The supply chain (SC) encompasses all actions related to meeting customer requests and transferring materials upstream to meet those demands. Organisations must operate towards increasing SC efficiency and effectiveness to meet SC objectives. Although most businesses expected the COVID-19 pandemic to severely negatively impact their SCs, they did not know how to model disruptions or their effects on performance in the event of a pandemic, leading to delayed responses, an incomplete understanding of the pandemic’s effects and late deployment of recovery measures. This paper presents a method for modelling and quantifying SC performance assessment for airline catering. In the COVID-19 context, the researchers proposed a Bayesian network (BN) model to measure SC performance and risk events and quantify the consequences of pandemic disruptions. The research simulates and measures the impact of different triggers on SC performance and business continuity using forward and backward propagation analysis, among other BN features, enabling us to combine various SC perspectives and explicitly account for pandemic scenarios. This study’s findings offer a fresh theoretical perspective on the use of BNs in pandemic SC disruption modelling. The findings can be used as a decision-making tool to predict and better understand how pandemics affect SC performance.Airline Catering Supply Chain Performance during Pandemic Disruption: A Bayesian Network Modelling ApproachacceptedVersio
