Key Note - Lean Kanban Central Europe 2011 - Predictability & Measurement with Kanban

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Which metrics do you use with Kanban? How do you plan and management large projects with Kanban? How has Kanban enabled the concept of "No Estimates" or the use of probabilistic forecasting?

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Key Note - Lean Kanban Central Europe 2011 - Predictability & Measurement with Kanban

  1. 1. Predictability & Measurement with Kanban Lean Kanban Central Europe Munich October 2011 David J. Anderson David J. Anderson & Associates dja@djandersonassociates.com
  2. 2. Book Published April 2010 Available from djandersonassociates.com Advanced Kanban A 72,000 word intro to the topic
  3. 3. http://leankanbanuniversity.com http://www.limitedwipsociety.org LinkedIn Groups: Software Kanban Yahoo! Groups: kanbandev Yahoo! Groups: kanbanops
  4. 4. Delivering predictability with Kanban requires some different techniques for different types of work such as software maintenance and support or Advanced Kanban major project work
  5. 5. Service-oriented work Advanced Kanban
  6. 6. Create a regular delivery cadence Develop a strong config management capability Develop capability to deploy effectively Build code with high quality Advanced Kanban
  7. 7. Understand capability by studying the natural philosophy of the work MARCH Lead Time Distribution 2.5 # CRs 2 1.5 1 0.5 106 101 96 91 86 81 76 71 66 61 56 51 46 41 36 31 26 21 16 11 6 1 0 Days Lead Time Distribution APRIL 3.5 Majority of CRs range 30 -> 55 2 Outliers 1.5 1 0.5 Days 8 14 1 14 4 13 0 3 6 7 12 12 11 10 99 92 85 78 71 64 57 50 43 36 29 22 15 8 0 1 CRs & Bugs 2.5 Advanced Kanban 3
  8. 8. For standard class items, offer a target lead time based on the 2nd confidence interval Advanced Kanban
  9. 9. Lead Time Distribution 3.5 3 2 1.5 1 0.5 1 4 7 0 3 6 8 14 14 13 12 12 11 10 99 92 85 78 71 64 57 50 43 36 29 22 8 15 0 1 CRs & Bugs 2.5 Days Advanced Kanban For example, SLA of 51 days with 98% on-time (+2 sigma from mean)
  10. 10. 51 days will not be good enough for some feature requests, so offer a package of classes of service Advanced Kanban
  11. 11. Package of Classes with SLAs  As soon as possible   100% on-time   providing 24 days advance notice Up to 51 days  98% on-time guarantee Up to 51 days  50% on-time Advanced Kanban  Full transparency
  12. 12. Lead time Standard Class Items Fixed Date Items Advanced Kanban Expedite Item Features Delivered
  13. 13. Allocate capacity across classes of service in order to deliver against anticipated demand 5 4 Analysis Input Queue In Prog Done 3 4 Development Dev Ready In Prog Done 2 Build Ready 2 = 20 total Test Release Ready ... Allocation 4 = 20% 10 = 50% 6 = 30% Advanced Kanban +1 = +5%
  14. 14. John Seddon has observed that allocating capacity in this fashion “damages capacity”! While this is theoretically possible it will almost never happen because (a) a simple policy can be implemented to temporarily re-allocate (b) demand is rarely zero for a given type, though Fixed Date class of service can be seasonal Advanced Kanban (c) the tickets represent work, not workers, the workforce is flexible. Classes of service & capacity allocation insure people can keep busy improving utilization not damaging it
  15. 15. Major Project Work Advanced Kanban
  16. 16. Requires all the same underlying data as used in service oriented work plus Advanced Kanban
  17. 17. Major Project with two-tiered kanban board Advanced Kanban
  18. 18. Cumulative Flow and Predictive Modeling with S-Curve Time Inventory Started Designed Coded Complete 30 -M ar 23 -M ar 16 -M ar 9M ar 2M ar eb 24 -F 17 -F eb Typical S-curve Advanced Kanban eb 240 220 200 180 160 140 120 100 80 60 40 20 0 10 -F Features Device Management Ike II Cumulative Flow
