Category Archives: Decisions

Lean Kanban United Kingdom 2013

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I enjoyed attending LKUK13 and would like to share some of the snippets that I found interesting. These are from my notes and are my interpretations – any mis-interpretation is entirely my responsibility and I am happy to receive any corrective feedback.

Mike Burrows – Kanban is like Onions!

  • If we organise the work, we make it possible for people to re-roganise around the work
  • Ask if any single improvement can benefit the Customers, team and organisation – the improvement is good if all 3 can benefit from it
  • To help with paying attention to flow, then keep work sized to see movement every day
  • Small acts of leadership – such as the routine from Toyota – leaders can ask
    • What is the process?
    • How can we see it’s working?
    • How is it improving?
  • Agreement from people versus agreement between people

Liz Keogh – Cynefin in Action

  • Frog thinking vs bicycle thinking – we can take a bike apart and put it back together, and it will work again – not a frog
  • We’re discovering how to discover stuff by doing it
  • Deliberate discovery – Risk (newest things) first – tell the story that’s never been told
  • Focus on how we can quickly get feedback

Edward Kay – Mulit-client Kanban

  • The ‘ready’ column makes a good handover point
  • Use ‘Help’ tokens to indicate that you need assistance with a story – either with context or skills – so that you don’t interrupt others and they can select their own time to help you

Torbjörn Gyllebring – #NoMetrics – the ephemeral role of data in decision making

  • Lines of code is the best metric (and everyone hates this) – great for archaeology, but it’s all from the past
  • Ethics – in a position of power, you start to influence people – do no harm
  • Our customers are those whose lives we touch
  • Clarification should be at the centre of any measurement effort
  • Data needs to always be relevant
  • Informational measures are useful – but it depends on how people perceive it
  • ODIM – a good model – Outcomes, then Decision, then Information, then Metrics – use the metric and then discard it
  • Know why you need the data

Yuval Yeret – Kanban – a SANE way towards agile in the enterprise

  • When trying to change culture, engage in marketing – identify and nurture opportunities
  • Start with leaders and managers
  • Need to balance between prescriptive guidance and no guidance
  • After a chance allow time to stabalise and recharge – then provide good reasons to get out of recharge mode

Chris McDermott – The Other Side of Kanban

  • Encourage shared understanding – not managers are dating agents and chaperones
  • Add a ‘ready to celebrate’ column onto the board

Stephen Parry – How to develop Lean leadership and create an adaptive, learning and engaging organisation

  • Reciprocity only works when there is a sincere and genuine feeling – does not work if there is a feeling of manipulation – It can be negative
  • ‘Dont bring me problems, bring me solutions’ is an example of leadership abandonment not empowerment

Chris Young – Models, Maps, Measures and Mystery

  • Asked why customer approval waiting times went up a lot – led to the idea to have customers sit with the developers
  • At one stage the customer started leading the standups
  • Added an extra column to personal kanban board ‘didn’t happen’ next to the ‘done’ column

Jabe Bloom – What is the value of social capital?

  • A value stream is a linear view of the social network
  • Swarms – form temporary teams on high-value problems with volunteers
  • Emergent slack – have 20% of time spent on interruptible tasks (tasks that no-one is waiting on)
  • Social capital is the ability to distribute and leverage trust (reciprocity)
  • In a low social capital environment we use consensus models
  • In a high social capital environment we trust each other to make decsions
  • Authority removes social capital (consumes it)

Jim Benson – Beyond Agile

  • Flow if you can, pull if you must (pull systems are all remedial)
  • No recipe for success – just a recipe for not likely failing
  • Trying to do agile versus delivering value

Zsolt Fabok – I Broke the WIP Limit Twice, and I’m Still on the Team!

  • If you understand small, incremental evolutionary changes and pull, then you can decduce the rest
  • The goal is to have a stable system – easier to improve it

Alexis Nicolas – Management hacking in progress

  • Managers should focus on learning. We can live with problems for 1 or 2 days because we have better risk management
  • Change is viral – not prepared planning – we can design a viral change

Troy Magennis – Cycle Time Analytics – Fast #NoEstimate Forecasting and Decision Making

  • Statistics is more of a logic problem than a maths problem
  • When we forcast, state the level of uncertainty – ask what point would sway the decision
  • Every choice we make changes the outcome – Decision induced uncertainty
  • Diagnostic models allow us to  run ‘what if’ scenarios
  • Estimating what could go wrong is more important
  • We should update our forecast each time we finish a piece of work because we have learnt more

Decisions

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Thank you to Torbjörn Gyllebring and Thom Leggett for the conversations and inspiration to write this post.

It can be useful to step back from our work and study the decisions that we are making. Here are some of the ways to study decisions and how we can start to visualise the information.

Mapping Decisions

Start by writing out the decisions and laying them out in a logical sequence – grouping similar ones together and consequential ones after each other. Draw some arrows between them – it can be helpful if another person is working with you to help clarify which decisions lead to others and their inter-relationships.

Sometimes there are loops such as the ones labelled ‘A’ and ‘B’ where, when we make decsion A, it changes how we should make decsion B which then impacts how we should make decision A. It might be possible that we get false ‘loops’ as well as the real ones when we are not clear about the decisions that we are making.

 Decisions pull info

Once a decision has been identified, we start seeking the information required in order to make the decision. It is as if the decision is ‘pulling’ the information into it, and once there is enough, we can then make the decision. We can visualise this by writing the decision as a question on the top of a card and the information items required as bullet points under the question.

 Decision Cards

Some information items are actually prior decisions, we can use a code to link these cards together and put them up on a wall to show the realtionships. This allows us to have good conversations about both the activities required to gather information and the progress of the decisions that we are making.