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Buffering Uncertainty in Supply and Demand with JMP® Analytics and Modeling

 Buffering Uncertainty in Supply and Demand with JMP® Analytics and Modeling


Ling Lin, Clinical Research and Outcomes Manager; Khoa To, Manufacturing Process Engineer, AcuFocus Inc.

Inventory plays a key role in the operations behavior of virtually all manufacturing systems. It serves as an effective way to buffer variability in a supply chain. There are many mathematical modeling approaches to inventory control in operational management. For example, from the oldest and simplest economic order quantity (EOQ) model to the more sophisticated reorder point (ROP) model. However many of the models usually assume that demand is known in advance. In this poster, we used the statistical model where demand and supply are both assumed uncertain and will be characterized statistically using JMP® Analytics (such as model fitting). The overall problem statement is to determine the optimized inventory policy that will minimize the inventory holding cost while satisfying a desired service level. The output of the model is a reasonable inventory policy expressed as a pair (Q,r) where Q is called cycle stock (how much to order) and r is called safety stock (when to order)


I am surprised this did not get more attention.  JMP modeling can be well used in supply chain.  This is a great example.  And there are few if other examples in the JMP community as evidenced by a simple Community search on "inventory".    I know this was posted about a decade ago.  But would love to see something like this fully executed in JMP as opposed to bouncing back to Excel.

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