Let your tasks flow like water!
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This document provides an overview of a training on using the Taskman tool to manage tasks and projects. It begins with discussing motivation for using Taskman, including avoiding chaos with tasks in emails and spreadsheets, better prioritizing work, and increasing transparency. It then covers agile methodologies like Scrum and Kanban used at EEA. Key policies for EEA's workflow process include limiting work in progress, fast feedback loops, and focusing on finishing work. Using these policies helped reduce lead times on projects significantly. The document concludes with reviewing Taskman roles and permissions.
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Let your tasks flow like water!
- 1. Apr 2016 - Antonio De Marinis, Michael Norén TASKMAN TRAINING Let your tasks flow like water!
- 2. Training agenda Motivation - our success story Intro to Agile methodologies Agile at EEA: Flow Tips and tricks Taskman roles, who does what Taskman features IDM2 setup Let’s do it - Exercises
- 3. Motivation
- 4. Motivation
- 5. Motivation Lost tasks in emails and excel sheets, avoid chaos Project tasks vs private tasks Stay in control Better prioritization Clear who is doing what, no more ambiguity Visualise work Measure progress, find bottlenecks Less meetings needed Less documentation overhead Better feedback loop = better output
- 6. Motivation Within any shared system we need framework and policies to avoid chaos and the depletion of the common resources (Tragedy of the commons). The tragedy of the commons is an economic theory of a situation within a shared-resource system where individual users acting independently and rationally according to their own self-interest behave contrary to the common good of all users by depleting that resource.
- 7. Motivation Share lessons learned and best practices
- 9. Motivation - our success story 83 days 47 days 38 days 17 days Tasks got faster and faster like turbocharging our team! 28 days26 days 19 days
- 10. The process has high impact Note the IDM2 A-Team lead time (~9 days), much shorter compared to other software teams. IT Helpdesk has the shortest (< 7h) due to the specific kind of work (non-software development).
- 11. How did we achieve this?
- 12. Motivation - our success story 🏁 Stop starting, start finishing 🔞 Limit work in progress ⏰ Fast feedback loop Main policies:
- 13. Motivation - our success story 🏁 Stop starting, start finishing 🔞 Limit work in progress (WIP) ⏰ Fast feedback loop Project A Project B Finish A Finish B Start Start 12 months Project A Project B Finish B 6 months Finish A 6 months Start Start + task switching time Imagine resource available: one fte developer. Work on 2 projects. Option 1 Option 2
- 14. Motivation - our success story Little’s law*: Average lead time (LT) = Average WIP (WIP) / Average Throughput (TH) * Little’s law was found in 1928 by John Little (Prof. MIT) in the discipline of queueing theory It implies that increasing WIP leads to a higher LT and vice versa - check to reduce WIP to increase LT in order to get stuff done faster, you need to work on less (on average) 🏁 Stop starting, start finishing 🔞 Limit work in progress (WIP) ⏰ Fast feedback loop
- 15. Monitor WIP-limits in Taskman GOOD BAD
- 16. Motivation - our success story 🏁 Stop starting, start finishing 🔞 Limit work in progress (WIP) ⏰ Fast feedback loop To-do list time WIP = 1 Needs Feedback Area
- 17. Motivation - our success story 🏁 Stop starting, start finishing 🔞 Limit work in progress (WIP) ⏰ Fast feedback loop To-do list time ? Needs clarifications Start new ticket WIP = 1 Needs Feedback Area
- 18. Motivation - our success story 🏁 Stop starting, start finishing 🔞 Limit work in progress (WIP) ⏰ Fast feedback loop To-do list time WIP = 1 Needs Feedback Area
- 19. Motivation - our success story 🏁 Stop starting, start finishing 🔞 Limit work in progress (WIP) ⏰ Fast feedback loop To-do list time WIP = 1 ? Needs clarifications Start new ticket Needs Feedback Area
