If you practice Kanban, you already understand the incredible insight that visualization can bring to the way you work. But do you know how to use that insight to optimize your workflow? You might be surprised to learn that you already have all the data you need to begin optimizing flow — you just need to learn a few basic Kanban calculations to turn that raw data into actionable flow metrics.
We asked Dan Vacanti, CEO and co-founder of ActionableAgile and author of Actionable Agile Metrics for Predictability, to share his insights on how to use Kanban metrics to optimize flow. In a series of posts, Dan will explain how to harness the power of data in Kanban to help you maximize the value you can bring to your customers.
In this post, you’ll learn the basic metrics of flow, and how to analyze the data your board data to begin optimizing your workflow. In future posts, you’ll learn how to use Kanban calculations to continously improve your process.
From Daniel Vacanti:
The Metrics of Flow
Central to the idea of Kanban is the concept of flow. Flow is all about how value propagates through a process. I’m assuming here, of course, that you have customers, that you must deliver value for those customers, and that you’ve set up a process to manage how that value is delivered. Let me just cut to the chase, then: the best strategy to optimize the delivery of value to your customers is to optimize flow.
Part of the process of optimizing flow involves answering two very basic questions:
- How long does it take us to deliver value to our customers?
- How much customer value are we delivering at any given time?
Note: These questions must be answered from the perspective of the customer.
As we will see in the coming posts, “How long does it take us to deliver value to our customers?” is best answered by the flow metric called Cycle Time. “How much customer value are we delivering at any given time?” is best answered by the flow metric called Throughput. Both Cycle Time and Throughput are going to be measured in terms of our final flow metric that, as it turns out, is the biggest influencer of both: Work in Progress.
The good news is that these metrics are very simple to understand both for you and for your customers (but more on that in a later post). The even better news is that flow metrics are very easy to collect and track—in fact, you probably already have all the data you need to get started in analyzing your flow metrics right now.
How Do We Collect Flow Metrics?
But first let’s begin, as is customary, at the beginning.
All metrics are measurements—the three flow metrics mentioned above being no exception. Thus, any discussion of flow metrics must begin with a discussion of measurement. It is important to understand that all measurements have at least three aspects: a start point, an end point, and a unit of measure. Understand how these aspects apply to your process, and you’ve gone a long way to understanding flow metrics in general.
In my book, Actionable Agile Metrics for Predictability, I borrow (steal) a metaphor from Dr. John Little that suggests all processes can be thought of in terms of a simple queuing system. Simply put, in a queuing system items arrive, they are worked on or wait to be worked on, and then they depart. The figure below represents a pictorial version of this concept:
Think about how you might apply this model to your own process: What does it mean for work to have arrived? Is it when your customer asks for it? Or is it when the request is approved or prioritized? Or is it when you start working on it? Or is it something else. There’s no right answer here—you just need to decide what that arrival point is. Likewise, what does it mean for something to have departed? Is it when all the work to build it is done? Is it when it is shipped to the customer? Or is it when the customer has had a chance to use it and sign off on it? Again, there’s no right answer here.
Once you have explicitly defined what those boundaries are for your process, then to get a measurement for our metrics, all you have to do is take a timestamp for when something arrives (based on your definition of arrival) and a timestamp for when something departs (based on your definition of departure). An example of what your measurement data might look like is summarized in the table below:
| Work Item ID | Arrived | Departed |
| 1 | 01/01/2016 | 01/03/2016 |
| 2 | 02/02/2016 | 03/03/2016 |
| 3 | 01/02/2016 | 03/04/2016 |
That’s it. Really, that’s it. All of the basic flow metrics in Kanban can be derived from this minimal data set. (You can find utilities for how to extract this data from your own tooling at https://github.com/actionableagile.)
In the coming blogs, I’ll talk about how to turn your flow data into flow metrics, how these flow metrics are related by Little’s Law, and how to use flow metrics and Little’s Law to predictably optimize the delivery of customer value through your process.
Recommended Reading:
To learn more about flow and flow metrics, check out these resources:
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