<![CDATA[Fuel (BBM) is a strategic commodity that plays a crucial role in supporting various economic sectors. PT XYZ, one of the fuel suppliers in Timor Leste, faces significant challenges in ensuring a stable and timely supply. Issues such as demand fluctuations, lead time uncertainty, and limited storage capacity often trigger stockout risks and additional operational costs such as demurrage. In 2023, PT XYZ recorded two stockout events and two potential demurrage occurrences, resulting in financial losses and missed sales opportunities. This study adopts a Monte Carlo simulation approach to model the variability of daily demand and lead time more realistically. Three inventory control methods are evaluated: the Min-Max method, the (s,Q) method, and the (s,S) method, across three demand scenarios: normal, +20% increase, and -15% decrease. Key performance indicators analyzed include Economic Order Quantity (EOQ), Safety Stock (SS), Reorder Point (ROP), total cost, and service level. The simulation was conducted over 851 days to reflect actual operational conditions. The results show that the Min-Max method performed best under the low-demand scenario, with the lowest total cost and no stockouts. The (s,Q) method provided the best balance between ordering frequency, operational cost, and service level in the normal demand scenario. Meanwhile, the (s,S) method demonstrated less efficient performance under the high-demand scenario due to higher stockouts and increased holding costs. These findings recommend adopting inventory control strategies that are adaptive to demand dynamics and consider storage capacity limitations to enhance PT XYZ’s fuel supply chain efficiency and resilience.]]>
