Project managers are ultimately assessed by how fast and how much their team delivers. In the Kanban Method, throughput is the key measure of team productivity. No matter how many tasks are being worked on, throughput only measures what has been completed.

While kanban throughput is commonly displayed as a bar chart, you can gain much better insights into your process by using a throughput histogram. Histograms display the frequency distribution of your data. Learning to read these charts enables you to track your team performance over time, determine your team capacity and assess your process efficiency.

The Kanban throughput histogram

Average throughput is the most accurate approach to track team performance, and the preferred method of visualising this metric over a period of time is using a throughput histogram.

Histograms show the shape and distribution of your data. The greatest benefit that comes with their use is the ability to monitor basic information about the dataset, such as the median value, the width of spread and the overall distribution of the assignments of your team.

Kanban throughput histogram

Throughput histogram allows you to visualise how consistently your team is delivering results. On the horizontal axis is the throughput (number of tasks completed), while the vertical axis shows the frequency (number of days) that had a certain throughput. The vertical percentile lines indicate the probability that throughput will be repeated in the future – for example, using the graph above, there is a 50% chance that the future throughput of this team will be 5 items/day or fewer. There is an 85% chance that the throughput will be fewer than 7 items a day. These values can be used to define service level agreements.

Plotting a throughput histogram

Let’s look at plotting a histogram to show the throughput of a team over a month. Here is how the team performed in table form:

Daily throughput in table form

It is difficult to make sense of the numbers using such a simple table. One thing that does stand out is the two zero values cropping up every seven days – weekends. Let’s try displaying this data on a scatterplot:

Throughput scatterplot

This makes more sense – it’s easy to see how the team’s throughput has varied from day to day over the month. However, we can’t easily see the average throughput of our team or work out trends over time.

Let’s make a histogram of this data. First, count the number of days that have certain throughput.

Throughput frequency distribution in table form

Let’s plot this data as a histogram diagram.

Throughout histogram

Now that we have a histogram, can you work out what useful conclusions and insights can be drawn from it?

How to read throughput histograms

Average throughput measures how much work has been done on average during a week or month, tracking how trends develop over time. We can also use this value to perform rough estimation and commit on how many items a team can deliver for a certain time period.

Our team’s average throughput over the month is the most important metric we want to discover. There are three ways to calculate an average – they can give the same result, but frequently have some differences and mean slightly different things.

One important thing to note about a typical throughput data set is the appearance of zero values due to non-work days – weekends, public holidays, vacations etc. This can make it seem like your team is less productive than they are!

However, it is useful to be aware of average throughput calculated both with and without weekend values. Including the weekend lows gives you an accurate throughput value to calculate over longer periods of time, while leaving them out will give you a more accurate throughput average for the typical business day.

  • Mode is the easiest average to calculate – it is the number that appears most often. In this case, a throughput of 0 is the mode. As we discussed above, these zero values are weekend days that don’t reflect a typical day of work. Removing the weekend values gives a mode of 3.
  • Median shows the middle number of a data set. For example, the median of the following set of values (1, 2, 4, 7, 8) is 4. The median of this next set (3, 3, 4, 5, 5) is also 4. If the data set is an even number, the value between the two central numbers is taken. The median in the histogram above is 6 tasks per day.
  • Mean is the average calculation that you are most likely to be familiar with. This involves adding up all of the values and dividing them by the number of instances in the data set. The total throughput of the entire month is 111 work items, with 30 instances (days) in the set – giving a mean of 3.7 tasks completed per day.
 

WE UNCOVER THE EFFICIENCY OF YOUR WORKFLOW

Optimise your performance with Kanban analytics

See a dashboard with your data

What is the point of calculating the different types of average? Depending on the range of the data set, different ones can be more likely to predict the central tendency of the set. This central tendency or central value reflects the actual average throughput of the team.

In a data set with a small number of much higher or much lower values, the mean will be skewed up or down. In our example, we can see that a small number of days had a much higher throughput than the others. In this case, the median value is used to assess team performance.

Average throughput is a great way to perform rough estimations. For more accurate long-term forecasts though we recommend using the Monte Carlo method.

Have you found histogram diagrams useful to visualise your throughput data? Which type of average best reflects the performance of your team? Tell us about your experience in the comments!

1 Star2 Stars3 Stars4 Stars5 Stars How helpful is this article? 3 votes