  19. 19. Simulating S-Curve with a Z Slope in middle 3.5x - 5x slope at ends 5x 20% Time Inventory Started Designed Coded Complete 30 -M ar 23 -M ar 16 -M ar 9M ar 2M ar 24 -F eb 20% eb 17 -F eb 60% Advanced Kanban 240 220 200 180 160 140 120 100 80 60 40 20 0 10 -F Features Device Management Ike II Cumulative Flow
  20. 20. Track actual throughput against projection Time Inventory Started Designed Coded Complete 30 -M ar 23 -M ar 16 -M ar 9M ar 2M ar eb 24 -F 17 -F eb Track delta between planned and actual each day Advanced Kanban eb 240 220 200 180 160 140 120 100 80 60 40 20 0 10 -F Features Device Management Ike II Cumulative Flow
  21. 21. Unplanned Work Report Scope Creep Dark Matter Advanced Kanban
  22. 22. Make a long term plan to build platform replacement Device Management Ike II Cumulative Flow Time Inventory Started Designed Coded Complete 2008 30 -M ar 23 -M ar 16 -M ar 5x 9M ar 2M ar eb 24 -F eb 2006 17 -F eb Slope in middle 3.5x - 5x slope at ends Advanced Kanban 240 220 200 180 160 140 120 100 80 60 40 20 0 10 -F Features Required throughput (velocity)
  23. 23. We need average throughput (velocity) to peak at 13 features per month over 24 months. Advanced Kanban
  24. 24. Little‟s Law Determines staffing level Target to achieve plan Throughput = WIP Lead Time Treat as Fixed variable Advanced Kanban From observed capability
  25. 25. Changing the WIP limit without maintaining the staffing level ratio represents a change to the way of working. It is a change to the system design. And will produce a change in the observed „common cause‟ capability of the system Advanced Kanban
  26. 26. Plan based on currently observed capability and current working practices. Do not assume process improvements. Advanced Kanban If changing WIP to reduce undesirable effects (e.g. multitasking), get new sample data (perform a spike) to observe the new capability
  27. 27. Little‟s Law Determines staffing level Target to achieve plan 13 / month = WIP 0.25 months If current working practice is 1 unit WIP per person then 3 people are needed Advanced Kanban WIP = 3.25, round up to 4. Might be safe to From observed capability round down to 3.
  28. 28. Slightly over-allocate the intangible class of service (green) to compensate against expediting 5 4 Analysis Input Queue In Prog Done 3 4 Development Dev Ready In Prog Done 2 Build Ready 2 = 20 total Test Release Ready ... Allocation 4 = 20% 12 = 60% 4 = 20% Advanced Kanban +1 = +5%
  29. 29. Conclusions Advanced Kanban
  30. 30. For Service-oriented work, create predictability with a regular delivery cadence a strong config management capability capability to deploy effectively code with high quality For major projects Advanced Kanban understand peak throughput (velocity) model the s-curve on work complete treat the avg. lead time as the fixed variable use Little‟s Law to calculate WIP limits and staffing levels
  31. 31. Thank you! Advanced Kanban dja@agilemanagement.net http://www.agilemanagement.net/
  32. 32. About… David Anderson is a thought leader in managing effective software teams. He leads a consulting firm dedicated to improving economic performance of knowledge worker businesses – improving agility, reducing cycle times, improving productivity and efficiency in technology development. He has 25+ years experience in the software industry starting with computer games in the early 1980‟s. He has led software teams delivering superior productivity and quality using innovative agile methods. He developed MSF for CMMI Process Improvement for Microsoft. He is a co-author of the SEI Technical Note, CMMI and Agile: Why not embrace both! David was a founder of the Lean Software & Systems Consortium, a not for profit dedicated to promoting better standards of professionalism and effectiveness in software engineering. Email… dja@agilemanagement.net Advanced Kanban David‟s book, Agile Management for Software Engineering – Applying the Theory of Constraints for Business Results, introduced many ideas from Lean and Theory of Constraints into software engineering.

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