Let me say something obvious. Estimating is hard. So how come that we at Lunar Logic barely remember times when it was an issue? The standard approach to estimation is based on expert guesses. This means that subconsciously we make ourselves vulnerable to cognitive biases which render our estimates too optimistic and unreliable. That neither is fun for development teams nor provides much value to clients. We can employ a different strategy. Instead of relying on vulnerable human memory we use the actual historical data and run statistical simulations to produce a forecast. Not only is the outcome significantly better than what expert guess provides but also it requires less work from development teams. We will share a story how our approach to estimation at Lunar Logic evolved from a common scenario to what we call statistical forecasting. We’ll share the theory behind, show how we do it and present some real data to back up the story. After all, once we understand why estimation is hard we can easily improve how we do it.

1. STATISTICAL FORECASTING

5. DON’T DO IT
T. DEMARCO, T. LISTER: WALTZING WITH BEARS

6. ACCURACY OF ESTIMATION
SOURCE: TSAI, KLAYMAN, HASTIE: EFFECTS OF AMOUNT OF INFORMATION ON JUDGMENT ACCURACY AND
CONFIDENCE

7. ESTIMATION BIAS

12. LEA

13. WORK TIME
LEAD TIME
FLOW EFFICIENCY=
LEAD
TIME
WORK
TIME
LEAWAIT TIME

14. LEA
LEA

15. THROUGHPUT VS STORY POINTS

20. UNIVERSE’S PLANS

21. source: DOGHOUSEDIARIES

24. BASED ON THE WORK OF
TROY MAGENNIS, LARRY MACCHERONE AND DOUGLAS HUBBARD

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MONTE CARLO EXAMPLE

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AVERAGE PACE FORECAST - SIMPLE REGRESSION

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MONTE CARLO EXAMPLE: THROUGHPUT SAMPLES
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MONTE CARLO EXAMPLE: THROUPUT DISTRIBUTION
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MONTE CARLO EXAMPLE: UNLUCKY BRIAN

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MONTE CARLO EXAMPLE: WORST CASE SCENARIO

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MONTE CARLO EXAMPLE: BEST CASE SCENARIO

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MONTE CARLO EXAMPLE

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38. MONTE CARLO EXAMPLE
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39. MONTE CARLO EXAMPLE
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MONTE CARLO EXAMPLE

42. HISTORICAL DATA: COMPLETED STORIES

43. HISTORICAL DATA: TIMELINE

44. STATISTICAL MODEL: INPUT #1

45. WHY LEAD TIMES?

46. LEAD TIME

47. HISTORICAL DATA: LEAD TIMES

48. LEAD TIME DISTRIBUTION

49. LEAD TIME DISTRIBUTION

50. STATISTICAL MODEL: INPUT #2

51. HISTORICAL DATA: WORK IN PROGRESS

52. WORK IN PROGRESS DISTRIBUTION

53. READY…
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Iteration

54. SAMPLING LEAD TIME VALUES

55. SAMPLING LEAD TIME VALUES

56. SUM(LEAD TIME VALUES) = UNITS OF WORK
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57. SAMPLING WORK IN PROGRESS
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87

58. SAMPLING WORK IN PROGRESS
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69. 90% CONFIDENCE INTERVAL?

70. UNCERTAINTY

85. BE BETTER

87. SOURCES & RESOURCES
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