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Kanban to #003 - Metrics

  1. Build the local Kanban community Connect the GTA to the global Kanban community Increase Kanban Maturity in the GTA Mission
  3. Fit for Purpose Cards Micro-narrative Micro-narrative Micro-narrative
  4. Let’s talk about Metrics!
  5. 1 2 3 4 How having this data impacts understanding of Customer expectations? How having this data helps you improve your Service? What kind of improvement does this data help guide? What kind of decision is this data helping with?
  6. 1 2 3 4
  7. Where is my stuff? What am I going to get at the end? Why does it take this long? Flow
  8. Flow Efficiency = Total Touch Time Total Lead Time
  9. Delivery Rate WIP Delivery Lead TimeDelivery Expectations High Risk Class Lower Risk Class
  10. Little’s Law Avg. Delivery Rate = Avg. WIP Avg. Lead Time Predictable, yet not deterministic “easiest” to control
  11. • Average arrival rate = average departure rate (constant WIP) • All work that enters the system must flow through to the end • The average age of WIP is constant Conservation of Flow
  12. Time WorkItemCount(Cumulative) Cumulative Flow Diagrams
  13. 5 to 7 Months ReleasableFeatures Pea pods. From Years to Months – Highly Stable
  14. Lead Time Distributions K=1.5 K=0.75
  15. “In God we trust, all others must bring data” – W. Edwards Deming

Editor's Notes

  1. From the Customer’s perspective, what matters is flow of value.
  2. 3 “flow metrics”. They are in different “classes of risk” Turns out that the one that Customers care about the most is the one that carries the most risk. That’s why we focus on measuring and managing Delivery Lead Times.
  3. Those 3 metrics are related. Of all 3, WIP is the one that it is “easy” to directly control. That’s why we use WIP constraints as the tool to manage the other two.
  4. Aging of items may be a useful health indicator to monitor.
  5. Mode: most seen (and remembered) value. High probability lead times will be over that. Median: useful in establishing short feedback loops to continuously validate forecasting models and project and release plans. 63rd Percentile: used in estimating the scale parameter 80th Percentile: SLAs