Practitioner's Guide to Statistics and Lean Six Sigma for Process Improvements E-Book

Mikel Harry

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1578

Veröffentlichungsjahr: 2011

Beschreibung

This hands-on book presents a complete understanding of Six Sigma and Lean Six Sigma through data analysis and statistical concepts In today's business world, Six Sigma, or Lean Six Sigma, is a crucial tool utilized by companies to improve customer satisfaction, increase profitability, and enhance productivity. Practitioner's Guide to Statistics and Lean Six Sigma for Process Improvements provides a balanced approach to quantitative and qualitative statistics using Six Sigma and Lean Six Sigma methodologies. Emphasizing applications and the implementation of data analyses as they relate to this strategy for business management, this book introduces readers to the concepts and techniques for solving problems and improving managerial processes using Six Sigma and Lean Six Sigma. Written by knowledgeable professionals working in the field today, the book offers thorough coverage of the statistical topics related to effective Six Sigma and Lean Six Sigma practices, including: * Discrete random variables and continuous random variables * Sampling distributions * Estimation and hypothesis tests * Chi-square tests * Analysis of variance * Linear and multiple regression * Measurement analysis * Survey methods and sampling techniques The authors provide numerous opportunities for readers to test their understanding of the presented material, as the real data sets, which are incorporated into the treatment of each topic, can be easily worked with using Microsoft Office Excel, Minitab, MindPro, or Oracle's Crystal Ball software packages. Examples of successful, complete Six Sigma and Lean Six Sigma projects are supplied in many chapters along with extensive exercises that range in level of complexity. The book is accompanied by an extensive FTP site that features manuals for working with the discussed software packages along with additional exercises and data sets. In addition, numerous screenshots and figures guide readers through the functional and visual methods of learning Six Sigma and Lean Six Sigma. Practitioner's Guide to Statistics and Lean Six Sigma for Process Improvements is an excellent book for courses on Six Sigma and statistical quality control at the upper-undergraduate and graduate levels. It is also a valuable reference for professionals in the fields of engineering, business, physics, management, and finance.

Leseprobe

Contents

Preface

1 Principles of Six Sigma

1.1 OVERVIEW

1.2 SIX SIGMA ESSENTIALS

1.3 QUALITY DEFINITION

1.4 VALUE CREATION

1.5 BUSINESS, OPERATIONS, PROCESS, AND INDIVIDUAL (BOPI) GOALS

1.6 UNDERPINNING ECONOMICS

1.7 PERFORMANCE METRICS

1.8 PROCESS

1.9 DESIGN COMPLEXITY

1.10 NATURE AND PURPOSE OF SIX SIGMA

1.11 NEEDS THAT UNDERLIE SIX SIGMA

1.12 WHY FOCUSING ON THE CUSTOMER IS ESSENTIAL TO SIX SIGMA

1.13 SUCCESS FACTORS

1.14 SOFTWARE APPLICATIONS

GLOSSARY

REFERENCES

2 Six Sigma Installation

2.1 OVERVIEW

2.2 SIX SIGMA LEADERSHIP—THE FUEL OF SIX SIGMA

2.3 DEPLOYMENT PLANNING

2.4 APPLICATION PROJECTS

2.5 DEPLOYMENT TIMELINE

2.6 DESIGN FOR SIX SIGMA (DFSS) PRINCIPLES

2.7 PROCESSING FOR SIX SIGMA (PFSS) PRINCIPLES

2.8 MANAGING FOR SIX SIGMA (MFSS) PRINCIPLES

2.9 PROJECT REVIEW

2.10 SUMMARY

GLOSSARY

REFERENCES AND NOTES

3 Lean Sigma Projects

3.1 OVERVIEW

3.2 INTRODUCTION

3.3 PROJECT DESCRIPTION

3.4 PROJECT GUIDELINES

3.5 PROJECT SELECTION

3.6 PROJECT SCOPE

3.7 PROJECT LEADERSHIP

3.8 PROJECT TEAMS

3.9 PROJECT FINANCIALS

3.10 PROJECT MANAGEMENT

3.11 PROJECT PAYBACK

3.12 PROJECT MILESTONES

3.13 PROJECT ROADMAP

3.14 PROJECT CHARTERS (GENERAL)

3.15 SIX SIGMA PROJECTS

Summary

3.16 PROJECT SUMMARY

GLOSSARY

REFERENCES

4 Lean Practices

4.1 OVERVIEW

4.2 INTRODUCTION

4.3 THE IDEA OF LEAN THINKING

4.4 THEORY OF CONSTRAINTS (TOC)

4.5 LEAN CONCEPT

4.6 VALUE-ADDED VERSUS NON-VALUE-ADDED ACTIVITIES

4.7 WHY COMPANIES THINK LEAN

4.8 VISUAL CONTROLS—VISUAL FACTORY

4.9 THE IDEA OF PULL (KANBAN)

4.10 5S–6S APPROACH

4.11 THE IDEA OF PERFECTION (KAIZEN)

4.12 REPLICATION—TRANSLATE

4.13 POKA-YOKE SYSTEM—MISTAKEPROOFING

4.14 SMED SYSTEM

4.15 7W þ 1 APPROACH—SEVEN PLUS ONE DEADLY WASTE(S)

