Little’s Law is a theorem for queuing systems. It determines the average number of items in a stationary queuing system, based on the average waiting time of an item within a system and the average number of items arriving at the system per unit of time. The long-term average number L of customers in a stationary system is equal to the long-term average effective arrival rate λ multiplied by the average time W that a customer spends in the system

Little’s law variables:

  • L = The average number of work items in a queuing system / Work In Progress (WIP).
  • λ = The average number of items arriving at the system per unit of time / The long-term average effective arrival rate / Throughput / The rate at which the items go in and out of the system.
  • W = The average waiting time a work item spends in a queuing system / Lead time.
Littles law - Kanban adaptation

Example 1

On average 30 cars come to the fast food takeaway every hour. They usually spend 6 minutes there (0,1 hour). L= 30×0.1= 3 cars are usually in the line.

Example 2

A team takes on 24 items they usually finish in four weeks and works on them simultaneously.

Littles law - example 1

but if half the items are being worked on at the same time

Littles law - example 2

which enables lowering cycle time to half, meaning that the value will be delivered faster, with no other improvements.

What is it for:

  • Exposes the real performance
  • Provides predictability
  • Helps to reduce/manage multitasking
  • Helps to balance the work in progress and Lead time
  • Enables setting the WIP limits
  • Reduces Throughput which increases the Lead time

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