Updated: May 27
This is post 5 of 9 in our Little's Law series.
Not to get too morbid, but in police detective work, when a married woman is murdered, there are only three rules to determine who the killer is:
1. It's always the husband
2. It's always the husband
3. It's always the husband
The same thing is true when your flow metrics are murdered by your process:
1. It's always the assumptions
2. It's always the assumptions
3. It's always the assumptions
Think back to the first experiment I had you run at the start of this blog series. I had you look at your data, do some calculations, and determine if you get the results that Little's Law predicts. I even showed you some example data of a real process where the calculated metrics did not yield a valid Little's Law result. I asked you at the time, "What's going on here?"
If you've read my last post, then you now have the answer. The problem isn't Little's Law. The problem is your process. The Throughput form of Little's Law is based on five basic assumptions. Break any one or more of those assumptions at any one or more times, and the equation won't work. It's as simple as that.
For convenience for the rest of this discussion, I'm going to re-list Little's assumptions for the Throughput form of his law. Also, for expediency, I am going to number them, though this numbering is arbitrary and is in no way meant to imply an order of importance (or anything else for that matter):
1. Average arrival rate equals average departure rate
2. All items that enter a workflow must exit
3. WIP should neither be increasing nor decreasing
4. Average age of WIP is neither increasing nor decreasing.
5. Consistent units must be used for all measures
In that earlier post, I gave this example from a team that I had worked with (60 days of historical data):
WIP: 19.54, TH: 1.15, CT: 10.3
For this data, WIP / TH is 16.99, not 10.3.
What that tells us is that at one or more points during that 60-day time frame, this team violated one or more of Little's Law's assumptions at least one or more times. One of the first pieces of detective work is to determine which ones were violated and when. Almost always, a violation of Little's Law comes down to your process's policies (whether those policies are explicit or not). For example, does your process call for expedites that are allowed to violate WIP limits and that takes priority over other existing work? If so, for each expedited item you had during the 60 days, you violated at least assumptions #3 and #4.
Did you have blockers that you ignored? If so, then you at least violated #4.
Did you cancel work and just delete it off the board? If so, then you violated #2.
And so on.
This was quite possibly the easiest post to write in this series -- but probably the most important one. A very quick and easy health check is to compare your calculated flow metrics with those that are calculated by Little's Law. Are they different? If so, then somewhere, somehow, you have violated an assumption. Now your detective work begins. Do you have process policies that are in direct contradiction to Little's Law's assumptions? If so, what changes can you make to improve stability/predictability? Do you have more ad hoc policies that contradict Little? If so, how do you make them more explicit so the team knows how to respond in certain situations?
The goal is not to get your process perfectly in line with Little. The goal is to have a framework for continual improvement. Little is an excellent jumping-off point for that.
Speaking of continual improvement, when it comes to spotting improvement opportunities as soon as possible, there is one assumption above that is more important than all of the others. If you have followed my work up until now, then you know what that assumption is. If not, then read on to the next post...
Explore all entries in this series
It's Always the Assumptions (this article)
About Daniel Vacanti, Guest Writer
Daniel Vacanti is the author of the highly-praised books "When will it be done?" and "Actionable Agile Metrics for Predictability" and the original mind behind the ActionableAgile™️ Analytics Tool. Recently, he co-founded ProKanban.org, an inclusive community where everyone can learn about Professional Kanban, and he co-authored their Kanban Guide.
When he is not playing tennis in the Florida sunshine or whisky tasting in Scotland, Daniel can be found speaking on the international conference circuit, teaching classes, and creating amazing content for people like us.