Formulation of Raw Material Inventory Model in The Steel Industry

Abstract

Suboptimal inventory management can lead to overstocks that increase storage costs or shortages that hamper production. PT XYZ, a manufacturer of automotive components, faces this challenge, especially in the management of steel raw materials. This study aims to develop an inventory management model using the Economic Order Quantity (EOQ) and Mixed Integer Non-Linear Programming (MINLP) approaches to handle uncertainties in steel raw material inventory. This method is complemented by demand forecasting using the ARIMA model to overcome stochastic demand patterns. The results indicate an annual raw material requirement of 809,735 kg, with an optimal inventory cost of Rp 6,943,611,000. Out of four suppliers analyzed, two were selected with raw material allocations of 659,480 kg and 150,255 kg, and reorder points of 2,228 kg and 1,965 kg, respectively. This model reduces the risk of stock shortages and excess inventory, ensures production continuity, and lowers inventory costs.