A Technical Review: AI-Driven Risk Management & Decision Support: The Road Ahead for Autonomous Supply Chains
Year: 2025
Type: article
Abstract: The contemporary supply chain landscape is experiencing a fundamental transformation driven by artificial intelligence technologies and their integration into autonomous operational frameworks. This technical review examines the evolution of AI-driven risk management and decision support systems within supply chain ecosystems, highlighting how intelligent automation is reshaping traditional supply chain management paradigms. The integration of AI-powered Application Programming Interfaces serves as the foundational infrastructure for autonomous supply chain ecosystems, enabling real-time communication and decision-making across diverse stakeholder networks. Machine learning models revolutionize supply chain risk management by enabling proactive identification and classification of potential disruptions, while predictive analytics implementations demonstrate substantial improvements in risk prediction accuracy and response times. The convergence of AI, IoT, blockchain, and 5G technologies creates hyper-connected networks that deliver unprecedented levels of visibility, control, and operational efficiency. Organizations implementing AI-driven supply chain management systems gain significant competitive advantages through cost efficiency improvements, enhanced responsiveness capabilities, and quality improvement initiatives. The transformation extends beyond operational efficiency to encompass strategic competitive advantages that fundamentally reshape industry dynamics. Future supply chains will operate with minimal human intervention through autonomous operations, dynamic adaptation capabilities, and personalized customer experiences enabled by advanced AI systems processing extensive customer interaction data to generate tailored recommendations.
Author: Shikha Duttyal
Institution: Northern Illinois University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6893599744
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