<![CDATA[Background: The construction materials distribution sector faces significant challenges in managing inventory due to fluctuating demand and the absence of systematic planning. Distributor Bintang Kerahiman, operating in Palembang and South Sumatra, currently relies on estimation-based inventory decisions, leading to frequent stockouts and overstocking. These inefficiencies lead to increased operational costs and lost sales opportunities, underscoring the need for a more structured, quantitative inventory control approach based on the Economic Order Quantity model. Aim: This study aims to design a cost-efficient inventory system by applying the Economic Order Quantity model and to compare its performance with the company's existing inventory method. Methods: A quantitative approach was employed using historical sales data from March to August 2025 across 30 selected products. Demand forecasting was conducted using the moving average method. Inventory parameters, including ordering cost, holding cost, safety stock, and reorder point (ROP), were calculated. The optimal order quantity was determined using the Economic Order Quantity formula, and total inventory costs were compared between the current method and the proposed model. Results: The implementation of the Economic Order Quantity model successfully reduces total inventory costs by optimizing order quantities and balancing ordering and holding costs. Across 30 products, the model demonstrated consistent cost savings and reduced inefficiencies. Additionally, profit improvements were observed due to decreased lost sales. For instance, profit for Afur BCP PVC Basket increased from IDR 990,000 to IDR 996,000, while Mold Cleaning Liquid increased from IDR 305,000 to IDR 330,000. Conclusions: The Economic Order Quantity model is more effective than the existing method in minimizing inventory costs, reducing stock imbalances, and improving service levels. The integration of safety stock and reorder point further enhances the system’s ability to handle demand variability. Implication. This study provides practical implications for construction material distributors by offering a data-driven inventory control framework. The findings support improved decision-making, cost efficiency, and customer satisfaction. Furthermore, the proposed system can be extended by integrating digital inventory applications and hybrid models for future research.]]>
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