an experienced, widely published statistician, Master Black Belt, teacher, and consultant, has succeeded in combining examples that guide the reader through data analysis, Six Sigma project definition, conducting experiments, graphical analysis, and errors to avoid, all in one concise text. Unlike other books on data analysis, Lamprecht steers clear of classic, or “perfect” examples, preferring instead to address the everyday issues that data analysts confront, and explain the value certain data does and does not offer. The book includes numerous graphs that illustrate ways to intuitively analyze data. Data analysis techniques are presented first, then the author introduces Six Sigma concepts and integrates the two disciplines in a concluding chapter.!--nl--This book is ideal for Certified Six Sigma Black Belts as well as those who are uncertified, but would like to understand how data can be analyzed. Even those who rely on sophisticated statistical software to conduct their Six Sigma analysis will benefit from this insightful yet easy-to-use book by developing a true understanding of statistics and a better understanding of the results they are receiving. Numerous examples illustrate how various techniques are applied. Each example is reviewed from the perspective of what was not said in the example; in other words, the very information you will be faced with when you conduct your own analysis. Titles of some sections in the book include the words "optional" or "advanced." These sections cover more advanced but nonetheless useful topics, but skipping these sections will not affect the overall flow of the various subjects presented.

Sujets

• Business
• Qualité, hygiène, sécurité, environnement
• Sciences & Techniques
• Statistiques
• Statistiques
• Total Quality Management
• Gestion et management
• Seis Sigma
• Economics
• Minitab
• Six Sigma

Informations

• Publié par: ASQ Quality Press
• Date de parution: 08 décembre 2004
• Nombre de lectures: 0
• EAN13: 9780873892360
• Langue: English
• Poids de l'ouvrage: 1 Mo

