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Fast learning.....using simulations
- 1. Lightning Fast Learning ….using simulations Pieter Rijken
- 2. Job Clients Contact Pieter Rijken Agile Coach / Trainer KPN, ING NL, Rabobank, Nationale Nederlanden, Aegon prijken@xebia.com +31 6 83036743 pieterjrijken pieter_rijken
- 3. Case: introduction Large telecoms company Scrum team Complex IT landscape Team distributed over The Netherlands and Romania Develops application to support internal business units Dependencies with other teams Xebia Nova Identity Template 3
- 4. After a couple of sprints
- 5. We are doing retrospectives!! Why don't we improve? At the start of sprint the board is reset Improvement action result is known ….or not?
- 7. Learning Every sprint (2 week cycle) Sequential Using partial information
- 8. Cynefin framework Obvious & complicated - Sequential Complex - Selectionism
- 10. How much later? Second and parallel experiments give unreliable results and based on partial understanding of the system Limited number of learnings and improvements
- 11. So, why should we care? Walking the fitness landscape
- 13. Fitness landscape: more policies
- 14. More complicated Aspects - Up & downstream - More services & dependencies - Types of work - Scaling work item, eg tasks, stories, features 6 Practices of the Kanban Method - Visualize - Limit wip - Manage flow - Make policies explicit - Implement feedback loops & improve collaboratively
- 15. Learning in complex environments We need a combination of sequential and selectionism Run parallel experiments Choose the best outcome …as starting point for the next
- 16. Learn faster Use simulations! Especially Monte Carlo based methods Examples
- 18. History
- 21. What are we trying to optimize?
- 22. Let's do some simulations
- 23. Fun with policies! GetKanban
- 25. Policies: Carlos
- 26. Benefits of Monte Carlo
- 27. Monte Carlo: errors caused by Lack of understanding - Unknown policies - Policies wrongly modeled - Policies not correctly followed in practices (lack of discipline) - Changing environment Monte Carlo - Statistical, outliers - Artifacts from the used algorithm/method - Random number generators
- 28. Understanding the system Relevant policies
- 31. Relevant policies Distribution of story sizes Distribution of work accomplished every day Sprint length, replenishment, and delivery frequencies Capability of the team (velocity) Dealing with unfinished stories Taking up additional work during sprint (replenishment) …..
- 32. Optimal policies? Walking the fitness landscape
- 37. Fitness landscape Issue of local optimum - How do you know whether you're in local optimum only? - How to get to the global optimum? With gradual steps, first results get worse, before they improve Use simulations to guide what will work - Reinforcement learning techniques, or - Monte Carlo methods
- 38. Case: story size policy Team task distribution Task distribution after policy change
- 39. Case: 'story size < 13SP' policy
- 40. Case: 'no more than 4 stories' policy
- 41. Case: 'take up only 35 SP' policy
- 42. Summary
- 43. Summary Learning takes time for complex problems Speed up by running simulated experiments Benefits: - From months to seconds - Helps to understand the system - Helps to explore the fitness landscape - Helps to choose most promising change Beware of systematic errors and interpretation of results
Editor's Notes
- Task durations are distributions => once we realize this we can simulate! Two fundamental approaches => learning and selectionism 4 types of learning: sequential & selectionism (in 4 combinations) Variation is needed to get information as value (variation is good!) Partial information is less valuable in complex projects/systems Note connection to cynefin framework
- Ik help teams betere resultaten bereiken. Hoe doe ik dat? Door ze feedback te geven hoe ze het doen en het effect van hun gedrag/doen op het halen van hun doelen. Als agile/kanban coach. Welke doelen dan? Sneller en deadlines halen. Verhaallijn: Beschrijven hoe scrum team dit doet en niet lukt. Wat nu? Hoe komt dit? Sequential learning Gartner model + AMM => veel parameters, complex performance landscape aha! We need parallel learnings and choose the best one! What about Kanban teams? Kanban team dan? -> partial information because continuous learning Not good either. Tasks are distributions!! => simulations!! Combination of iteration and selectionism How does this work? Enter monte carlo simulations ============================================ Fitness landscape: Locally we can see, around it's foggy but bnit further away we cannot see. Simulations is about probing the part we cannot see amd understanding the the part we can see Show in series of pictures Movie about changing environment => star trek?? ============================================
- Vraag aan publiek!!!
- 1) Team with control chart, velocity chart 2) GetKanban Getting a feel for the natural statistical fluctuations Understanding of current policies and relevance