Introduction of Kanban metrics
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One of the powerful aspects of Kanban is the statistical analysis of its metrics. This presentation talks about common Kanban metrics and how to interpret them.
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Introduction of Kanban metrics
- 1. Intro to Kanban Metrics Chuck Durfee
- 2. The Kanban Rules Visualize your work Limit work in progress Manage flow
- 3. Why Metrics
- 5. Throughput
- 7. WIP Limit Benefits better lead times faster feedback forces removal of impediments
- 9. Cycle Time vs. Lead Time
- 10. Case Study
- 11. Case Study
- 12. Questions In the video, what was: Cycle time? Work in progress? What was the relationship between them?
- 13. Little’s Law Comes from queuing theory Requires some assumptions All of these values are averages, not absolutes Cycle Time = WIP ÷ Throughput
- 14. Increasing Throughput Throughput = WIP ÷ Cycle Time
- 15. Work in Progress WIP = Throughput × Cycle Time Start stopping, start finishing
- 16. Assumptions
- 17. Stable System Measurements are in consistent units During measurement period: Work in progress total must remain steady Work in progress average age must remain steady Questions What queuing approaches work for this? What effects do class of service have on this? What things are not implied by Little’s Law?
- 18. Queue Discipline In software, we deal with non-homogenous: Delays Task durations
- 19. Continuous Flow Little’s Law was originally proven for arrivals To apply it to departures: average arrival rate ≅ average departure rate and must be a closed system
- 21. CFD: WIP
- 22. CFD: WIP
- 23. CFD: Lead Time
- 24. CFD: Lead Time
- 25. CFD: Mean Delivery Time
- 26. CFD: Mean Delivery Time
- 27. CFD: Mean Arrival Time
- 28. der Takt (German) pulse beat Takt time = Time available ÷ Customer demand
- 30. How many stories need to be completed each day to make our release date?
- 31. Healthy CFD Patterns Lines for each step become parallel Steady Takt time synchronized effort Gap between lines narrows Decreasing work in process Slope of lines increases Throughput increasing through faster processing
- 37. Depth of Kanban
- 38. Acknowledgements Dan Vacanti, “Little’s Flaw” http://vimeo.com/52683659 Donald Reinertsen, “The Science of WIP Constraints” http://vimeo.com/53321681 Paul Brodzinski, “Cumulative Flow Diagram” http://brodzinski.com/2013/07/cumulative-flow-diagram.html
Editor's Notes
- Build Measure Learn
- Predates lean by about 50 years by Dr. Erlang, 1909 Queues form when processes with variability are loaded to high levels of utilization
- Cycle time is time from one station to the next Work in progress is passengers on train Relationship: reduce cycle time (delays) and see WIP go down
- http://positiveincline.com/index.php/2012/06/another-look-at-littles-law/ http://www.vissinc.com/2012/09/07/littles-law-isnt-it-a-linear-relationship/
- By adding capacity directly (e.g. adding people) or indirectly (improve the process) By lowering work in progress
- By adding capacity directly (e.g. adding people) or indirectly (improve the process) By lowering work in progress
- Tough: product delivery has non-homogenous delays and task duration times queue discipline is crucial FIFO or maybe round robin Class of service implies different handling different average ages Little’s Law doesn’t talk about size of items, people working on items, order of work, distribution of arrival & departure rates (http://vimeo.com/52683659)
- http://brodzinski.com/2013/07/cumulative-flow-diagram.html Lines wider, more WIP, slower delivery Or maybe there are more blocked stories Or maybe the team just grew and people are ramping up
- Dev takes longer than test. Why? Could be an issue with dev taking too long Could be an issue with not enough QA A dev done column might help
- Done is stair-step, which implies a gated release process More stuff in test without being released is problematic – lower quality? Devs start working on new stuff over bug fixing? Code hard to deploy?
- Flat spot, company holiday? Something blocking all stories? Loss of staging environment? Another project took priority?
- Dev is healthy, but test is flat problem is in testing