4.16 6M APPROACH

4.17 SUMMARY

GLOSSARY

REFERENCES AND NOTES

5 Value Stream Mapping

5.1 OVERVIEW

5.2 INTRODUCTION

5.3 VALUE STREAM MAPPING

5.4 FOCUSED BRAINSTORMING

5.5 GRAPHICAL REPRESENTATION OF A PROCESS IN A VALUE STREAM MAP

5.6 EFFECTIVE WORKING TIME

5.7 CUSTOMER DEMAND

5.8 TAKT TIME

5.9 PITCH TIME

5.10 QUEUING TIME

5.11 CYCLE TIME

5.12 TOTAL CYCLE TIME

5.13 CALCULATION OF TOTAL LEAD TIME(S)

5.14 VALUE-ADDED PERCENTAGE AND SIX SIGMA LEVEL

5.15 DRAWING THE CURRENT-VALUE-STREAM MAP

5.16 DRAWING THE VALUE STREAM MAP

5.17 WHAT MAKES A VALUE STREAM LEAN

5.18 THE FUTURE VALUE STREAM MAP

5.19 SUMMARY

GLOSSARY

REFERENCES AND NOTES

6 Introductory Statistics and Data

6.1 OVERVIEW

6.2 INTRODUCTION

6.3 GENETIC CODE OF STATISTICS

6.4 POPULATIONS AND SAMPLES

6.5 THE IDEA OF DATA

6.6 NATURE OF DATA

6.7 DATA COLLECTION

6.8 THE IMPORTANCE OF DATA COLLECTION

6.9 SAMPLING IN SIX SIGMA

6.10 SOURCES OF DATA

6.11 DATABASE

6.12 SUMMARY

GLOSSARY

REFERENCES

7 Quality Tools

7.1 OVERVIEW

7.2 INTRODUCTION

7.3 NATURE OF SIX SIGMA VARIABLES

7.4 QUALITY FUNCTION DEPLOYMENT (QFD)

7.5 SCALES OF MEASUREMENT

7.6 DIAGNOSTIC TOOLS

7.7 ANALYTICAL METHODS

7.8 GRAPHICAL TOOLS

7.9 GRAPHICAL REPRESENTATION OF A PROCESS

7.10 SIPOC DIAGRAM

7.11 IPO DIAGRAM—GENERAL MODEL OF A PROCESS SYSTEM

7.12 FORCE-FIELD ANALYSIS

7.13 MATRIX ANALYSIS—THE IMPORTANCE OF STATISTICAL THINKING

7.14 CHECKSHEETS

7.15 SCORECARDS

7.16 AFFINITY DIAGRAM

7.17 CONCEPT INTEGRATION

GLOSSARY

REFERENCES

8 Making Sense of Data in Six Sigma and Lean

8.1 OVERVIEW

8.2 SUMMARIZING QUANTITATIVE DATA: GRAPHICAL METHODS

8.3 SUMMARIZING QUANTITATIVE DATA: NUMERICAL METHODS

8.4 ORGANIZING AND GRAPHING QUALITATIVE DATA

8.5 SUMMARIZING BIVARIATE DATA

8.6 MULTI-VARI CHARTS

GLOSSARY

EXERCISES

9 Fundamentals of Capability and Rolled Throughput Yield

9.1 OVERVIEW

9.2 INTRODUCTION

9.3 WHY CAPABILITY

9.4 SIX SIGMA CAPABILITY METRIC

9.5 DISCRETE CAPABILITY

9.6 CONTINUOUS CAPABILITY—EXAMPLE

9.7 FUNDAMENTALS OF CAPABILITY

9.8 SHORT- VERSUS LONG-TERM CAPABILITY

9.9 CAPABILITY AND PERFORMANCE

9.10 INDICES OF CAPABILITY

9.11 CALIBRATING THE SHIFT

9.12 APPLYING THE 1.5σ SHIFT CONCEPT

9.13 YIELD

9.14 HIDDEN FACTORY

GLOSSARY

REFERENCES

10 Probability

10.1 OVERVIEW

10.2 EXPERIMENTS, OUTCOMES, AND SAMPLE SPACE

10.3 CALCULATING PROBABILITY

10.4 COMBINATORIAL PROBABILITY

10.5 MARGINAL AND CONDITIONAL PROBABILITIES

10.6 UNION OF EVENTS

10.7 INTERSECTION OF EVENTS

GLOSSARY

EXERCISES

11 Discrete Random Variables and their Probability Distributions

11.1 OVERVIEW

11.2 SIX SIGMA PERFORMANCE VARIABLES

11.3 SIX SIGMA LEVERAGE VARIABLES

11.4 RANDOM VARIABLES

11.5 PROBABILITY DISTRIBUTIONS OF A DISCRETE RANDOM VARIABLE

11.6 MEAN OF A DISCRETE RANDOM VARIABLE

11.7 STANDARD DEVIATION OF A DISCRETE RANDOM VARIABLE

11.8 THE BINOMIAL DISTRIBUTION

11.9 THE POISSON PROBABILITY DISTRIBUTION

11.10 THE GEOMETRIC DISTRIBUTION

11.11 THE HYPERGEOMETRIC PROBABILITY DISTRIBUTION

GLOSSARY

EXERCISES

12 Continuous Random Variables and Their Probability Distributions

12.1 OVERVIEWS

12.2 CONTINUOUS PROBABILITY DISTRIUTIONS

