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### D. Aitcheson. How to make forecasts that are actually accurate.

1. Darren Aitcheson - Agile Lithuania How to make forecasts that are actually correct (hopefully)
2. •From Northern Ireland •I do Agile stuff •Professional Kanban Trainer with ProKanban.org About me
3. • Estimates are a fact of life (unfortunately) • “It will be done when it’s done” generally isn’t a business strategy • Your teams are always being put under pressure to deliver on time based on guessing • They (usually) get blamed if they are late Why you should care
4. https://app.sli.do/even t/bpjmwajn Or go to slido.com and enter 100284
5. Lesson 1: All estimates are guesses (Please remind your management of this fact)
6. So how do we make our guesses more accurate?
7. Story points?
8. Orange = 3 points Blue = 5 points Yellow = 8 points Purple = 13 points
9. Use story points in your team for capacity planning if you want to But they don’t answer “when will it be done?”
10. If story points aren’t the answer, what do we do?
11. We build jigsaws (obviously 😉)
12. Jigsaw = epic/feature/story/item of work
13. Do we have anything that can help us make a more accurate guess about future items?
14. Lesson 2: The best way to forecast the future is to look at the past
15. One data point isn’t going to help us - we need more
16. We’ve now completed 5 “epics”. Forecast how long it will take to complete the next one.
17. How close were you to being correct?
18. To be even more accurate, what should we do?
19. You guessed it - get more data
20. We’ve now completed 10 “epics”. Let’s learn how to be always “right”.
21. Lesson 3: A forecast must consist of: * A range * A probability
22. We do this by using percentiles
23. Let’s build two more to test our theory
24. So why wasn’t that last jigsaw done on time?
25. In what ways do our work items resemble the jigsaws we just built?
26. None of them are identical
27. We have no idea how complex they’ll be before we start
28. Numerous other things can happen while we’re working on them
29. …but yet, we can still provide a forecast that is accurate and achievable
30. Lesson 4: Your historical data is important - if you’re not collecting it, start now!
31. So how do we apply this in real life?
32. “When will x be done?”
33. 1. Set a Service Level Expectation
34. “Once we begin work on an item, we complete it within 10 days or less, with an 85% probability”
35. 2. Size your items to give them a reasonable chance of meeting your SLA
36. If an item looks too big, split it
37. The 15% will probably cover you for when you get it wrong
38. Why is this approach good for everyone?
39. Everyone knows what to expect
40. We don’t have to agonise over story points or person-days, etc.
41. Your team isn’t under pressure to meet unrealistic timescales
42. You can use it for any process, not just software development
43. Visualising the data allows you to see the variation and to begin to address the causes of it
44. You can easily use a similar process to forecast multiple items “I have a backlog of 20 items; when will they be done?”
45. Our final lesson, which trumps all the others…
46. Lesson 5: If you are not delivering value, it doesn’t matter how accurate your forecasts are, or how quickly you get things done!