๐Ÿ‡ฒ๐Ÿ‡พAirline Catering Supply Chain Performance During Pandemic Disruption: A Bayesian Network Approach

Fathien Azuien Yusriza, Universiti Kuala Lumpur, Malaysian Institute of Aviation Technology

To be presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 12, 2024.


This study addresses the crucial purpose of enhancing supply chain (SC) efficiency and effectiveness in the face of unforeseen disruptions, particularly examining the impact of the COVID-19 pandemic on the airline catering sector. With a focus on SC performance assessment, the researchers propose and implement Bayesian network (BN) modeling as a strategic tool to measure and quantify the consequences of pandemic disruptions. The study employs forward and backward propagation analysis within the BN model to simulate and measure the impact of different triggers on SC performance and business continuity. The findings provide a valuable theoretical perspective on the use of BNs in pandemic SC disruption modeling, offering insights that can serve as a decision-making tool for predicting and comprehending the effects of pandemics on SC performance. This research contributes to a better understanding of SC dynamics in the context of unforeseen disruptions and provides a foundation for informed decision-making in the airline catering sector amid uncertainties.

About the Presenter

Fathien Azuien Yusriza is a highly skilled professional with a Bachelor's Degree in Aviation Management from Universiti Kuala Lumpur, Malaysian Institute of Aviation Technology (UniKL MIAT), and a Master's degree in Engineering Technology (Aerospace). With a rich background, she has contributed her expertise to global logistics companies, including DHL APSSC and Airbus Malaysia, as a transport analyst. Currently engaged in research focusing on the effectiveness of aviation's supply chain management, she actively contributes to scholarly publications, including journal articles and book chapters, showcasing her commitment to advancing knowledge in the field.

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