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Can Guinness help you estimate?
The document discusses various methods for estimating project size and duration, focusing on story points, velocity, and forecasting metrics like Kanban and Monte Carlo simulations. It highlights the importance of using relative measures for complexity and planning, along with the caveat that a small amount of data can still yield useful forecasts. Additionally, it underscores that being approximately correct is often more valuable than being precisely wrong.
Can Guinness help you estimate?
- 1. Can Guinness helpyou estimate? @jamespwyllie @bigpinots
- 2. Story Points &Velocity Story Points “They are a unit of measure for expressing the overall size of a user story or feature. They tell us how big a story is, relative to others, either in terms of size or complexity” - Mike Cohn
- 3. Story Points &Velocity Planning Poker
- 4. Story Points &Velocity Benchmark 3 Story Points
- 5. Story Points &Velocity Velocity total story points / no. of sprints Last 5 sprints completed points were 30, 40, 50, 60, 70 = 250 points / 5 sprints = velocity of 50 points How big is my project? Total estimate (points) / velocity = number of sprints e.g. 500 story points for project / 50 points = 10 sprints How long will it take? Number of sprints x length of sprint (weeks) e.g. 2 weeks sprints = 10 sprints x 2 weeks = 20 weeks https://www.mountaingoatsoftware.com/tools/velocity-range-calculator
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- 7. Kanban Metrics LEAD TIME WIP DELIVERYRATE https://hakanforss.wordpress.com/
- 8. Kanban Metrics Little’s Law AverageLead Time (ALT) = Average WIP / Average DR Average WIP (AW) = Average Lead Time x Average DR Average Delivery Rate (DR) = Average WIP / Average Lead Time “Slap anyone who uses little’s law to create forecasts” - Daniel Vacanti https://leanpub.com/actionableagilemetrics Basic Forecast Forecast = (WI(n) / DR) + ALT
- 9. Monte Carlo simulators (5.99x 127,583) - 312,270 = 451,952 (5.99 x 127,583) - 312,270 = 451,952 (5.49 x 26,715) - 305,923 = -159,255 (5.99 x 127,583) - 312,270 = 451,952 (5.49 x 26,715) - 305,923 = -159,255 (8.49 x 45,202) - 228,423 = 155,341 (5.99 x 127,583) - 312,270 = 451,952 (5.49 x 26,715) - 305,923 = -159,255 (8.49 x 45,202) - 228,423 = 155,341 (6.99 x 117,371) - 245,352 = 575,073 (5.99 x 127,583) - 312,270 = 451,952 (5.49 x 26,715) - 305,923 = -159,255 (8.49 x 45,202) - 228,423 = 155,341 (6.99 x 117,371) - 245,352 = 575,073 (8.00 x 14,054) - 329,609 = -217,180 (5.99 x 127,583) - 312,270 = 451,952 (5.49 x 26,715) - 305,923 = -159,255 (8.49 x 45,202) - 228,423 = 155,341 (6.99 x 117,371) - 245,352 = 575,073 (8.00 x 14,054) - 329,609 = -217,180 (7.49 x 73,265) - 369,820 = 178,935 (5.99 x 127,583) - 312,270 = 451,952 (5.49 x 26,715) - 305,923 = -159,255 (8.49 x 45,202) - 228,423 = 155,341 (6.99 x 117,371) - 245,352 = 575,073 (8.00 x 14,054) - 329,609 = -217,180 (7.49 x 73,265) - 369,820 = 178,935 (8.49 x 7,904) - 207,013 = -139,911 (5.49 x 26,715) - 305,923 = -159,255 (8.49 x 45,202) - 228,423 = 155,341 (6.99 x 117,371) - 245,352 = 575,073 (8.00 x 14,054) - 329,609 = -217,180 (7.49 x 73,265) - 369,820 = 178,935 (8.49 x 7,904) - 207,013 = -139,911 (5.99 x 148,909) - 206,799 = 685,166 (8.49 x 45,202) - 228,423 = 155,341 (6.99 x 117,371) - 245,352 = 575,073 (8.00 x 14,054) - 329,609 = -217,180 (7.49 x 73,265) - 369,820 = 178,935 (8.49 x 7,904) - 207,013 = -139,911 (5.99 x 148,909) - 206,799 = 685,166 (5.99 x 40,272) - 300,293 = -59,064 (6.99 x 117,371) - 245,352 = 575,073 (8.00 x 14,054) - 329,609 = -217,180 (7.49 x 73,265) - 369,820 = 178,935 (8.49 x 7,904) - 207,013 = -139,911 (5.99 x 148,909) - 206,799 = 685,166 (5.99 x 40,272) - 300,293 = -59,064 (5.99 x 151,186) - 228,727 = 676,880 (8.00 x 14,054) - 329,609 = -217,180 (7.49 x 73,265) - 369,820 = 178,935 (8.49 x 7,904) - 207,013 = -139,911 (5.99 x 148,909) - 206,799 = 685,166 (5.99 x 40,272) - 300,293 = -59,064 (5.99 x 151,186) - 228,727 = 676,880 (8.99 x 35,272) - 333,723 = -16,618 (7.49 x 73,265) - 369,820 = 178,935 (8.49 x 7,904) - 207,013 = -139,911 (5.99 x 148,909) - 206,799 = 685,166 (5.99 x 40,272) - 300,293 = -59,064 (5.99 x 151,186) - 228,727 = 676,880 (8.99 x 35,272) - 333,723 = -16,618 (7.99 x 124,891) - 368,288 = 629,595 (8.49 x 7,904) - 207,013 = -139,911 (5.99 x 148,909) - 206,799 = 685,166 (5.99 x 40,272) - 300,293 = -59,064 (5.99 x 151,186) - 228,727 = 676,880 (8.99 x 35,272) - 333,723 = -16,618 (7.99 x 124,891) - 368,288 = 629,595 (8.99 x 76,506) - 388,655 = 299,138 (5.99 x 148,909) - 206,799 = 685,166 (5.99 x 40,272) - 300,293 = -59,064 (5.99 x 151,186) - 228,727 = 676,880 (8.99 x 35,272) - 333,723 = -16,618 (7.99 x 124,891) - 368,288 = 629,595 (8.99 x 76,506) - 388,655 = 299,138 (7.99 x 23,805) - 375,232 = -185,034 (5.99 x 40,272) - 300,293 = -59,064 (5.99 x 151,186) - 228,727 = 676,880 (8.99 x 35,272) - 333,723 = -16,618 (7.99 x 124,891) - 368,288 = 629,595 (8.99 x 76,506) - 388,655 = 299,138 (7.99 x 23,805) - 375,232 = -185,034 (5.99 x 42,630) - 253,040 = 2,314 (5.99 x 151,186) - 228,727 = 676,880 (8.99 x 35,272) - 333,723 = -16,618 (7.99 x 124,891) - 368,288 = 629,595 (8.99 x 76,506) - 388,655 = 299,138 (7.99 x 23,805) - 375,232 = -185,034 (5.99 x 42,630) - 253,040 = 2,314 … and on … (5.99 x 151,186) - 228,727 = 676,880 (8.99 x 35,272) - 333,723 = -16,618 (7.99 x 124,891) - 368,288 = 629,595 (8.99 x 76,506) - 388,655 = 299,138 (7.99 x 23,805) - 375,232 = -185,034 (5.99 x 42,630) - 253,040 = 2,314 … and on … and on … (5.99 x 151,186) - 228,727 = 676,880 (8.99 x 35,272) - 333,723 = -16,618 (7.99 x 124,891) - 368,288 = 629,595 (8.99 x 76,506) - 388,655 = 299,138 (7.99 x 23,805) - 375,232 = -185,034 (5.99 x 42,630) - 253,040 = 2,314 … and on … and on … and on …
- 10. “the future offorecasting in knowledge work” ~ Dan Vacanti
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- 12. Calibration I’m 80%confident it will be between 0mm and 100mm Estimate the average rainfall in September
- 14. Student’s t-statistic William SealyGosset (13 June 1876 – 16 October 1937) https://upload.wikimedia.org/wikipedia/commons/thumb/4/42/William_Sealy_Gosset.jpg/766px-William_Sealy_Gosset.jpg A small amount of data can be used to forecast the future … if you take the appropriate level of error into account
- 15. Conclusions Measurement “A quantitatively expressedreduction of uncertainty based on one or more observations” - Douglas Hubbard 4 useful measurement assumptions: ● Your problem is not as unique as you think ● You have more data than you think ● An adequate amount of data is more accessible than you think ● You need less data than you think
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- 17. Conclusions “It is betterto be roughly right than precisely wrong.” ~ John Maynard Keynes, British Economist Can Guinness help you estimate?
- 18. Conclusions “It is betterto be roughly right than precisely wrong.” ~ John Maynard Keynes, British Economist Can Guinness help you estimate? forecast? @jamespwyllie & @bigpinots
Editor's Notes
- #4 Everyone has heard of story points Planning Poker is the mechanism a team can use for coming to a shared understanding of that relative size Cards with the fibonacci sequence (i.e. each number is a product of the previous 2). Mike and mountain goat has trademarked his own sequence PRACTICAL EXERCISE HERE Discuss the story, everyone chooses a card they think represents the size … everyone declares them at once. This way no-one is led by anyone else. Try to gain consensus by having outliers justify why they think it’s a certain number. Agreement made when we have a definitive size and a shared understanding of the work to be done.
- #6 Sprint Planning Size stories (or work items) and team agree what will be brought into the sprint. The Scrum Master uses team velocity as a guide to ensure they don’t over-commit Not going to talk about ideal hours Mike’s velocity calculator Nice touch with the confidence level. Will be a running theme of this talk URL will be on the last slide
- #9 Little’s Law Defines the relationship between Lead Time, WIP and Delivery Rate As WIP and Lead Time increase, Delivery Rate goes down. We get more, more quickly by doing less at the same time If you have a team of 5, and you have 10 WIs in progress, that’s an average of 2 per person. How can they each be doing two things at once? Work items relatively sized. How big doesn’t matter as we are averaging. Clearly you don’t want work items too big though. Application of Little’s Law to produce a basic forecast G(n) = (WI(n) / DR) + ALT Is this the right thing to do though? Can we do better than using averages? Vacanti: “Slap anyone who uses little’s law to create forecasts”