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Is It Time to
 Rethink Deming?
         Lean Kanban Benelux
           Antwerp, Belgium
               October 3, 2011

No part of this presentation may be reproduced
 without the written permission of the author.

                Donald G. Reinertsen
              Reinertsen & Associates
                600 Via Monte D’Oro
          Redondo Beach, CA 90277 U.S.A.
                   (310)-373-5332
     Internet: Don@ReinertsenAssociates.com
                Twitter: @dreinertsen
          www.ReinertsenAssociates.com
Perspective
• Deming’s work is extremely important and it
  has had great influence on repetitive
  manufacturing.
• His ideas are relevant outside of this domain,
  but they must be used with some knowledge
  of the target domain.
• This involves rethinking a little bit of the
  mathematics and a lot of the implications.
• Deming did not claim that he had optimized
  his ideas for product development.

                                                   2
Who Was Deming?
              1927
                             US Department of Agriculture
              1939
                             Adviser to US Census Bureau
              1945
                             1950 Taught SPC in Japan,
                             Deming Prize Created
                             1960 Awarded Japan’s Order of the
                             Sacred Treasure, Second Class
                             Statistics Professor at New York
1900-1993
                             University, Consultant, Celebrity

              1993

      Legitimized relevance of statistics to industry.
      Made SPC a household term. A 1980’s celebrity.
                                                                 3
Some Product Development Questions
 1. Should we respond to random variation?
 2. Should we try to eliminate as much variability as
    possible?
 3. What is the essential difference between process
    control and experimentation?
 4. Is it always better to prevent problems than
    correct them?
 5. Is the system, as Deming states, the cause of 94
    percent of our problems?
 6. Are there other useful approaches?


                                                        4
1. Statistical Control
• For Deming, bringing a process under statistical
  control is indispensable.
• This state occurs when the outcomes of the
  process lie between upper and lower control limits.
• These limits are set at 3 times the standard
  deviation of the process.
• Standard deviation is calculated from the sampled
  output of the system.
• Thus, a process can be classified as in statistical
  control even when it has very high variation.
• This inherently stabilizes the status quo.
                                                        5
Statistical Control


               Upper Control Limit


Value
               3
                                     Mean
               3
               Lower Control Limit



  In Control        Time


                                            6
Deming’s World View
                                              3 Upper
                        Variation            and Lower
Process                                     Control Limits
  under
statistical
 control           Common
                    Cause                             Process
                                    Special
                                    Cause            not under
                                                     statistical
                                                       control


Shewhart used the terms chance (random) cause and assignable cause.

                                                                   7
Inherently Recursive
            Sample System Output

        Set Control Limits 3 from Mean


 Inside UCL and LCL        Outside UCL or LCL

   Common Cause               Special Cause

      No Action                Take Action

Output Doesn’t Change        Output Changes
 or Drifts Randomly
                                                8
Making Adjustments
• When the output of a process lies randomly
  between its upper and lower control limits it
  is under statistical control.
• If we make adjustments to a process that is
  under statistical control it will increase
  variation and hurt performance.
• If the output falls outside its limits this is
  defined as a special cause and the operator
  should investigate and correct this cause.
• Control limits are not specification limits!

                                                   9
Deming’s Funnel
      +1       +1

                          No Adjustment
                           Variance = 1

       -1      -1                         +2
                                 Offset
                                 to +1
                    +1
 Offsetting                               0
Adjustment
                                          0
Variance = 2
                     -1          Offset
                                 to -1    -2
                                               10
Statistical Control

Now it’s 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
An Economic View
                                                   Cost/Benefit
No Remaining              Variation                 Analysis
  Economic
 Opportunity
                       Not
                   Economical
                                        Economical
                    to Correct
                                         to Correct

                                                       Economic
                                                      Opportunity

Fixing or mitigating a defect is a tradeoff between the
 cost and benefit of fixing it, regardless of the cause.
                                                                    12
Deming’s Frame of Reference
• As you might expect, Deming views each
  outcome as an independent identically
  distributed (IID) random variable — the classic
  statistics of random sampling.
• But, what would happen if we had a Markov
  Process, where the outcome was a function of
  both the current state and a random variable.
• This is common in product development, e.g.
  when a second stochastic activity can’t start
  until the first one finishes.

