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- 1. LKCE 2013 – Modern Management Methods Cycle Time Analytics Making decisions using Lead Time and Cycle Time to avoid needing estimates for every story Troy Magennis @t_magennis Slides at bit.ly/agilesim
- 2. 2 @t_Magennis slides at bit.ly/agilesim
- 3. Q. What is the chance of the 4th sample being between the range seen after the first three samples? Actual Maximum (no duplicates, uniform distribution, picked at random) 2 4 3 1 Actual Minimum @t_Magennis slides at bit.ly/agilesim
- 4. Q. What is the chance of the 4th sample being between the range seen after the first three samples? Actual Maximum (no duplicates, uniform distribution, picked at random) Highest sample 2 ? ? 4 3 ? 1 ? Lowest sample Actual Minimum @t_Magennis slides at bit.ly/agilesim
- 5. Q. What is the chance of the 4th sample being between the range seen after the first three samples? Actual Maximum (no duplicates, uniform distribution, picked at random) Highest sample 25% chance higher than highest seen 2 25% lower than highest and higher than second highest 4 3 25% higher than lowest and lower than second lowest 1 Lowest sample Actual Minimum 25% lower than lowest seen @t_Magennis slides at bit.ly/agilesim A. 50% % = (1 - (1 / n – 1)) * 100
- 6. Q. What is the chance of the 12th sample being between the range seen after the first three samples? Actual Maximum (no duplicates, uniform distribution, picked at random) Highest sample 2 9 5 5% chance higher than highest seen ? 3 12 4 10 6 11 ? 7 1 8 Lowest sample Actual Minimum 5% lower than lowest seen @t_Magennis slides at bit.ly/agilesim A. 90% % = (1 - (1 / n – 1)) * 100
- 7. # Prior Samples 3 4 5 6 7 8 9 10 11 12 13 15 17 20 Prediction Next Sample Within Prior Sample Range 50% 67% 75% 80% 83% 86% 88% 89% 90% 91% 92% 93% 94% 95% @t_Magennis slides at bit.ly/agilesim
- 8. Four people arrange a restaurant booking after work Q. What is the chance they arrive on-time to be seated? 8 @t_Magennis slides at bit.ly/agilesim
- 9. 9 15 TIMES more likely at least on person is late 1 in 16 EVERYONE is ON-TIME Person 1 Person 2 Person 3 Person 4 Commercial in confidence
- 10. 10
- 11. Estimating the wrong things and getting a poor result doesn’t mean we shouldn’t estimate at all We just need to estimate things that matter most 12 Commercial in confidence
- 12. 85% Forecasts are attempts to Change of At Least 2 th August 15answer questions about Teams 2013 future events. They are an estimate with a stated Definitely Greater uncertainty than $1,000,000 16 @t_Magennis slides at bit.ly/agilesim
- 13. There is NO single forecast result Uncertainty In = Uncertainty Out There will always be many possible results, some more likely and this is the key to proper forecasting @t_Magennis slides at bit.ly/agilesim
- 14. Likelihood Probabilistic Forecasting combines many uncertain inputs to find many possible outcomes, and what outcomes are more likely than others 50% Possible Outcomes 50% Possible Outcomes Time to Complete Backlog 18 @t_Magennis slides at bit.ly/agilesim
- 15. Likelihood Did the Obama 2012 Campaign Fund Advertising to Achieve 50% Chance of Re-election? 85% Possible Outcomes 15% Time to Complete Backlog 19 @t_Magennis slides at bit.ly/agilesim
- 16. Task Uncertainty – Summing Variance 1 2 3 4 Source attribution: Aidan Lyon, Department of Philosophy. University of Maryland, College Park. “Why Normal Distributions Occur” http://aidanlyon.com/sites/default/files/Lyon-normal_distributions.pdf 20 @t_Magennis slides at bit.ly/agilesim