- 20. Motivation - our success story 🏁 Stop starting, start finishing 🔞 Limit work in progress (WIP) ⏰ Fast feedback loop To-do list time WIP = 1 Needs Feedback Area
- 21. Motivation - our success story 🏁 Stop starting, start finishing 🔞 Limit work in progress (WIP) ⏰ Fast feedback loop To-do list time WIP = 1 Needs Feedback Area ? Needs clarifications Start new ticket
- 22. Motivation - our success story 🏁 Stop starting, start finishing 🔞 Limit work in progress (WIP) ⏰ Fast feedback loop To-do list time WIP = 1 Needs Feedback Area
- 23. Motivation - our success story 🏁 Stop starting, start finishing 🔞 Limit work in progress (WIP) ⏰ Fast feedback loop To-do list time WIP = 1 Needs Feedback Area Start new ticket Feedback given
- 24. Motivation - our success story 🏁 Stop starting, start finishing 🔞 Limit work in progress (WIP) ⏰ Fast feedback loop To-do list time WIP = 1 Needs Feedback Area Start new ticket
- 25. Motivation - our success story 🏁 Stop starting, start finishing 🔞 Limit work in progress (WIP) ⏰ Fast feedback loop To-do list time WIP = 1 Needs Feedback Area Start new ticket Feedback given Feedback given
- 26. Motivation - our success story 🏁 Stop starting, start finishing 🔞 Limit work in progress (WIP) ⏰ Fast feedback loop To-do list time WIP = 1 Needs Feedback Area Start new ticket
- 27. Motivation - our success story 🏁 Stop starting, start finishing 🔞 Limit work in progress (WIP) ⏰ Fast feedback loop To-do list time WIP = 1 Needs Feedback Area Start new ticket Now WIP = 4 Task switching will kill performance?!
- 28. Motivation - our success story 🏁 Stop starting, start finishing 🔞 Limit work in progress (WIP) ⏰ Fast feedback loop To-do list time WIP = 1 Needs Feedback Area Start new ticket Now WIP = 4 Task switching will kill performance?! These are stuck here These tasks will have to wait much longer now
- 29. Motivation - our success story 🏁 Stop starting, start finishing 🔞 Limit work in progress (WIP) ⏰ Fast feedback loop To-do list time ? Needs clarifications Start new ticket WIP = 1 Lesson learned: Don’t start a new ticket too easily. Chase after feedback first.
- 30. Feedback queue in Taskman
- 34. project methodologiesWaterfall = Space mission Each step takes months/years with little flexibility to change
- 35. project methodologiesWaterfall = Space missionScrum = Cruise ship Sprints of 2-3 weeks each = mini waterfalls Sprint 1 Sprint 2 Sprint 3
- 36. project methodologiesWaterfall = Space missionKanban = RaftingKanban = Motorboat
- 37. EEA Flow process Product Owners, Stakeholders Idea / Requests Meetings Emails Usability tests Stakeholders Users feedback Product Strategy External factors Team Manager, Developers Developers Product Owners, Team Manager Developers
- 38. EEA Flow process - multi-projects
- 39. EEA FLOW PROCESS - detailed view Implementation at EEA: Flow ideas Epics (XXL/XL user stories) Contextual prioritised list (L/M/S user stories) Product Owner + Stakeholders (product team) Product Owner + Team Product Owner + Team GREEN PRODUCT Delivery Team (cross-functional) Delivery Team Manager / (System Owner) queue analysis in progress testing demo to deploy FLOW ideas PRODUCT BACKLOG TASKS BACKLOG TEAM QUEUE VISION BACKLOG WORK IN PROGRESS BLUE PRODUCT Live
- 40. Roles and permissions Product Owner / Delivery Team Manager: Represents the stakeholders (internal EEA product owners) towards the developers and make sure the WIP-limits and workflow policies are respected. Manager: This is a senior developer (IDM/contractor), that supports the Product Owner and further co-ordinate the queue and work in progress, also known as Scrum Master. Developer: This role is given to the resources that are implementing the tickets, it is mostly consultants and EEA IT-staff. Reporter: This is given to all stakeholders and EEA internal product owners that are allowed to report new tickets. Being a Reporter means you will request- and give feedback on work to be done.