12.3 THE NORMAL DISTRIBUTION

12.4 THE EXPONENTIAL DISTRIBUTION

GLOSSARY

EXERCISES

13 Sampling Distributions

13.1 OVERVIEW

13.2 SAMPLING DISTRIBUTION OF A SAMPLE MEAN

13.3 SAMPLING DISTRIBUTION OF A SAMPLE PROPORTION

13.4 THE CENTRAL-LIMIT THEOREM (CLT)

GLOSSARY

EXERCISES

14 Single-Population Estimation

14.1 OVERVIEW

14.2 MEANING OF A CONFIDENCE LEVEL

14.3 ESTIMATING A POPULATION MEAN

14.4 ESTIMATING A POPULATION PROPORTION

14.5 ESTIMATING A POPULATION VARIANCE

GLOSSARY

EXERCISES

15 Control Methods

15.1 OVERVIEW

15.2 INTRODUCTION

15.3 CONTROL LOGIC

15.4 STATISTICAL CONTROL SYSTEMS

15.5 STATISTICAL CONTROL

15.6 PREVENTION VERSUS DETECTION

15.7 A PROCESS CONTROL SYSTEM DEFINITION

15.8 VARIATION

15.9 PROCESS OUT OF CONTROL

15.10 FUNDAMENTALS OF PROCESS CONTROL

15.11 CONTINUOUS STATISTICAL PROCESS CONTROL (SPC) TOOLS

15.12 INTERPRETING PROCESS CONTROL

15.13 STATISTICAL PROCESS CONTROL AND STATISTICAL PROCESS MONITORING

15.14 THE FOUNDATION OF SPC

15.15 TOOLS FOR PROCESS CONTROLS – CONTROL CHARTS

15.16 CONTROL LIMITS

15.17 PROCESS OUT-OF-CONTROL CONDITION

15.18 WESTERN ELECTRIC RULES

15.19 CONTROL CHARTS AND HOW THEY ARE USED

15.20 PRECONTROL METHOD

15.21 CONTROL CHARTS FOR VARIABLES

15.22 CONTROL CHART FOR ATTRIBUTES

GLOSSARY

REFERENCES AND NOTES

16 Single-Population Hypothesis Tests

16.1 OVERVIEW

16.2 INTRODUCTION TO HYPOTHESIS TESTING

16.3 TESTING A CLAIM ABOUT A POPULATION MEAN

16.4 HYPOTHESIS TEST ABOUT A POPULATION PROPORTION

GLOSSARY

EXERCISES

17 Estimation and Hypothesis Tests: Two Populations

17.1 OVERVIEW

17.2 INFERENCES ABOUT DIFFERENCES BETWEEN TWO POPULATION MEANS FOR INDEPENDENT SAMPLES

17.3 INFERENCES ABOUT DIFFERENCES BETWEEN TWO POPULATION MEANS FOR PAIRED SAMPLES

17.4 INFERENCES ABOUT DIFFERENCES BETWEEN TWO POPULATION PROPORTIONS

GLOSSARY

EXERCISES

18 Chi-Square Tests

18.1 OVERVIEW

18.2 A GOODNESS-OF-FIT TEST

18.3 CONTINGENCY TABLES

18.4 TESTS OF INDEPENDENCE AND HOMOGENEITY

GLOSSARY

EXERCISES

19 Analysis of Variance

19.1 OVERVIEW

19.2 THE F DISTRIBUTION

19.3 ONE-WAY ANALYSIS OF VARIANCE

19.4 PAIRWISE COMPARISONS

19.5 MULTIFACTOR ANALYSIS OF VARIANCE

19.6 WHAT TO DO WHEN THE ASSUMPTIONS ARE UNREASONABLE

GLOSSARY

EXERCISES

20 Linear and Multiple Regression

20.1 OVERVIEW

20.2 SIMPLE REGRESSION MODEL

20.3 LINEAR REGRESSION

20.4 COEFFICIENT OF DETERMINATION AND CORRELATION

20.5 MULTIPLE REGRESSION

20.6 REGRESSION ANALYSIS

20.7 USING THE REGRESSION MODEL

20.8 RESIDUAL ANALYSIS

20.9 CAUTIONS IN USING REGRESSION

GLOSSARY

EXERCISES

21 Measurement Analysis

21.1 OVERVIEW

21.2 INTRODUCTION

21.3 MEASUREMENT

21.4 MEASUREMENT ERROR

21.5 ACCURACY AND PRECISION

21.6 MEASUREMENT SYSTEM AS A PROCESS

21.7 CATEGORIES OF MEASUREMENT ERROR THAT AFFECT LOCATION

21.8 CATEGORIES OF MEASUREMENT THAT AFFECT SPREAD

21.9 GAGE ACCURACY AND PRECISION

21.10 EXPLORING LINEARITY ERROR

21.11 GAGE REPEATABILITY AND REPRODUCIBILITY (R&R)

21.12 ANOVA METHOD VERSUS X–R METHOD

21.13 ANOVA/VARIANCE COMPONENT ANALYSIS

21.14 RULES OF THUMB

21.15 ACCEPTABILITY CRITERIA

21.16 CHAPTER REVIEW

GLOSSARY

