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Davis Balestracci

Six Sigma

What Did Deming Really Say?

“I really didn’t say everything I said.” —Yogi Berra

Published: Wednesday, April 20, 2011 - 04:30

My March 30, 2011 article ended with wisdom from Yogi Berra as a warning to the quality profession. Some prickly reactions to it got me thinking about the last 30 years or so of quality improvement.

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The 1980 NBC television show, “If Japan Can, Why Can’t We?” introduced the teachings of W. Edwards Deming to U.S. viewers and caused a quantum leap in awareness of the potential for quality improvement in industry. During the late 1980s, the movement also caught fire in health care. Those of you familiar with Deming’s funnel rules (which shows that a process in control delivers the best results if left alone) will smile to realize that his rule No. 4—making, doing, or basing your next iteration based on the previous one—also known as a “random walk,” has been in operation for the last 30 years.

Jeff Liker, professor of industrial and operations engineering at the University of Michigan, beautifully describes the random walks that have taken place within the time spans of Six Sigma and lean. In a private correspondence with leadership expert Jim Clemmer, Liker writes:

“Originally Six Sigma was derived from Toyota Quality Management (TQM) by Motorola to achieve six sigma levels of quality, and then through Allied Signal and GE it morphed to projects by Black Belts based on statistics to become a cost-reduction program—every project needs a clear ROI. In other words, we denigrated the program from a leadership philosophy to a bunch of one-off projects to cut costs. It was a complete bastardization of the original, and it rarely led to lasting, sustainable change because the leadership and culture were missing.

"A similar thing happened to lean when it got reduced to a toolkit (e.g., value-stream mapping, KPI boards, cells, kanban).

"Lean and Six Sigma in no way reflect the original thinking of excellent Japanese companies or their teachers like Deming."

Clemmer also cites multiple studies from 1996–2007 concluding that about 18 to 24 months after these various quality systems are launched, 50–70 percent of them fail. Liker concurs and feels that the four key failure factors, in this order, are:
• Leadership lacking deep understanding and commitment
• Focus on tools and techniques without understanding the underlying cultural transformation required
• Superficial program instead of deep development of processes that surface problems solved by thinking people
• Isolated process improvements instead of creating integrated systems for exceptional customer value

Virtually everyone agrees that the No. 1 barrier to improvement is still top management’s inability to be visibly committed to quality. Is this the “elephant in the living room” or as Clemmer calls it, “the moose on the table”? The longer I’m in improvement, the more I realize the wisdom of Deming’s statement, “If I could reduce my message to management to just a few words, I’d say it all has to do with reducing variation.” Why reduce variation? Because it affords better prediction. He said it so often: “Management is prediction!”

Deming also says in point No. 2 of his famous 14 Points: “Adopt the new philosophy.” 

Unfortunately, Deming’s philosophy seems to have morphed into a training mill turning out “belts” by the thousands with statistical training that makes my palms sweat. I’ve said it before: People don’t need statistics; they need to know how to solve their problems. All that’s needed is a few simple tools and a working knowledge of variation to be able to distinguish between common and special causes. Only 1–2 percent of people need advanced statistical knowledge. Deming would roll over in his grave if he could see the statistical subculture of “hacks” (his term) that have been turned out in his name.

In Deming’s words

I think the best book on design of experiments (DOE) is Quality Improvement Through Planned Experimentation, by Ronald Moen, Thomas Nolan, and Lloyd Provost (McGraw-Hill Professional, 1999). It is the only book I’ve seen that uses a process-oriented approach, which is so sorely needed in the real world.

The foreword was written by none other than W. Edwards Deming, and in it he explains the approach to statistics needed:

“Prediction is the problem, whether we are talking about applied science, research and development, engineering, or management in industry, education, or government,” he says. “The question is, ‘What do the data tell us? How do they help us to predict?’

“Unfortunately, the statistical methods in textbooks and in the classroom do not tell the student that the problem in data use is prediction. What the student learns is how to calculate a variety of tests (t-test, F-test, chi-square, goodness of fit, etc.) in order to announce that the difference between the two methods or treatments is either significant or not significant. Unfortunately, such calculations are a mere formality. Significance or the lack of it provides no degree of belief—high, moderate, or low—about prediction of performance in the future, which is the only reason to carry out the comparison, test, or experiment in the first place.

“… [I]nterchange of any two numbers in the calculation of the mean of a set of numbers, their variance or their fourth moment does not change the mean, variance, or fourth moment.

“In contrast, interchange of two points in a plot of points may make a big difference in the message that the data are trying to convey for prediction.

