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  • 8/13/2019 What Did Deming Really Say - Balestracci

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    Subscribe to Davis's free DATA SANITY newsletter:

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    CLICK HERE to request a free copyof the preface andchapter summary of my book

    DATA SANITY: A Quantum Leap to Unprecedented Results

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    "Thank you for a very informative and entertaining workshop today. I learnedso much and also have direction for what I still need to learn!"

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    What Did DemingReallySay?

    By Davis Balestracci on Apr 20, 2011 | Categorized under: Deming

    I really didnt say everything I said. Yogi Berra

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

    The 1980 NBC television show, If Japan Can, Why Cant We?introduced theteachings of W. Edwards Deming to U.S. viewers and caused a quantum leap inawareness of the potential for quality improvement in industry. During the late

    1980s, the movement also caught fire in health care. Those of you familiar withDemings funnel rules (which shows that a process in control delivers the bestresults if left alone) will smile to realize that his rule No. 4making, doing, orbasing your next iteration based on the previous onealso known as a randomwalk, has been in operation for the last 30 years.

    Jeff Liker, professor of industrial and operations engineering at the Universityof Michigan, beautifully describes the random walks that have taken placewithin the time spans of Six Sigma and lean. In a private correspondence withleadership 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 AlliedSignal and GE it morphed to projects by Black Belts based on statistics tobecome a cost-reduction programevery project needs a clear ROI. In otherwords, we denigrated the program from a leadership philosophy to a bunch ofone-off projects to cut costs. It was a complete bastardization of the original,and it rarely led to lasting, sustainable change because the leadership andculture 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 excellentJapanese companies or their teachers like Deming.

    Clemmer also cites multiple studies from 19962007 concluding that about 18to 24 months after these various quality systems are launched, 5070 percent ofthem fail. Liker concurs and feels that the four key failure factors, in this order,are:

    Leadership lacking deep understanding and commitmentFocus on tools and techniques without understanding the underlyingcultural transformation required

    Superficial program instead of deep development of processes thatsurface problems solved by thinking people

    Isolated process improvements instead of creating integrated systems forexceptional customer value

    Virtually everyone agrees that the No. 1 barrier to improvement is still topmanagements inability to be visibly committed to quality. Is this the elephantin the living room or as Clemmer calls it, the moose on the table? Thelonger Im in improvement, the more I realize the wisdom of Demingsstatement, If I could reduce my message to management to just a few words,Id 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 newphilosophy.

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    Unfortunately, Demings philosophy seems to have morphed into a trainingmill turning out belts by the thousands with statistical training that makes mypalms sweat. Ive said it before: People dont need statistics; they need to knowhow to solve their problems. All thats needed is a few simple tools and aworking knowledge of variation to be able to distinguish between common andspecial causes. Only 12 percent of people need advanced statisticalknowledge. Deming would roll over in his grave if he could see the statisticalsubculture of hacks (his term) that have been turned out in his name.

    In Demings words

    I think the best book on design of experiments (DOE) is Quality ImprovementThrough Planned Experimentation, by Ronald Moen, Thomas Nolan, andLloyd Provost (McGraw-Hill Professional, 1999). It is the only book Ive seenthat uses a process-oriented approach, which is so sorely needed in the realworld.

    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 dothey help us to predict?

    Unfortunately, the statistical methods in textbooks and in the classroom do nottell the student that the problem in data use is prediction. What the studentlearns is how to calculate a variety of tests (t-test, F-test, chi-square, goodnessof fit, etc.) in order to announce that the difference between the two methods or

    treatments is either significant or not significant. Unfortunately, suchcalculations are a mere formality. Significance or the lack of it provides nodegree of beliefhigh, moderate, or lowabout prediction of performance inthe future, which is the only reason to carry out the comparison, test, orexperiment in the first place.

    [I]nterchange of any two numbers in the calculation of the mean of a set ofnumbers, 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 bigdifference in the message that the data are trying to convey for prediction.

