Modeling humanitarian relief supply chain performance using bayesian network approach

Abstract

The concept of humanitarian relief supply chain management has gained a lot of interest among academics and practitioners since the number of natural or human-made disasters have been increased drastically. These disasters frequently cause extensive destruction, loss of life, and collateral damage. Although these events cannot be prevented, appropriate measures can be taken to mitigate their negative effects on nations. Humanitarian organizations provide aid for disaster relief operations, which is known as the humanitarian relief supply chain. Nonetheless, it is essential to comprehend the efficacy of the humanitarian relief supply chain performance measurement model. A humanitarian organization can monitor and control its relief supply chain more efficiently and effectively by measuring performance. The purpose of this research is to develop a Bayesian belief network model for predicting the performance of the humanitarian relief supply chain in case of catastrophic events, such as natural disasters and man-made crises, in order to efficiently deliver assistance to affected regions. The study begins with the identification of performance metrics that have a direct or indirect effect on the overall performance of a humanitarian organization. Then, with the aid of a Bayesian belief network, a probabilistic graphical model capable of predicting any organization's relief supply chain based on performance metrics was developed. The model has been validated through numerical examples, extreme condition testing, scenario analysis, sensitivity analysis, and diagnostics analysis. The performance measurement model will assist organizations' decision-makers and policymakers in controlling, monitoring, and enhancing their relief supply chain.