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OOP 2012 - Predictability & Meansurement with Kanban
The document discusses advanced Kanban techniques for achieving predictability and measurement in various work types, including software maintenance and major projects. It emphasizes the importance of establishing a regular delivery cadence, strong configuration management, and effective deployment capabilities. The author, David J. Anderson, provides insights into modeling throughput and staffing levels based on observed capability while advocating for transparency and the management of work in progress.
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OOP 2012 - Predictability & Meansurement with Kanban
- 1. Predictability & Measurement withKanban OOP 2012 Munich January 2012 Twitter: agilemanager David J. Anderson David J. Anderson & Associates Email: dja@djaa.com
- 2. Book Published April 2010 Availablefrom djaa.com Advanced Kanban A 72,000 word intro to the topic
- 3. German published January, 2011 Kanban 2012 Translationby Arne Roock & Henning Wolf of IT-Agile
- 4. http://leankanbanuniversity.com http://www.limitedwipsociety.org LinkedIn Groups: SoftwareKanban Yahoo! Groups: kanbandev Yahoo! Groups: kanbanops
- 5. Delivering predictability with Kanban requiressome different techniques for different types of work such as software maintenance and support or Advanced Kanban major project work
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- 7. Create a regulardelivery cadence Develop a strong config management capability Develop capability to deploy effectively Build code with high quality Advanced Kanban
- 8. Understand capability bystudying 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
- 9. Observe Flow witha spectral analysis histogram of lead time Lead Time Distribution 3.5 3 CRs & Bugs 2.5 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 1 0 Days SLA expectation of 44 days with 85% on-time Advanced Kanban Mean of 31 days SLA expectation of 51 days with 98% on-time
- 10. 44 or 51days will not be good enough for some feature requests, so offer a package of classes of service Advanced Kanban
- 11. Package of Classeswith 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. Lead time Standard ClassItems Fixed Date Items Advanced Kanban Expedite Item Features Delivered
- 13. Allocate capacity acrossclasses 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%
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- 15. Requires all thesame underlying data as used in service oriented work plus Advanced Kanban
- 16. Major Project withtwo-tiered kanban board Advanced Kanban
- 17. Observe Flow witha Cumulative Flow Diagram Avg. Lead Time Time Inventory Started Designed Coded Complete 30 -M ar 23 -M ar 16 -M ar 9M ar 2M ar eb Avg. Throughput Kanban 2012 24 -F eb WIP 17 -F eb 240 220 200 180 160 140 120 100 80 60 40 20 0 10 -F Features Device Management Ike II Cumulative Flow
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- 19. Cumulative Flow and PredictiveModeling with S-Curve Inventory Started Designed Coded Complete 30 -M ar 23 -M ar 16 -M ar 9M ar 2M ar eb Time Kanban 2012 24 -F eb Typical S-curve 17 -F eb 240 220 200 180 160 140 120 100 80 60 40 20 0 10 -F Features Device Management Ike II Cumulative Flow
- 20. Simulating S-Curve witha Z 60% 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 eb 24 -F eb 20% Kanban 2012 17 -F eb 240 220 200 180 160 140 120 100 80 60 40 20 0 10 -F Features Device Management Ike II Cumulative Flow
- 21. Track actual throughputagainst projection Inventory Started Designed Coded Complete 30 -M ar 23 -M ar 16 -M ar 9M ar 2M ar eb Time Kanban 2012 24 -F eb Track delta between planned and actual each day 17 -F eb 240 220 200 180 160 140 120 100 80 60 40 20 0 10 -F Features Device Management Ike II Cumulative Flow
- 22. Unplanned Work Report ScopeCreep Dark Matter Advanced Kanban
- 23. Planning a largeproject Device Management Ike II Cumulative Flow 2008 30 -M ar 23 -M ar 16 -M ar 9M ar 2M ar 5x Kanban 2012 24 -F eb 2006 eb Slope in middle 3.5x - 5x slope at ends 17 -F eb 240 220 200 180 160 140 120 100 80 60 40 20 0 10 -F Features Required throughput (velocity) During the middle 60% of the project schedule Time we need Throughput (velocity) to average 220 Inventory Started Designed Coded Complete features per month
- 24. Little’s Law Determines staffinglevel Target to achieve plan Throughput = WIP Lead Time From observed capability Kanban 2012 Treat as Fixed variable
- 25. Changing the WIPlimit 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 Kanban 2012
- 26. Plan based oncurrently observed capability and current working practices. Do not assume process improvements. If changing WIP to reduce undesirable effects (e.g. multitasking), get new sample data (perform a spike) to observe the new capability Kanban 2012
- 27. Little’s Law Determines staffinglevel Target to achieve plan 55 / week WIP = 0.4 week WIP = 22, round up to 25. 5 teams, 5 per team If current working practice is 1 unit WIP per person then 5 people are needed to per team Kanban 2012 From observed capability
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- 29. 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
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- 31. About… David Anderson isa thought leader in managing effective software teams. He leads a consulting, training and publishing business dedicated to developing, promoting and implementing sustainable evolutionary approaches for management of knowledge workers. He has 30 years experience in the high technology industry starting with computer games in the early 1980’s. He has led software teams delivering superior productivity and quality using innovative agile methods at large companies such as Sprint and Motorola. David is a founder of the Lean Kanban University, a business dedicated to assuring quality of training in Lean and Kanban throughout the world. http://leankanbanuniversity.com Email: dja@djaa.com Twitter: agilemanager Advanced Kanban David is the author of two books, Agile Management for Software Engineering – Applying the Theory of Constraints for Business Results, and Kanban – Successful Evolutionary Change for your Technology Business.