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McGraw-Hill Professional Publishing
Quality Improvement Through Planned Experimentation 3/E / Edition 3

Quality Improvement Through Planned Experimentation 3/E / Edition 3

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The latest experimental design techniques for quality improvement

"The methods taught in this book are a major contribution to statistical methods as an aid to engineers, as well as to those in industry, education, or government who are trying to understand the meaning of fi gures derived from comparisons or experiments." — W. EDWARDS DEMING

Co-written by three recipients of the Deming Medal awarded by the American Society for Quality (ASQ), Quality Improvement through Planned Experimentation, Third Edition discusses the principles and methodologies for planning and conductingexperiments to improve products, processes, or systems.

Fully revised with up-to-date case studies and incorporating new software, this authoritative guide fosters the sequential building of knowledge essential for implementing effective improvements. End-of-chapter exercises reinforce what you've learned, and forms for designing planned experiments help you to integrate themethods in the book into your daily work. The methods of planned experimentation provide an opportunity to better meet the needs of customers, reduce costs, and increase productivity by effecting verifiably beneficial changes.

* Improvement of quality * Principles for design and analysis of planned experiments * Experiments with one factor * Experiments with more than one factor * Reducing the size of experiments * Evaluating sources of variation* Sequential experimentation * Using a time series response variable * Designs with factors at more than two levels * Applications in health care * New product design

NEW: Study-it software available for download!

Product Details

ISBN-13: 9780071759663
Publisher: McGraw-Hill Professional Publishing
Publication date: 07/23/2012
Edition description: List
Pages: 544
Sales rank: 543,026
Product dimensions: 5.90(w) x 9.10(h) x 1.20(d)

About the Author

Ronald D. Moen is a statistician, consultant, and teacher to industry, government, healthcare, and education. He is a member of Associates in Process Improvement (API, 1984) and Adjunct Lecturer in the Physics and Engineering Science Department at the University of Michigan-Flint (1995-2005). Moen’s experiences of over 30 years include General Motors Corporation and the U.S. Department of Agriculture. He served as a Deming helper at 70 of his four-day seminars (1983-1993) and is a senior fellow at the Institute for Healthcare Improvement (IHI). Moen is coauthor of Improving Quality through Planned Experimentation, Second Edition and Quality Improvement through Planned Experimentation (both McGraw-Hill), He is the 2002 recipient of the Deming Medal.

Thomas W. Nolan, Ph.D., is a statistician, author, consultant, and member of Associates in Process Improvement, a consulting firm specializing in the improvement of quality and productivity. He is also a Senior Fellow and member of the executive team of the Institute for Healthcare Improvement. Dr. Nolan is the author of three books on improving quality and productivity and has published articles in a variety of peer-reviewed journals. In 2000, he was the recipient of the Deming Medal awarded by the American Society for Quality.

Lloyd P. Provost works as an advisor to organizations, helping them make improvements in their products and services and increase their capacity to continually learn and improve. In his work with leaders of organizations, Lloyd uses the Associates in Process Improvement (API) Quality as a Business Strategy template for alignment and focusing of improvement. He is the author of several papers relating to quality and measurement and coauthor of Quality Improvement Through Planned Experimentation, Second Edition (McGraw-Hill). He was the year 2003 recipient of the Deming Medal awarded by the American Society for Quality.

Table of Contents

Chapter 1. Improvement of Quality
Building Knowledge and the Scientific Method
Defining Quality
Model for Improvement
Sequential Experimentation Using the PDSA Cycle

Chapter 2. Principles for Design and Analysis of Planned Experiments
Types of Planned Experiments
Principles for Designing Analytic Studies
Tools for Experimentation
Form for Documentation of a Planned Experiment
Analysis of Data from Analytic Studies

Chapter 3. Experiments with One Factor
General Approach to One-Factor Experiments
Using Run Charts for a One-Factor Design
Using Shewhart Charts for One-Factor Experiments
Paired-Comparison Experiments
Randomized Block Designs
Incomplete Block Designs

Chapter 4. Experiments with More Than one Factor
Introduction to Factorial Designs
Design of Factorial Experiments
Advanced Topics in the Analysis of Factorial Experiments

Chapter 5. Reducing the Size of Experiments
Introduction to Fractional Factorial Designs
Fractional Factorial Designs—Moderate Current Knowledge
Fractional Factorial Designs—Low Current Knowledge
Using Blocking to Design a Sequence of Experiments

Chapter 6. Evaluating Sources of Variation
Applications of Nested Designs
Planning and Analyzing an Experiment with Nested Factors
More Complex Nested Designs
Appendix 6a: Calculation of Variance Components
Appendix 6b: Calculating and Combining Statistics

Chapter 7. Sequential Experimentation—A Case Study
Improving a Milling Process—Getting Started
The First Improvement Cycle: Current Performance of the Mlls
The Second PDSA Cycle: Sources of Variation
The Third PDSA Cycle: Evaluating Mill Cutter Vendors
The Fourth PDSA Cycle: Screening Process Variables
The Fifth PDSA Cycle: Evaluate Effect of Improvements on the Mill Process
The Sixth PDSA Cycle: Evaluating Important Factors
The Seventh PDSA Cycle: Determining Optimum Levels
The Eighth PDSA Cycle: Confirmation of Improvements
Final Actions of the Mill Improvement Team

Chapter 8. Sequential Experimentation Using a Time Series Response Variable
Incorporating Experimental Patterns in a Time Series
Shewhart Charts
Designs for Sequential Experimentation Using Time Series Response Variables

Chapter 9. Experiments with Factors at More than Two Levels
Factorial Designs with More Than Two Levels
Augmenting 2^k Factorial Designs with Center Points
Three Level Designs for Quantitative Factors
Experiments for Formulations or Mixtures
Experimental Design for Complex Systems

Chapter 10. Applications in Healthcare

Chapter 11. New Product Design
Phase 0: Generate Ideas
Phase 1: Develop Concepts and Define Product
Phase 2: Test
Phase 3: Produce Product

Appendix A: Evaluating Measurement Systems

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