“The plot of points conserves the information derived from the comparison or experiment.” 

And, in addition to the process output being measured, determining the sample itself to be measured is its own separate process. The concepts of “randomness” and “sample size for significance” go out the window.

Deming coined the term “analytic” to describe studies to improve a product or process in the future:
• Prediction is the aim.
• There is a need to conduct multiple plan-do-study-act (PDSA) cycles over a wide range of conditions.
• There are limitations of commonly used statistical methods such as analysis of variance to address the important sources of uncertainty in analytic studies.
• Graphical methods of analysis are primary.

Confirmation of the results of exploratory analysis comes primarily from prediction rather than from using formal statistical methods such as confidence intervals. Satisfactory prediction of the results of future studies conducted over a wide range of conditions is the means to increase the degree of belief that the results provide a basis for action.

When planning to test a change, people are making a prediction that the change will be beneficial in the future. What people don’t realize is that a limited set of conditions will be present during the test; the conditions in the past, during the test, and in the future could all be different. Circumstances unforeseen or not present at the time of the test will arise in the future. Will the change still result in an improvement under these new, future conditions?

Knowledge about the change is based on the specific subject matter on which the change itself is based, as well as knowledge about the environment in which the change will be implemented. Extrapolating the test results to the future is the primary source of uncertainty when a change is tested. The question then becomes, “How does one randomly sample the future?” Easy: One can’t.

The connection between knowledge of the subject matter from which the change is developed and analysis of the data from a test of the change is essential to effective improvement. This cannot happen in a statistical vacuum.

Integrating statistics’ role into leadership philosophy

The fact that most leadership is clueless to the power of statistical thinking in everyday management certainly doesn’t help quality professionals’ efforts. That said, quality practitioners need to start by improving the  process of teaching statistics, especially before they attempt to bring current seminars into the “C-suite.” Much of what is currently taught shouldn’t be applied to daily management—or probably most anything else (except maybe manufacturing product quality). The wrong things continue to be taught: p-values, confidence intervals, normal distribution, sample size, and regression, to name a few.

I once gave a talk following an ASQ Fellow who tried to make a case for bringing a quincunx into the board room—and passing out three pages of statistical definitions. I could feel the tension in the room rising. I then began my talk by saying, “If I brought a quincunx into a board room, they’d throw me out on my ear,” and the room erupted in laughter.

Where to start? Here is a quote from Dr. Donald Berwick, a pioneer in health care improvement:

“Plotting measurements over time turns out, in my view, to be one of the most powerful devices we have for systemic learning…. Several important things happen when you plot data over time. First, you have to ask what data to plot. In the exploration of the answer, you begin to clarify aims, and also to see the system from a wider viewpoint. Where are the data? What do they mean? To whom? Who should see them? Why? These are questions that integrate and clarify aims and systems all at once…. If you follow only one piece of advice from this lecture when you get home, pick a measurement you care about and begin to plot it regularly over time, you won’t be sorry.”

Until the culture at large appreciates the concept of “process” and eradicates blame, true improvement will not take place. To “solve” their problems everyone in a culture truly committed to improvement must work from perspectives of:
• Customer orientation
• Continuous improvement
• Elimination of waste
• Prevention, not detection
• Reduction of variation
• Statistical thinking and use of data
• Adherence to best-known methods
• Use of best available tools
• Respect for people and their knowledge
• Results-based personal feedback

Creating this culture is far, far more important than teaching a bunch of statistical techniques.

Discuss

About The Author

Davis Balestracci’s picture

Davis Balestracci

Davis Balestracci is a past chair of ASQ’s statistics division. He has synthesized W. Edwards Deming’s philosophy as Deming intended—as an approach to leadership—in the second edition of Data Sanity (Medical Group Management Association, 2015), with a foreword by Donald Berwick, M.D. Shipped free or as an ebook, Data Sanity offers a new way of thinking using a common organizational language based in process and understanding variation (data sanity), applied to everyday data and management. It also integrates Balestracci’s 20 years of studying organizational psychology into an “improvement as built in” approach as opposed to most current “quality as bolt-on” programs. Balestracci would love to wake up your conferences with his dynamic style and entertaining insights into the places where process, statistics, organizational culture, and quality meet.

Comments

Failing that interview

David - so sorry to hear that.  I encounter that misperception all of the time tho...


The book Davis recommends is a decent start (although I would ignore the stuff on fisbone diagrams)


"Quality improvement through planned experimentation" by Moen, Nolan and Provost.  The first several chapters are very good at pointing out the difference bewteen enumerative and analytic studies and why statistical formulas and tests are really not needed.  Good experimental structure will result in answers that are will be visually obvious wehn properly plotted.


