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In order to analyze the current situation of organizational processes, you need to collect data about jobs, their routings, arrival and departure times, et cetera. In situations when jobs (or parts) wait in staging areas, or warehouses, or at people’s desks in office, there may not be accurate data on these times. But how do you get this data? Therefore, it is our challenge to understand the basic relationship between the time of a job (the time it takes to produce a product, or work on an order), the time the product or order spends in a queue and the throughput (or the amount of products or orders that are made in a production system).
In the figure below, you see a production process. The total process consists of a processing job and a queue.
The processing job transforms input to output, for instance the assembly of parts into an total product or the treatment of a patient or something else you can think of. The number of parts in the queue depends on the time it takes to process the product and how much products enter the total process in a certain amount of time. The quicker the products enter the system, or when it takes a longer time to process the jobs, there is need for this queue to store products until processing.
Little’s law is a tool that can help with determining how long job/parts have been waiting, when timestamp data is not readily available. You want to know the relation between the times that it takes to process the job, the waiting time in the queue and the number of products entering and leaving the system.
How does Little’s Law work? There is some information you need to perform a calculation. First of all, there is the processing time or job time. This is the time it takes to do the work on one single products. Second, you have the queueing time. This is the time one single product waits in a queue. Then, there is the total throughput time (THT). This is the overall time it takes for one product to go through the queue and the processing job. Furthermore, there is the work in progress (WIP). This is the number of items in the system. Lastly, the throughput (TH). This is the number of products going through the system.
Combining these information:
The
number of items in the system (WIP) equals the number of products going through
the system (TH), multiplied by the total time it takes (THT) (see the figure
below).