REFERENCES

22 Design of Experiments

22.1 OVERVIEW

22.2 INTRODUCTION

22.3 DESIGN OF EXPERIMENTS (DOE) DEFINITION

22.4 ROLE OF EXPERIMENTAL DESIGN IN PROCESS IMPROVEMENT

22.5 EXPERIMENT DESIGN TOOLS

22.6 PRINCIPLES OF AN EXPERIMENTAL DESIGN

22.7 DIFFERENT TYPES OF EXPERIMENTS

22.8 INTRODUCTION TO FACTORIAL DESIGNS

22.9 FEATURES OF FACTORIAL DESIGNS—ORTHOGONALITY

22.10 FULL FACTORIAL DESIGNS

22.11 RESIDUAL ANALYSIS (22)

22.12 MODELING (22)

22.13 MULTIFACTOR EXPERIMENT

22.14 FRACTIONAL FACTORIAL DESIGNS

22.15 THE ANOVA TABLE

22.16 NORMAL PROBABILITY PLOT OF THE EFFECTS

22.17 MAIN-EFFECTS PLOT

22.18 BLOCKING VARIABLE

22.19 STATISTICAL SIGNIFICANCE

22.20 PRACTICAL SIGNIFICANCE

22.21 FUNDAMENTALS OF RESIDUAL ANALYSIS

22.22 CENTERPOINTS

22.23 NOISE FACTORS

22.24 STRATEGY OF GOOD EXPERIMENTATION

22.25 SELECTING THE VARIABLE LEVELS

22.26 SELECTING THE EXPERIMENTAL DESIGN

22.27 REPLICATION

22.28 ANALYZING THE DATA (ANOVA)

22.29 RECOMMENDATIONS

22.30 ACHIEVING THE OBJECTIVE

22.31 CHAPTER SUMMARY

22.32 CHAPTER EXAMPLES

GLOSSARY

REFERENCES

23 Design for Six Sigma (DFSS), Simulation, and Optimization

23.1 OVERVIEW

23.2 INTRODUCTION

23.3 SIX SIGMA AS STRETCH TARGET

23.4 PRODUCIBILITY

23.5 STATISTICAL TOLERANCES

23.6 DESIGN APPLICATION

23.7 DESIGN MARGIN

23.8 DESIGN QUALIFICATION

23.9 DESIGN FOR SIX SIGMA (DFSS) PRINCIPLES

23.10 DECISION POWER

23.11 EXPERIMENTATION

23.12 EXPERIMENT DESIGN

23.13 RESPONSE SURFACE DESIGNS

23.14 FACTORIAL PRODUCIBILITY

23.15 TOOLBOX OVERVIEW

23.16 MONTE CARLO SIMULATIONS

23.17 DESIGN FOR SIX SIGMA PROJECT SELECTION EXAMPLE

23.18 DEFINING SIMULATION INPUTS

23.19 DEFINING OUTPUTS AND RUNNING A SIMULATION

23.20 STOCHASTIC OPTIMIZATION: DISCOVERING THE BEST PORTFOLIO WITH THE LEAST RISK

23.21 CONCLUSIONS

GLOSSARY

REFERENCES

24 Survey Methods and Sampling Techniques

24.1 OVERVIEW

24.2 INTRODUCTION

24.3 THE SAMPLE SURVEY

24.4 THE SURVEY SYSTEM

24.5 CLEAR GOALS

24.6 TARGET POPULATION AND SAMPLE SIZE

24.7 INTERVIEWING METHOD

24.8 RESPONSE RATE, RESPONDENTS, AND NONRESPONDENTS

24.9 SURVEY METHODS

24.10 SOURCES OF INFORMATION AND DATA

24.11 ORDER OF THE QUESTIONS

24.12 PILOT TESTING THE QUESTIONNAIRE

24.13 BIASED SAMPLE OR RESPONSE ERROR

24.14 SAMPLING—RANDOM AND NONRANDOM SAMPLES

24.15 POPULATION DISTRIBUTION

24.16 SAMPLING DISTRIBUTION

24.17 SAMPLING AND NONSAMPLING ERRORS

GLOSSARY

REFERENCES

Appendix A Statistical Tables

Appendix B Answers to Selected Odd-Numbered Exercises

Index

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Library of Congress Cataloging-in-Publication Data:

Practitioner’s guide for statistics and lean six sigma for process improvements/Mikel J. Harry… [et al.].

p. cm.

Includes index.

ISBN 978-0-470-11494-0 (cloth)

1. Process control-Statistical methods. 2. Six sigma (Quality control standard) 3. Statistics-Data processing. I. Harry, Mikel J.

TS156.8.P72 2009

658.5072’7--dc22

2009034002

PREFACE

Top management is supposed to be a tree full of owls - hooting when management heads into the wrong part of the forest

—Robert Townsend, business writer.

INTRODUCTION

