Little’s Law is an equation that frequently appears in discussions of Kanban systems. While initially formulated as a part of queueing theory to describe the length of time people would spend in stores it has since been applied to many other contexts from manufacturing to knowledge work (particularly Kanban for the purposes of today’s conversation).

Little’s Law describes the relationship between the three primary flow metrics, Cycle Time, Work in Progress (WIP) and Throughput.

Kanban teams often want to use Little’s Law for prediction purposes and there are a few problems with that. The first, which we’re not going to go into detail on in this series, is that Little’s Law is describing a relationship among averages. It does not say anything about individual pieces of work. The second, is that the predictability of your system heavily depends on a number of assumptions built into Little’s Law, if those assumptions are not being met then the system cannot be considered stable. In statistics this would be described as a system that is not stationary.

Four Assumptions of Little’s Law

  1. The average arrival rate is equal to the average departure rate.
  2. All work entering the system will eventually depart the system.
  3. The average age of work remains constant, neither increasing nor decreasing.
  4. Consistent units are used to measure WIP, Cycle Time, and Throughput.

Over the next few days we’re going to discuss each of those assumptions. We’ll discuss why straying from these assumptions might reduce the ability of your team to effectively forecast and what you might do to help your system more closely match those assumptions. We’ll relate all of these ideas to your teams ability to predictably deliver business value and also discuss when you may want to let your system stray from these assumptions.

Click Here to read on about the first assumption of Little’s Law.