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How should we integrate Demand Sensing with our Replenishment solution?
My last post laid out the importance of integrating Demand Sensing with Replenishment solutions. I got a few requests to explain it with a example. Here is a use case to explain how Demand Sensing adds tremendous value to Replenishment.
Inventory buffers in a Replenishment Supply Chain should be sized to cover maximum demand during Replenishment Lead Time (RLT), adjusted for supply reliability. When we look at the distribution system of a medium sized CPG company in India, these last mile buffers at the distributors could be typically 5 to 20 lac combinations of product-nodes. Each of these buffers needs to be sized according to this definition.
Sizing of these individual buffers requires that we predict demand at that granularity and refresh it daily to account for demand shifts and demand driver events. This is where a Demand Sensing engine adds huge value, using AI/ML algorithms to decipher demand patterns at that granularity and consider the impact of demand drivers during the RLT.
The output of Demand Sensing helps size these buffers dynamically, which ensures agile replenishment to synchronize supplies with demand.