<![CDATA[This study develops a multi-period linear programming model to optimize coffee inventory and replenishment planning under demand uncertainty. The model integrates inventory balance, replenishment capacity, storage capacity, and safety stock constraints to determine cost-efficient replenishment quantities and ending inventory levels for three coffee products: Robusta, Arabica, and Blend. Simulated data over six planning periods were analyzed under low, medium, and high demand scenarios using PuLP in Python. The results show that optimal solutions were obtained under low and medium demand conditions, with total inventory costs of Rp 286,836,000 and Rp 480,466,000, respectively. Under low demand, inventory was maintained exactly at safety stock levels, reflecting a just-in-time strategy. Under medium demand, the model temporarily increased Robusta inventory to anticipate future demand. However, the high-demand scenario was infeasible, indicating insufficient replenishment capacity. The model provides a practical decision support tool for cost-efficient and resilient coffee inventory management.]]>
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