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Cycle Time Mathematics

Cycle Time Mathematics. Presented at KLRAT 2013 Troy.magennis@focusedobjective.com. Conclusions. Forecasting using cycle time is proving useful Cycle time follows a Weibull /Lognormal shape We can estimate the actual distribution with just a minimum and a maximum guess (initially)

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Cycle Time Mathematics

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  1. Cycle Time Mathematics Presented at KLRAT 2013 Troy.magennis@focusedobjective.com

  2. Conclusions • Forecasting using cycle time is proving useful • Cycle time follows a Weibull/Lognormal shape • We can estimate the actual distribution with just a minimum and a maximum guess (initially) • Controlling the shape of the distribution (narrowing) improves predictability • Proposing a way of identifying what risks and delays will have the greatest impact… Commercial in confidence

  3. Prediction Intervals

  4. Q. What is the chance of the 4th sample being between the range seen after the first three samples? (no duplicates, uniform distribution, picked at random) Actual Maximum 2 4 3 1 Actual Minimum

  5. Q. What is the chance of the 4th sample being between the range seen after the first three samples? (no duplicates, uniform distribution, picked at random) Actual Maximum Highest sample ? 2 ? 4 3 ? ? 1 Lowest sample Actual Minimum

  6. Q. What is the chance of the 4th sample being between the range seen after the first three samples? (no duplicates, uniform distribution, picked at random) Actual Maximum 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 25% lower than lowest seen Lowest sample A. 50%% = (1 - (1 / n – 1)) * 100 Actual Minimum

  7. Q. What is the chance of the 12th sample being between the range seen after the first three samples? (no duplicates, uniform distribution, picked at random) Actual Maximum 5% chance higher than highest seen Highest sample ? 2 9 5 3 12 4 10 6 11 ? 7 1 8 5% lower than lowest seen A. 90%% = (1 - (1 / n – 1)) * 100 Lowest sample Actual Minimum

  8. Halved the risk with 3 samples

  9. Monte Carlo

  10. If a measurement changes over time or is different each time you measure it, it is a DISTRIBUTION Can’t apply normal mathematical operators…. Commercial in confidence

  11. 1. Easy to Capture Metric 3. Follows Known Distribution Pattern Cycle Time Forecasting 2. Forecasting using Historical Data(even a little) 4. Forecast at Project or Feature or Story Level Commercial in confidence

  12. Sum Random Numbers Historical Story Lead Time Trend ….. 295 410 187 Sum: Basic Cycle Time Forecast Monte Carlo Process1. Gather historical story lead-times2. Build a set of random numbers based on pattern3. Sum a random number for each remaining story to build a single outcome 4. Repeat many times to find the likelihood (odds) to build a pattern of likelihood outcomes Days To Complete

  13. How I Quantify Lead Time Reduction When no ROI is easily discerned (maintenance teams?) Even a small decrease in Cycle Time has a increased impact on throughput over a year..

  14. How I Quantify Cycle Time Reduction Revenue Estimates for product: Even a small decrease in Cycle Time can have huge a benefit on cash flow over time. This example shows the cost of missing a seasonal uptick in summer sales (revenue estimates shown to the left). Cost per work day is calculated at $2,500 day.

  15. Shape and Impact of Cycle-Time & Scope

  16. Note: Histogram from actual data Commercial in confidence

  17. Commercial in confidence

  18. Likelihood… Commercial in confidence

  19. Story / Feature Inception5 Days Pre Work 30 days Waiting in Backlog25 days Total Story Lead Time 30 days “Active Development”30 days Waiting for Release Window5 Days Post Work 10 days System Regression Testing & Staging 5 Days

  20. Binary Permutations Commercial in confidence

  21. Why Weibull • Now for some Math – I know, I’m excited too! • Simple Model • All units of work between 1 and 3 days • A unit of work can be a task, story, feature, project • Base Scope of 50 units of work – Always Normal • 5 Delays / Risks, each with • 25% Likelihood of occurring • 10 units of work (same as 20% scope increase each)

  22. Normal, or it will be after a few thousand more simulations

  23. Base + 1 Delay

  24. Base + 2 Delays

  25. Base + 3 Delays

  26. Base + 4 Delays

  27. Base + 5 Delays

  28. Shape – How Fat the distribution. 1.5 is a good starting point. Scale – How Wide in Range. Related to the Upper Bound. *Rough* Guess: (High – Low) / 4 Location – The Lower Bound KEY POINT: WITH JUST MIN AND MAX THE CURVE CAN BE INFERRED Commercial in confidence

  29. Changing Cycle Time Shape

  30. When forecasting, the wider the curve, the MORE higher value numbers will occur Teams following this curve WILL be able to predict more predictability because forecast range will be tighter Commercial in confidence

  31. Binary Permutations EVERY RISK OR DELAY YOU CAN REMOVE REDUCE COMBINATIONS BY 2n Commercial in confidence

  32. Order of Priority for Improvement • Prioritized list of blockers and delay states • Balanced to include most frequent & biggest delay • Order Risks and Delays by weighted impact Impact = Frequency Risk or Delay “Type” x Duration • This will remove the most combinations of delay and shrink the area of our distribution giving biggest benefit of predictability Commercial in confidence

  33. Mining Cycle Time Data

  34. Mining / Testing Cycle Time Data Commercial in confidence

  35. Cycle Time Capture Practices • Clearly understand from where and to where • Capture begin and end date; compute the number of days in-between • Does cycle time include defect fixing? • Are there multiple types of work in the same cycle time data • Stories • Defects • Classes of Service Commercial in confidence

  36. Things that go wrong… • Zero values • Repetitive (erroneous) values • Batching of updates • Include/exclude weekends • Project team A staff raided impacting their cycle time (Team A up, team B’s down) • Work complexity changes • Team skill changes Commercial in confidence

  37. Many low values. Often zero or values below what makes sense. Check the most frequent low values. Multiple modes. In this case two overlapping Weibulls. Often due to multiple classes of service, or most often - defects versus stories. Commercial in confidence

  38. Shape – How Fat the distribution. Scale – How Wide in Range. Related to the Upper Bound Location – The Lower Bound Commercial in confidence

  39. Estimating Distributions using Historical Data

  40. 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 50 Samples • Uniform Range with Boundaries at 5th and 95th CI • Weibull Range with Boundaries at 5th and 95th CI • Bootstapping (Random Sampling with Replacement) • More than 100 Samples • Use historical data at random without replacement • Curve Fitting Commercial in confidence

  41. Sampling at Random Strategies • If you pick what samples to use, you bias the prediction… • Strategies for proper random sampling – • Use something you know is random (dice, darts) • Pick two groups using your chosen technique and compute your prediction separately and compare • Don’t pre-filter to remove “outliers” • Don’t sort the data, in fact randomize more if possible Commercial in confidence

  42. Estimating Concurrent Effort from Cumulative Flow Chart Concurrent WIP Sample : Find the smallest and the biggest or take at least 11 samples to be 90% sure of range Commercial in confidence

  43. 20% 40% 60% 80%

  44. Scope Creep Over Time Look at the rate new scope is added over time Commercial in confidence

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