<![CDATA[Inventory management in retail warehouses is increasingly challenged by demand volatility and supplier lead time uncertainty. Conventional inventory approaches such as EOQ or min–max control often fail to capture the simultaneous interaction among multiple operational factors, resulting in stock shortages or overstock conditions. PT XYZ experiences recurring fulfillment gaps in non-food products, particularly toiletries, where warehouse stock is unable to consistently meet sub-branch demand. This study proposes the application of Response Surface Methodology (RSM) to model and optimize warehouse inventory fulfillment levels. A Box–Behnken experimental design involving three factors—store demand, supplier incoming goods, and delivery lead time—at three levels generated 15 experimental runs. ANOVA results confirm the statistical significance of the model (F = 176.29) with no lack of fit, while the coefficient of determination (R² = 0.996) indicates strong explanatory power. The optimal inventory level identified through matrix analysis is 18,697.27 units under specific operational conditions. The findings demonstrate that RSM effectively captures factor interactions and provides a data-driven decision framework for inventory optimization. This study contributes methodologically by extending RSM application in retail warehouse management and offers managerial insights to improve service level performance and reduce logistics inefficiencies.]]>
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