Slicing stories
In an agile environment, we split our work down into what we call “stories”, that are the smallest unit of value passing through a workstream. Unfortunately, we have a tendency to over-complicate story writing, making it unnecessarily hard. Done well, it can be a simple process of taking small steps, repeatedly.
Keeping people busy
The Kanban Guide talks about optimizing the workflow for three different attributes: effectiveness, efficiency, and predictability. It talks about the fact that any optimizations we perform will be a balance across these three and that over-optimizing on one may make the others worse.
Steps to improving predictability
If you have a need to know when the work will be done or how much you can do in a certain period of time then predictability will be important to you. We have great tools like Monte Carlo for probabilistic forecasting but the truth is that the forecast we generate is only as good as the data we give it. Garbage in yields garbage out. So how do we improve our data to make it inherently more predictable?
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.
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.
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).