                                                    13
A Random Walk
• We flip a coin 1000 times, add 1 for each head,
  subtract 1 for each tail, and keep track of our
  cumulative total.
• How many times the cumulative total will
  return to the zero line during the 1000 flips?


       Cumulative   H T T H T H H
         Total


                                  Time



                                                    14
One Thousand Coin Tosses

           1st Half Crossings = 38      Cumulative
           2nd Half Crossings = 0
 50
           Average Time Between Crossings = 25.6
 40
           Maximum Time Between Crossings = 732
 30

 20

 10

  0
       0                    250               500       750               1000
 -10



Note: +1 for each head, -1 for each tail
Based on example from “Introduction to Probability Theory and Its Applications”,
by William Feller. John Wiley: 1968

                                                                                   15
Cumulative Totals Diffuse

                                                            Early
    Probability



                                                                   Late




                           Value of Random Variable
Notes:            1. Zero is always most probable value.
                  2. But, it becomes less probable with time.
                  3. For large N a binomial distribution approaches a
                  normal distribution.
                                                                          16
It’s Not Deming’s Funnel
• The randomness that causes a problem will not fix
  this problem in a reasonable amount of time.
• We must intervene quickly and decisively when we
  reach the control limit.
• It is precisely this control of high queue states that
  is 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 the
  center of the control range not its edge.
• Think of a Drunkard’s Walk on top of a skyscraper.


                                                           17
2. Eliminating Variability

• In manufacturing we try to minimize the
  variability of a process.
• There is a underlying economic reason
  why this works.
• In product development variability plays a
  very different economic role.
• Consider a race with ten runners.



                                               18
Asymmetric Payoffs and Option Pricing

                 Expected Price                           Payoff vs. Price
Probabilty




                                                 Payoff
                                             x            Strike
                                                          Price


                       Price                                       Price

                                   Expected Payoff
                        Expected
                         Payoff




             =                      Strike
                                    Price



                                             Price

                                                                             19
Higher Variability Raises This Payoff


                            Strike
                            Price

   Expected
    Payoff




                                   Price
                    Payoff SD=15      Payoff SD=5

           Option Price = 2, Strike Price = 50,
      Mean Price = 50, Standard Deviation = 5 and 15
                                                       20
Manufacturing Payoff-Function*

  Gain                     Target



Payoff

  Loss
                        Performance

   Larger Variances Create Larger Losses
         *The Taguchi Loss Function
                                           21
Making Good Economic Choices

              Economic
Probability
               Payoff          Economic Expectation
 Function
   p( x )
              Function
                              E ( g ( x ))   g ( x ) p( x )dx
                g( x )


 Deming’s        Another critical         What we want
  Focus          leverage point.          to maximize.




                                                              22
3. Sampling vs. Experimentation

       SAMPLING                     EXPERIMENTATION
•   The population you are      •   Identify the question you
    sampling is given.              are trying to answer.
•   Devise efficient sampling   •   Determine what data you
    strategies to balance           need to answer the
    accuracy vs. cost.              question.
•   Here sampling design is a   •   Develop an efficient way to
    key skill.                      create this data.
                                •   Here experimental design
                                    is key skill.



                                                                  23
Inferential Statistics


Input                                                Output




              How many modules are defective?

        Design a sampling strategy to answer this
        question at the required confidence level.


                                                         24
Design of Experiments


Input                                             Output




               16 Modules with 1 defective

          Which, if any, modules are defective?

        Design a testing strategy to quickly
        and efficiently answer this question.
                                                      25
Information and Testing

Information



                   Probability of Failure  Pf
                   Probability of Success  Ps
                   Information Generated by Test  I t
                                   1                
                   I t  Pf log 2        Ps log 2  1 
                                  P                P 
                                   f                s


0%                              50%                          100%
                    Probability of Failure
                                                                    26
4. The Cult of Prevention

• Is it always better to prevent problems
  than it is to find and fix them?
• This will be quick.
• NO.
• Minimizing the cost of failure is always a
  local optimization.