At times the practitioner might wonder why many people go into Lean Six Sigma and statistics. Is their DNA different? What do practitioners of Lean Six Sigma do, practice, or believe in? At a time when success depends on satisfying the need for developing quantitative skills and reasoning abilities, the mystique surrounding Lean Six Sigma persists. Practitioner’s Guide to Statistics and Lean Six Sigma for Process Improvements shows some of the payoffs for working with the fundamental concepts of statistics combined with the methodology of Lean Six Sigma. The areas of discussion are real. Most of the situations that the practitioner encounters in the book are based on hands-on experience. The goal is for the practitioner to enjoy solving business problems and have fun while doing it. The reader is not expected to become a statistician; it is understood that the practitioner might not remember in a year or so most of the techniques presented here. However, this is not the goal; what we would like to see is that you solve some of your business’s most important problems and gain appreciation for the how, the when, and the why. Lean Six Sigma is not about complex formulas and equations; it is about graphical, visual, and simple analytical methods. It is about looking — with a method — at a business problem and its processes and then creating graphical representations to work with in order to solve those business problems and improve the business processes for cost effectiveness. This is what counts.

We begin the preface of this first edition of Practitioner’s Guide to Statistics and Lean Six Sigma forProcess Improvements with a question that most Lean Six Sigma practitioners have been asking. Just what are statistics and Lean Six Sigma used for? For application purposes, Practitioner’s Guide to Statistics and Lean Six Sigma for Process Improvements is our response to this question, and an attempt to fill this need. This book represents a combined effort to create excitement, interest, curiosity, and desire in the non specialist for cost reduction and value-centric thinking that is based on the fact that errors or mistakes in process execution are directly linked to many of the economic fundamentals that underlie a business. Of course, such fundamentals include things like cycle time, work-in-process, labor cost, and non value activities that consume time and resources, to mention only a few. In this book, we attempt to create a strong desire for reading Practitioner’s Guide to Statistics and Lean Six Sigma forProcess Improvements, by knowing that each of our readers will understand that reducing defects and defect opportunities directly reduces cycles-time, work-in-process, labor, scrap, rework, and equipment utilization. Our efforts have been guided by Thomas Jefferson’s notion that an enlightened citizenry, in which practitioners, having acquired a broad knowledge of topics, can then exercise sound judgment in making business and economic decisions. Managers who have learned this material should agree that many of their past management practices and quality programs might need some updating.

