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Lean Kanban India 2018 | WIP decides Lead Time, Delivery Rate and Flow Efficiency | Amit Kaulagekar


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Session Title: WIP decides Lead Time, Delivery Rate and Flow Efficiency
Session Overview:
We all are familiar with Kanban metrics. When we adopt Kanban, we want to achieve shorter Lead Time, higher delivery rate and better flow efficiency. How do we achieve all of this? The master key is WIP limit. Together, let’s look at different scenarios to understand impact of WIP limit on all three... Is there some magic formula? What does the data say... what are the findings so far?

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Lean Kanban India 2018 | WIP decides Lead Time, Delivery Rate and Flow Efficiency | Amit Kaulagekar

  1. 1. WIP drives Lead Time Delivery Rate Flow Efficiency and Quality 22nd Sept 2018
  2. 2. Amit Kaulagekar • Based in Pune, India • • Linkedin: Amit Kaulagekar • Areas of expertise – • Lean, Kanban and Six Sigma • Project and Program Management • Service Management • Business Process Management • Standards and Compliance • Software Engineering
  3. 3. What we have heard • When • WIP is Higher, higher / longer Lead Time • WIP is Higher, higher Delivery Rate • WIP is Higher, lower quality • WIP is Higher, lesser predictability • And controlled WIP results in • Lesser lead time • Better predictability • Better quality but • Lower delivery rate (some call it productivity)
  4. 4. Impact of WIP limit – lessons learned • If your WiP is high, reduce it • If your WiP is already low, consider your economic drivers • If Productivity drives your bottom line, don’t push WiP too low • If time to market or quality drives your bottom line, push WiP as low as it will go
  5. 5. Let’s test this in a controlled environment With GetKanban game
  6. 6. Definitions • WIP - an unfinished work item that is still being added to or developed • WIP limit - A strategy for preventing bottlenecks • System Lead Time - the time we commit to start the work (when we pull it as WIP) and stops when the work reaches an unbounded queue • Customer Lead Time - begins at the point of commitment where the customer’s order is accepted by the service delivery organization. Includes unbounded queues… • Delivery Rate = WIP / System Lead Time (or Avg. Lead Time) • Flow Efficiency % = Work Time / Lead Time * 100
  7. 7. WIP limit can be • by person, • By workflow, • By work item type, • By total number of items in progress • By risk dimension or • Across the entire board
  8. 8. But how do we decide WIP today • Team size + some buffer (like 50%) • Twice / Thrice the work items per individual • Xxxxxxx • Most of the times, it is just a number at the top of the column
  9. 9. And what do we want to achieve? • Better lead time • Better predictability • Better quality and • Lesser delivery rate • Better delivery rate • Lesser lead time • Lesser predictability and • Lesser quality OR Economic drivers
  10. 10. Kanban Metrics – Little’s law Avg. Lead Time Avg. Delivery RateWIP Pool of ideas Ready to deliver Delivery Rate (from the kanban system) System Lead Time WIP =
  11. 11. Let’s calculate Team started with WIP limit of 12, increased it to 16 and again brought it down to 12. Delivery rate on • Day 10 • (WIP / Lead Time) = Delivery Rate • 10 / 7.33 = 1.36 • Day 12 • 13 / 9 = 1.44 • Day 14 • 20 / ?????
  12. 12. Run chart and Lead Time Distribution chart So we use Lead Time distribution to calculate average lead time which can be used for ‘Delivery Rate’ calculation. In the given scenario, Avg. Lead time is 8.75 days And Run chart helps us find trends / patterns in the process. As we can see, variation is very high in this process.
  13. 13. Flow Efficiency and Finance And the team could calculate ‘Flow Efficiency’ for only 1 ticket (12 day time period). Flow Efficiency could not be calculated for other tickets. And the economic driver was shorter lead time or ‘Time to market’
  14. 14. So here are the numbers for all days… and trend ‘Time to Market’ being an economic driver, by increasing the WIP, team achieved exactly opposite of it.
  15. 15. Another example where WIP was 20 • Work is moving in batches • Lead time distribution is skewed towards right • Run chart shows high variation
  16. 16. And here is the data table
  17. 17. When WIP is 10
  18. 18. Impact of WIP limit – what we observed • As WIP increases • Higher Lead Time • Low Flow Efficiency but • Better Delivery Rate • As WIP decreases • Lesser Lead Time • Low Delivery Rate • Higher Flow efficiency
  19. 19. What next… To test this hypothesis for other scenarios – based on the way WIP is decided and come back…
  20. 20. Thank You