Mapping disruption management solutions in Closed-Loop Supply Chains leveraging Industry 4.0 technologies

Uncertainty mapping and control in circular models has been proven to be multi-driven and holonic-oriented. Closed-Loop Supply Chains (CLSCs), involving reverse logistics structures and operations, are significantly impacted by variability in return quantity, timing and quality. Such variability frequently results in inefficiencies and value losses, manifesting as ripple and bullwhip effects that disrupt value creation and undermines supply resilience. Such disruptions impact both upstream and downstream supply agents, i.e., managers and consumers. Recent advancements in Industry 4.0 technologies, such as Artificial Intelligence, Machine Learning, Big Data analytics and the Internet of Things, provide promising solutions to enhance dynamics prediction accuracy and supply resilience. This study performs a two-steps literature analysis, where results from a bibliometric study provide the foundation for a subsequent state-of-art overview. The aim is to identify and integrate the most effective I4.0 technologies and tools to mitigate risks associated with ripple and bullwhip effects within CLSCs. The benefits of disruption risk mitigation are underscored for both upstream and downstream supply chain actors, summarized within a comprehensive 2-layers framework designed to support both circular supply chain dynamics stabilization in case of operational disruptions and system resilience in case of structural attacks.