A blockchain-based intelligent recommender system framework for enhancing supply chain resilience (2404.00306v3)

Abstract: This research proposed a data-driven supply chain disruption response baseline framework based on intelligent recommender system technology as an initial SCRes reactive solution. To improve the data quality and reliability of the proposed IRS as a stable, secure, and resilient decision support system, blockchain technology is integrated into the baseline architecture. The smart contract is prototyped to demonstrate the information exchange mechanism under a BLC network environment. The BLC-IRS framework is then implemented with an industrial case to demonstrate its executable function. A system dynamics (SD) simulation model is adopted to validate the BLC-IRS framework as an effective digital SCRes enhancement measure. The simulation results indicated that the proposed BLC-IRS framework can be effectively implemented as a SC disruption mitigation measure in the SCRes response phase as reactive measure, enabling SC participants to react better to SC disruptions at the physical level. Compared to previous studies that limited at the conceptual level as the proactive SCRes measure with a standalone fashion, the developed BLC-IRS contributes an executable SCRes digital solution with synthetic technologies as a reactive SCRes measure for the SCRes community, by identifying the internal and external supplementary resource information in an agile, safe, and real-time manner after SC disruption.