Evaluating disruption scenarios for improving downstream oil supply chain resilience and cost minimization using Monte Carlo simulation

This study examines the impact of both random and anticipated disruptions on transportation costs within different stages of a downstream oil supply chain. Conducting a comprehensive literature review, a MILP model was developed to simulate a multifaceted refined oil supply chain, integrating refining and import facilities, storage depots, and customer demand nodes. The study unfolds in two phases: a deterministic model establishing a supply chain performance baseline, and a Monte Carlo simulation generating disrup-tion scenarios. Results reveal increased transportation costs and significant flow modifications between en-tities. Imports of refined oil products surged to counter local production shortages, with increased use of cost-effective bulk cargo modes and a notable reliance on road transport to offset disrupted pipelines. The study highlights the substantial impact of disruptions on transportation costs, emphasizing diversified transportation methods where pipelines are constrained. Acknowledging study limitations focusing on a singular supply chain's transport costs, it advocates for research on inventory management and alternate pipeline development to enhance supply chain resilience under disruption scenarios.