The book is designed to introduce Lean Six Sigma practices and statistics. It is also intended to serve as a reference for the Lean Six Sigma community. We envisioned that our readers come from multiple communities — executive, management, and Lean Six Sigma practitioners — and possess diverse education, work experience, and skills. This aim required that the material be presented at several different levels of understanding. Recognizing that practitioners learn in a variety of ways, we have provided alternative approaches to help readers visualize and interpret common types of business problems. Thus, this book contains more than 1000 figures to explain arithmetic, algebraic, statistics, and Lean Six Sigma concepts and results. In writing this book, a struggle with the traditional teaching method in which authors present a problem and immediately solve it was encountered. Practitioner’s Guide toStatistics and Lean Six Sigma for Process Improvements believes that an important part of problem solving is to encourage reflection by the practitioner before action. Thus, examples covering fundamental concepts offer hints, rather than firm directions, to guide the reader’s choice of tools and methods.

The chapters of the book are divided into two different groups— applications and theory. Applications are emphasized in Chapters 1–9, 15, 21, 22, and 23, while theory is emphasized in Chapters 10–14, 16–20 and 24. The chapter sequence 1–24 is a natural one for mastering all of the material. On the other hand, someone who has interest in the material might begin with the summary or overview sections of the first six chapters.

Objective

The Practitioner’s Guide to Statistics and Lean Six Sigma for Process Improvements is for today’s world, where a vast number of individuals and organizations must interact within a rapidly changing, global economy. Perhaps Francis Gouillart and James Kelley said it best in their 1995 McGraw-Hill book, Transforming the Organization:

We once assumed that corporate evolution consists of long periods of stasis, punctuated by periodic adaptations, but the pace of change is too fast forth at now. Now, the corporation needs to adapt every day and this is why today’s corporations of all types and sizes are looking for top-line growth, reduction in cost structure, and increased capacity – without major capital investment...

Businesses today are looking for improvements in customer satisfaction, investor relations, process quality, sound supply chain, and supplier quality, not to mention the need for gains in organizational capability, cost effectiveness and capacity to remain competitive. These companies must deliver additional value to the bottom line while concurrently improving customer satisfaction.

Another simple example of this mission was apparent during the preparation of this book. The authors are a diverse group that includes men and women, native-born and immigrant members. They were educated at different colleges and universities and pursued diverse careers. In addition, they live or work in a half-dozen states; thus thousands of email messages and telephone calls were required to coordinate the preparation of this book. Now, replace “authors” with “workforce,” and the practitioner obtains a realistic description of the role of diversity in today’s working environment.

Lean Six Sigma is about extra ordinary sense, not common sense. Throughout this book, practitioners will encounter the latest ideas and principles of Lean Six Sigma; many of these differ from what managers learned in school or during their professional careers. For example, in science and industry, probability and risk models have become the cluster of designing operations and decision making. To assist in achieving these goals, Practitioner’s Guide to Statistics and Lean Six Sigma for Process Improvements has stressed the connections among statistics, Six Sigma principles, and Monte Carlo simulations. Naturally, the pursuit of deeper insights into these relationships depends on understanding the basic statistical principles and Lean Six Sigma methodology. No list of publications can capture the continuous sense of invention and innovation in this field; therefore, the goal of this book is to be flexible and simple in its presentation of the contemporary state of Lean Six Sigma and how it impacts our daily lives.

Approach

Since its inception, Six Sigma has expanded far beyond its initial objective of using statistics to improve the quality of manufacturing products. The Six Sigma name was derived from this initial objective. Critics, unaware of this Six Sigma future-extendibility, compared Six Sigma with total quality management (TQM). When significantly higher quality standards are demanded from processes and products, employees must individually and collectively examine the ways in which their work is accomplished. Although narrowing acceptable process variation by a factor of less than two appears to be a minor change, the number of defects resulting from a modified process is reduced by orders of magnitude. Because Six Sigma is about innovating new ways for doing things, not just fixing them after they fail, senior management participation is required to achieve the potential gains from Six Sigma.

Over time, the effectiveness of new and existing analysis methods and tools used to achieve these aggressive quality targets has improved. Practitioner’s Guide to Statistics and Lean Six Sigma for Process Improvements recognizes that these same techniques may be applied to other processes within a business, to other types of businesses, and perhaps even to managing an entire corporation. The reader should understand that the terms Lean Sigma and Lean Six Sigma are used interchangeably to describe this ever-broadening capability.

A fundamental concept of Lean Six Sigma is its use of metrics to determine success. Lean Six Sigma has been successfully implemented at many major corporations because it uses business strategies that increase market share and reduce costs. Every process, every number, every metric, every operational definition, and, in general, every step in creating the final product, service, or transaction is consistently questioned along with the integrity of the data. Only by measuring the organization’s processes and analyzing the measurements can the organization improve its processes. It is virtually impossible to evaluate the health of an organization or to know where an organization is going without measurements.

