Some of the best indicators of team performance are the flow of both new information into the team and of value out of the team. If we can improve visibility into these indicators, and therefore the opportunities for the team to improve the way they work, it becomes possible for the team to support their organization in ways they couldn’t before. There are three standard metrics that are core to understanding the effectiveness of any flow-based system. The relationship between the three metrics is defined by Little’s Law. When applied to the systems used to enable knowledge work the law is usually restated in terms of Throughput, Work In Progress (WIP), and Cycle Time.
I’m going to describe Throughput first because I think it’s the easiest to understand. Throughput is the number of items that leave the system in a given time period. These items are considered done. They may be done because we started them and then decided that it was no longer valuable for us to do or done because we have released them to our customers or done because we realized that the technology wasn’t available to do the thing we wanted to do. Regardless of the reason for them being finished (and the reason is probably important to keep track of!) these items count towards the systems overall Throughput.
Throughput is defined as a number of items per unit of time; in my experience it’s usually most useful to use days as the unit of time for an individual team but you could also use hours, weeks, or months depending on the type of work in the system being measured.
Work In Progress (WIP)
Where Throughput is the measure of the number of things that are now done and have therefore left your system, WIP is the measure of things that have started but are not yet done. This is everything that is currently in your system. And when I say everything, I mean everything. If you have dependencies that you’re waiting on but you already started the work then it is included in your WIP. If you have decided to put something on hold to work on something more important, that item is included in your WIP. If something is in a wait state in your system before the next process can start, that is included in your WIP (I hope you get the point)!
WIP is defined simply as a number of items, these items could be User Stories or Features or Tasks, whatever things are the focus of the system you’re trying to measure. In the context of knowledge work it is important that these items are things your customers and stakeholders also care about. If these items are only important within your team than you might be focusing on and therefore improving, the wrong thing!
Cycle Time is the elapsed time between when something is started until it is done. For the purposes of measuring Cycle Time it does not matter how much time the work item spent active versus in blocked or waiting states (although this is another thing that might be interesting to keep track of).
Cycle Time is most often measured in a number of days but once again, the unit of time is going to depend heavily on your team’s context and it may be appropriate to use hours, weeks, or months depending on the work in the system.
While the three metrics defined above are extremely valuable in describing the historical effectiveness of a flow-based system, Work Item Age is a metric that describes what the effectiveness of your system may look like in the near future. Work Item Age is a measure of how long a work item has been in progress. This follows the same rule as the Cycle Time calculation in that it does not matter whether the work item has been actively being worked on, once it has been started the clock starts. The typical unit of measure here is days but once again, you could use hours, weeks, or months depending on the work in the system. Work Item Age is one of the most useful metrics to use when managing and improving on a workflow because it describes what is currently happening in the system rather than what has already happened. It therefore has an immense amount of decision value. We can decide to focus on a work item that is aging passed a certain point or to not worry about items that haven’t yet aged much.
In future posts I’ll talk more about how to effectively use these metrics to manage and improve your workflow. I’ll also discuss how the stability of these metrics directly relates to your team’s predictability and the ability to have useful conversations with your customers about the value you are delivering to them.