<![CDATA[This study proposes an integrated approach combining ABC classification, Economic Order Quantity (EOQ), and Monte Carlo simulation to evaluate inventory performance under stochastic demand conditions in a palm oil processing plant. ABC classification identified 32 category A items that contribute significantly to total inventory value and require priority control. EOQ analysis was applied to determine optimal order quantity, followed by safety stock and reorder point calculations. A 1,000-iteration Monte Carlo simulation was used to assess inventory performance in the event of random demand. According to the simulation results, the average service level is roughly 0.88, and the stockout probability runs from 0.000 to 0.189, which corresponds to service level values between 0.811 and 1.000. Depending on the parameters of demand, the mean inventory cost ranges from roughly Rp 7,764 to Rp 1,281,248. The EOQ-based policy lowers inventory costs by roughly 15% to 29%, with an average savings of almost 22% when compared to the current policy, according to a comparison of total inventory costs. These findings show that the combined ABC-EOQ and Monte Carlo simulation approach lowers overall inventory costs while increasing service level performance. A thorough tool for assessing inventory policies in situations of stochastic demand is offered by the suggested framework.]]>
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