In 1883, Lord Kelvin said: “When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind.”

On the basis of this statement, it is widely recognized that Lean Six Sigma assists the bottom line in simple tasks such as

Accepting what we do not know

Not doing what we do not know

Not knowing until it is measured

Not measuring what we do not value

Not valuing what we do not measure

Many success stories linking quality directly to business performance are included in the book. In addition, many of the concepts introduced by Lean Six Sigma now appear in quality documents. See, for example, the following quote from the IBM Quality Policy.

Product leadership is one of the goals. An essential element of product leadership is quality: the quality of offering which are defect free. Organizations have to do things right the first time. Each stage in the process must produce a defect-free output. Quality is everyone’s job. Each function, each individual including all of the suppliers must assume the responsibility for a defect-free operation.

PHILOSOPHY OF THE BOOK

As important as the specific recommendations contained in the Practitioner’s Guide is the philosophy that learning Lean Six Sigma and the fundamentals of statistics is a task that can be easily accomplished by almost anyone who possesses adequate curiosity, perseverance, will power, and discipline. When readers find themselves asking why processes using these methods are so cost effective, they are on their way to becoming practitioners of Lean Six Sigma. Practitioners will be encouraged to apply the techniques and ideas introduced here to business problems, root cause analysis, decision making, economic events, controversies, and other real-life situations. To this end, the authors have chosen topics and examples from real-life experiences. Our enthusiasm may mask potential risks of incorrectly applying the Lean Six Sigma tools. There is no substitute for wisdom gained through education and experience. Consider the Law of the Instrument— Give a hammer to a small child and he/she will soon conclude that everything needs pounding. This is also sometimes stated as If you give a child a hammer, everything looks like a nail; and when you really want to drive anail, everything starts to look like a hammer. It doesn’t matter if it’s a rock or a wrench, a blender or a board or a baguette. When someone really, really wants to drive that nail, everything suddenly looks like a hammer. In this metaphor it is observed that many variables are correlated but some have no cause-and-effect relationship. The practitioner must therefore rely on a set of Lean Six Sigma methods to identify the critical few causes and leave the remaining trivial many for subsequent analysis.

Software Tools

MINITAB® examples are included throughout the book chapters. References are made to MINITAB, release 15 for Windows® 98, Windows Vista, ME, Windows NTTM4, 2000, XP Home or Professional. MINITAB is used by customers from a broad range of industries such as aerospace, financial services, manufacturing, oil and gas, pharmaceuticals, and utilities. For more information on MINITAB, see www.minitab.com.

JMP® Student Edition for Windows XP, Windows Vista, and Mac OS X 10.5 or higher is frequently referenced in the book. JMP is used by customers from a broad range of industries such as aerospace, financial services, manufacturing, oil and gas, pharmaceuticals, and utilities. For more information on the full version of JMP, see www.jmp.com.

Crystal Ball®, from Oracle, examples are included in Chapter 23. Crystal Ball is a leading spreadsheet-based software suite for predictive modeling, forecasting, Monte Carlo simulation and optimization. Crystal Ball is used by customers from a broad range of industries such as aerospace, financial services, manufacturing, oil and gas, pharmaceuticals, and utilities. With Crystal Ball, the practitioner can make tactical decisions to reach objectives and gain a competitive edge under even the most uncertain market conditions. For more information on Crystal Ball, see www.oracle.com/crystalball/index.html.

MindPro®, from the Six Sigma Management Institute, enables the rapid deployment and application of Lean Six Sigma knowledge. This video, which is based on the knowledge system, not only provides a platform for digital and blended training but also includes an integrated Best Practice Coach for on-the-job applications. MindPro also includes a Body of Knowledge that enables an enterprise to link video coaching and training directly into their company documents, critical processes and existing training materials. For more information on MindPro, see www.sixsigmamindpro.com.

ACKNOWLEDGMENTS

The main objective of this book is to encourage the use of statistics and Lean Six Sigma in the management of business, service, and manufacturing processes. We believe that logic and ordered reasoning lead to clearer problem definition, superior problem-solving ability, greater self-confidence, and improved management. The concepts and ideas included in the book come from many sources over an extended period of time. Thus, the cited references are generally those of more readily available books and journals rather than the original sources.

During the writing of Practioner’s Guide, we have received helpful comments and suggestions from many persons.

In particular, Mikel Harry would like to acknowledge the late Bill Smith, Motorola Inc. Although Bill is no longer among us in this world, his central role in the creation and initial development of Six Sigma will always be recognized and admired. Bill was a true pioneer in the field of quality management. Dr. Harry would also like to acknowledge his bride-to-be, Sandy Penwell. Her dedication and support is inspiring and deeply appreciated.

