An assessment of probabilistic disaster in the oil and gas supply chain leveraging Bayesian belief network

Nazmus Sakib, Niamat Ullah Ibne Hossain, Farjana Nur, Srinivas Talluri, Raed Jaradat and Jeanne Marie Lawrence

International Journal of Production Economics, 2021, vol. 235, issue C

Abstract: The oil and gas supply chain (OGSC) is considered to have one of the most significant stakes in the U.S. economy because of its interconnectedness with supply chains in other sectors, such as health and medicine, food, heavy manufacturing, and services. While oil and gas development is expanding exponentially, various factors ranging from man-made to natural disasters can hinder OGSC processes, which, in turn, can result in inefficient and costly operations in other sectors. This study presents a Bayesian Network (BN) model to predict and assess disasters in the OGSC based on seven main factors: technical, economic, social, political, safety, environmental, and legal. BBN is a probabilistic graphical model that is predominantly used in risk analysis to illustrate and assess probabilistic relationships among different variables. To draw meaningful managerial insights into the proposed model, sensitivity analysis and belief propagation are used. The results indicate that of the seven factors responsible for OGSC disasters, technical factors have the highest impact while legal and political factors have the lowest.

Keywords: Oil and gas; Supply chain; Disaster assessment; Bayesian network; Resilience (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:235:y:2021:i:c:s0925527321000839

DOI: 10.1016/j.ijpe.2021.108107

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