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Introduction to Engineering Statistics and Six Sigma

Statistical Quality Control and Design of Experiments and Systems

  • Theodore T. Allen

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Introduction

    1. Pages 1-26
  3. Statistical Quality Control

    1. Front Matter
      Pages 27-27
    2. Pages 117-134
    3. Pages 135-146
    4. Pages 147-160
    5. Pages 161-173
    6. Pages 175-197
    7. Pages 199-237
  4. Design of Experiments (DOE) and Regression

  5. Optimization and Strategy

    1. Front Matter
      Pages 455-455
    2. Pages 479-481
    3. Pages 483-497
  6. Back Matter
    Pages 499-529

About this book

Introduction

Many have heard that six sigma methods are necessary to survive, let alone thrive, in today’s competitive markets, but are not really sure what the methods are or how or when to use them.

Introduction to Engineering Statistics and Six Sigma contains precise descriptions of all of the many related methods and details case studies showing how they have been applied in engineering and business to achieve millions of dollars of savings. Specifically, the methods introduced include many kinds of design of experiments (DOE) and statistical process control (SPC) charting approaches, failure mode and effects analysis (FMEA), formal optimization, genetic algorithms, gauge reproducibility and repeatability (R&R), linear regression, neural nets, simulation, quality function deployment (QFD) and Taguchi methods. A major goal of the book is to help the reader to determine exactly which methods to apply in which situation and to predict how and when the methods might not be effective.

Illustrative examples are provided for all the methods presented and exercises based on case studies help the reader build associations between techniques and industrial problems. A glossary of acronyms provides familiarity with six sigma terminology and solutions to homework and practice exams are included.

Keywords

Business Engineering Statistics Quality Regression Reliability Simulation Six Sigma Statistical Process Control algorithm algorithms genetic algorithms linear optimization linear regression optimization statistics

Authors and affiliations

  • Theodore T. Allen
    • 1
  1. 1.Department of Industrial Welding and Systems EngineeringThe Ohio State UniversityColumbusUSA

Bibliographic information

  • DOI https://doi.org/10.1007/1-84628-200-4
  • Copyright Information Springer-Verlag London Limited 2006
  • Publisher Name Springer, London
  • eBook Packages Engineering
  • Print ISBN 978-1-85233-955-5
  • Online ISBN 978-1-84628-200-3
  • Buy this book on publisher's site