Implement inventory management rules based on a periodic review policy to reduce the number of stores replenishments
For most retailers, inventory management systems take a fixed, rule-based approach to forecast and replenishment orders management.
The objective is to build a replenishment policy that will minimize ordering, holding and shortage costs.
In a previous article, we have built a simulation model based on a continuous review inventory policy, assuming a normal distribution of the demand.
However, this kind of policy can be inefficient when you handle a large portfolio of items that may have different replenishment cycle lengths.
In this Article, we will improve this model and implement a periodic review policy with Python to limit the number of replenishments.
As an Inventory Manager of a mid-size retail chain, you are in charge of setting the replenishment quantity in the ERP. Because your warehouse operational manager is complaining about the orders frequencies, you start to challenge the replenishment rules implemented in the ERP, especially for the fast runners. Previously we have implemented several inventory rules based on continuous review policies.
What would be the number of replenishments if you have 2,500 SKUs?
This analysis will be based on Dummy Data shared in this folder.
In this repository code you will find all the code used to explain the concepts presented in the article.
Senior Supply Chain Engineer with an international experience working on Logistics and Transportation operations.
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