Supply chain chronic losses and risk interdependencies: a Bayesian Belief Network approach

Supply Chain Risk Management (SCRM) has become integral to firm governance, and risk interdependencies, despite limited attention, significantly impact risk analysis and mitigation. Recurrent risks, resulting from consistent disturbances, generate chronic losses and are critically affected by other related risks. This work enhances the assessment of chronic losses with interdependent risks, building upon an existing Bayesian Belief Network (BBN) approach for risk interdependencies in SCRM, combining expected utility theory to address the complex, interconnected nature of risks. This work advocates a change in existing approaches to properly evaluate chronic losses often underestimated due to decision makers' expectations. The research shows that the impact of recurrent risks may be underestimated by other approaches based on their frequency, undermining confidence in mitigation strategies. This work not only presents a novel approach to risk assessment but also highlights the importance of studying causal interdependencies, and emphasises the need to consider low-probability and high-impact risks but also recurrent, seemingly minor risks in SCRM processes.