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More About This Title Visual Six Sigma: Making Data Analysis Lean
English
This book shows you how to leverage dynamic visualization and exploratory data analysis techniques to:
See the sources of variation in your dataSearch for clues in your data to construct hypotheses about underlying behaviorIdentify key drivers and modelsShape and build your own real-world Six Sigma experienceWhether you work involves a Six Sigma improvement project, a design project, a data-mining inquiry, or a scientific study, this practical breakthrough guide equips you with the strategies, process, and road map to put Visual Six Sigma to work for your company.
Broaden and deepen your implementation of Visual Six Sigma with the intuitive and easy-to-use tools found in Visual Six Sigma: Making Data Analysis Lean.
English
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.
English
Preface ix
Acknowledgments xi
PART I BACKGROUND
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
PART II CASE STUDIES
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
