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Most of you know the situation when we had planned some work, let’s say a few different tasks. Then we started most of them, work in parallel and when time is up only a small portion of the work is really completed. In teams working towards deadlines or iterations this usually at least feels bad or has consequences, such as more budget needs to be requested or delay of delivery.

Little’s law is a theorem in queueing theory by John Little. What it says is that long-term the average number (L) of customers in a stable system equals the long-term average effective arrival rate (λ) multiplied by the average time (W) that a customer spends in the system. As a mathematical formula it’s: L = λ W In simple words, there is a relationship between the average number of customers in a stable system, their arrival rate, and the average time in the system.