Predictability: No magic required
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This talk teaches you about queues and how the choices we make to manage them can make or break your predictability. This is a primer to queueing theory without statistics. It ends with suggestions on how to monitor indicators of predictability.
![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
________!](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Predictability: No magic required
- 1. Predictability No magic required Julia Wester Improvement Coach & Team Manager EverydayKanban.com @everydaykanban learn@leankit.com
- 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. How many telephone lines are needed to avoid blocked calls given § Random arrivals § Random durations Pulling answers from randomness @everydaykanban
- 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. @everydaykanban TODAY’S TALK § Why queues matter § Choices we can make about queues § Monitoring your predictability indicators
- 7. @everydaykanban Queues are the waiting work in a system
- 8. Mo’ queue, Mo’ problems @everydaykanban § Longer average cycle times § Wider range of cycle times § More mgmt overhead § Reduced motivation & quality
- 9. @everydaykanban Our workflows are chains of queues
- 10. As interpreted by Don Reinertsen Aesop’s Fable: The Tortoise and the Hare @everydaykanban
- 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. @everydaykanban Choices we can make about queues
- 13. @everydaykanban Choice: Use a push system or pull system? 1 queue per server 1 queue multiple servers Which one do you use?
- 15. @everydaykanban Choice: What factors used to prioritize? Your policy here! FIFO/S PRIORITY
- 17. @everydaykanban Choice: Deliver large or small batches? Once a month Once a week
- 18. Once a week Once a month @everydaykanban Wider variation Less variation
- 20. @everydaykanban Cycle time ranges: Lagging indicator NovOctoberSeptemberAugustJuly Good clustering Can we reduce the outliers? 95%: 45 days or less
- 21. @everydaykanban Queue size: a leading indicator Which lane is going faster?
- 22. @everydaykanban CFD: Demonstrates the relationship Work units Time Avg. Queue Size Avg. Cycle Time To Do Design Create Verify Deliver 18 10 1.5 2.5
- 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. @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.
- 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