All of this content used to be spread over three different blogs at three different domains and it's now been merged into one. Why was it ever three? Because at the time it seemed reasonable that each of them was for a different audiences, and yet over time I've found that the lines between topic areas got blurrier and tended to overlap. So now they're all together in one place.
If you encounter things that seem broken, please let me know and I'll get them fixed.
Browse by topic area:
- Psychology & Behaviour (Formerly UnconsciousAgile.com)
- Flow, Kanban, Scrum (Formerly ImprovingFlow.com)
- Technical Practices (Formerly AgileTechnicalExcellence.com)
There's a lot here and if you're not sure where to start, here are some popular starting points. From these, you'll find crosslinks to even more topics. Enjoy!
- Psychological Safety: An overview. For the science, see the SAFETY model. For Google's research into why it's important for high performing teams, see Project Aristotle. What happens when we don't have that safety?
- Anxiety and Stress: For the science, see Polyvagal Theory or a description of some neuroscience, illustrated with a bear encounter. To let go of that anxiety, see the Anti-Anxiety toolkit.
- Recommended reading: I'm often asked for book recommendations.
- Generally more about the brain: Cognitive bias, motivation, default mode network, systems 1 & 2 and neurotransmitters (chemicals) that drive behaviour.
- Language patterns: Why language is so important, and Clean Language, a specific language pattern that has excellent application for coaching.
- Improving your meetings: Specifically retrospectives (my video course), and standups. What if your people won't participate?
- Improving learning: with neuroscience and LEGO.
- Flow & Kanban: Flow metrics, probabilistic forecasting, and understanding waste.
- Technical practices: Continuous integration, TDD as design, and ensemble programming.
- Something fun: The millennial whoop, and inattentional blindness.
One Thing vs Multiple Things
When creating a forecast first ask yourself whether you are forecasting One Thing or Multiple Things. It’s not always clear which of these situations you are in but the approach you take to creating the forecast will differ significantly. This post will help you to figure out which approach to take.
Presentation: Neuroscience of psychological safety
Defining a workflow
The Kanban Guide defines three core practices. The first is “define and visualize a workflow” and while it describes what needs to be in that workflow, it doesn’t give any guidance on how to facilitate as session with a team to do that definition. In this video, I describe how I facilitate a session with teams to define their workflow.
Staying within our WIP limits
In a Kanban model, one thing we find most teams struggle with are WIP limits. Everyone wants to just start one more item even if we’re already at the limit. Surely one more can’t hurt. Except of course, it does.
Improving Predictability - Consistent Units and Conclusion
This is the last in a series of posts on the four assumptions behind Little’s Law. If you haven’t read those previous posts I encourage you to go back to understand the background. As a reminder, the four assumptions are listed below.
Improving Predictability - Average Age
In a previous post I’ve introduced the four assumptions behind Little’s Law and discussed the first two assumptions in detail. If you haven’t read those previous posts I encourage you to go back to understand the background. As a reminder, the four assumptions are listed below.
Moving backwards on a kanban board
A question we’re frequently asked is whether items are allowed to move backwards on a board. Many people will just say “no” but the real answer is more nuanced than that and depends on a number of factors.
Improving Predictability - All work must finish
In a previous post I introduced the four assumptions behind Little’s Law and the idea that they are critical to understanding and improving your system’s predictability. We’ve also already discussed the first assumption regarding the equality of average arrival and departure rates. If you haven’t read those previous posts I encourage you to go back to understand the background. As a reminder, the four assumptions are listed below.
Improving Predictability - Average Arrival and Departure Rates
In a previous post I introduced the four assumptions behind Little’s Law and the idea that they are critical to understanding and improving your system’s predictability. If you haven’t read that post I encourage you to go back to understand the background. As a reminder, the four assumptions are listed below.
Improving Predictability
Little’s Law is an equation that frequently appears in discussions of Kanban systems. While initially formulated as a part of queuing theory to describe the length of time people would spend in stores it has since been applied to many other contexts from manufacturing to knowledge work (particularly Kanban for the purposes of today’s conversation).