EOQ Inventory Model in a Metalworking MSE with Intermittent Demand: A Case Study

Abstract

The main objective of this research article is to optimize costs and logistic KPIs applying an economic order quantity (EOQ) inventory model in a metal-mechanic MSE with intermittent demand. Firstly, the forecast model with the lowest MAD and ECM is selected. The object under study, after ABC classification, belongs to the family of products located in class A due to its valuation and participation in the inventory. The Croston method is considered the most effective forecast model. Secondly, an aggregate planning is developed to satisfy the projection. Then, the EOQ or Wilson model is implemented to reduce inventory costs. Finally, to validate the calculated data, a simulation model is built in Arena with 50 replications. As a result, the inventory costs were reduced to 22.6%.