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Beyond Control Charts and Cumulative Flow Diagrams

Control Charts (CCs) and Cumulative Flow Diagrams (CFDs) are powerful ways to display information about a flow system, such as a Scrum or Kanban development process. Unfortunately the very fact that the charts display so much information means that it is often difficult to extract specific information from them. That is why it's useful to also plot some of the key attributes of the systems on their own - this allows us to look at these aspects specifically, alongside the rawer view of the data that you get from CCs and CFDs.

The graphic on the right shows a number of diagrams all of which were derived from very simple data about each item that flowed through this system:
  • when it arrived into the system; 
  • when it departed the system; and
  • whether the item was "delivered" or "discarded".
Note: I use the term "discard" here as a general term to include an exit from the system at any point in the system and for any reason. It includes aborting/abandoning the item after commitment, as well as postponing the item by moving it back to a part of the process upstream from the system under study. For the definition of this and other terms used here please see this Glossary.
The first diagrams in the graphic is the Control Chart - actually this is simply a scatter plot of the time each item stays in the system under study. I refer to this as "Time in Process - TiP - or alternatively "Time in _______" where the blank stands for whatever the process or part of the process is under study. For example it could be the Time in Preparation, Time in Development, Time in Acceptance, etc. The scatter plot highlights (in orange) the items which were not "delivered".

Below it is the CFD. Unlike some very stripy versions, this one has only 3 bands (as limited by the input data), corresponding to arrivals, all departures (including discards), and deliveries.

The remaining diagrams all highlight one or more aspects of the same data. Firstly the terms from Little's Law:
  1. Average Delivery Rate. This is measured in items per week, and the average is taken over 1 week. Note this only shows actually delivered items. Alternatively a plot of "Throughput" could have been used which includes all items that have passed through the system.
  2. Average Time in Process (TiP). This is measured in weeks and again the average is taken over 1 week.
  3. Average Work in Progress (WiP). This is measured in number of items, again averaged over one week. Care must be taken when calculating average WiP for a day, particularly on days when an item arrives in or departs from the system, to ensure that it is consistent with the calculations of average TiP.
In addition to these standard quantities from Little's Law a number of flow balance metrics are shown. These are:
  1. Net Flow. Simply the difference between the number arriving and departing over the previous week.
  2. Delivery Bias. This is a measure of the degree to which Delivery Rate is higher or lower than would be predicted by Little's Law for the given period (1 week in this case). If it is non-zero it indicates away from stability. Further discussion of this quantity is found here.
  3. Flow Debt/Credit. This is a measure of the degree to which the average TiP varies from that predicted by the CFD. This also indicates a degree of instability if it varies significantly from zero. See Dan Vacanti's book [vaca] for further discussion.
  4. Age of WiP Indicator. This compares the average age of the WiP with half the average Tip. It is another indicator of imbalance.
Recently I have been discussing these four quantities with colleagues and with Troy Magennis and Dan Vacanti as they show promise for predicting significant changes in the TiP, a very important aspect of the effectiveness of the system.

A spreadsheet containing the means to generate these diagrams from your data will shortly be made available from gitHub. Watch this space!

References
  • [vaca] Vacanti, Daniel S. "Actionable Agile Metrics for Predictability: An Introduction". LeanPub. (2015)
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