Review of Reducing Cycle Time, Part 2
In my last post, I answered the question, “What would happen if the factory decided to change its batch size from 10 parts to 4?” As the figure below illustrates, we discovered that the throughput would not change, but the cycle time would be significantly lowered.
I also introduced you to Little’s Law in Part 2 of this series. This law states that throughput (T) is always equal to work-in-process inventory (WIP) divided by cycle time (C/T), or stated mathematically:
Throughput = WIP / Cycle Time
T = WIP/C/T
Understanding the implications of Little’s Law
In Part 3, I will continue our examination of Little’s Law and its implications for cycle time and throughput. Much of what I will be presenting in this series of posts is taken from my second book, The Ultimate Improvement Cycle—Maximizing Profits Through the Integration of Lean, Six Sigma and the Theory of Constraints.
Little’s Law implies that reducing cycle time and work-in-process inventory are essentially equivalent activities, as long as throughput remains constant. But we know that reducing WIP without reducing variability will cause throughput to decrease. This is known as the Variability Buffering Law. The takeaway here is that 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 provide five reasons as to why this should be your objective.
Why keep cycle times as short as possible, especially in the constraint operation?
- Be more responsive 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.
- Maintain 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.
- Improve 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.
- Reduce reliance 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.
- Make more accurate 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.
I mentioned in Part 2 of this series, the “batch and queue” production system is the worst possible scenario for a company, but there is an even less efficient system that is still in practice. Some companies practice “batch and store” production, whereby instead of processing the material to the next process, they move the material to a storage location. In this process, the cycle time becomes even more protracted!
Coming in the next post
In the next post, I will compare the impact of one-piece flow to that of batching.
Until next time.
 Factory Physics, Hopp and Spearman, McGraw-Hill, 2001
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