- 41. Suggested reading... Free download http://www.infoq.com/minibooks/kanban-scrum- minibook Key points summary https://goo.gl/MwO1MK Thank you for your attention
Editor's Notes
- The tragedy of the commons is an economic theory of a situation within a shared-resource system where individual users acting independently and rationally according to their own self-interest behave contrary to the common good of all users by depleting that resource. The concept and name originate in an essay written in 1833 by the Victorian economist William Forster Lloyd, who used a hypothetical example of the effects of unregulated grazing on common land (then colloquially called "the commons") in the British Isles.[1] The concept became widely-known over a century later due to an article written by the ecologist Garrett Hardin in 1968.[2] In this context, commons is taken to mean any shared and unregulated resource such as atmosphere, oceans, rivers, fish stocks, or even an office refrigerator.
- Option 1 = WIP 2, Option 2 = WIP 1
- In queueing theory, a discipline within the mathematical theory of probability, Little's result, theorem, lemma, law or formula[1][2] is a theorem by John Little which states: The long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, λ, multiplied by the (Palm‑)average time a customer spends in the system, W; or expressed algebraically: L = λW. Although it looks intuitively reasonable, it is quite a remarkable result, as the relationship is "not influenced by the arrival process distribution, the service distribution, the service order, or practically anything else."[3] The result applies to any system, and particularly, it applies to systems within systems
- In queueing theory, a discipline within the mathematical theory of probability, Little's result, theorem, lemma, law or formula[1][2] is a theorem by John Little which states: The long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, λ, multiplied by the (Palm‑)average time a customer spends in the system, W; or expressed algebraically: L = λW. Although it looks intuitively reasonable, it is quite a remarkable result, as the relationship is "not influenced by the arrival process distribution, the service distribution, the service order, or practically anything else."[3] The result applies to any system, and particularly, it applies to systems within systems
- In queueing theory, a discipline within the mathematical theory of probability, Little's result, theorem, lemma, law or formula[1][2] is a theorem by John Little which states: The long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, λ, multiplied by the (Palm‑)average time a customer spends in the system, W; or expressed algebraically: L = λW. Although it looks intuitively reasonable, it is quite a remarkable result, as the relationship is "not influenced by the arrival process distribution, the service distribution, the service order, or practically anything else."[3] The result applies to any system, and particularly, it applies to systems within systems
- In queueing theory, a discipline within the mathematical theory of probability, Little's result, theorem, lemma, law or formula[1][2] is a theorem by John Little which states: The long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, λ, multiplied by the (Palm‑)average time a customer spends in the system, W; or expressed algebraically: L = λW. Although it looks intuitively reasonable, it is quite a remarkable result, as the relationship is "not influenced by the arrival process distribution, the service distribution, the service order, or practically anything else."[3] The result applies to any system, and particularly, it applies to systems within systems
- In queueing theory, a discipline within the mathematical theory of probability, Little's result, theorem, lemma, law or formula[1][2] is a theorem by John Little which states: The long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, λ, multiplied by the (Palm‑)average time a customer spends in the system, W; or expressed algebraically: L = λW. Although it looks intuitively reasonable, it is quite a remarkable result, as the relationship is "not influenced by the arrival process distribution, the service distribution, the service order, or practically anything else."[3] The result applies to any system, and particularly, it applies to systems within systems
- In queueing theory, a discipline within the mathematical theory of probability, Little's result, theorem, lemma, law or formula[1][2] is a theorem by John Little which states: The long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, λ, multiplied by the (Palm‑)average time a customer spends in the system, W; or expressed algebraically: L = λW. Although it looks intuitively reasonable, it is quite a remarkable result, as the relationship is "not influenced by the arrival process distribution, the service distribution, the service order, or practically anything else."[3] The result applies to any system, and particularly, it applies to systems within systems
- In queueing theory, a discipline within the mathematical theory of probability, Little's result, theorem, lemma, law or formula[1][2] is a theorem by John Little which states: The long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, λ, multiplied by the (Palm‑)average time a customer spends in the system, W; or expressed algebraically: L = λW. Although it looks intuitively reasonable, it is quite a remarkable result, as the relationship is "not influenced by the arrival process distribution, the service distribution, the service order, or practically anything else."[3] The result applies to any system, and particularly, it applies to systems within systems
