Little's Law - Introduction (PowerPoint PPTX)

Little's Law, is a significant regularity in queueing theory. It was proved by John D. C. Little in 1961.

Little's Law states that the average number of customers in a queuing system that is in a steady state is equal to the product of their average arrival rate and their average time spent in the system.

Although this seems intuitively reasonable, it is a result worth noting: it implies that this behavior is completely independent of the probability distributions used, and thus no assumptions need be made about the distribution of arrival times or dispatch discipline.

Little's Law is important in the context of process optimization, among other things. Especially in the context of process control based on Kanban, it is applied as an important approach with regard to the optimization of processes and throughput.

Contents

•  Short Historical Intro of Little's Law
•  Little's Law & Its Elements
•  Necessary Assumptions for Little's Law to work
•  Little's Law in a Knowledge Based Context (Kanban / Kanban IT)

Application

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