Viewpoints on Robust Supply Chain Network Risk Assessment Using Dynamic Bayesian Networks

Abstract

The text dissects the exploration of employing dynamic Bayesian networks (DBNs) for reliably assessing the risk posed to supply chain networks. A growing recognition of the complexity and intricacy of contemporary supply chain networks has sparked interest in novel methodologies such as DBNs that can model temporal and spatial dependencies effectively. DBNs emerge as a powerful analytical tool enabling probabilistic inference under prevailing uncertainties, capturing both the inherent and external risks. The paper demonstrates how these networks, bestowed with both temporal and causal mathematical frameworks, offer superior analytical traction over traditional methods in understanding the interconnected risk elements. We discuss their ability to appraise the ever-changing vulnerabilities in a supply chain setting, dynamically adapting to modifications brought by stakeholder actions or market changes. As supply chains burgeon in complexity, the role of DBNs in enabling a robust, comprehensive understanding of risk factors becomes increasingly paramount. Thus, the paper advocates for the DBNs' application as a transformative approach to risk assessment, providing significant foresight and adaptability in an unpredictable supply chain landscape.