Key Drivers of A Replenishment Planning Process
Data sets for modeling a Replenishment Planning Process
Half the battle is won when we know the set of data that drives a Replenishment Planning process. The need for sending new stock is mainly because somewhere in the value chain there is consumption happening. In an ideal scenario, the following data sets are a must to start modeling our process :
i) Demand Forecast
ii) Open Sales Orders
iii) Stock on hand
iv) Selling Price & Cost Price
The 1st three data sets will help us to understand volume impact whereas the 4th data set will help us to compute commercial values which will be an important point of discussions during any Sales, Operation, or Finance meetings.
The desired Replenishment Plan should also comprise of the necessary inventory levels which are mostly computed using the historical consumption trend at a periodic level. In some cases, a ball-park number is used which could either be an average consumption of the previous few months or periods or, a small portion of forecast number (let us say 1/4th) will be treated as Minimum Stock Level. In short, most of the time we have a MIN-MAX level of Inventory as our signals.
Complexities increase when there are multiple drivers and various calculations. The challenge for Operation or Planning teams will be to analyze all of this together.
With ValQ, Modern Visual Planning for Microsoft Power BI, we can create a model that replicates our Replenishment Planning process. Moreover, with the help of a quick ‘What-If analysis’, Planners can find out a Replenishment Plan which best fits their organizational goals using ValQ.
In our next blog, we shall discuss on Replenishment Plan considering Forward-Forecast so that the Organization is aware of their next period’s anticipated stock movement.
Stay tuned to know more about ValQ for Replenishment Planning
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Webinar dated 5th Feb-2020 (Asia Pacific). Click here for registration.
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