Title: “The Surprising Effectiveness of Limiting WIP: What Makes It Work So Well”

1. The Surprising Effectiveness
of Limiting WIP:
Why It Works So Well
LKIN 2016
Bangalore, India
September 10, 2016
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
No part of this presentation may be reproduced
without the written permission of the author.

2. 2Copyright 2016, Reinertsen & Associates
Objectives
• Explain why WIP constraints work
• Discuss how they affect economics
• Explain the difference between WIP control
and WIP constraints

3. 3Copyright 2016, Reinertsen & Associates
A Bit of Context

4. 4Copyright 2016, Reinertsen & Associates
Two Approaches
• We can treat WIP constraints with two approaches:
• Science-based
• Faith-based / Cargo Cult
• A science-based approach:
• Forces you to understand underlying
mechanisms of action.
• Permits you to engineer specific solutions for
different and changing contexts.
• A faith-based approach:
• Requires much less thinking.
• Works well in stable contexts like manufacturing.

5. 5Copyright 2016, Reinertsen & Associates
Thus, since the Toyota Production System has
been created from actual practices in the
factories of Toyota, it has a strong feature of
emphasizing practical effects, and actual
practice and implementation over theoretical
analysis.
– Taiichi Ohno
From Foreword to 1983 First
Edition of Toyota Production
System by Yasuhiro Monden

6. 6Copyright 2016, Reinertsen & Associates
The TPS Emergency Room
• We desire to rigorously
imitate the practices of
Toyota.
• We will set a strict
upper limit on WIP.
• When we reach our
limit, no new patients
can enter until another
departs.

7. 7Copyright 2016, Reinertsen & Associates
(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
Deming on Kanban

8. 8Copyright 2016, Reinertsen & Associates
WIP Constraints in Product Development
• Non-homogeneous flows are different.
• A process does not need to be under statistical
control to benefit from WIP constraints.
• WIP constraints are a primary tool for rate
matching.
• Rate matching prevents generating queues.
• Queues damage cycle time, quality, and
efficiency.
• A constraint is just one of many possible
responses to a high WIP level.
• Whether other responses are better depends on
the economics.

9. 9Copyright 2016, Reinertsen & Associates
Controlling Internet Flows
Sender Receiver
Packet from Sender
Acknowledgement (ACK) from Receiver
Data Network
Packets + ACKs < Window Size
Window Size Limits Number
of Unacknowledged Packets

10. 10Copyright 2016, Reinertsen & Associates
Congestion Avoidance vs. Control
0
100
Probability
of
Dropping
Packet
TCP
RED
REM
RED = Random Early Deletion, REM = Random Early Marking
Quantity in Buffer

11. 11Copyright 2016, Reinertsen & Associates
The Science

12. 12Copyright 2016, Reinertsen & Associates
How WIP Constraints Work
• Queueing systems randomly drift into
high queue states.
• These high queue states will delay many
jobs at the same time.
• Once entered, these states are relatively
persistent take a long time to clear.
• If we prevent a system from entering these
high queue states, we will produce
significant cycle time benefits at relatively
low cost.

13. 13Copyright 2016, Reinertsen & Associates
The Effect of Capacity Utilization
Queue Size vs. Capacity Utilization
0
5
10
15
20
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Capacity Utilization
QueueSize
Deterministic
Stochastic
Notes: Assumes M/M/1/ Queue,  = Capacity Utilization




1
2
qL

14. 14Copyright 2016, Reinertsen & Associates
Probability of High Queue States
Number of Items in Queue
Probability
  n
1State Probability* =
for M/M/1/ Queue
* Number of items In system

15. 15Copyright 2016, Reinertsen & Associates
Impact of High Queue States
Number of Items in Queue (Queue State)
Probability
State Probability
Impact on
Cycle Time
for M/M/1/ Queue
A State’s Cycle Time Impact = pn(n)/
CycleTimeImpact

16. 16Copyright 2016, Reinertsen & Associates
So, Why Not Chop Off the Tail?
Number of Items in Queue
Probability
State Probability
Impact on
Cycle Time

17. 17Copyright 2016, Reinertsen & Associates
The M/M/1/k Queue
WIPCAP 1 2 5 10 20 Infinite
Average Cycle Time 1.0 1.5 2.8 4.6 7.2 10.0
Time in Queue 0 0.5 1.8 3.6 6.2 9.0
Utlilization Percent 47% 63% 79% 85% 89% 90%
Empty Percent 53% 37% 21% 15% 11% 10%
Blocking Percent 47% 30% 13% 5% 1% 0%
Note: Assumes 90 percent utilization.
WIP constraints tradeoff reductions in cycle time
against blocking and underutilization costs.

18. 18Copyright 2016, Reinertsen & Associates
Effect of WIPCAPS
72%
52%
28%
21%
5%
1%
13%
5%
1%
0% 20% 40% 60% 80%
0.5x
1x
2x
WIPCAP
Percent Change
Delay Reduction Underutilization Blocking
Consequences of WIP Constraints
Note: WIPCAP relative to average WIP for
M/M/1/ queue loaded to 90 percent utilization.

19. 19Copyright 2016, Reinertsen & Associates
How WIP Constraints Work
• WIP constraints affect three important
economically important factors:
• They decrease average cycle time. (+)
• They generate blocking costs. (-)
• They create underutilization costs. (-)
• We need an economic framework to
assess their impact.
• A temporary WIP excursion may be
cheaper than a high blocking cost.

20. 20Copyright 2016, Reinertsen & Associates
Cumulative
-10
0
10
20
30
40
50
0 250 500 750 1000
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
One Thousand Coin Tosses
1st Half Crossings = 38
2nd Half Crossings = 0
Average Time Between Crossings = 25.6
Maximum Time Between Crossings = 732

21. 21Copyright 2016, Reinertsen & Associates
Random Processes Are Different
• A Markov chain adds a random variable to
the current state value.
• Such chains DO NOT regress to the mean.
• Instead, they diffuse from the origin.
• When we concatentate variances overall
variance grows.
• The time it takes for such processes to
return to control is very long.

22. 22Copyright 2016, Reinertsen & Associates
Early
Late
Cumulative Totals Diffuse
Value of Random Variable
Probability
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.
Notes:

23. 23Copyright 2016, Reinertsen & Associates
The Big Idea!
We can get significant cycle time
reduction, for a relatively low price,
by reducing the amount of time our
process spends in high queue states.

24. 24Copyright 2016, Reinertsen & Associates
WIP Control

25. 25Copyright 2016, Reinertsen & Associates
Demand-focused
Approaches
Block Entry
Purge WIP
Redefine the
Endpoint
T-Shaped
Resources
Supply-focused
Approaches
WIP Control
Methods of WIP Control
Resource
Pulling
Part-time
Resources
Flexible
Experts
Skill
Overlap
Toyota’s Kanban Method

26. 26Copyright 2016, Reinertsen & Associates
Limiting Active Projects by
Blocking Entry
1
2
3
4
1
2
3
4
COD Savings of Project 1 and 2 Late Start Advantages
for Project 3 and 4

27. 27Copyright 2016, Reinertsen & Associates
WIP Control
• Shortens cycle time
• Improves requirements
• Accelerates feedback
• Creates payoff asymmetries
• Raises productivity
• Raises efficiency

28. 28Copyright 2016, Reinertsen & Associates
Avoid Long Planning Horizons (V6)
Datum
Search Area
D = Vt
D = Vt
A =V2 t2
Planning Horizon
Error

29. 29Copyright 2016, Reinertsen & Associates
The Front-Loaded Lottery
• A lottery ticket pays $200 to the winning two digit
number.
• You can pick the numbers in two ways:
• Pay $2 to select both digits at once.
• Pay $1 for the first digit, find out if it is correct,
and then choose if you wish to pay $1 for the
next digit.
• Which approach has better economics? Why?
• What parallels does this have with product
development?

30. 30Copyright 2016, Reinertsen & Associates
100%
Probability
of
Occurrence
Value of Feedback
Cumulative Investment
100%
10%
Savings
= $0.90
$1 $2
90%
$1
0

31. 31Copyright 2016, Reinertsen & Associates
Summary
• WIP constraints are cost-effective way to
prevent the accumulation of variability.
• Their economic trade-offs favor progressive
tightening.
• There are many alternatives to completely
stopping flow when a WIP limit is reached.
• Look to telecommunication systems, not
factories, for the most advanced approaches.

32. 1991 / 1997 1997 2009
Going Further
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