Kanban Thinking Questions

Question MarkQuestions

Kanban Thinking emerged from a realisation that “best practices” are not universal, and that sometimes, continuing to try harder, do better and have more discipline isn’t the right thing to do when those practices are not appropriate. As a result, the challenge became one of how to help people learn and discover their own solutions to the challenges they face when pre-packaged solutions don’t work. The result, Kanban Thinking, is a mental model that guides my thinking, and gives me a framework with which to ask questions when designing kanban systems. This post describes Kanban Thinking in terms of some basic questions.

The System

The starting point is to understand why we are designing a kanban system.

  • What systemic problem, difficulty or frustration are we trying to address?

Impacts

Next we consider how we will know whether the kanban system is doing its job.

Flow

Improving the progress of the work might be a positive impact.

  • How would investing in our process, its efficiency and reliability make a difference?

Value

Improving the product of the work might be a positive impact.

  • How would investing in our product, its effectiveness and validity make a difference?

Potential

Improving the sustainability of the work might be a positive impact.

  • How would investing in our people, their euphoria and humanity make a difference?

Heuristics

Then we evaluate what interventions we might make to begin evolving the kanban system.

Study

Studying the context allows a better understanding of the current situation.

  • What could be done to learn more about customer and stakeholder needs, the resultant demand, and how that demand is processed?

Share

Sharing the knowledge gives everyone a common understanding of the situation.

  • What information is important to share, and how can tokens, the inscriptions on them, and their placements, provide a single visual model?

Contain

Containing the work, with loose constraints, creates a stable, yet supple system.

  • What policies could help limit work in process, and remove unnecessary or unexpected delays or rework?

Sense

Sensing the current capability provides understanding of how well the system is performing.

  • What measures and meetings might create insights and guide decisions on the interventions required to have the desired impact?

Explore

Exploring possible interventions leads to discovery of the evolutionary potential of the system.

  • What small experiments could be run to safely learn the impact of different interventions?

Answers

There are not necessarily any right or wrong answers to these questions. The intent is that they should lead to dialogue and conversations, which themselves lead to awareness and ideas for how to go about change and improvement.

How do these questions help you? Let me know!

Estimates as Sensors

Note: this is not an April Fool – honest!

I’ve been watching the #NoEstimates conversations with interest and decided its about time to pitch in with my own perspective. I don’t want to take any ‘side’ because like most things, the answer is not black or white. Estimates can be used for both good and evil. For me they are useful as a sensing mechanism. Put another way, by estimating, I can get a sense of how well I know my actual capability.

Lets take an example. I’m taking part in a 10K run this Sunday (*) and I am estimating that I will complete the distance in 55 minutes. This is based on an understanding of my capability from participating in regular 5K runs, and more general training runs over a 10K distance. I’ve never run an official 10K race, let alone this course, and I don’t know what the conditions will be like, but I’m aiming for 55 minutes. If I run quicker and do better than my estimate, then my actual 10K capability is better than I thought. If I run slower and do worse than my estimate, then my actual 10K capability is worse than I thought. Either way,  I will learn something about how well I know my 10K capability.

What helps even more with that learning is regular feedback! I use MayMyRun on my phone to track  progress and give me feedback every kilometre for total time, average pace and last split. This could be considered a distance-based cadence. I could probably also use a time-based cadence to give me equivalent feedback every few minutes. This feedback on a regular cadence helps me decided whether my estimate was way off, or if I should slow down because my pace is probably unsustainable, or speed up because I feel I can push harder.

How does this relate to product development? Well, we can use estimates in the same way to get a sense of a teams delivery capability, and use regular feedback to learn whether we’re making the progress we thought, and need to re-plan, slow down or speed up. Time-boxing, with Story Point estimates and Velocity can provide this time-based cadence and feedback. Alternatively, how long it takes to complete 10 User Stories can be used as a distance-based cadence and feedback.

To sum up, I recommend using estimates to sense capability and create feedback for yourself. I don’t recommend using them to make promises and give guarantees to others. Or maybe we could just call them sensors instead of estimates?

(*) Of course this post is primarily an excuse to invite readers to sponsor me. If you’re so inclined, or would like to show support for this post in a charitable and financial way, then please head over to my JustGiving page and do so there 🙂

Update: My final time was 49:23 based on the timing chip, or 49:30 from the starting gun. I’ve learned that I’m better than I thought I was, and next time I’ll be estimating 50 minutes!