- In queueing theory, a discipline within the mathematical theory of probability, Little's result, theorem, lemma, law or formula[1][2] is a theorem by John Little which states: The long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, λ, multiplied by the (Palm‑)average time a customer spends in the system, W; or expressed algebraically: L = λW. Although it looks intuitively reasonable, it is quite a remarkable result, as the relationship is "not influenced by the arrival process distribution, the service distribution, the service order, or practically anything else."[3] The result applies to any system, and particularly, it applies to systems within systems
- In queueing theory, a discipline within the mathematical theory of probability, Little's result, theorem, lemma, law or formula[1][2] is a theorem by John Little which states: The long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, λ, multiplied by the (Palm‑)average time a customer spends in the system, W; or expressed algebraically: L = λW. Although it looks intuitively reasonable, it is quite a remarkable result, as the relationship is "not influenced by the arrival process distribution, the service distribution, the service order, or practically anything else."[3] The result applies to any system, and particularly, it applies to systems within systems
- In queueing theory, a discipline within the mathematical theory of probability, Little's result, theorem, lemma, law or formula[1][2] is a theorem by John Little which states: The long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, λ, multiplied by the (Palm‑)average time a customer spends in the system, W; or expressed algebraically: L = λW. Although it looks intuitively reasonable, it is quite a remarkable result, as the relationship is "not influenced by the arrival process distribution, the service distribution, the service order, or practically anything else."[3] The result applies to any system, and particularly, it applies to systems within systems
- In queueing theory, a discipline within the mathematical theory of probability, Little's result, theorem, lemma, law or formula[1][2] is a theorem by John Little which states: The long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, λ, multiplied by the (Palm‑)average time a customer spends in the system, W; or expressed algebraically: L = λW. Although it looks intuitively reasonable, it is quite a remarkable result, as the relationship is "not influenced by the arrival process distribution, the service distribution, the service order, or practically anything else."[3] The result applies to any system, and particularly, it applies to systems within systems
- In queueing theory, a discipline within the mathematical theory of probability, Little's result, theorem, lemma, law or formula[1][2] is a theorem by John Little which states: The long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, λ, multiplied by the (Palm‑)average time a customer spends in the system, W; or expressed algebraically: L = λW. Although it looks intuitively reasonable, it is quite a remarkable result, as the relationship is "not influenced by the arrival process distribution, the service distribution, the service order, or practically anything else."[3] The result applies to any system, and particularly, it applies to systems within systems
- In queueing theory, a discipline within the mathematical theory of probability, Little's result, theorem, lemma, law or formula[1][2] is a theorem by John Little which states: The long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, λ, multiplied by the (Palm‑)average time a customer spends in the system, W; or expressed algebraically: L = λW. Although it looks intuitively reasonable, it is quite a remarkable result, as the relationship is "not influenced by the arrival process distribution, the service distribution, the service order, or practically anything else."[3] The result applies to any system, and particularly, it applies to systems within systems
- In queueing theory, a discipline within the mathematical theory of probability, Little's result, theorem, lemma, law or formula[1][2] is a theorem by John Little which states: The long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, λ, multiplied by the (Palm‑)average time a customer spends in the system, W; or expressed algebraically: L = λW. Although it looks intuitively reasonable, it is quite a remarkable result, as the relationship is "not influenced by the arrival process distribution, the service distribution, the service order, or practically anything else."[3] The result applies to any system, and particularly, it applies to systems within systems
- In queueing theory, a discipline within the mathematical theory of probability, Little's result, theorem, lemma, law or formula[1][2] is a theorem by John Little which states: The long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, λ, multiplied by the (Palm‑)average time a customer spends in the system, W; or expressed algebraically: L = λW. Although it looks intuitively reasonable, it is quite a remarkable result, as the relationship is "not influenced by the arrival process distribution, the service distribution, the service order, or practically anything else."[3] The result applies to any system, and particularly, it applies to systems within systems