    The plot of points conserves the information derived from the comparison orexperiment.

    And, in addition to the process output being measured, determining the sampleitself to be measured is its own separate process. The concepts of randomnessand sample size for significance go out the window.

    Deming coined the term analytic to describe studies to improve a product orprocess in the future:

    Prediction is the aim. There is a need to conduct multiple plan-do-study-act (PDSA) cyclesover a wide range of conditions.

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    There are limitations of commonly used statistical methods such asanalysis of variance to address the important sources of uncertainty inanalytic studies.

    Graphical methods of analysis are primary.

    Confirmation of the results of exploratory analysis comes primarily fromprediction rather than from using formal statistical methods such as confidence

    intervals. Satisfactory prediction of the results of future studies conducted overa wide range of conditions is the means to increase the degree of belief that theresults provide a basis for action.

    When planning to test a change, people are making a prediction that the changewill be beneficial in the future. What people dont realize is that a limited set ofconditions will be present during the test; the conditions in the past, during thetest, and in the future could all be different. Circumstances unforeseen or notpresent at the time of the test will arise in the future. Will the change still resultin an improvement under these new, future conditions?

    Knowledge about the change is based on the specific subject matter on whichthe change itself is based, as well as knowledge about the environment in whichthe change will be implemented. Extrapolating the test results to the future isthe primary source of uncertainty when a change is tested. The question thenbecomes, How does one randomly sample the future? Easy: One cant.

    The connection between knowledge of the subject matter from which thechange is developed and analysis of the data from a test of the change isessential 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 ineveryday management certainly doesnt help quality professionals efforts. Thatsaid, quality practitioners need to start by improving the process of teachingstatistics, especially before they attempt to bring current seminars into the C-suite. Much of what is currently taught shouldnt be applied to dailymanagementor probably most anything else (except maybe manufacturingproduct quality). The wrong things continue to be taught: p-values, confidenceintervals, 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 roomand passing out three pages ofstatistical definitions. I could feel the tension in the room rising. I then beganmy talk by saying, If I brought a quincunx into a board room, theyd throw meout on my ear, and the room erupted in laughter.

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

    Plotting measurements over time turns out, in my view, to be one of the mostpowerful devices we have for systemic learning. Several important thingshappen 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? Towhom? Who should see them? Why? These are questions that integrate andclarify aims and systems all at once. If you follow only one piece of advice

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    from this lecture when you get home, pick a measurement you care about andbegin to plot it regularly over time, you wont be sorry.

    Until the culture at largeappreciates the concept of process and eradicatesblame, true improvement will not take place. To solve their problemseveryonein a culture truly committed to improvement must work fromperspectives of:

    Customer orientationContinuous improvementElimination of wastePrevention, not detectionReduction of variationStatistical thinking and use of dataAdherence to best-known methodsUse of best available toolsRespect for people and their knowledgeResults-based personal feedback

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

    Latest article by Davis:

    The Road to Health Care Reform Is Paved with Missed Opportunities

    After reading Joe De Feos July 8, 2011, Quality Digest Dailyarticle, A PositivePrognosis: Transforming Health Care in America,I took another look at the wonderful

    book,Escape Fire(Jossey-Bass, 2003), a compendium of Dr. Donald Berwicksinspiring plenary speeches at the Institute for Healthcare Improvements (IHI) 19922002 annual forum. Berwick is probably the leading health care-improvement thinker inthe world. He is the former CEO of IHI and, as some of you know, a controversialObama appointee as head of the Centers for Medicare and Medicaid Administration. Inmy opinion, he is most definitely the person for the job. As if it wasnt difficult enoughto deal only with health care cultures, he now has the thankless job of integrating messypolitical agendas into the very serious business of health improvement.

    Read more...Copyright 2012 Harmony Consulting, LLC All rights reserved. | Phone: 207.899.0962 | Admin|Use & Privacy| Site MapSite design, development & hosting by Small Web Solutions

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