                                               27
5. The System Dominates
“I should estimate that in my experience most troubles
and most possibilities for improvement add up to
proportions something like this:

       94 % belong to the system (responsibility of
            management)
        6 % special”
                                   - Out of the Crisis p.315

(Responsibility of leadership) “A third responsibility is to
accomplish ever greater and greater consistency of
performance within the system, so that apparent
differences between people continually diminish.”
                                    - Out of the Crisis p.249

  These statements have terrifying implications.
                                                                28
The Red Bead Experiment
• Deming’s epic work is an entertaining con.
• It demonstrates vividly that a set of behaviors
  (that he disapproves of) do not work to improve
  performance.
• How does he work this magic?
• The output of the Red Bead Game is a random
  variable that is completely independent of the
  applied treatment.
• It will demonstrate that NO management method
  can EVER influence the output of a process.


                                                    29
The Red Bead Experiment


  Input                 System
  Various    Workers
Treatments
                                   Output
Rewards
Slogans                 Random     Percent
Posters                 Number      White
Beatings               Generator   Beads
Anything

             Experimental Design
                                             30
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
The OODA Loop
• Originally developed by Col. John Boyd, USAF.
• F-86 achieves 10:1 kill ratio vs. the technically
  superior MiG-15.
• There are time competitive cycles of action.
• The effects of faster decisions are cumulative.
• So, complete the loop faster than the competition.


                      Orient

      Observe                       Decide

                        Act

                                                       32
Boyd: Influence the Future
• For Boyd we are always walking into new terrain in
  the fog. The situation changes and we must
  quickly 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 on
  reacting quickly to obstacles and opportunities.
• Which model is most relevant to the way we add
  value in product development?


                                                         33
Lean Start-Up
• The Boyd model is, in fact, the approach of the
  Lean 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 than
  Deming.



                                                       34
Did Deming Understand Lean?

• There is actually little evidence that
  Deming had deep understanding of how
  Lean works.
• There are six passing references to
  Kanban in his book.
• He doesn’t appear to understand the
  critical relationship between batch size
  and quality.
• He has little focus on the speed of
  feedback loops.
                                             35
Deming on Kanban
(When a process is in statistical control…) One may now start
to think about Kanban or just-in-time delivery.
                                       – Out of the Crisis p.333

Kanban or just in time follows as a natural result of statistical
control of quality, which in turn means statistical control of
speed of production.
                                   – Out of the Crisis p.343-344

• Actually, WIP constraints work whether or not a
  process is in statistical control.
• In fact, it is precisely when a process is out of
  statistical control that high queue states are most
  likely, and WIP constraints produce the greatest
  economic benefit.
                                                                    36
Conclusion
• Cumulative random variables behave differently.
• Payoff asymmetries change the role of variability.
• Sampling is not experimentation.
• For the product developer design of experiments is
  more 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
“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 Mathematics”
                                            38
And the Bad News...

            “...it has long been observed
            that the mathematics that is
            not learned in school is very
            seldom learned later, no
            matter how valuable it would
            be to the learner.”



               Very Seldom != Never


R.W. Hamming, (1968 Turing Award)
  from “Methods of Mathematics”
                                            39

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Don reinertsen is it time to rethink deming

