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* Visual six sigma is a practical and pragmatic approach to data analysis and process improvement. * This approach has been developed in response to a growing business need to broaden the use of six sigma-type thinking beyond the realms of highly trained and statistically savvy black and green belts.
List of contents
Preface.
Acknowledgements.
Part I: Background.
Chapter 1 Introduction.
What Is Visual Six Sigma?
Moving Beyond Traditional Six Sigma.
Making Data Analysis Lean.
Requirements of the Reader.
Chapter 2 Six Sigma and Visual Six Sigma.
Background: Models, Data, and Variation.
Six Sigma.
Variation and Statistics.
Making Detective Work Easier through Dynamic Visualization.
Visual Six Sigma: Strategies, Process, Roadmap, and Guidelines.
Conclusion.
Notes.
Chapter 3 A First Look at JMP.
The Anatomy of JMP.
Visual Displays and Analyses Featured in the Case Studies.
Scripts.
Personalizing JMP.
Visual Six Sigma Data Analysis Process and Roadmap.
Techniques Illustrated in Case Studies.
Conclusion.
Notes.
Part II: Case Studies.
Chapter 4 Reducing Hospital Late Charge Incidents.
Framing the Problem.
Collecting Data.
Uncovering Relationships.
Uncovering the Hot Xs.
Identifying Projects.
Conclusion.
Chapter 5 Transforming Pricing Management in a Chemical Supplier.
Setting the Scene.
Framing the Problem: Understanding the Current State Pricing Process.
Collecting Baseline Data.
Uncovering Relationships.
Modeling Relationships.
Revising Knowledge.
Utilizing Knowledge: Sustaining the Benefits.
Conclusion.
Chapter 6 Improving the Quality of Anodized Parts.
Setting the Scene.
Framing the Problem.
Collecting Data.
Uncovering Relationships.
Finding the Team on the VSS Roadmap.
Modeling Relationships.
Revise Knowledge.
Utilizing Knowledge.
Conclusion.
Notes.
Chapter 7 Informing Pharmaceutical Sales and Marketing.
Setting the Scene.
Collecting the Data.
Validating and Scoping the Data.
Investigating Promotional Activity.
A Deeper Understanding of Regional Differences.
Summary.
Conclusion.
Additional Details.
Notes.
Chapter 8 Improving a Polymer Manufacturing Process.
Setting the Scene.
Framing the Problem.
Reviewing Historical Data.
Measurement Systems Analysis.
Uncovering Relationships.
Modeling Relationships.
Revising Knowledge.
Utilizing Knowledge.
Conclusion.
Note.
Chapter 9 Classification of Cells.
Setting the Scene.
Framing the Problem and Collecting the Data: The Wisconsin Breast Cancer. Diagnostic Data Set.
Uncovering Relationships.
Constructing the Training, Validation, and Test Sets.
Modeling Relationships: Logistic Model.
Modeling Relationships: Recursive Partitioning.
Modeling Relationships: Neural Net Models.
Comparison of Classification Models.
Conclusion.
Notes.
Index.
