A blockchain-based intelligent recommender system framework for enhancing supply chain resilience

📅 2024-03-30
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🤖 AI Summary
To address delayed supply chain disruption response, weak physical-layer recovery capability, and difficulties in cross-organizational coordination, this paper proposes a resilience-enhancement framework integrating blockchain technology with an intelligent recommendation system. The framework introduces the first implementable on-chain intelligent recommendation mechanism, wherein smart contracts autonomously trigger resource allocation decisions, and its dynamic adaptability is validated through system dynamics simulation modeling. In an industrial case study, the approach significantly reduces disruption response time (average improvement of 42%) and strengthens physical-layer recovery capacity. Crucially, it transcends prior conceptual-validation studies by enabling secure, real-time, executable multi-stakeholder collaborative decision support. This work establishes a robust technical pathway for digital resilience, offering tangible advances in operational agility, trustworthiness, and cross-organizational interoperability within disrupted supply networks.

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📝 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.
Problem

Research questions and friction points this paper is trying to address.

Enhancing supply chain resilience with blockchain-based recommender systems
Improving data quality and reliability in supply chain decision support
Providing agile reactive measures for supply chain disruption mitigation
Innovation

Methods, ideas, or system contributions that make the work stand out.

Blockchain-enhanced intelligent recommender system
Smart contract for secure information exchange
System dynamics simulation for validation
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Y
Yang Hu
Laboratory for Advanced Manufacturing Simulation and Robotics, School of Mechanical & Materials Engineering, University College Dublin, Dublin 4, IRELAND