- 17. Decision Induced Uncertainty Every choice we make changes the outcome Planned / Due Date July Cost of Delay Dev Cost Staff Actual Date August September October Forecast Completion Date 21 @t_Magennis slides at bit.ly/agilesim November December
- 18. What is modelling and how to use cycle time MODELING AND CYCLE TIME 22
- 19. A model is a tool used to mimic a real world process Models are tools for low-cost experimentation @t_Magennis slides at bit.ly/agilesim
- 20. Simple Depth of Forecasting models Linear Projection System Cycle Time Diagnostic Partitioned Cycle Time 25 Simulated process Commercial in confidence
- 21. Simple Cycle Time Model Amount of Work (# stories) Lead Time or Cycle Time Random Chance / Risk / Stupidity 26 @t_Magennis slides at bit.ly/agilesim Parallel Work in Proc. (WIP)
- 22. Capturing Cycle Time and WIP Story Start Date Completed Cycle Time Date (days) 1 2 3 1 Jan 2013 5 Jan 2013 5 Jan 2013 15 Jan 2013 12 Jan 2013 4 5 6 7 8 9 27 10 6 Jan 2013 3 Jan 2013 7 Jan 2013 10 Jan 2013 10 Jan 2013 13 Jan 2013 15 Jan 2013 14 Date “Complete” – Date “Started” 7 Jan 2013 18 Feb 2013 22 Jan 2013 18 Jan 2013 26 Jan 2013 Use with attribution
- 23. Capturing Cycle Time and WIP Story Start Date Completed Cycle Time Date (days) Date 1 Jan 1 2 3 1 Jan 2013 5 Jan 2013 5 Jan 2013 15 Jan 2013 12 Jan 2013 3 Jan 4 Jan 5 Jan 4 5 6 7 8 9 28 10 6 Jan 2013 3 Jan 2013 7 Jan 2013 10 Jan 2013 10 Jan 2013 13 Jan 2013 15 Jan 2013 7 Jan 2013 18 Feb 2013 22 Jan 2013 Count of Started, but 18 Jan 2013 26 Jan completed Not 2013 Use with attribution 6 Jan 7 Jan 8 Jan 9 Jan 10 Jan … 15 Jan WIP 5
- 24. Capturing Cycle Time and WIP Story Start Date Completed Cycle Time Date (days) Date 1 Jan WIP 1 1 2 3 1 Jan 2013 5 Jan 2013 5 Jan 2013 15 Jan 2013 12 Jan 2013 3 Jan 4 Jan 5 Jan 2 2 3 4 5 6 7 8 9 29 10 6 Jan 2013 3 Jan 2013 7 Jan 2013 10 Jan 2013 10 Jan 2013 13 Jan 2013 15 Jan 2013 6 Jan 7 Jan 8 Jan 9 Jan 10 Jan … 15 Jan 4 5 5 5 7 … 7 14 7 7 Jan 2013 18 Feb 2013 22 Jan 2013 4 42 12 18 Jan 2013 8 26 Jan 2013 13 Use with attribution
- 25. 30 Trial 1 Trial 2 Trial 100 9 13 13 5 Sum: 51 1 4 7 5 11 28 … 35 19 5 13 11 83 11 Fancy term for turning a small set of samples into a larger set: Bootstrapping Use with attribution By repetitively sample we build trial hypothetical “project” completions
- 26. Sum Random Numbers Historical Story Cycle Time Trend 25 11 29 43 34 26 31 45 22 27 More often Less often Sum: 31 43 65 45 8 7 34 73 54 48 295 410 ….. Basic Cycle Time Forecast Monte Carlo Process 1. Gather historical story lead-times 2. Build a set of random numbers based on pattern 3. Sum a random number for each remaining story to build a potential outcome 4. Repeat many times to find the likelihood (odds) to build a pattern of likelihood outcomes Days To Complete 19 12 24 27 21 3 9 20 23 29 187
- 27. 1. Historical Cycle Time Monte Carlo Analysis = Process to Combine Multiple Uncertain Measurements / Estimates 6. Phases 2. Planned Resources/ WIP 4. Historical Scope Creep Rate 3. The Work (Backlog) Backlog Feature 1 Feature 2 Feature 3 (optional) 33 5. Historical Defect Rate and Cycle Times (optional) @t_Magennis slides at bit.ly/agilesim
- 28. 34 Commercial in confidence
- 29. 35 @t_Magennis slides at bit.ly/agilesim
- 30. Y-Axis: Number of Completed Stories Project Complete Likelihood Range of complete stories probability 0 to 50% X-Axis: Date 36 @t_Magennis slides at bit.ly/agilesim 50 to 75% > 75%
