Reducing WIP may require some counterintuitive actions, such as temporarily pulling projects or orders out of the workflow and setting them aside. Only this option can be used to effectively address these short-term demand variations.It is impractical, however, when addressing relatively short-term variations in demand. This is fine when considering longer-term system capacity. Commit significant investment to increase the scale of the system to better handle the increase of WIP.That is, identify waste in a system – especially waste that is present in the system constraint – then systematically eliminate it. This can be done through process improvement.To improve cycle time, only two options are available: Significant levels of new orders cause production efficiency to decrease.This is one of the strange but accurate implications of Little’s Law: Well, the increase in orders, which is normally a good thing, immediately causes a decrease in production efficiency, which would be a bad thing. The WIP would increase from 200 units to 235, and because TH would remain constant at 50 units per day, CT would immediately increase from four days per unit to 5.4 days per unit. This means we can accommodate new orders of 50 units each day and the system will remain balanced. But suppose we receive an order for 85 units, 35 more than the standard order of 50. Given these conditions, our cycle time, the average time it takes to complete one unit, would be four days because of the calculation below: Our work in process (WIP), the number of units in various stages of production, remains relatively constant at 200. So what are the applications of Little’s Law in business? Considering a typical production situation of accepting new orders into a production process, let’s assume we are running a process in which throughput (TH), the number of units we produce, equals 50 units per day. This figure must be measured (counted) directly or can be computed from Little’s Law.Īnd, you can also transform the equation below to find the average wait time for a system, whereas the above tells us the average length of the queue, or how many people are waiting in line. WIP = work in process (average number of units or customers in a system). This is the number of items currently in production or being serviced in some way. Generally, determining cycle time requires either direct measurement or can be computed from Little’s Law as CT = WIP/TH. This is the time it takes to complete the production cycle or the average time it takes to produce one unit.
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