Competitive Innovation and Improvement: Statistical Design and Control, 1st Edition (Hardback) book cover

Competitive Innovation and Improvement

Statistical Design and Control, 1st Edition

By Kieron Dey

Productivity Press

231 pages | 84 B/W Illus.

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pub: 2014-09-25
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Competitive Innovation and Improvement: Statistical Design and Control explains how tocombine two widely known statistical methods—statistical design and statistical control—in a manner that can solve any business, government, or research problem quickly with sustained results. Because the problem-solving strategy employed is pure scientific method, it makes integration into any existing problem-solving or research method quite simple.

The material in the book is presented in a manner that anyone can read and immediately put to use, including executives, managers, statisticians, scientists, engineers, researchers, and all of their supervisors and employees. Organizations can apply the concepts discussed with existing staff to release latent energy rather than adding to their workload. Optional footnotes provide the opportunity for more advanced technical insight.

Supplying readers with an understanding of orthogonal design, the book illustrates key ideas through large-scale case studies. The book’s 12 case studies examine the coupling of statistical design with economic control across a range of industries and problem types.

The book suggests the real world, rather than mathematics alone, to reveal how things work and how to make them work better. Innovation and improvement by design is explained, which will help readers open up left-brain analytics to more right-brain creativity.

Although mathematics (as advanced as needed to solve the problem) is used throughout the text, it is translated into simple arithmetic without any mathematical notation. The book limits references to a few essential texts and papers that readers can refer to as they become more experienced in statistical design and control.



—Dr. Randy Brown. Director of Health Research, Mathematica Policy Research, Inc.

Work that is way ahead of others… interesting and energetic writing. A lot of hands-on as well as technical wisdom – a very rare combination. The material is new, challenging, and important.

—Dr. Brian L. Joiner. Minitab Co-Inventor, former Professor of Statistics, University of Wisconsin, Madison

A wonderful book with unique insights into an area of enormous potential. … not duplicated anywhere to the best of my knowledge. Writing style makes it easy to follow each topic.

—Dr. Steve Grady, Consulting Econometrician

The concept is simple (it makes you wonder why others haven’t tried it). The large scale is unique … not discussed in current literature.

—Tim Baer, Principal Statistician, Roche Diagnostics

Table of Contents

Simplicity of Statistical Design and Control

Making a Start

How Does It Work?

Care Management Case: Improving Health for Thousands of People


Measurement Quality

Care Management Statistical Design

Baseline Data

Managing the Test

Test Results

Exploratory Analysis

What Might the Results Mean?

Findings Are Often Surprising

Significance of the Results


Implementation Troubleshooting

Designed Innovation

Innovation Uses More Right Brain than Left

Retailing Case: New Product Sales


Measurement Quality

Preparing for the Test

Retail Furniture Statistical Design and Its Management

Exploratory Analysis and Inference

What Might the Results Mean?

Statistical Significance

Ironing Out Some Possible Wrinkles

Predicting and Delivering the Improvement

Retailing Designed Innovation Case: Conclusion

Statistical Control

Using Statistical Control

Economic Advantage


Practical Use of Statistical Control

Digression into Causality

Concluding Scientific Work in the Care Management Case

False Alarm Rate Is Neither Known Nor Useful in Statistical Control

Statistical Control Terminology

Statistics Breaks Down in Unstable Processes

Economic Loss without Statistical Control

Cost Explosion Story Unexploded

Tests for Statistical Control

Statistical Control Integrated with Statistical Design

Managing Statistical Control Schemes

Mechanics of Statistical Control

Where Did Statistical Control Originate?

Measurement Error and Control

All Measurement Systems Are Inherently Flawed

Clinical Care Case: Initial Measurement Study and Long-Term Controls

Establishing a Measurement Control Scheme

Statistical Design

Advantages of Large Statistical Design

Two-Level Designs

Full Factorial Designs

Fractional Factorial Designs

Backpacking Case


Managing the Test

Measurement Quality

Exploratory Analysis

What Might the Initial Results Mean?

Exploring Interactions

Simpler Analysis

Statistical Significance

Solving the Puzzle


Analysis of All Pair Interactions

Measurement Problem Found and Fixed after the Test

Using Sales Change as the Test’s Measurement

Calculating Precision and Sample Size Before the Test

Diagnosing Unusually High or Low Results in a Statistical Design Row

Guidance on Fractional Factorial Designs

Multifactorial Designs

Care Management Case: More Analytical Insight


Milk Story

Soil Story

Geometric versus Nongeometric Designs

Aliasing Scheme for the Care Management Design

Augmenting Multifactorials to Also Estimate Pair Interactions

Testing Strategy

Uniqueness and Stumbling Around

Where Did Statistical Design Originate?

Statistical Design and Control: A Dozen Large-Scale Case Studies

Selection of Cases

Simultaneous Design

Solving Complex Problems Simply

Simultaneous Design Idea

Science Education Case


Baseline Data

Simultaneous Statistical Designs for Science Classes

Pair Interactions across Designs and an Easier Analysis


Rules for Simultaneous Designs

General Multichannel Optimization Case

Simultaneous Design Procedure

Scientific Method, Randomization, and Improvement Strategies

Simplicity of the Scientific Method

Scientific Method with Statistical Design and Control

Randomization Distribution

Randomization Device

Proof Isn’t in the Pudding

What Science Lies beneath Implementation Being the Hardest Part?

Common Improvement Strategies

Randomized Control Trials (RCT)

Statistical Design and Control Are for Real Problems with Everyone Contributing

Managing Improvement and Innovation


Speed without Net Resources

How to Manage Specific Improvements/Innovations

Statistical Design and Control Summary

Appendix: Answers to Exercises



About the Author

Kieron Dey studied mathematics and statistics at Reading University, England and management at Rensselaer Polytechnic Institute, New York. He was on the experimental staff at Hirst Research Center, London, England (an early specialized center for applied scientific research), and apprenticed with Joan Keen, a pioneer in industrial statistics. He later joined IIT Research Institute, another contract research organization, serving in several roles including scientific advisor. He has held technical leadership positions in corporations up to $2 billion in size, now with Nobigroup Inc. He has vast experience with corporate and government leaders. Dey is a Fellow of the Royal Statistical Society.

Subject Categories

BISAC Subject Codes/Headings:
BUSINESS & ECONOMICS / Decision-Making & Problem Solving
MATHEMATICS / Probability & Statistics / General