- 31. How certain based on model forecast Further calculations to make economic tradeoffs 37 Commercial in confidence
- 32. What is 10% Cycle Time Reduction Worth? Baseline Staff Cost Cost of Delay Total Cost $912.000 + $190.000 = $1.102.000 Experiment: 10% Cycle Time Reduction Staff Cost Cost of Delay Total Cost $883.200 + $177.419 = $1.060.619 Opportunity: $41.381 38
- 33. What is One Designer Worth? Baseline Staff Cost Cost of Delay Total Cost $912.000 + $190.000 = $1.102.000 Experiment: + 1 Designer Staff Cost Cost of Delay Total Cost $610.400 + $5.000 = $615.400 Opportunity: $486.600 39
- 34. FORECASTING STRATEGIES 40
- 35. When you have historical data 1. Model Baseline using historically known truths The Past 2. Test Model against historically known truths 3. Forecast The Future
- 36. Compare Model vs Actual Often Range of complete probability Actual results to compare if model is predictable 43 @t_Magennis slides at bit.ly/agilesim
- 37. When you have no historical data The Future @t_Magennis slides at bit.ly/agilesim
- 38. If we understand how cycle time is statistically distributed, then an initial guess of maximum allows an accurate inference to be made Alternatives • Borrow a similar project’s data • Borrow industry data • Fake it until you make it… (AKA guess range) 47 @t_Magennis slides at bit.ly/agilesim
- 39. Probability Density Function 1997: Industrial Strength Software 2002: Metrics and Models in by Lawrence H. Software Quality Engineering (2nd Edition) [Hardcover] Putnam , IEEE , Ware Myers Stephen H. Kan (Author) 0.32 0.28 0.24 0.2 0.16 0.12 0.08 0.04 0 -10 0 10 20 30 40 50 60 70 80 x Histogram 48 Gamma (3P) Lognormal Rayleigh @t_Magennis slides at bit.ly/agilesim Weibull 90 100 110 120 1
- 40. Waterfall Weibull Shape Parameter = 2 AKA Rayleigh 49 Commercial in confidence
- 41. Agile / Lean / Kanban Weibull Shape Parameter = 1.5 50 Commercial in confidence
- 42. Typical Operations / Release Weibull Shape Parameter = 1 AKA Exponential 51 Commercial in confidence
- 43. Shape – How Fat the distribution. 1.5 is a good starting point. Probability Density Function 0.28 0.24 f(x) 0.2 Scale – How Wide in Range. Related to the Upper Bound. *Rough* Guess: (High – Low) / 4 Location – The Lower Bound 0.16 0.12 0.08 0.04 0 0 10 20 30 40 50 60 70 x Histogram 52 Weibull @t_Magennis slides at bit.ly/agilesim 80 90 100 110 120
- 44. What Distribution To Use... • No Data at All, or Less than < 11 Samples (why 11?) – Uniform Range with Boundaries Guessed (safest) – Weibull Range with Boundaries Guessed (likely) • 11 to 30 Samples – Uniform Range with Boundaries at 5th and 95th CI – Weibull Range with Boundaries at 5th and 95th CI • More than 30 Samples – Use historical data as bootstrap reference – Curve Fitting software 53 @t_Magennis slides at bit.ly/agilesim
- 45. Questions… • Download the slides (soon) and software at http://bit.ly/agilesim • Contact me – Email: troy.Magennis@focusedobjective.com – Twitter: @t_Magennis • Read: 54
- 46. 1. Historical Cycle Time Design Develop Test Design Develop A Process to Combine Multiple Uncertain Measurements / Estimates is Needed Test 2. Planned Resources/ Effort 4. Historical Scope Creep Rate 3. The Work (Backlog) Backlog Feature 1 Feature 2 Feature 3 (optional) 55 5. Historical Defect Rate & Cycle Times (optional)

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