Visual Six Sigma: Making Data Analysis Lean (Wiley and SAS Business Series) / Edition 1

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Your company needs effective new tools for connecting more closely with customers, identifying emerging market trends, and seizing opportunities for growth. Enter Visual Six Sigma: Making Data Analysis Lean.

Visual Six Sigma shows you how to exploit your data to make sound business decisions more quickly and easily than you could otherwise, with or without the support of a formal initiative like Six Sigma. Using the powerful visualization capabilities of JMP software from SAS Institute Inc. (a fully functional demonstration version is available for download), this hands-on resource provides you with the tools to:

  • Understand and navigate JMP statistical software
  • Use dynamic visualization methods and other statistical techniques at various stages in your data analysis process, guided by the Visual Six Sigma Data Analysis Process
  • Put the relevant how-to steps of Visual Six Sigma in action, following the Visual Six Sigma road map
  • Employ visualization to support techniques such as measurement system analysis, analysis of designed experiments, modeling, optimization and simulation, and data mining

This pragmatic guide presents six in-depth case studies highlighting Visual Six Sigma in action, while addressing complex problems and opportunities faced by individuals and teams in a variety of application areas. Easy to read, easy to digest, and easy to implement, Visual Six Sigma is a powerful profit-building resource and your must-have tool for driving lasting improvement.

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Product Details

  • ISBN-13: 9780470506912
  • Publisher: Wiley
  • Publication date: 12/21/2009
  • Series: Wiley and SAS Business Series, #28
  • Edition number: 1
  • Pages: 492
  • Sales rank: 1,293,938
  • Product dimensions: 7.30 (w) x 10.10 (h) x 1.80 (d)

Meet the Author

Ian Cox, PhD, is Solutions Manager for JMP Sales and Marketing. He has worked for Digital Equipment Corporation, Motorola, and Motorola University and is a Six Sigma Black Belt.

Marie A. Gaudard, PhD, is a Partner with the North Haven Group and an Emerita Professor of Statistics at the University of New Hampshire. She has worked extensively as a teacher and consultant in industry, focusing on statistical quality improvement, predictive modeling, and data analysis.

Philip J. Ramsey, PhD, is a Partner with the North Haven Group and a member of the statistics faculty at the University of New Hampshire. He is an industrial statistician with extensive experience in applying statistical methods to products, processes, and research and development programs.

Mia L. Stephens, MS, is an Academic Ambassador with the JMP division of SAS. Formerly a trainer, consultant, North Haven Group partner, and statistics instructor at the University of New Hampshire, she is an expert in Lean Six Sigma and Design for Six Sigma program deployment.

Leo T. Wright is Product Manager of Six Sigma and Quality Solutions for the JMP division of SAS. He has worked for several Fortune 500 manufacturing organizations and is a Six Sigma Black Belt and an ASQ Certified Quality Engineer.

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Table of Contents

Preface ix

Acknowledgments xi


CHAPTER 1 Introduction 3

What Is Visual Six Sigma? 3

Moving beyond Traditional Six Sigma 4

Making Data Analysis Lean 4

Requirements of the Reader 5

CHAPTER 2 Six Sigma and Visual Six Sigma 7

Background: Models, Data, and Variation 7

Six Sigma 10

Variation and Statistics 13

Making Detective Work Easier through Dynamic Visualization 14

Visual Six Sigma: Strategies, Process, Roadmap, and Guidelines 16

Conclusion 21

Notes 21

CHAPTER 3 A First Look at JMP® 23

The Anatomy of JMP 23

Visual Displays and Analyses Featured in the Case Studies 39

Scripts 44

Personalizing JMP 47

Visual Six Sigma Data Analysis Process and Roadmap 47

Techniques Illustrated in the Case Studies 50

Conclusion 50

Notes 50


CHAPTER 4 Reducing Hospital Late Charge Incidents 57

Framing the Problem 58

Collecting Data 59

Uncovering Relationships 62

Uncovering the Hot Xs 90

Identifying Projects 103

Conclusion 103

CHAPTER 5 Transforming Pricing Management in a Chemical Supplier 105

Setting the Scene 106

Framing the Problem: Understanding the Current

State Pricing Process 107

Collecting Baseline Data 112

Uncovering Relationships 121

Modeling Relationships 147

Revising Knowledge 152

Utilizing Knowledge: Sustaining the Benefits 159

Conclusion 162

CHAPTER 6 Improving the Quality of Anodized Parts 165

Setting the Scene 166

Framing the Problem 167

Collecting Data 169

Uncovering Relationships 183

Locating the Team on the VSS Roadmap 196

Modeling Relationships 197

Revising Knowledge 210

Utilizing Knowledge 229

Conclusion 231

Note 232

CHAPTER 7 Informing Pharmaceutical Sales and Marketing 233

Setting the Scene 235

Collecting the Data 235

Validating and Scoping the Data 237

Investigating Promotional Activity 263

A Deeper Understanding of Regional Differences 282

Summary 291

Conclusion 292

Additional Details 292

Note 301

CHAPTER 8 Improving a Polymer Manufacturing Process 303

Setting the Scene 305

Framing the Problem 307

Reviewing Historical Data 314

Measurement System Analysis 320

Uncovering Relationships 334

Modeling Relationships 345

Revising Knowledge 366

Utilizing Knowledge 378

Conclusion 388

Note 389

CHAPTER 9 Classification of Cells 391

Setting the Scene 393

Framing the Problem and Collecting the Data: The Wisconsin Breast Cancer Diagnostic Data Set 394

Uncovering Relationships 395

Constructing the Training, Validation, and Test Sets 417

Modeling Relationships: Logistic Model 443

Modeling Relationships: Recursive Partitioning 460

Modeling Relationships: Neural Net Models 467

Comparison of Classification Models 480

Conclusion 483

Notes 484

Index 485

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