Predictability: No Magic Required

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!

Transcript

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
6. Why Queues Matter @everydaykanban
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?
14. @everydaykanban normal stopped Slower, but consistent Wide variation
15. @everydaykanban Choice: What factors used to prioritize? Your policy here! FIFO/S PRIORITY
16. @everydaykanban FIFO Non-FIFO Wider variation Less variation. Feasible?
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
19. Monitoring your predictability indicators @everydaykanban
20. @everydaykanban Cycle time ranges: Lagging indicator Nov October September August July 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.
25. @everydaykanban References and Inspiration
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