Julia Wester
November 08, 2016

# 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!

## Julia Wester

November 08, 2016

## 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)

8. ### Mo’ queue, Mo’ problems @everydaykanban § Longer average cycle times

§ Wider range of cycle times § More mgmt overhead § Reduced motivation & quality

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.

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

Once a week

variation

20. ### @everydaykanban Cycle time ranges: Lagging indicator Nov October September August

July Good clustering Can we reduce the outliers? 95%: 45 days or less

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