Understanding your demand and capability is a key part of Kanban, we can then use work-in-progress limits (WIP) to control the flow of work. But first, we truly have to understand the type of work our system is doing. Not all work is created equal! Not everything is a story, let alone the same size.
So we understand the different types of work the service does to enable us to analyse what type of work is getting done, how much is getting done, and how long that takes us. Service teams have many different priorities and it is important to keep the balance of all the different types.
So first we have to establish the type of work you do. The good news is you already have this information in your head or your online tool.
Once we have identified your work items, we want to align the meaning of these terms:
|Work Item Type
|Big User Story
|Short, simple description of a feature told from the perspective of the person who desires the new capability, usually a user or customer of the system (*taken from Mike Cohns website)
|A problem that has been identified in the live environment or with a customer
|A problem that has been identified as the service team goes about their work
|Less-than-optimal approach to speed up a piece of functionality or a project, which then later needs to be reworked.
|A timeboxed investigation
|Support from the wider business
|Emergency tickets that must be completed ASAP
|Work that should have been planned, but slipped through the net
There might be more and that is fine. In some organisations I have worked at, we have aligned these across all service teams and not just one in isolation. This gives the benefit of everyone using the same terminology and you can see interesting trends across the data.
Most online tools will allow you to add your own work item types and so I would do an exercise to align the queue and the work that is in flight to these new definitions.
We need to allow work to be completed before we can start analysing the results. You need very little in reality, somewhere between 7-15 tickets to be completed.
Screenshots taken from Power BI, and other tools are available!
So the first thing we can look at is the delivery rate of work. Simply this means, how much of this work item type are we getting done. I would typically have a chart for each different type. From this you can now take action e.g do we have a quality issue or is there too much-unplanned work coming our way. If we can reduce the unplanned and spend less time on bugs, then the work you do on stories will increase.
Now we have this data, we can start being smart in terms of planning. If we have an 85% capacity of the service team for work, we can now shape the demand allocation and say 10% on tech debt, 10% on unplanned and so 65% available for stories. No team works on 100% stories and so why are we often naive and plan to do this? Don’t like the numbers you are spending not on stories, then what can we do to improve and shift the balance?
Things like unplanned work and expedites have a negative effect on the system as it distracts us. So maybe we need to introduce a WIP limit for expedites and an explicit policy to define what it means to be an expedite. If you are a team that gets hit by a lot of expedites, maybe you are a service support team, then at least we can understand the levels and plan accordingly.
The great thing about having data like this is that we can see the changes over time and the impacts of the process improvements you put in place.
Over time you might start to notice patterns in the demand. Maybe you get lots of requests at a certain point of the year. Knowing this means we can be prepared and ready.
How Long Work Takes
We can also start to build up a picture of how long each of these work items takes to get completed, the stability of that, and the trend. The chart below is from a service support team.
So I can see stories are being delivered with 85% confidence in 14 days or less. This has been reduced over the last three months because the team is taking action to break down work, better-manage dependencies, and limit their work in progress to really focus on getting work done.
This particular team can use these numbers to set service-level expectations (SLE) with their customers. Note, we would do this differently if you are working on a large outcome/project.
In this particular client, I have also noticed we effectively have small, medium, and large stories. The difference between small and large can be vast and so can impact the figures. So we now have a concept of an SLE for S, M, and L story work types.
It’s fair to say if you have Kanban systems to manage your portfolio boards, projects, features, and epics. We can also get the same information, just at a higher level.
By breaking into work items types we can start to use the data to understand and make decisions. We also take the feeling we are ‘not getting much done’ to something a bit more evidence-based. Naturally, the data is only as good as the input and so we have to improve that. But over time you will have nailed the process part and can really start to use your data to make decisions.
So maybe today is the day that you start thinking about your work item types?