  • 1. Is It Time to Rethink Deming? Lean Kanban Benelux Antwerp, Belgium October 3, 2011 No part of this presentation may be reproduced without the written permission of the author. Donald G. Reinertsen Reinertsen & Associates 600 Via Monte D’Oro Redondo Beach, CA 90277 U.S.A. (310)-373-5332 Internet: Don@ReinertsenAssociates.com Twitter: @dreinertsen www.ReinertsenAssociates.com
  • 2. Perspective • Deming’s work is extremely important and it has had great influence on repetitive manufacturing. • His ideas are relevant outside of this domain, but they must be used with some knowledge of the target domain. • This involves rethinking a little bit of the mathematics and a lot of the implications. • Deming did not claim that he had optimized his ideas for product development. 2
  • 3. Who Was Deming? 1927 US Department of Agriculture 1939 Adviser to US Census Bureau 1945 1950 Taught SPC in Japan, Deming Prize Created 1960 Awarded Japan’s Order of the Sacred Treasure, Second Class Statistics Professor at New York 1900-1993 University, Consultant, Celebrity 1993 Legitimized relevance of statistics to industry. Made SPC a household term. A 1980’s celebrity. 3
  • 4. Some Product Development Questions 1. Should we respond to random variation? 2. Should we try to eliminate as much variability as possible? 3. What is the essential difference between process control and experimentation? 4. Is it always better to prevent problems than correct them? 5. Is the system, as Deming states, the cause of 94 percent of our problems? 6. Are there other useful approaches? 4
  • 5. 1. Statistical Control • For Deming, bringing a process under statistical control is indispensable. • This state occurs when the outcomes of the process lie between upper and lower control limits. • These limits are set at 3 times the standard deviation of the process. • Standard deviation is calculated from the sampled output of the system. • Thus, a process can be classified as in statistical control even when it has very high variation. • This inherently stabilizes the status quo. 5
  • 6. Statistical Control Upper Control Limit Value 3 Mean 3 Lower Control Limit In Control Time 6
  • 7. Deming’s World View 3 Upper Variation and Lower Process Control Limits under statistical control Common Cause Process Special Cause not under statistical control Shewhart used the terms chance (random) cause and assignable cause. 7
  • 8. Inherently Recursive Sample System Output Set Control Limits 3 from Mean Inside UCL and LCL Outside UCL or LCL Common Cause Special Cause No Action Take Action Output Doesn’t Change Output Changes or Drifts Randomly 8
  • 9. Making Adjustments • When the output of a process lies randomly between its upper and lower control limits it is under statistical control. • If we make adjustments to a process that is under statistical control it will increase variation and hurt performance. • If the output falls outside its limits this is defined as a special cause and the operator should investigate and correct this cause. • Control limits are not specification limits! 9
  • 10. Deming’s Funnel +1 +1 No Adjustment Variance = 1 -1 -1 +2 Offset to +1 +1 Offsetting 0 Adjustment 0 Variance = 2 -1 Offset to -1 -2 10
  • 11. Statistical Control Now it’s 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/Benefit No Remaining Variation Analysis Economic Opportunity Not Economical Economical to Correct to Correct Economic Opportunity Fixing or mitigating a defect is a tradeoff between the cost and benefit of fixing it, regardless of the cause. 12
  • 13. Deming’s Frame of Reference • As you might expect, Deming views each outcome as an independent identically distributed (IID) random variable — the classic statistics of random sampling. • But, what would happen if we had a Markov Process, where the outcome was a function of both the current state and a random variable. • This is common in product development, e.g. when a second stochastic activity can’t start until 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 our cumulative total. • How many times the cumulative total will return to the zero line during the 1000 flips? Cumulative H T T H T H H Total Time 14
  • 15. One Thousand Coin Tosses 1st Half Crossings = 38 Cumulative 2nd Half Crossings = 0 50 Average Time Between Crossings = 25.6 40 Maximum Time Between Crossings = 732 30 20 10 0 0 250 500 750 1000 -10 Note: +1 for each head, -1 for each tail Based on example from “Introduction to Probability Theory and Its Applications”, by William Feller. John Wiley: 1968 15
  • 16. Cumulative Totals Diffuse Early Probability Late Value of Random Variable Notes: 1. Zero is always most probable value. 2. But, it becomes less probable with time. 3. For large N a binomial distribution approaches a normal distribution. 16
  • 17. It’s Not Deming’s Funnel • The randomness that causes a problem will not fix this problem in a reasonable amount of time. • We must intervene quickly and decisively when we reach the control limit. • It is precisely this control of high queue states that is 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 the center of the control range not its edge. • Think of a Drunkard’s Walk on top of a skyscraper. 17
