1. Is It Time to Rethink Deming? Lean Kanban Benelux Antwerp, Belgium October 3, 2011No part of this presentation may be reproduced without the written permission of the author.Donald G. ReinertsenReinertsen & Associates600 Via Monte DOroRedondo Beach, CA 90277 U.S.A. (310)-373-5332 Internet: Don@ReinertsenAssociates.comTwitter: @dreinertsenwww.ReinertsenAssociates.com

2. Perspective Demings work is extremely important and ithas had great influence on repetitivemanufacturing. His ideas are relevant outside of this domain,but they must be used with some knowledgeof the target domain. This involves rethinking a little bit of themathematics and a lot of the implications. Deming did not claim that he had optimizedhis ideas for product development. 2 3. Who Was Deming?1927 US Department of Agriculture1939 Adviser to US Census Bureau1945 1950 Taught SPC in Japan, Deming Prize Created 1960 Awarded Japans Order of the Sacred Treasure, Second Class Statistics Professor at New York1900-1993 University, Consultant, Celebrity1993Legitimized relevance of statistics to industry.Made SPC a household term. A 1980s celebrity. 3 4. Some Product Development Questions 1. Should we respond to random variation? 2. Should we try to eliminate as much variability aspossible? 3. What is the essential difference between processcontrol and experimentation? 4. Is it always better to prevent problems thancorrect them? 5. Is the system, as Deming states, the cause of 94percent of our problems? 6. Are there other useful approaches?4 5. 1. Statistical Control For Deming, bringing a process under statisticalcontrol is indispensable. This state occurs when the outcomes of theprocess lie between upper and lower control limits. These limits are set at 3 times the standarddeviation of the process. Standard deviation is calculated from the sampledoutput of the system. Thus, a process can be classified as in statisticalcontrol even when it has very high variation. This inherently stabilizes the status quo.5 6. Statistical Control Upper Control LimitValue 3 Mean 3 Lower Control LimitIn ControlTime6 7. Demings World View3 UpperVariationand LowerProcess Control Limitsunderstatistical control CommonCause ProcessSpecialCausenot under statistical controlShewhart used the terms chance (random) cause and assignable cause. 7 8. Inherently RecursiveSample System OutputSet Control Limits 3 from Mean Inside UCL and LCLOutside UCL or LCL Common Cause Special CauseNo ActionTake ActionOutput Doesnt ChangeOutput Changes or Drifts Randomly8 9. Making Adjustments When the output of a process lies randomlybetween its upper and lower control limits itis under statistical control. If we make adjustments to a process that isunder statistical control it will increasevariation and hurt performance. If the output falls outside its limits this isdefined as a special cause and the operatorshould investigate and correct this cause. Control limits are not specification limits! 9 10. Demings Funnel+1 +1No Adjustment Variance = 1 -1-1 +2 Offset to +1+1 Offsetting 0Adjustment0Variance = 2 -1Offset to -1-2 10 11. Statistical ControlNow its time to put on your critical thinking hat. The aim of a system of supervision of nuclear power plants or anything else should be to improve all plants. No matter how successful this supervision, there will always be plants below average. Specific remedial action would be indicated only for a plant that turned out by statistical tests, to be an outlier.- Out of the Crisis p.58 11 12. An Economic View Cost/BenefitNo RemainingVariation AnalysisEconomic Opportunity Not EconomicalEconomicalto Correct to Correct EconomicOpportunityFixing or mitigating a defect is a tradeoff between the cost and benefit of fixing it, regardless of the cause.12 13. Demings Frame of Reference As you might expect, Deming views eachoutcome as an independent identicallydistributed (IID) random variable the classicstatistics of random sampling. But, what would happen if we had a MarkovProcess, where the outcome was a function ofboth the current state and a random variable. This is common in product development, e.g.when a second stochastic activity cant startuntil the first one finishes.13 14. A Random Walk We flip a coin 1000 times, add 1 for each head,subtract 1 for each tail, and keep track of ourcumulative total. How many times the cumulative total willreturn to the zero line during the 1000 flips? Cumulative H T T H T H H TotalTime14 15. One Thousand Coin Tosses 1st Half Crossings = 38Cumulative 2nd Half Crossings = 0 50 Average Time Between Crossings = 25.6 40 Maximum Time Between Crossings = 732 30 20 100 0250 500 750 1000 -10Note: +1 for each head, -1 for each tailBased on example from Introduction to Probability Theory and Its Applications,by William Feller. John Wiley: 1968 15 16. Cumulative Totals DiffuseEarlyProbability Late Value of Random VariableNotes:1. Zero is always most probable value.2. But, it becomes less probable with time.3. For large N a binomial distribution approaches anormal distribution.16 17. Its Not Demings Funnel The randomness that causes a problem will not fixthis problem in a reasonable amount of time. We must intervene quickly and decisively when wereach the control limit. It is precisely this control of high queue states thatis exploited by the magical Kanban approach.(Blocking can be viewed as a M/M/1/k queue.) And when we intervene we should return to thecenter of the control range not its edge. Think of a Drunkards Walk on top of a skyscraper. 17 18. 2. Eliminating Variability In manufacturing we try to minimize thevariability of a process. There is a underlying economic reasonwhy this works. In product development variability plays avery different economic role. Consider a race with ten runners. 