Estimation is dead - Tbilisi, by John Coleman 26 April 2024 final.pdf
•
0 likes•13 views
In this Agile International Summit opening keynote talk in Tbilisi, Gerogia, John Coleman explored relative sizing (story points, t-shirt size, time reference), right sizing, and dynamic design to cost. It's an of the InfoQ paper "sizing and forecasting in Scrum" except this talk was not focused on only Scrum.
Recommended
More Related Content
Similar to Estimation is dead - Tbilisi, by John Coleman 26 April 2024 final.pdf
Similar to Estimation is dead - Tbilisi, by John Coleman 26 April 2024 final.pdf (20)
More from Orderly Disruption
More from Orderly Disruption (20)
Recently uploaded
Recently uploaded (20)
Estimation is dead - Tbilisi, by John Coleman 26 April 2024 final.pdf
- 1. Estimation is dead? John Coleman @JohnColemanIRL https://linktr.ee/johncolemanxagility https://www.infoq.com/articles/sizing- forecasting-scrum/
- 2. Complex Work Where More than Expertise is Needed Handling it requires: • Fresh thinking • Experimentation • Openness to “I wonder if” old solutions to old problems will work on the new one. • Trust • Frequent adjustment
- 5. Sizing caveats People who do the work do the sizing, no one else! If we don’t ”clean up up the kitchen” as a habit, work will take longer than before The most popular sizing techniques are either based on data or educated guesses
- 6. Sizing options we’ll talk about today Relative estimation (time, story points, t-shirt) Counting the number of valuable items delivered (throughput) Rightsizing #Noestimates Dynamic Design To Cost
- 7. Sizing is devalued by • Not having caveats associated with the start date, e.g., nine weeks from the date we start • Not recognizing the amount of work in progress and the progress of that work • The severity of impediments • Not ordering items higher up the backlog according to delivery risk • A sub-optimal approach to handling dependencies • Confusing outputs with outcomes • Not engaging in discovery activities when the risk of not harvesting potential value is high • Delusions of accuracy and pursuing more accuracy
- 9. Story points Potential upsides Useful to avoid bringing “elephants” into WIP Could be used to limit work in progress Easy to pick reference items from the past Simple to do Developers like the conversation it triggers Often paired with t-shirt sizing or wall estimation Potential downsides Creator regrets story points Only for the team Story point inflation BS story points Often paired with planning poker (time consuming)
- 10. T-shirt sizes Potential upsides Useful to avoid bringing “elephants” into Work In Progress Could be used to limit work in progress Easy to pick reference items from the past Developers like the conversation it triggers Simple to do Requires very little detail Potential downsides Converted to numbers quite often, numbers that get used to forecast when work might be done
- 11. Time reference Potential downsides Requires suitable reference items from the past Prone to abuse be people with a focus on people utilization Unsuitable for probabilistic forecasting Potential upsides Speaks in the customers language Easy to pick reference items from the past Waiting time is included in our memory of how long it takes Simple to do
- 13. Rightsizing Potential upsides Simple Less “analysis paralysis” Supports recurring probabilistic forecasting Potential downsides Items right-sized just in time or in product backlog refinement Misunderstood that all right sized items must be of equal size Disconnect in Kanban community about use of item split rate to support probabilistic forecasting If most days a team has no throughput, probabilistic forecasting will have low quality
- 14. Wall / table estimation Potential upsides Useful to avoid bringing “elephants” into Work In Progress Could be used to limit work in progress Easy to pick reference items from the past Developers like the conversation it triggers Simple and quick to do; requires very little detail Guesstimate for potential value sized as well as effort typically, priming ordering for value divided by size Potential downsides Converted to numbers quite often, numbers that get used to forecast when work might be done Often one and done – should be revisited regularly
- 16. Variable quality with sizing an item Factors for how long things take The batch size – the level of effort actually needed Waiting time … Sizing for the level of effort considers Complexity of the work Riskiness of the work Whether we did something similar before Perception of skill levels required to complete the work and availability of those skills Availability of tools and skills using those tools Dependencies
- 17. 100% Resource utilization = 0% flow (Henrik Kniberg) Image courtesy of LeSS.works
- 19. Throughput
- 20. Guesstimating / counting the number/ range of items to deliver a goal Potential upsides • Suitable inputs for probabilistic forecasting • Can be used across teams Potential downsides • People prefer relative sizing, and almost “cannot let go” • Misunderstood that all items need to be of equal size • Different product backlog item types • Prone to the use of averages
- 21. Monte Carlo simulations model a future based on data and assumptions
- 23. Make the requirements less dumb –Elon Musk
- 24. It appears that once (delivery leaders) know how to get benefits right, they know how to get everything right. Bent Flyvbjerg – author of How Big Things Get Done
- 25. Keys to Success in “How Big Things Get Done” Look for similar initiatives in your organization, across the world, and assess the problems they had and how long they took. Aim to “Lego-ize” rather than customize – you’re not so special!
- 29. Planguage Example Unambiguous • TAG NPS • GIST Improve Net Promoter Score to what is deemed good by NPS experts • AMBITION Segment leading NPS in the region. • STAKEHOLDER Product Manager • CONSTRAINT {current moment, end-to-end- customer-satisfaction-with-product} • DEFINED https://en.wikipedia.org/wiki/Net_promoter_score • AUTHORITY Market Insights team Clarity • SCALE Net Promoter Score • METER high to low NPS range previous 180 days • NOW 5 • TOLERABLE >0 • GOAL 20 • STRETCH 30 • WISH 50 • PAST -30 • TREND -30 |<-| to +10 |-->| last 18 months
- 31. Credibility of costs or impact 1. Wild guess, no credibility 2. We know it has been done somewhere 3. We have one measurement somewhere 4. There are several estimates in the estimated range 5. Several measurements relevant for the use case (e.g. qualifier values) 6. Several relevant measurements obtained using a reliable method 7. Have used the same design / solution / experiment option previously in your organization 8. Reliable in-house measurements of same design / solution option / experiment option 9. In-house measurements of design / same design / solution option /experiment option correlate to external sources 10. Have previously used this design / same design / solution option / experiment option on this initiative and measured it 11. Have solid, contract-guaranteed, long-term experience of this design or solution/experiment option on this initiative
- 32. • Plan - What can you deliver this week? • Do it • Study and Learn • Act / Adapt / Adjust
- 33. Key take aways Avoid story points, counting non-valuable product backlog items, counting unDone work as Done, use of averages Avoid Consider historical reference items but beware of accidental complication Consider Try probabilistic forecasting based on counting valuable product backlog items to Done Try Try #NoEstimates and “rolling wave forecasts” of valuable product backlog items to Done Try For complex work, promote managing expectations about uncertainty over managing expectations about dates Dynamic Design to Cost Promote
- 34. Question Time