Ofelia de Hodgins wants to thank Dr.Martin Garry Hodgins from the University of North Carolina, Charlotte (UNCC), for his willing support and his major contributions to creating the many figures of the Lean Six Sigma chapters of this book; his dedication is deeply appreciated. In addition, Ofelia wishes to acknowledge the many friends and colleagues who helped mold and shape the manuscript into a viable book. In particular, Ms. de Hodgins appreciates the efforts of - in alphabetical order Tracee Lee Beebe, Marketing Director of Conversion Service International; Ginger Evans, CEO and President of Dynamic Training; Connie Januzzi from the Six Sigma Management Institute (SSMI); Claudia Nierman, graphic arts designer; and Leonardo Nierman, whose work of art and sculptures are shown in most of the museums of the world. In particular, Ofelia de Hodgins wants to thank Dr. Owen Hill of IBM Corporation and SUNY-New Paltz for long hours of impeccable editing of the Lean Six Sigma chapters. Mr. Nierman’s and Dr. Hill’s comments and suggestions were insightful and always well taken. Ms. de Hodgins wants to personally thank her triplet-children Garry II, Lizzie, and Cody for their patience, understanding, and consistent support during the writing of this book.

Prem Mann would like to take this opportunity to thank his family for their support and understanding while he was busy working on this project. Chris Lacke would like to thank his wife Cheryl, his kids, Bailey, Maddie, and Nolan, and his parents, Jay and June for all their support and understanding.

R.L.Hulbert wants to thank those who provided support during this process. Special thanks to Joan Fyfe for her inspiration and guidance and Bill Fyfe for friendship above and beyond.

The authors would like to express their utmost gratitude and appreciation to Steve Quigley (Associate Publisher, John Wiley & Sons, Inc.,) and Jacqueline Palmieri (Editorial Program Coordinator, Mathematics & Statistics, John Wiley & Sons, Inc.,). Without their help, push, support, and understanding, this project would never have been completed. Authors would also like to thank Kellsee Chu of John Wiley & Sons, Inc., and Sanchari Sil of Thomson Digital for handling the publication of this book in a very efficient manner.

The efforts and inputs of all were critical in the goal of creating excitement around solving business problems and having fun while doing it.

There is a website for this book that contains helpful and supportive information and manuals. The web address for this site is: ftp://ftp.wiley.com/public/sci_tech_med/guide_statistics. Readers are encouraged to visit this site.

1 Principles of Six Sigma

1.1 OVERVIEW

A Closer Link to Executive Thinking. Within a business, Six Sigma has different meanings for different groups; this difference depends on one’s level and respective job role in an organization. At the enterprise level, Six Sigma is often deployed as a strategic business initiative. In this context, it focuses on making significant improvements in areas such as business growth, capacity, investor relationships, and customer satisfaction. At the operations level, Six Sigma is tactical in nature and is most often directed toward improving delivery time, cost of poor quality (COPQ), defects per unit (DPU), and a host of other critical measures of operational effectiveness and efficiency. At the process level, Six Sigma is used to reduce process variability. Reducing variability minimizes the number of defects, shortens process cycle times, and decreases direct costs. At this level, the motto is simple—if you make an improvement, then on a timely basis the gains should be verifiable. In this context, the elimination of a defect, mistake, fault, or error within the “system” must directly translate into a measurable benefit such as reduced headcount, less material, and lower overhead cost. In other words, Six Sigma is a strategic and tactical system for managing total business enterprises. From this perspective, Six Sigma has the capacity and capability to deliver customer and provider satisfaction, which are key ingredients for business success. In short, Six Sigma epitomizes the ideals of business success and optimizes the control function of an enterprise. In its most elemental form, Six Sigma represents 3.4 defects per million opportunities for defect. This perspective of Six Sigma is related to a single opportunity for defect for a single critical-to-quality (CTQ) characteristic. The fundamental idea of Six Sigma is that as performance is improved, quality, capacity, cycle time, inventory levels, and other key factors are also improved. Thus, when these factors are improved, both the provider and the customer experience greater satisfaction in performing business transactions.

1.2 SIX SIGMA ESSENTIALS

The abatement of business risk is essential to Six Sigma. In this text we will explore many of the key concepts underpinning a new definition of quality. We will describe quality as the state in which value is realized for the customer and provider in every aspect of the business relationship. Simply stated, performance meets expectations; in doing so, we will demonstrate that Six Sigma is far more than a simple quality target. The fundamental tenets of Six Sigma are as follows:

Thinking Six Sigma. Explore the big ideas that power the realization of breakthrough performance and then gain insight into how these ideas create value for any type of enterprise.