Empirical Analysis of AI-Driven Systems for Identifying Supply Chain Risks and Strengthening Resilience


  • Mark Johnson, Gabriel Girard Department of Computer Science, University of Oxford, Italy


Supply chain, Resilience, AI-driven systems, Risk identification, Empirical analysis, Disruption mitigation


In today's volatile business environment, supply chain resilience is a critical factor for organizations to maintain operational continuity and mitigate disruptions. This study presents an empirical analysis of the efficacy of AI-driven systems in identifying supply chain risks and enhancing resilience. The research methodology involved a comprehensive review of existing literature on supply chain risk management and AI applications, followed by empirical data collection from a diverse range of industries. Leveraging quantitative and qualitative analysis techniques, the study evaluated the performance of AI-driven systems in identifying, assessing, and responding to supply chain risks. The findings reveal that AI-driven systems offer significant advantages in risk detection and mitigation compared to traditional methods. Through advanced data analytics and machine learning algorithms, these systems can proactively identify potential risks across the supply chain network, including supplier disruptions, demand fluctuations, geopolitical instabilities, and natural disasters. Additionally, AI-powered predictive modeling facilitates scenario planning and risk prioritization, enabling businesses to allocate resources efficiently and minimize the impact of disruptions on operations. This empirical analysis underscores the transformative potential of AI-driven systems in strengthening supply chain resilience and mitigating risks in an increasingly complex and dynamic business environment. By leveraging AI technologies effectively, organizations can enhance their adaptive capacity and ensure long-term sustainability in the face of uncertainty and volatility.