Extrait

Applied Data
Analysis for Process
ImprovementAlso available from ASQ Quality Press:
The Certified Six Sigma Black Belt Handbook
Donald W. Benbow and T.M. Kubiak
Applied Statistics for the Six Sigma Green Belt
Gupta Bhisham and H. Fred Walker
Design of Experiments with MINITAB
Paul Mathews
Six Sigma for the Shop Floor: A Pocket Guide
Roderick A. Munro
Six Sigma for the Office: A Pocket Guide
Roderick A. Munro
Defining and Analyzing a Business Process: A Six Sigma Pocket Guide
Jeffrey N. Lowenthal
Six Sigma Project Management: A Pocket Guide
Jeffrey N. Lowenthal
The Six Sigma Journey from Art to Science
Larry Walters
The Six Sigma Path to Leadership: Observations from the Trenches
David H. Treichler
Failure Mode and Effect Analysis: FMEA From Theory to Execution,
Second Edition
D. H. Stamatis
Customer Centered Six Sigma : Linking Customers, Process Improvement,
and Financial Results
Earl Naumann and Steven Hoisington
Design for Six Sigma as Strategic Experimentation: Planning, Designing,
and Building World-Class Products and Services
H.E. Cook
To request a complimentary catalog of ASQ Quality Press publications,
call 800-248-1946, or visit our Web site at http://qualitypress.asq.org.Applied Data
Analysis for Process
Improvement
A Practical Guide to Six Sigma Black
Belt Statistics
James L. Lamprecht
ASQ Quality Press
Milwaukee, WisconsinAmerican Society for Quality, Quality Press, Milwaukee 53203
© 2005 by
All rights reserved. Published 2005
Printed in the United States of America
12 11 10 09 08 07 06 05 5 4 3 2 1
Library of Congress Cataloging-in-Publication Data
Lamprecht, James L., 1947–
Applied data analysis for process improvement : a practical guide to Six Sigma
Black Belt statistics / James Lamprecht.
p. cm.
Includes index.
ISBN 0-87389-648-3 (soft cover : alk. paper)
1. Six sigma (Quality control standard). 2. Quality control—Statistical
methods. I. Title.
TS156.L3196 2005
658.4'013—dc22 2004030721
ISBN 0-87389-648-3
No part of this book may be reproduced in any form or by any means, electronic,
mechanical, photocopying, recording, or otherwise, without the prior written
permission of the publisher.
Publisher: William A. Tony
Acquisitions Editor: Annemieke Hytinen
Project Editor: Paul O’Mara
Production Administrator: Randall Benson
ASQ Mission: The American Society for Quality advances individual,
organizational, and community excellence worldwide through learning,
quality improvement, and knowledge exchange.
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To place orders or to request a free copy of the ASQ Quality Press Publications
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Printed on acid-free paperContents
List of Figures and Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii
Part I Applied Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Chapter 1 Intuitive Statistics: The Values of Graphs for
Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.0 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1 The Use of Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Other Graphing Techniques: The Box Plot Diagram . . . . . . . . 12
1.3 Standard Deviation and Standard Error (of the Mean) . . . . . . 21
1.4 Experimental Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
1.5 How to Collect Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
1.6 Measurement and Environmental Conditions as Sources
of Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
1.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Chapter 2 Frequency Distributions . . . . . . . . . . . . . . . . . . . . . . . . 29
2.0 Histograms and the Normal Distribution . . . . . . . . . . . . . . . . . 29
2.1 Properties of the Normal Distribution . . . . . . . . . . . . . . . . . . . 35
2.2 Frequency Distribution of Averages . . . . . . . . . . . . . . . . . . . . . 42
2.3 Degrees of Freedom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
22.4 Chi-Square (χ ) and F Distributions . . . . . . . . . . . . . . . . . . . . 45
2.5 Conclusion 50
Chapter 3 Statistical Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.0 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.1 Population versus Sample 51
3.2 Point Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.3 Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.4 How to Test a Hypothesis: Statistical Inference . . . . . . . . . . . 60
vvi Contents
3.5 Tests Concerning Means (Z-Test for Sample
Size > 30) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.6 Test Concerning Differences Between Means
(Z-Test or T-Test and Paired T-Test) . . . . . . . . . . . . . . . . . . . . 71
3.7 Tests Concerning Variances (F-Test) . . . . . . . . . . . . . . . . . . . 78
3.8 Tests Based on Count Data . . . . . . . . . . . . . . . . . . . . . . . . . . 82
3.9 How to Determine Sample Size . . . . . . . . . . . . . . . . . . . . . . . 94
3.10 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
Chapter 4 Design of Experiments 99
4.0 Introduction 99
4.1 One-Way ANOVA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
4.2 Two-Way ANOVA 113
4.3 How About Interactions? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
4.4 Comments Regarding the Sum of Squares for
Error (SSE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
4.5 Example 4.4 Latin Square (Advanced Section:
Refer to Appendix A) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
4.6 What Is the Meaning of the Word Fixed Appended
to Each Factor in Table 4.14? . . . . . . . . . . . . . . . . . . . . . . . . 119
4.7 Advanced Topic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
4.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
Chapter 5 Factorial Designs and Fractional Factorial
Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
5.0 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
5.1 Factorial Designs at Two Levels . . . . . . . . . . . . . . . . . . . . . . 133
5.2 The Use of Replication to Estimate Error Terms . . . . . . . . . 142
5.3 Fractional Factorial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
5.4 Nonmanufacturing Example . . . . . . . . . . . . . . . . . . . . . . . . . 149
5.5 What About Two or More Response Variables?
An Example from Research Design . . . . . . . . . . . . . . . . . . . 154
5.6 Economic Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
5.7 Conclusion: How to Set Up an Experiment . . . . . . . . . . . . . . 163
Chapter 6 Regression Analysis 165
6.0 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
6.1 Relationship Between Two Variables
(Correlation Coefficient) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
6.2 Linear Regression Analysis: An Example . . . . . . . . . . . . . . . 170
6.3 Curvilinear Regressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
6.4 Using Dummy Variables 177Contents vii
6.5 Regression Models With and Without Interactions . . . . . . . 181
6.6 Conclusion: Model Building with Regression
Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
Chapter 7 Response Surfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
7.0 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
7.1 Response Surface Designs: Box-Behnken . . . . . . . . . . . . . . 185
7.2 Central Composite Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
7.3 Finding the Optimum Setting to Center a Process
and Minimize the Standard Deviation . . . . . . . . . . . . . . . . . . 192
7.4 Potential Problems to Avoid When Running a DoE . . . . . . . 196
7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
Part II The DMAIC Methodology . . . . . . . . . . . . . . . . . . . . . 199
Chapter 8 On Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . 201
8.0 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
8.1 The DMAIC Model: The Foundation of Six Sigma . . . . . . 202
8.2 Data: The Source for Most Problem Definitions . . . . . . . . . 203
8.3 Types of Problem Opportunities . . . . . . . . . . . . . . . . . . . . . 203
8.4 Type I: Internal Efficiency Opportunities . . . . . . . . . . . . . . 205
8.5 The Qualitative Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 208
8.6 Analyzing the Result: The Affinity Diagram . . . . . . . . . . . . 211
8.7 Problem Definition: The Key to a Successful Project . . . . . 215
8.8 What to Consider When Selecting a Project 216
8.9 Process Analysis Phase (Analyze) . . . . . . . . . . . . . . . . . . . 216
8.10 The Cause-and-Effect Matrix . . . . . . . . . . . . . . . . . . . . . . . 224
8.11 Generic Types of Problems a