Visual Six Sigma : Making Data Analysis Lean

Through Visual Six Sigma, learn what your data is telling your businessIn the typical business environment of process improvement, you want simple-to-use tools that everyone can use at all levels to rapidly explore and interpret data. Visual Six Sigma: Making Data Analysis Lean helps you use your own data to drive incredible improvement within your business.Divided into three parts-background, case studies, and JMP highlights-Visual Six Sigma covers Six Sigma and Visual Six Sigma A first look at JMP Transforming pricing management Improving white polymer manufacturing Designing experiments and modeling relationshipsBroaden and deepen your application of Six Sigma thinking within your organization with the intuitive and easy to use tools in Visual Six Sigma: Making Data Analysis Lean.

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.

Preface	p. ix
Acknowledgments	p. xi
Background
Introduction	p. 3
What Is Visual Six Sigma?	p. 3
Moving beyond Traditional Six Sigma	p. 4
Making Data Analysis Lean	p. 4
Requirements of the Reader	p. 5
Six Sigma and Visual Six Sigma	p. 7
Background: Models, Data, and Variation	p. 7
Six Sigma	p. 10
Variation and Statistics	p. 13
Making Detective Work Easier through Dynamic Visualization	p. 14
Visual Six Sigma: Strategies, Process, Roadmap, and Guidelines	p. 16
Conclusion	p. 21
Notes	p. 21
A First Look at JMP“	p. 23
The Anatomy of JMP	p. 23
Visual Displays and Analyses Featured in the Case Studies	p. 39
Scripts	p. 44
Personalizing JMP	p. 47
Visual Six Sigma Data Analysis Process and Roadmap	p. 47
Techniques Illustrated in the Case Studies	p. 50
Conclusion	p. 50
Notes	p. 50
Case Studies
Reducing Hospital Late Charge Incidents	p. 57
Framing the Problem	p. 58
Collecting Data	p. 59
Uncovering Relationships	p. 62
Uncovering the Hot Xs	p. 90
Identifying Projects	p. 103
Conclusion	p. 103
Transforming Pricing Management in a Chemical Supplier	p. 105
Setting the Scene	p. 106
Framing the Problem: Understanding the Current State Pricing Process	p. 107
Collecting Baseline Data	p. 112
Uncovering Relationships	p. 121
Modeling Relationships	p. 147
Revising Knowledge	p. 152
Utilizing Knowledge: Sustaining the Benefits	p. 159
Conclusion	p. 162
Improving the Quality of Anodized Parts	p. 165
Setting the Scene	p. 166
Framing the Problem	p. 167
Collecting Data	p. 169
Uncovering Relationships	p. 183
Locating the Team on the VSS Roadmap	p. 196
Modeling Relationships	p. 197
Revising Knowledge	p. 210
Utilizing Knowledge	p. 229
Conclusion	p. 231
Note	p. 232
Iirforming Pharmaceutical Sales and Marketing	p. 233
Setting the Scene	p. 235
Collecting the Data	p. 235
Validating and Scoping the Data	p. 237
Investigating Promotional Activity	p. 263
A Deeper Understanding of Regional Differences	p. 282
Summary	p. 291
Conclusion	p. 292
Additional Details	p. 292
Note	p. 301
Improving a Polymer Manufacturing Process	p. 303
Setting the Scene	p. 305
Framing the Problem	p. 307
Reviewing Historical Data	p. 314
Measurement System Analysis	p. 320
Uncovering Relationships	p. 334
Modeling Relationships	p. 345
Revising Knowledge	p. 366
Utilizing Knowledge	p. 378
Conclusion	p. 388
Note	p. 389
Classification of Cells	p. 391
Setting the Scene	p. 393
Framing the Problem and Collecting the Data: The Wisconsin Breast Cancer Diagnostic Data Set	p. 394
Uncovering Relationships	p. 395
Constructing the Training, Validation, and Test Sets	p. 417
Modeling Relationships: Logistic Model	p. 443
Modeling Relationships: Recursive Partitioning	p. 460
Modeling Relationships: Neural Net Models	p. 467
Comparison of Classification Models	p. 480
Conclusion	p. 483
Notes	p. 483
Index	p. 485
Table of Contents provided by Ingram. All Rights Reserved.

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Visual Six Sigma : Making Data Analysis Lean

Summary

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Table of Contents

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