The other two books I recommend: 


Statistical Engineering by MacKay and Steiner


Process Quality Control by Ott


 


 

Books

B,


Thanks!  I'll add them to my List.

Examples

I come pre-set to believe anything Deming said, but your entire article is written at the 20,000 foot level.  Please illustrate the points with some examples.  For instance, what statistical do's and don'ts would apply in attempting to identify, perhaps by experiment, which measurable product-, process- or environmental factors had the greatest effect on a product or system, and describing that effect.  Say, for instance, I want to invent the world's furthest-flying golf ball.  And what predictive factors, vs. evaluative ones, would I be interested in.


BTW, I just "failed" an interview where the chief criteria seemed to be my ability to explain when F, Chi-square, and t tests were required, how many ppm defective is +/- 3 Sigma, and Cp vs. Cpk.  I explained that DOE, IMHO, is the tool of last resort in any conceivable manufacturing quality scenario, and didn't say (but think) that my ability to parrot statistical concepts, which I purposely ignore to save brain space for important things, is a poor predictor(!) of my performance.


On reflection, the real problem is that I didn't demonstrate awesomeness at MiniTab.

Er...uh..."Examples without theory teach nothing"..."It depends"

My point is that I AM writing at the 20,000 foot level--EXECUTIVES AND MANAGEMENT.  Until they "get" it, interviews such as you experience will be the norm...and a waste of time.  You didn't REALLY want that job, did you?  Read Brian Joiner's "Fourth Generation Management" and I think you will answer your own questions...and be ready to hit the ground running at a more appropriate level via the skills you will learn.  And read "The Improvement Guide" by Nolan, Nolan, Moen, and Provost as well.  It's the inability to see the "big picture" (systems thinking?) that is the very problem I am addressing.  Good luck.  Davis

Thanks

for your response.  I'll check out those resources.


That interview turned me off, but I'm temping here and need to go along to get along.  I've not found telling hiring managers they're wrong to be an effective way to get jobs.

Deming's Teachings

W. Edwards DemingThanks for a very insightful story about Deming's concepts.  They are refreshing to hear since much has been forgotten or warped in the last 15 or so years since he left our world.  If any readers are interested in learning more about Deming's teachings, especially if you are in the Austin, Texas area, I would like to announce that our local ASQ section is putting on "Dr. Deming Day" at which one of Deming's most qualified assistants, Bill Scherkenbach, will present a mini-version of the 4-day seminar, focused on the problems of today.  If you read Out of the Crisis, you will notice that Deming dropped his name several times.  I'm signed up now and I hope other Deming advocates will do the same.  Seating is limited, and information is at asqaustin.org. This is a great opportunity to learn more of Deming's teachings which are not always viewed as "common sense" in this day and age.  - Mike Harkins

 Dear Mr. Balestracci, I

 

Dear Mr. Balestracci,

 

I enjoyed your article very much. It was refreshing reading about the proper selection of statistical concepts for effective decision making.

 

Here is a couple of observations I would like to make:

 

First,  “Originally Six Sigma was derived from Toyota Quality Management (TQM) by Motorola …" Should it be Toyota Management System? TQM was used for Total Quality Management in the 80's and early 90's. I don’t believe TQM contributed any original theories.

 

Second, Dr. Deming did say “If I could reduce my message to management to just a few words, I’d say it all has to do with reducing variation.” but, some time later he stated that, "I said earlier that my message, in a few words, had to do with variation. I've given it some more thought, and I would say it has to do with pride of work."

 

 

Regards,

 

Fernando J. Grijalva

@demingsos (twitter)

Thank you for the clarification

Dear Fernando,


Thank you so much for your kind comments.  I also share your perception about TQM, but its reference was as quoted by Liker (who may have even workeded for Toyota).  I have read Dr. Deming's writings extensively and never came across that clarification you gave--Thank you!  Davis

Deming Quote

Hi Davis,

I m a please to provide the source of Dr. Deming's quote:

""Deming was once asked, at one of his seminars, how he would summarize his message in a few words. "I'm not sure," he replied, "but it would have something to do with variation." Later he said, "I said earlier that my message, in a few words, had to do with variation. I've given it some more thought, and I would say it has to do with pride of work.""

Peter Scholtes

What's pride got to do with it?

Journal for Quality and Participation.

Dec. 1996

 

Regards,

Fernando J. Grijalva

@demingsos (Twitter)