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