Inventory Optimization in Manufacturing Projects Using ABC Analysis, EOQ, Reorder Point, and Just-In-Time Principles: An Automotive Industry Case Study

This study developed and validated an integrated inventory optimization framework combining Pareto Analysis, ABC Classification, Economic Order Quantity, Reorder Point, and Just-In-Time principles for an automotive manufacturing case study. Pareto Analysis confirmed that a small number of inventory items account for the majority of inventory investment, while ABC Classification enabled differentiated control strategies for high-, medium-, and low-priority items. The EOQ model determined an optimal order quantity of 1,366 units, and the ROP model established a replenishment level of 2,100 units, together forming an effective replenishment policy. The comparative evaluation showed that the proposed policy reduces average inventory from 1,000 to 683 units and total annual inventory cost from ₹264,000 to ₹245,440 — a 7.03% saving — despite a moderate increase in ordering cost. These findings demonstrate that integrating inventory classification with quantitative replenishment optimization yields greater benefits than applying these techniques in isolation, and that the proposed framework is readily adaptable to other manufacturing sectors where inventory investment represents a significant share of operational expenditure.

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