Balancing Stockout and Demurrage Risks in Fuel Supply Chains through Monte Carlo Simulation

Fuel supply chains face critical challenges in balancing service reliability and cost efficiency, particularly in emerging markets with limited storage capacity. PT XYZ, a fuel distributor in Timor Leste, experienced two stockout incidents and two demurrage events in 2023, resulting in financial losses and operational disruptions. This study applies Monte Carlo simulation to model demand variability and lead time uncertainty for two key products (Gasoline RON 92 and Gasoil 0.05% Sulphur) over a 851-day horizon. Three inventory policies were evaluated: Min–Max, (s,Q), and (s,S), under three demand scenarios (normal, +20%, −15%). Performance indicators included total cost, service level, stockout days, and demurrage exposure. The results highlight significant trade-offs. The Min–Max policy consistently minimized stockout to below 1% but incurred high holding costs, reaching IDR 31.1 billion. The (s,Q) policy achieved the lowest total cost (IDR 143.1 billion) but suffered from high stockout risk, up to 35.25% under high demand. The (s,S) policy offered a balanced approach: for Gasoline RON 92 in the low-demand scenario, it completely eliminated stockout (0.00%) and demurrage, while in Gasoil under normal demand, stockout reached 25.38%. This study demonstrates that no single policy dominates across scenarios; instead, managers must weigh the trade-off between stockout and demurrage risks. The findings suggest that (s,S) provides a flexible baseline policy, with adaptive switching to Min–Max or (s,Q) depending on demand conditions. The results offer practical insights for fuel supply chain decision-makers facing uncertainty in demand and capacity constraints.