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Statistical Engineering: An Algorithm for Reducing Variation in Manufacturing Processes

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Title: Statistical Engineering: An Algorithm for Reducing Variation in Manufacturing Processes
Author: Stefan H. & R. Jock MacKay, Steiner
ISBN: 0873896467 / 9780873896467
Format: Hard Cover
Pages: 360
Publisher: ASQ
Year: 2005
Availability: Out of Stock
     
 
  • Description
  • Contents

Reducing the variation in process outputs is a key part of process improvement. For mass produced components and assemblies, reducing variation can simultaneously reduce overall cost, improve function and increase customer satisfaction with the product.

The authors have structured this book around an algorithm for reducing process variation that they call "Statistical Engineering." The algorithm is designed to solve chronic problems on existing high to medium volume manufacturing and assembly processes. The fundamental basis for the algorithm is the belief that we will discover cost effective changes to the process that will reduce variation if we increase our knowledge of how and why a process behaves as it does. A key way to increase process knowledge is to learn empirically, that is, to learn by observation and experimentation.

The authors discuss in detail a framework for planning and analyzing empirical investigations, known by its acronym QPDAC (Question, Plan, Data, Analysis, Conclusion). They classify all effective ways to reduce variation into seven approaches. A unique aspect of the algorithm forces early consideration of the feasibility of each of the approaches.

Acknowledgements
Preface

Chapter 1. : Introduction

Part I Setting The Stage
Chapter 2. : Describing Processes
Chapter 3. : Seven Approaches to Variation Reduction
Chapter 4. : An Algorithm for Reducing Variation
Chapter 5. : Obtaining Process Knowledge Empirically

Part II Getting Started
Chapter 6. : Defining a Focused Problem
Chapter 7. : Checking the Measurement System
Chapter 8. : Choosing a Working Variation Reduction Approach

Part III Finding A Dominant Cause of Variation
Chapter 9. : Finding a Dominant Cause Using the Method of Elimination
Chapter 10. : Investigations to Compare Two Families of Variation
Chapter 11. : Investigations to Compare Three or More Families of Variation
Chapter 12. : Investigations Based on Single Causes
Chapter 13. : Verifying a Dominant Cause

Part IV Assessing Feasibility and Implementing A Variation Reduction Approach
Chapter 14. : Revisiting the Choice of Variation Reduction Approach
Chapter 15. : Moving the Process Center
Chapter 16. : Desensitizing a Process to Variation in a Dominant Cause
Chapter 17. : Feedforward Control Based on a Dominant Cause
Chapter 18. : Feedback Control
Chapter 19. : Making a Process Robust
Chapter 20. : 100% Inspection
Chapter 21. : Validating a Solution and Holding the Gains

References
Index

 
 
 
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