Friday, October 24, 2014

10 . 24 . 14 | Dr. Little Shares His Take on Dey’s Book

Dr. Kevin Little, Principal and Founder of Informing Ecological Design, LLC recently penned a review of Kieron Dey’s Competitive Innovation and Improvement: Statistical Design and Control. We had expected the book to be controversial among academics and researchers since it contains no mathematical notation and also explains common errors in experimental work by inappropriate use of mathematical models in both design and analysis. So we were pleasantly surprised to find an independent review by Dr. Kevin Little (PhD is statistics from University of Wisconsin at Madison) an experienced consultant with unusually strong technical skills in this field (as well as many others).

Dr. Little provides a balanced review rightly noting nothing new in the statistical control sections and centering on the parallel use of statistical design and control in unorthodoxly large and diverse business settings, as especially innovative.

The review also notes the case in Chapter 1 alone as being worth the price of the book; this is good insight since it frees healthcare of the widely perceived need to randomize patients to the experiment allowing improvement in live care/disease management operations. It also shows why measurement error is never an issue in problems of this type (unless "cleaned up"!) The case was early in a set of about a dozen similar cases (some of which were of better design or larger improvement) and was chosen in part because it shows how real cases look, imperfections fixed along the way. The dozen cases met with fierce resistance from statistical colleagues which was important to sound design and overcoming similar unfounded concerns in the future. Chapter 1 conveys the sparks caused by pulling it uphill in this way as a feature of good science.

View the full text of Dr. Little's review on his blog:

Thursday, April 3, 2014

04 . 03 . 14 | New Book: “Competitive Innovation and Improvement”

When asked why the book was written (and a little about what’s between the covers,) Kieron Dey said:

“I first got the idea about separating tiny signals from large amounts of noise from time spent in radar design and wondered why similar was not much used in industry to solve problems. Where statistical design was used, it tended to be on a small scale and not much in processes involving lots of people.

The idea to combine statistical design and control came from a book on survey sampling. This fusion was controversial for years among professionals, for no reason. Everything used is in the literature. 

“Intent-to-treat" is also used throughout (which means, roughly, allowing an element of laissez-faire, to get real world results, not forced ones that don't hold longer term).

 Simultaneous design (where more than one design runs at once, overlaid) was added in 2012. The simultaneous designs have been important in cross-channel optimization in retail, and in complex healthcare improvements. This was the last addition as the theory was tricky and it finally fell in place in 2011. It found that what had seemed weaknesses (where interactions across designs might be a problem) in fact hid a large strength, which is in Chapter 8 with real cases. The method had to be simplified so that users could apply easily.

Finally, the scientific method is used throughout (which folds nicely into comparative effectiveness research, DMAIC or PDCA etc.) and the book explains what (and how simple) this is. The scientific method allows the same method to be used for existing and new processes: hence the “improvement and innovation” in the title. Innovation becomes less elusive in this way – it can be designed rather than waiting for inspiration. Also, getting back to pure, simple science means using right-brain (creativity) as well as left (analytic) so more people can contribute, valuably for the enterprise (which can be business, industry, research or government).

There is no mathematical notation so that anyone can read and use these well-established methods. Scientists and researchers will find Chapter 8 challenging on scientific method and randomization, so there's something for everyone. Mathematics is used a lot behind the scenes of the book but the real world is used more: to understand how businesses work and make them work better.

There are about 20 exercises peppered through the book, for the reader to accelerate what would be learned in field experience and get started on real business competitive problems.

Surprisingly, it turns out to be a management tool, not one technical people alone can accomplish; it’s not top down though and the book explains why.

The Book is available for pre order on Amazon at: