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Predictability
No magic required
Julia Wester
Improvement Coach &
Team Manager
EverydayKanban.com
@everydaykanban
learn@le...
Adjective
Expected, especially on the basis
of previous or known behavior
[good or bad!]
Predictable
[pri-dik-tuh-buh l]
@...
How many telephone lines
are needed to avoid blocked
calls given
 Random arrivals
 Random durations
Pulling answers
from...
The mathematical study of waiting
lines, or queues.
Can quantify relationships
between queue size, capacity
utilization an...
@everydaykanban
TODAY’S TALK
 Why queues matter
 Choices we can make about queues
 Monitoring your predictability
indic...
Why Queues Matter
@everydaykanban
@everydaykanban
Queues are the waiting work in a system
Mo’ queue, Mo’ problems
@everydaykanban
 Longer average
cycle times
 Wider range of
cycle times
 More mgmt
overhead
 R...
@everydaykanban
Our workflows are chains of queues
As interpreted by Don
Reinertsen
Aesop’s Fable:
The Tortoise
and the Hare
@everydaykanban
Predictability ≠ fastest
UNLESS
you can consistently
be that fast.
To become more
predictable…USUALLY
DONE IN
2 to 200
DAY...
@everydaykanban
Choices we can make
about queues
@everydaykanban
Choice: Use a push system or pull system?
1 queue per server 1 queue multiple servers
Which one
do you use?
@everydaykanban
normal
stopped
Slower, but consistentWide variation
@everydaykanban
Choice: What factors used to prioritize?
Your policy
here!
FIFO/S PRIORITY
@everydaykanban
FIFO Non-FIFO
Wider
variation
Less
variation.
Feasible?
@everydaykanban
Choice: Deliver large or small batches?
Once a
month
Once a week
Once a week
Once a
month
@everydaykanban
Wider
variation
Less
variation
Monitoring your
predictability indicators
@everydaykanban
@everydaykanban
Cycle time ranges: Lagging indicator
NovOctoberSeptemberAugustJuly
Good clustering
Can we reduce the outli...
@everydaykanban
Queue size: a leading indicator
Which lane is going faster?
@everydaykanban
CFD: Demonstrates the relationship
Work
units
Time
Queue size
Cycle Time
To Do
Design
Create
Verify
Delive...
@everydaykanban
Queue Size: predicting predictability issues
Bigger queues lead to
longer cycle times, less
predictability...
@everydaykanban
• Remember, you have control over predictability!
• Get baseline measures of queue size/cycle times.
• Mak...
@everydaykanban
References and Inspiration
www.leankit.com
To receive a copy of:
• The slide deck for today’s presentation
• LeanKit’s 1st Annual Lean Business repor...
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DOES SFO 2016 San Francisco - Julia Wester - Predictability: No Magic Required

Download to read offline

Predictability: No Magic Required

Julia Wester, Improvement Coach, LeanKit

When you merge onto a freeway and are stuck in bumper-to-bumper traffic, you know right away that its going to be a long trip. Similarly, you can predict the cycle time of your work before it is finished without time consuming, and often incorrect, estimation. Sound like magic? Fortunately for all of us, it's not.

This talk explains the basics of queueing theory; demonstrates how allocation models and pull policies affect the cycle time of work; discusses the effects of batch size and variability on queues; and teaches how to successfully monitor your workflow to get leading indicators of effectiveness. With this information, you'll be doing better forecasting, and achieving better outcomes, in no time!

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DOES SFO 2016 San Francisco - Julia Wester - Predictability: No Magic Required

  1. 1. Predictability No magic required Julia Wester Improvement Coach & Team Manager EverydayKanban.com @everydaykanban learn@leankit.com
  2. 2. Adjective Expected, especially on the basis of previous or known behavior [good or bad!] Predictable [pri-dik-tuh-buh l] @everydaykanban USUALLY GREAT! USUALLY HORRIBLE! USUALLY ________!
  3. 3. How many telephone lines are needed to avoid blocked calls given  Random arrivals  Random durations Pulling answers from randomness @everydaykanban
  4. 4. The mathematical study of waiting lines, or queues. Can quantify relationships between queue size, capacity utilization and cycle times Queueing Theory was the solution @everydaykanban capacity utilization (rho) Queue size (N)
  5. 5. @everydaykanban TODAY’S TALK  Why queues matter  Choices we can make about queues  Monitoring your predictability indicators
  6. 6. Why Queues Matter @everydaykanban
  7. 7. @everydaykanban Queues are the waiting work in a system
  8. 8. Mo’ queue, Mo’ problems @everydaykanban  Longer average cycle times  Wider range of cycle times  More mgmt overhead  Reduced motivation & quality
  9. 9. @everydaykanban Our workflows are chains of queues
  10. 10. As interpreted by Don Reinertsen Aesop’s Fable: The Tortoise and the Hare @everydaykanban
  11. 11. Predictability ≠ fastest UNLESS you can consistently be that fast. To become more predictable…USUALLY DONE IN 2 to 200 DAYS! @everydaykanban USUALLY DONE IN 25 to 35 DAYS! reduce the range of probable outcomes.
  12. 12. @everydaykanban Choices we can make about queues
  13. 13. @everydaykanban Choice: Use a push system or pull system? 1 queue per server 1 queue multiple servers Which one do you use?
  14. 14. @everydaykanban normal stopped Slower, but consistentWide variation
  15. 15. @everydaykanban Choice: What factors used to prioritize? Your policy here! FIFO/S PRIORITY
  16. 16. @everydaykanban FIFO Non-FIFO Wider variation Less variation. Feasible?
  17. 17. @everydaykanban Choice: Deliver large or small batches? Once a month Once a week
  18. 18. Once a week Once a month @everydaykanban Wider variation Less variation
  19. 19. Monitoring your predictability indicators @everydaykanban
  20. 20. @everydaykanban Cycle time ranges: Lagging indicator NovOctoberSeptemberAugustJuly Good clustering Can we reduce the outliers? 95%: 45 days or less
  21. 21. @everydaykanban Queue size: a leading indicator Which lane is going faster?
  22. 22. @everydaykanban CFD: Demonstrates the relationship Work units Time Queue size Cycle Time To Do Design Create Verify Deliver 18 10 1.5 2.5
  23. 23. @everydaykanban Queue Size: predicting predictability issues Bigger queues lead to longer cycle times, less predictability Smaller queues lead to shorter cycle times, more predictability Work-In-Process (hidden queues?) Queued work 9 20 10 2
  24. 24. @everydaykanban • Remember, you have control over predictability! • Get baseline measures of queue size/cycle times. • Make informed choices about handling queues. • Monitor queues to anticipate and correct issues before they negatively impact cycle times.
  25. 25. @everydaykanban References and Inspiration
  26. 26. www.leankit.com To receive a copy of: • The slide deck for today’s presentation • LeanKit’s 1st Annual Lean Business report Send an email to: julia@leankit.com Subject: DOES16
  • powerirs

    Apr. 13, 2017
  • mpmcardle

    Nov. 21, 2016

Predictability: No Magic Required Julia Wester, Improvement Coach, LeanKit When you merge onto a freeway and are stuck in bumper-to-bumper traffic, you know right away that its going to be a long trip. Similarly, you can predict the cycle time of your work before it is finished without time consuming, and often incorrect, estimation. Sound like magic? Fortunately for all of us, it's not. This talk explains the basics of queueing theory; demonstrates how allocation models and pull policies affect the cycle time of work; discusses the effects of batch size and variability on queues; and teaches how to successfully monitor your workflow to get leading indicators of effectiveness. With this information, you'll be doing better forecasting, and achieving better outcomes, in no time!

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