A Data-Driven Intelligent Supply Chain Disruption Response Recommender System Framework

In light of the Industry 4.0 era, the global pandemic, and wars, interest in deploying digital technologies to increase supply chain resilience (SCRes) is rising. The utilization of recommender systems as a supply chain (SC) resilience measure is neglected, although these systems can enhance SC resilience. To address this problem, this research proposed a data-driven supply chain disruption response framework based on intelligent recommender system techniques and implemented the framework with a practical use case. The framework was validated by a System Dynamics (SD) model to demonstrate the effectiveness of the proposed system as a new communication scheme after a SC disruption, considering a demonstrative use case. Results show that the proposed framework can be implemented as an effective SC disruption mitigation measure in the SCRes response phase and help SC participants better react after the SC disruption.