Ebook: Introduction to Engineering Statistics and Six Sigma: Statistical Quality Control and Design of Experiments and Systems

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.

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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.

Content:
Front Matter....Pages i-xxii
Introduction....Pages 1-26
Front Matter....Pages 27-27
Quality Control and Six Sigma....Pages 29-44
Define Phase and Strategy....Pages 45-73
Measure Phase and Statistical Charting....Pages 75-116
Analyze Phase....Pages 117-134
Improve or Design Phase....Pages 135-146
Control or Verify Phase....Pages 147-160
Advanced SQC Methods....Pages 161-173
SQC Case Studies....Pages 175-197
SQC Theory....Pages 199-237
Front Matter....Pages 239-239
DOE: The Jewel of Quality Engineering....Pages 241-257
DOE: Screening Using Fractional Factorials....Pages 259-283
DOE: Response Surface Methods....Pages 285-319
DOE: Robust Design....Pages 321-341
Regression....Pages 343-377
Advanced Regression and Alternatives....Pages 379-399
DOE and Regression Case Studies....Pages 401-421
DOE and Regression Theory....Pages 423-454
Front Matter....Pages 455-455
Optimization and Strategy....Pages 457-477
Tolerance Design....Pages 479-481
Back Matter....Pages 499-529
Six Sigma Project Design....Pages 483-497

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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.

Content:
Front Matter....Pages i-xxii
Introduction....Pages 1-26
Front Matter....Pages 27-27
Quality Control and Six Sigma....Pages 29-44
Define Phase and Strategy....Pages 45-73
Measure Phase and Statistical Charting....Pages 75-116
Analyze Phase....Pages 117-134
Improve or Design Phase....Pages 135-146
Control or Verify Phase....Pages 147-160
Advanced SQC Methods....Pages 161-173
SQC Case Studies....Pages 175-197
SQC Theory....Pages 199-237
Front Matter....Pages 239-239
DOE: The Jewel of Quality Engineering....Pages 241-257
DOE: Screening Using Fractional Factorials....Pages 259-283
DOE: Response Surface Methods....Pages 285-319
DOE: Robust Design....Pages 321-341
Regression....Pages 343-377
Advanced Regression and Alternatives....Pages 379-399
DOE and Regression Case Studies....Pages 401-421
DOE and Regression Theory....Pages 423-454
Front Matter....Pages 455-455
Optimization and Strategy....Pages 457-477
Tolerance Design....Pages 479-481
Back Matter....Pages 499-529
Six Sigma Project Design....Pages 483-497
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