I have found some formulas of Little's Law in relation to the application in Kanban, but it still makes little sense for me. Two quotations from different sources.

Average Cycle Time = Average Work In Progress / Average Throughput Little’s law locks the three measures (WIP, throughput and cycle time) together in a unique and consistent way for any system to which it applies.


According to Little’s Law, Average Throughput = Average WIP divided by average cycle time

A number of questions arise. Why would I need this in order to calculate the average time if I can just sum up all cycles time from each item and divide it by the number of items?

Isn't "Work in Progress" is a fixed value and thus can't be average?

My WIP is 3, average cycle time is 1 day. 3/1 = 3. Throughput is 3. 3 what? :) Parrots?

The first formula is just not applicable. Where can I find a value of throughput?

1 Answer 1


Little's Law is most commonly used to explain why the practices in Kanban exist. As you point out, you can just calculate the average cycle time and don't need Little's Law to tell you what it is.

Now, you say your WIP is 3. I think you are conflating WIP with WIP limit. WIP limit is a set number but you should not be at your limit at all times. But for a moment, let's assume your average WIP is 3.

Since T = W/C, also C = W/T. To use your numbers, C = 3/3 = 1. Now, let's say your manager pressures you to take on 2 more things. Now C = 5/3 = 1.66 days. So, increasing WIP will increase cycle time. When applying Little's Law to Kanban, it's worth noting that Cycle Time and Throughput aren't directly influencable. They are only observable afterward. So what happens when people try to get more done is they often cram more work in, which has the opposite effect.

One thing Little's Law doesn't effectively express is context switching and other forms of overhead that increase as WIP increases. What happens in practice is as WIP goes up, Cycle time grows faster due to that overhead, so if you your WIP doubles, your cycle time will usually more than double, which will cause a lowering of throughput. This is not universally true, which is why you don't see it in Little's Law, but it is true in most processes.

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