Risk Management assessment of social sustainability in supply chains: a Bayesian Network model proposal

Recently, the social dimension of sustainability has become a focal point for academia and practitioners. Despite this growing attention, several unresolved challenges still exist within supply chain management (SCM), limiting the attainment of satisfactory levels of sustainability. This study fills this gap by proposing a Bayesian Network (BN) model, an Artificial Intelligence (AI) technique, for assessing the risk probability related to the social dimension of sustainability in supplier contracting. This novel approach utilises BN model incorporating the Human Development Index (HDI) and Global Slavery Index (GSI) to ascertain the likelihood of non-compliance risks in social aspects across apparel supply chains. Findings and testing of the BN model in apparel industry revealed that this model represents a proactive tool for decision-makers and practitioners by facilitating the monitoring and control of social burdens across SC tiers. Empirical analysis successfully identified the risk probability levels of non-compliance integrating company-specific data and external sources. The BN model offers a comprehensive understanding of the supply chain to mitigate a company’s risk and leverage its transparency regarding the social dimension of sustainability. The BN model offers an essential resource for practitioners, decision-makers, and academia to address more effectively the social dimension of sustainability in the SCM.