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

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Overview

Praise for Visual Six Sigma: Making Data Analysis Lean

"The authors of this book have done an outstanding job of illustrating how the practice of Six Sigma can be simplified and made more user-friendly through the use of visual data analysis methods. In this context, the book is thoughtfully written and laid out in a manner that virtually anyone can follow and use. For these reasons, I would highly recommend this book for any novice practitioner or newcomer to the field of Six Sigma."
Mikel J. Harry, PhD, Co-creator of Six Sigma and CEO, Six Sigma Management Institute

"The authors of Visual Six Sigma: Making Data Analysis Lean have forged a vital link between the important technology and philosophy of Six Sigma and the power of exploratory graphical analysis. Through the use of JMP software and a variety of case studies, the book will help quality practitioners to get the most out of their data."
Dick De Veaux, PhDProfessor of Statistics, Williams College

Exploit your data to make better decisions—faster

Because of its unique visual emphasis, Visual Six Sigma opens the doors for you to take an active role in data-driven decision making, empowering you to leverage your contextual knowledge to pose relevant questions and make sound decisions.

Visual Six Sigma: Making Data Analysis Lean shows you how to leverage dynamic visualization and exploratory data analysis techniques to:

  • See the sources of variation in your data
  • Search for clues in your data to construct hypotheses about underlying behavior
  • Identify key drivers and models
  • Shape and build your own real-world Six Sigma experience

Whether your 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.

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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
  • 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.

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.

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