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I broke the WIP Limit TWICE, still on the team

Zsolt Fabok
November 01, 2013

I broke the WIP Limit TWICE, still on the team

History:
- updated after #lkuk13 (2013-11-01)
- first version after #lknl13 (2013-10-09)

Abstract:

Hi, my name is Zsolt, I broke the WIP limit several times, I'm still on the team and I'm happy about it. Most probably you've had a similar experience (except maybe you weren't happy at all) when you first started to work in a team that got hit by the Kanban method introduced by one of your fellow teammates. At first, it is hard to follow and understand all the principles and practices, especially when they "hit" us, and therefore we make mistakes. This is good and natural because this is how we live our lives: we fail, we recover and start over.

Starting over requires us to do at least two things: re-learn the principles and practices, and look for examples on how others recovered. I believe that understanding the pull system, the WIP limits, and the difference between manufacturing and software development will give us enough to recover faster from failures and accelerate the learning process. Moreover, I assume that I did more wrong than right during my journey in Kanban land, and it cost me a lot. I believe that if I share these stories with you, it will save you a great deal of trouble for yourself, and if not, at least you'll have some ideas on how to recover.

Clearly, this talk is not for advanced practicioners, but I think they are as rare as unicorns anyway. So, if you have doubts, or want to know why we do things in the Kanban method as we do now, and you are also interested in some practical ideas, this talk is the right place for you.

Zsolt Fabok

November 01, 2013
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Transcript

  1. If you understand the purpose of small incremental evolutionary changes

    and the pull system, you’ll do fine with the Kanban Method, because you can easily deduce the rest. (helping questions: Am I improving? Am I pulling the work?)
  2. It is hard to improve the invisible, so we have

    to see what we have at our hands.
  3. Car manufacturing vs software engineering “The gemba walk” (Unfortunately, you

    won’t see much at a software engineering workplace) (http://zsoltfabok.com/blog/2013/06/gemba-walk-has-low-value/)
  4. Ready Design Implementation Test Delivery Live Flow (Stream) The flow

    ([value] stream) starts at the left side, and ends at the right side of the board.
  5. Now we can see the previously unseen, but we need

    to start pulling work items otherwise they won’t get delivered.
  6. Ready Design Implementation Test Delivery Live # ~ ~ ~

    1 2 1 3 # ~ ~ ~ # ~ ~ ~ # ~ ~ ~ # ~ ~ ~
  7. # ~ ~ ~ # ~ ~ ~ # ~

    ~ ~ work done staff liquidity + internal queue
  8. Too low WIP limit creates unnecessary bottleneck, too high WIP

    limit encourages multitasking and/or longer lead time.
  9. Car manufacturing vs software engineering “The inventory” (At Toyota the

    inventory is finite, however in software engineering it is infinite, therefore we cannot approach it with the same attitude)
  10. Now the work items are getting delivered, it is time

    to improve the system. Improvement requires measurement.
  11. The lead time Ready Design Implementation Test Delivery Live 1

    2 1 3 # ~ ~ ~ # ~ ~ ~ # ~ ~ ~ commitment
  12. Distribution of lead times days count 0 3 5 8

    10 13 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 22 28 33 56 average median* *Calculation of medians is a popular technique in summary statistics and summarizing statistical data, since it is simple to understand and easy to calculate, while also giving a measure that is more robust in the presence of outlier values Courtesy of Digital Natives
  13. Car manufacturing vs software engineering “The takt time” (In software

    engineering the demand is not quantitive, therefore it doesn’t matter how much time we spend between two features)
  14. Car manufacturing vs software engineering “The throughput” (In software engineering

    we don’t have to deliver 10 features, we have to deliver THE feature, therefore the number of delivered feature doesn’t help us, but it is a good “secondary” measure)
  15. Car manufacturing vs software engineering “The throughput” (In software engineering

    we don’t have to deliver 10 features, we have to deliver THE feature, therefore the number of delivered feature doesn’t help us, but it is a good “secondary” measure to see how we react to changes, or how good we are at forecasting)
  16. 0 2 4 5 7 week 1 week 2 week

    3 week 4 incoming outgoing The throughput
  17. The throughput (output) is helpful, if and only if it

    is compared to the demand (input).
  18. 1 2 1 3 # ~ ~ ~ # ~

    ~ ~ 0 2 4 5 7 w 1 w 2 w 3 w 4 6
  19. The goal is to have a stable system (first target)

    because improvement on a stable system will have a long term effect (second target).
  20. 1 2 1 3 # ~ ~ ~ # ~

    ~ ~ 0 2 4 5 7 w 1 w 2 w 3 w 4 7 It is Monday on Week 5... # ~ ~ ~ # ~ ~ ~ # ~ ~ ~ # ~ ~ ~ # ~ ~ ~ # ~ ~ ~ Aging item
  21. 0 2 4 5 7 w 1 w 2 w

    3 w 4 throughput *This argument is so weak that a decent philosopher stops thinking about philosophy and looks for another profession every time one reads it up “We get N items a week” “We deliver N items a week” “Therefore we can deliver an item in a week” “We deliver an item in a week” Therefore our lead time is the length of the week which is 5 days)”*
  22. days count 0 3 5 8 10 13 15 1

    2 3 4 5 6 7 8 9 But in reality you are actually here: 5 != [6,9]
  23. Protect the bottleneck and don’t punish the phase before the

    bottleneck. (with Theory of Constraints)
  24. 3 3 4 2 # ~ ~ ~ # ~

    ~ ~ Bottleneck
  25. 3 3 4 2 # ~ ~ ~ It hurts

    here... ...because the work items queue up here. # ~ ~ ~ # ~ ~ ~ # ~ ~ ~ # ~ ~ ~
  26. 3 3 4 2 # ~ ~ ~ # ~

    ~ ~ 2 Now the bottleneck is better protected... # ~ ~ ~ # ~ ~ ~ # ~ ~ ~ ...and the phase before the bottleneck doesn’t suffer.
  27. 0 4 8 11 15 week 1 week 2 week

    3 week 4 incoming outgoing Real data will come after finishing this experiment!
  28. If you understand the purpose of small incremental evolutionary changes

    and the pull system, you’ll do fine in Kanban land, because you can easily deduce the rest. And be skeptical about the practices that come from the manufacturing world (context matters).