Statistical Forecasting: Estimation Made Easy
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The document discusses statistical forecasting, emphasizing the inaccuracies and biases in estimation processes. It includes Monte Carlo simulation examples to demonstrate lead time and throughput, along with various scenarios and distributions of work in progress. Additionally, it highlights the significance of historical data in enhancing forecasting accuracy.
Statistical Forecasting: Estimation Made Easy
- 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
- 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
- 24. BASED ON THE WORK OF TROY MAGENNIS, LARRY MACCHERONE AND DOUGLAS HUBBARD
- 29. 0 10 20 30 40 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 MONTE CARLO EXAMPLE
- 30. 0 10 20 30 40 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 AVERAGE PACE FORECAST - SIMPLE REGRESSION
- 31. 0 10 20 30 40 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 MONTE CARLO EXAMPLE: THROUGHPUT SAMPLES 2 2 5 8 4
- 32. 0 10 20 30 40 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 MONTE CARLO EXAMPLE: THROUPUT DISTRIBUTION 2 2 5 8 4
- 33. 0 10 20 30 40 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 MONTE CARLO EXAMPLE: UNLUCKY BRIAN
- 34. 0 10 20 30 40 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 MONTE CARLO EXAMPLE: WORST CASE SCENARIO
- 35. 0 10 20 30 40 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 MONTE CARLO EXAMPLE: BEST CASE SCENARIO
- 36. 0 10 20 30 40 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 MONTE CARLO EXAMPLE
- 37. MONTE CARLO EXAMPLE 0 10 20 30 40 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
- 38. MONTE CARLO EXAMPLE 0 10 20 30 40 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
- 39. MONTE CARLO EXAMPLE 0 10 20 30 40 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
- 40. 0 10 20 30 40 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 MONTE CARLO EXAMPLE
- 42. HISTORICAL DATA: COMPLETED STORIES
- 44. STATISTICAL MODEL: INPUT #1
- 45. WHY LEAD TIMES?
- 46. LEAD TIME
- 47. HISTORICAL DATA: LEAD TIMES
- 50. STATISTICAL MODEL: INPUT #2
- 51. HISTORICAL DATA: WORK IN PROGRESS
- 52. WORK IN PROGRESS DISTRIBUTION
- 53. READY… 0 10 20 30 40 50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Iteration
- 54. SAMPLING LEAD TIME VALUES
- 55. SAMPLING LEAD TIME VALUES
- 56. SUM(LEAD TIME VALUES) = UNITS OF WORK 3 5 5 4 3 1 3 4 3 2 2 6 1 3 5 5 3 3 3 3 5 5 5 5 87
- 57. SAMPLING WORK IN PROGRESS 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 87
- 58. SAMPLING WORK IN PROGRESS 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 66 6 6 6 66 6 6 6 6 6 6 6 66 6 6 6 6 87
- 59. 6 6 66 6 6 6 6 6 6 6 6 6 6 66 6 6 6 66 6 6 6 6 6 6 6 66 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
- 70. UNCERTAINTY
- 85. BE BETTER