ISSN: 2582-8266 (Online) || ISSN Approved Journal || Google Scholar Indexed || Impact Factor: 9.48 || Crossref DOI
Anticipating supply chain disruptions with graph AI models
Department of Industrial Engineering, Faculty of Engineering, Texas A&M University, Kingsville, Texas. United States of America.
Review
World Journal of Advanced Engineering Technology and Sciences, 2022, 07(01), 241-244.
Article DOI: 10.30574/wjaets.2022.7.1.0095
Publication history:
Received on 28 August 2022; revised on 25 October 2022; accepted on 28 October 2022
Abstract:
Supply chain networks are increasingly complex and interconnected, making them vulnerable to disruptions caused by natural disasters, geopolitical tensions, cyberattacks, and market volatility. Traditional forecasting and risk management techniques often fall short in dynamically capturing the multi-relational and non-linear dependencies within these networks. This paper explores the role of Graph AI models—particularly Graph Neural Networks (GNNs)—in modeling, predicting, and mitigating supply chain disruptions. We propose a framework for integrating Graph AI into supply chain operations, emphasizing the significance of topological insights, data heterogeneity, real-time analytics, and adaptive learning. By referencing recent advances and empirical findings, we outline a path for deploying Graph AI as a strategic asset in resilient and intelligent supply chain management.
Keywords:
Graph AI; Supply Chain Disruptions; Graph Neural Networks; Supply Chain Resilience; Supply Chain Risk Management; Disruption Forecasting
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0