  • 18. 2. Eliminating Variability • In manufacturing we try to minimize the variability of a process. • There is a underlying economic reason why this works. • In product development variability plays a very different economic role. • Consider a race with ten runners. 18
  • 19. Asymmetric Payoffs and Option Pricing Expected Price Payoff vs. Price Probabilty Payoff x Strike Price Price Price Expected Payoff Expected Payoff = Strike Price Price 19
  • 20. Higher Variability Raises This Payoff Strike Price Expected Payoff Price Payoff SD=15 Payoff SD=5 Option Price = 2, Strike Price = 50, Mean Price = 50, Standard Deviation = 5 and 15 20
  • 21. Manufacturing Payoff-Function* Gain Target Payoff Loss Performance Larger Variances Create Larger Losses *The Taguchi Loss Function 21
  • 22. Making Good Economic Choices Economic Probability Payoff Economic Expectation Function p( x ) Function E ( g ( x ))   g ( x ) p( x )dx g( x ) Deming’s Another critical What we want Focus leverage point. to maximize. 22
  • 23. 3. Sampling vs. Experimentation SAMPLING EXPERIMENTATION • The population you are • Identify the question you sampling is given. are trying to answer. • Devise efficient sampling • Determine what data you strategies to balance need to answer the accuracy vs. cost. question. • Here sampling design is a • Develop an efficient way to key skill. create this data. • Here experimental design is key skill. 23
  • 24. Inferential Statistics Input Output How many modules are defective? Design a sampling strategy to answer this question at the required confidence level. 24
  • 25. Design of Experiments Input Output 16 Modules with 1 defective Which, if any, modules are defective? Design a testing strategy to quickly and efficiently answer this question. 25
  • 26. Information and Testing Information Probability of Failure  Pf Probability of Success  Ps Information Generated by Test  I t  1    I t  Pf log 2    Ps log 2  1  P  P   f   s 0% 50% 100% Probability of Failure 26
  • 27. 4. The Cult of Prevention • Is it always better to prevent problems than it is to find and fix them? • This will be quick. • NO. • Minimizing the cost of failure is always a local optimization. 27
  • 28. 5. The System Dominates “I should estimate that in my experience most troubles and most possibilities for improvement add up to proportions something like this: 94 % belong to the system (responsibility of management) 6 % special” - Out of the Crisis p.315 (Responsibility of leadership) “A third responsibility is to accomplish ever greater and greater consistency of performance within the system, so that apparent differences between people continually diminish.” - Out of the Crisis p.249 These statements have terrifying implications. 28
  • 29. The Red Bead Experiment • Deming’s epic work is an entertaining con. • It demonstrates vividly that a set of behaviors (that he disapproves of) do not work to improve performance. • How does he work this magic? • The output of the Red Bead Game is a random variable that is completely independent of the applied treatment. • It will demonstrate that NO management method can EVER influence the output of a process. 29
  • 30. The Red Bead Experiment Input System Various Workers Treatments Output Rewards Slogans Random Percent Posters Number White Beatings Generator Beads Anything 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 technically superior MiG-15. • There are time competitive cycles of action. • The effects of faster decisions are cumulative. • So, complete the loop faster than the competition. Orient Observe Decide Act 32
  • 33. Boyd: Influence the Future • For Boyd we are always walking into new terrain in the fog. The situation changes and we must quickly 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 on reacting quickly to obstacles and opportunities. • Which model is most relevant to the way we add value in product development? 33
  • 34. Lean Start-Up • The Boyd model is, in fact, the approach of the Lean 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 than Deming. 34
  • 35. Did Deming Understand Lean? • There is actually little evidence that Deming had deep understanding of how Lean works. • There are six passing references to Kanban in his book. • He doesn’t appear to understand the critical relationship between batch size and quality. • He has little focus on the speed of feedback loops. 35
  • 36. Deming on Kanban (When a process is in statistical control…) One may now start to think about Kanban or just-in-time delivery. – Out of the Crisis p.333 Kanban or just in time follows as a natural result of statistical control of quality, which in turn means statistical control of speed of production. – Out of the Crisis p.343-344 • Actually, WIP constraints work whether or not a process is in statistical control. • In fact, it is precisely when a process is out of statistical control that high queue states are most likely, and WIP constraints produce the greatest economic 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 is more 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 Mathematics” 38
  • 39. And the Bad News... “...it has long been observed that the mathematics that is not learned in school is very seldom learned later, no matter how valuable it would be to the learner.” Very Seldom != Never R.W. Hamming, (1968 Turing Award) from “Methods of Mathematics” 39