Humanitarian relief supply chain performance measurement: A framework and validation

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 has increased drastically. Humanitarian organizations assist in disaster relief operations by planning, sourcing, procuring, transporting, and distributing essential goods and services during emergency operations, known as the humanitarian relief supply chain. This research develops a Bayesian belief network-based framework for predicting the performance of the humanitarian relief supply chain in case of catastrophic events, such as natural disasters and man-made crises. The study begins with identifying performance metrics through factor analysis that directly or indirectly affect 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 demonstrates the interdependencies among the performance metrics within a network setting. The network is constructed through mediating variables by establishing causal relationships among performance metrics and mediating variables. The model has been validated through numerical examples, extreme condition testing, scenario analysis, sensitivity analysis, and diagnostics analysis. Extreme condition tests, diagnostic, and scenario analysis validate the model as reliable and stable. The sensitivity analysis result shows financial performance and monetary support as crucial factors in measuring the performance of the humanitarian relief supply chain. The performance measurement model will assist organizations' decision-makers and policymakers in controlling, monitoring, and enhancing their relief supply chain.