18 19. Asymmetric Payoffs and Option Pricing Expected Price Payoff vs. PriceProbabilty Payoff xStrikePrice Price Price Expected PayoffExpected Payoff =StrikePrice Price 19 20. Higher Variability Raises This PayoffStrikePrice ExpectedPayoff PricePayoff SD=15Payoff SD=5 Option Price = 2, Strike Price = 50,Mean Price = 50, Standard Deviation = 5 and 15 20 21. Manufacturing Payoff-Function*Gain TargetPayoffLossPerformance Larger Variances Create Larger Losses *The Taguchi Loss Function 21 22. Making Good Economic ChoicesEconomicProbability PayoffEconomic Expectation Function p( x )FunctionE ( g ( x )) g ( x ) p( x )dxg( x ) DemingsAnother critical What we wantFocusleverage point.to maximize.22 23. 3. Sampling vs. Experimentation SAMPLING EXPERIMENTATION The population you are Identify the question yousampling is given.are trying to answer. Devise efficient sampling Determine what data youstrategies to balance need to answer theaccuracy vs. cost.question. Here sampling design is a Develop an efficient way tokey skill.create this data. Here experimental designis key skill.23 24. Inferential StatisticsInputOutputHow many modules are defective?Design a sampling strategy to answer thisquestion at the required confidence level. 24 25. Design of ExperimentsInput Output 16 Modules with 1 defectiveWhich, if any, modules are defective?Design a testing strategy to quicklyand efficiently answer this question.25 26. Information and TestingInformation Probability of Failure Pf Probability of Success Ps Information Generated by Test I t 1 I t Pf log 2 Ps log 2 1 PP f s0%50%100%Probability of Failure26 27. 4. The Cult of Prevention Is it always better to prevent problemsthan it is to find and fix them? This will be quick. NO. Minimizing the cost of failure is always alocal optimization. 27 28. 5. The System DominatesI should estimate that in my experience most troublesand most possibilities for improvement add up toproportions something like this: 94 % belong to the system (responsibility ofmanagement)6 % special - Out of the Crisis p.315(Responsibility of leadership) A third responsibility is toaccomplish ever greater and greater consistency ofperformance within the system, so that apparentdifferences between people continually diminish.- Out of the Crisis p.249These statements have terrifying implications.28 29. The Red Bead Experiment Demings epic work is an entertaining con. It demonstrates vividly that a set of behaviors(that he disapproves of) do not work to improveperformance. How does he work this magic? The output of the Red Bead Game is a randomvariable that is completely independent of theapplied treatment. It will demonstrate that NO management methodcan EVER influence the output of a process.29 30. The Red Bead ExperimentInput SystemVariousWorkersTreatments OutputRewardsSlogans Random PercentPosters NumberWhiteBeatings Generator BeadsAnything Experimental Design 30 31. 6. Deming: Maintain the Status Quo For Deming the past history of the system represents the goal and reference point defining whether the system is under statistical control. Action is not taken when the system is under statistical control. We react to deviations outside the control range because they indicate that the system is no longer in statistical control. Thus, we look at the road behind us, through the rear view mirror, and use control limits to prevent ourselves from deviating from our past course. 31 32. The OODA Loop Originally developed by Col. John Boyd, USAF. F-86 achieves 10:1 kill ratio vs. the technicallysuperior MiG-15. There are time competitive cycles of action. The effects of faster decisions are cumulative. So, complete the loop faster than the competition.OrientObserve DecideAct 32 33. Boyd: Influence the Future For Boyd we are always walking into new terrain inthe fog. The situation changes and we mustquickly make choices to exploit these changes. This means it is critical to detect new information,determine what it means, and take action. Decision loop closure time is a critical metric. Boyd is focused on the road ahead and onreacting quickly to obstacles and opportunities. Which model is most relevant to the way we addvalue in product development? 33 34. Lean Start-Up The Boyd model is, in fact, the approach of theLean Start-up movement. Start with a testable hypothesis. Construct a fast, cheap experiment to test this hypothesis. Use this information to make the best economic choice: persevere or pivot. Lean Start-up looks much more like Boyd thanDeming. 34 35. Did Deming Understand Lean? There is actually little evidence thatDeming had deep understanding of howLean works. There are six passing references toKanban in his book. He doesnt appear to understand thecritical relationship between batch sizeand quality. He has little focus on the speed offeedback loops. 35 36. Deming on Kanban(When a process is in statistical control) One may now startto think about Kanban or just-in-time delivery. Out of the Crisis p.333Kanban or just in time follows as a natural result of statisticalcontrol of quality, which in turn means statistical control ofspeed of production. Out of the Crisis p.343-344 Actually, WIP constraints work whether or not aprocess is in statistical control. In fact, it is precisely when a process is out ofstatistical control that high queue states are mostlikely, and WIP constraints produce the greatesteconomic benefit.36 37. Conclusion Cumulative random variables behave differently. Payoff asymmetries change the role of variability. Sampling is not experimentation. For the product developer design of experiments ismore important than statistical inference. Statistical control may be unnecessary. Understand the OODA loop vs. the Deming cycle. Lose the Red Bead Experiment. Learn more about probability and statistics. 37 38. The three fields, calculus, probability, and statistics are all in constant use. Mathematicians in the past have tended to avoid the latter two, but probability and statistics are now so obviously necessary tools for understanding many diverse things that we must not ignore them even for the average student.R.W. Hamming, (1968 Turing Award)from Methods of Mathematics38 39. And the Bad News......it has long been observedthat the mathematics that isnot learned in school is veryseldom learned later, nomatter how valuable it wouldbe to the learner. Very Seldom != NeverR.W. Hamming, (1968 Turing Award)from Methods of Mathematics39