Feb 21, 2007
Little’s law is a result coming out of the field of queuing theory. If you’ve taken an introductory course in Operations Management or perused an introductory text in Operations or Industrial Engineering, I’m sure that you’ve come across this result. Simply stated,
The average number of customers in a stable system (over some time interval), N, is equal to their average arrival rate, Lambda, multiplied by their average time in the system, T, or:
What is interesting about this result is expounded further in the Wikipedia posting,
Although it looks intuitively reasonable, it’s a quite remarkable result, as it implies that this behavior is entirely independent of any of the detailed probability distributions involved, and hence requires no assumptions about the schedule according to which customers arrive or are serviced, or whether they are served in the order in which they arrive.
However, this is true only for stable systems which is a good place to begin with considering its generality of application. The scope of its application is not restricted to customer service scenarios but also to manufacturing as well as other operational environments. What is interesting to me about Little’s law is that it is well within the direction of what can be aptly termed factory physics. Furthermore, just as there is the very real need for a comprehensive undertanding of factory physics, there is an analogous need for understanding supply chain physics as well.
So what is Factory Physics? Factory Physics refers to a generalized field that deals with understanding the behavior of manufacturing systems. However, the real question is not whether the behavior of manufacturing systems is understood but whether decision makers situated in the firm fully understand the behavior of manufacturing systems? The question would analogously apply to the case of supply chain management as well.
The fundamental principles underlying factory physics are explored here and it is well worth a close reading. (The PDF link here provides a better representatio in my opinion)
In my next post, I will delve a little deeper into Factory Physics and what insights one can take away from those fundamental principles and find applications for them in a supply chain context. In the longer term, I think that this route offers a firm basis for developing robust metrics that are based on a sound appreciation for generalized laws of system behavior rather than taking the route of measuring what is measurable and observing what is observable. In my opinion, the former offers an empiricism that has been subsumed into the edifice of manufacturing or general operations while the latter offers empiricism on an ad-hoc and practical basis.
The journey continues…
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