A queuing system is a model of the following structure: Customers arrive and join a queue to await service by any of several servers. After receiving service the customers depart the system.
A fundamental result of queuing theory is known as Little’s Law, after the man who provided the first formal proof of a well-known piece of folk-wisdom [36].
Theorem 2.1 (Little’s Law). For a queuing system in steady state the average length L of the queue equals the average arrival rate λ times the average waiting time W. More succinctly:

Generally the rate λ of flow is determined by the customers, and so Little’s Law tells us that a
warehouse holding lots of inventory (large L) will see most of it sitting (large W ).

Here is an example of how we can use Little’s Law to tease out information that might not be immediately apparent. Consider a warehouse with about 10,000 pallets in residence and that turn an average of about 4 times a year. What labor force is necessary to support this? By Little’s Law:

Little’s Law can be very useful like this. Another typical use would be to compute an estimate of
inventory turns after simply walking through the distribution center: One can estimate inventory (queue length) by counting storage positions, and rate of shipping (throughput) by observing the shipping dock, and then apply Little’s Law.

What makes Little’s Law so useful is that it continues to hold even when there are many types of customers, with each type characterized by its own arrival rate λi, waiting time Wi, and queue
length Li. Therefore the law may be applied to a single SKU, to a family of SKUs, to an area within a warehouse, or to an entire warehouse.