A General Framework for Assessing System Resilience Using Bayesian Networks: A Case Study of Sulfuric Acid Manufacturer

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Marketing and Fashion Merchandising


Supply chains play an important role in modern society and national economic development. In recent years, supply chains are more susceptible to variety of disruptive events, including natural disasters, man-made attacks, and common failures due to their complexity, globalization, and interconnected structures. Hence, it is important to design resilient supply chains which are capable of withstanding and recovering rapidly from disruptive events. This paper first explores the key drivers that contribute to the design of resilient supply chains based on the notion of absorptive, adaptive and restorative capacities. Second, it introduces a generic conceptual framework comprising five key phases: threat analysis, resilience capacity design, resilience cost evaluation, resilience quantification, and resilience improvement. The primary challenge to the literature of system resilience is how to measure it qualitatively. Findings from literature indicate that many of the drivers to the system resilience are qualitative such as staff cooperation and collaboration during disruptive events, level of preparation against natural disaster, among others. To fill the gap between qualitative and quantitative assessment of resilience, we employed Bayesian network to quantify the system resilience. Bayesian network is a rigorous tool for measuring risks under uncertainty, representing dependency between causes and effects, and making special types of reasoning. Additionally, it is capable of handling both qualitative and quantitative variables in terms of probability. We implemented Bayesian network for quantifying the supply chain system resilience of sulfuric acid manufacturer in Iran. Different scenarios have been defined and implemented to identify critical variables that are susceptible to the system resilience of sulfuric acid manufacturer.

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Journal of Manufacturing Systems



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