Leveraging Importance Sampling for Effective Risk assessment of Supply Chain Networks

Risk assessment has become a crucial aspect of managing supply chain networks due to their inherent complexity and vulnerability to various disruptive events. This research paper focuses on the application of importance sampling as a powerful technique to accurately estimate the reliability of supply chain networks and identify critical areas of risk. The proposed methodology involves two key steps. Firstly, a comprehensive mathematical model is developed to represent the intricate relationships and dynamics within the supply chain network. This model encompasses factors such as demand uncertainty, transportation disruptions, supplier reliability, and inventory management policies. Secondly, importance sampling is employed to efficiently simulate risk scenarios and estimate the likelihood of rare events, such as severe disruptions or critical failures within the network. Importance sampling offers significant advantages over traditional Monte Carlo simulation methods by concentrating computational resources on the events of interest. By selectively sampling rare and extreme scenarios, the approach enables a more accurate estimation of the network's reliability and vulnerability to potential disruptions.