Curious Cat Management Improvement Blog: Deming, lean thinking, innovation, customer focus, continual improvement, six sigma.
July 29, 2008

Full and Fractional Factorial Test Design

An Essential Primer on Full and Fractional Factorial Test Design

Since full factorial gathers additional data, it reveals all possible interactions, but as seen by the numbers above, there is a trade-off. More data equals more information but more data also equals a longer test duration. The minimum data requirements for full factorial are very high since you are showing every experiment.

Even if you are using full factorial to get the same amount of information as a fractional factorial test, it will take more time since you need more data to see statistically relevant differences between the many experiments. You might be wondering how fractional factorial can be accurate if interactions are possible?

Random interactions of high relevance are very rare, especially when looking for interactions of more than 2 factors. You really need to design tests where you look for meaningful interactions that are based on true business requirements rather than hoping for a random and low influence interaction between a red button, a hero shot and a headline.

I am a fan of design of experiments as long time readers know (see posts on design of experiments).

Some good resources for more on the topics discussed above: What Can You Find Out From 8 and 16 Experimental Runs? by George Box - Statistics for Experimenters - Design of Experiments in Advertising.

Related: Google Website Optimizer - factorial experiment articles - Using Design of Experiments - Marketers Are Embracing Statistical Design of Experiments

June 29, 2008

Printer Product Development Using Design of Experiments

MEMS development in less than half the time by Christopher N. Delametter, Eastman Kodak Company

The traditional approach to optimizing a product or process using computer simulation is to evaluate the effects of one design parameter at a time. The problem with this approach is that interactions between design factors and second-order effects are likely to result in a locally optimized design that will provide far less performance than the global optimum. Kodak researchers use DOE to develop tests that examine first-order, second-order, and multiple factor effects simultaneously with relatively few simulation runs. The result is that the analyst can iterate to a globally optimized design with a far higher level of certainty and in much less time than the traditional approach.

By using DOE to drive CFD, Kodak researchers were able to optimize the design of the printhead in considerably less time than competitors. The advantages of simulation were especially apparent late in the project when researchers discovered a more optimal ink formulation for one of the colors.

Related: Design of Experiments articles - Using Design of Experiments - Statistics for Experimenters - Why Use Designed Factorial Experiments? - Kodak Debuts Printers With Inexpensive Cartridges

May 1, 2008

Management Improvement Carnival #34

Please submit your favorite management posts to the carnival. Read the previous management carnivals.

  • Introduction to Factorial Designs by Jonathan Mendez - “I like the idea of velocity in marketing — test, learn, test, learn, test. Instead of one large test I prefer focusing attention on certain areas or elements to achieve deeper understanding.”
  • MIT’s Message about Lean Enterprise Transformation by Mark Edmondson- “1. Market leaders are good at embracing enterprise change; 2. Enterprise change requires a holistic approach that engages all stakeholders. This includes employees, suppliers, customers, unions, and investors/owners”
  • Two Types of Bottleneck by David J. Anderson - “I now teach that there are two types of bottleneck: capacity constrained resources CCRs; and non-instant availability resources”
  • Oranges, Pebbles, and Sand by Ron Pereira - “In this video my daughters and I demonstrate how meeting an objective is just the beginning to improvement.”
  • Why errorproof when you can double-check? - “If you are in the position to prevent the error in the first place, why wouldn’t you? And, I’d argue, if you can write a tool to detect the screw up - ie, it is possible to programmatically figure out that the template is wrong,”
  • Systems and Improvement by John Dowd - “Thus did Deming, over sixty years ago, show a basic model about how to think about quality and improvement.”
  • (more…)

May 27, 2005

Marketers Are Embracing Statistical Design of Experiments

Marketers Are Embracing Statistical Design of Experiments (site broke link so I removed it) by Richard Burnham.

Crayola® conducts an e-mail marketing DOE to attract parents and teachers to their new Internet site. The company discovers a combination of factors that makes their new e-mail pitch three-and-a-half times more effective than the control. (Harvard Business Review, October 2001, “Boost Your Marketing ROI with Experimental Design,” Almquist, Wyner.)

Marketers can’t always be certain what triggers buyers to respond. In the past, we were always admonished to test-test-test, but only one factor at a time – relying on our gut feelings and uncertain hopes. With DOE, marketers have replaced voodoo with the science of statistics.

For more on Design of Experiments see:

March 8, 2005

Statistics for Experimenters - Second Edition

Buy Statistics for Experimenters

The classic Statistics for Experimenters has been updated by George Box and Stu Hunter, two of the three original authors. Bill Hunter, who was my father, and the other author, died in 1986. Order online: Statistics for Experimenters: Design, Innovation, and Discovery , 2nd Edition by George E. P. Box, J. Stuart Hunter, William G. Hunter.
I happen to agree with those who call this book a classic, however, I am obviously biased.

Google Scholar citations for the first edition of Statistics for Experimenters.
Citations in Cite Seer to the first edition.

The first edition includes the text of Experiment by Cole Porter. In 1978 finding a recording of this song was next to impossible. Now Experiment can be heard on the De-Lovely soundtrack.

Text from the publisher on the 2nd Edition:
Rewritten and updated, this new edition of Statistics for Experimenters adopts the same approaches as the landmark First Edition by teaching with examples, readily understood graphics, and the appropriate use of computers. Catalyzing innovation, problem solving, and discovery, the Second Edition provides experimenters with the scientific and statistical tools needed to maximize the knowledge gained from research data, illustrating how these tools may best be utilized during all stages of the investigative process. The authors’ practical approach starts with a problem that needs to be solved and then examines the appropriate statistical methods of design and analysis.
(more…)

September 23, 2004

Management Improvement History

Originally posted to the Deming Electronic Network, 22 Sep 1999, in reponse to this message

I would like to say that I think it is good that we have disagreements on the DEN. I think it is a strength of the DEN, not a weakness. However, I think we sometimes get to personal with no real purpose. One example of this, for me, is: “Well, I guess we knew different Demings. Mine was a teacher named Dr. W. Edwards Deming.” I doubt this statement is meant to be taken literally, and if it is not I do not see what it adds to the discussion. I point this out not because I think this is some bad act that should be punished but that I think we need to continue to develop a sense of how we wish to express our disagreements and I think that we should try to do so more constructively.

For the past 60 years we’ve been looking for the magic bullet that will improve the quality of our products, services and lives. In the 1940s, we applied statistics through sampling, SPC and design of experiments to improve our products. In the 1950s, we used quality cost and total quality control to bring about quality improvement. In the 1960s, zero defects and MIL-Q-9858A drove the quality improvement process. In the 1970s, quality circles, process qualification and supplier qualification became key quality issues. In the 1980s, employee training in problem solving, team activities and just-in-time inventory were the things to do.”

I find this statement so far from the truth that it would seriously damage any PDSA with this as an accepted assessment of history. I do not believe Deming had such an inaccurate view (of course I may be wrong). I do believe we need to improve our practice of Quality (and to do that we need to understand what happened in the past and why it was not more successful). The idea that Design of Experiments (DoE) was at the core of some Quality Movement to me is not at all accurate. (more…)

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