<![CDATA[The Fast-Moving Consumer Goods (FMCG) sector is characterized by rapid product turnover and short shelf lives, requiring effective inventory management. Companies such as PT Macrosentra Niagaboga face challenges in maintaining stock availability. Although the inventory system has been integrated, stock verification is still conducted manually, requiring adaptive management to minimize the risk of overstock and stockout. Therefore, this study aims to develop a demand forecasting model using the Long Short-Term Memory (LSTM) algorithm integrated with Discrete-Event Simulation (DES) to optimize inventory management. The study utilized historical shipment data from May 2025 to January 2026. Preprocessing included data cleansing, date validation, and aggregation into daily and weekly data. Forecasting results were subsequently used as inputs for the DES simulations to determine optimal inventory policies. The results demonstrated that weekly LSTM aggregation was more accurate and stable than daily aggregation, as evidenced by a reduction in WAPE from 49.9% to 16.94% for high-demand products. Furthermore, integration with DES reduced stock levels by more than 30% using a safety stock ratio of 0.7 without compromising service levels. Finally, the proposed model was implemented in a web-based dashboard serving as a decision support system for monitoring forecasting and inventory policy recommendations.]]>
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