In my last blog I discussed the relationship between Cycle Time (C/T), Processing Time (P/T), Work-In-Process (WIP) and Throughput (TP). In this blog I want to discuss something called Little’s Law and why it is imperative to keep cycle times as low as possible as well as the importance of reducing variability.
In 1961 John Little published a mathematical proof known as Little’s Law which states that throughput is always equal to WIP divided by cycle time, or stated mathematically,
Throughput = WIP / Cycle Time
TP = WIP/C/T
Graphically, the relationship between batch size (WIP) and cycle time can be seen in the figure below. For any batch size (WIP Level) the curve clearly behaves in a liner fashion. As a matter of fact, for this example the equation for this curve is:
y = 4x
Little’s Law (TP = WIP/C/T) implies that reducing cycle time and reducing WIP are essentially equivalent activities as long as throughput remains constant. But we know that reducing WIP without reducing variability will cause throughput to decrease (Variability Buffering Law). The real message here is, variability reduction is an extremely important component of WIP and cycle time reduction initiatives.
Before leaving this subject, I want to discuss the importance of keeping cycle times as short as possible, especially in the constraint operation. Hopp and Spearman (1) provide five reasons why this should be your objective:
1. Better responsiveness to the customer. If it takes less time to produce product, then the lead time to the customer can be shortened. Shorter lead times can result in increased sales.
2. Maintaining flexibility. Changing the list (backlog) of parts that are planned to start next is less disruptive than trying to change the set of jobs already in the process. Since shorter cycle times allow for later releases, they enhance this type of flexibility.
3. Improving quality. Long cycle times typically imply long queues in the system, which in turn imply long delays between defect creation and defect detection. For this reason, short cycle times support good quality.
4. Relying less on forecasts. If cycle times are longer than customers are willing to wait, production must be done in anticipation of demand rather than in response to it. Given the lack of accuracy of most demand forecasts, it is extremely important to keep cycle times shorter than quoted lead times, whenever possible.
5. Making better forecasts. The more cycle times exceed customer lead times, the farther out the forecast must extend. Hence, even if cycle times cannot be reduced to the point where dependence on forecasting is eliminated, cycle time reduction can shorten the forecasting time horizon. This can greatly reduce forecasting errors.
In my next blog I want to expand on the concept of batch and queue production, provide a bit more insight into why it might be the worst possible way to manufacture products, and to introduce the concept of critical WIP level.
(1)Factory Physics – Hopp and Spearman