AI-Driven Predictive Analytics for Supply Chain Risk Management - Verma, Pradeep (MZ Journal of Artificial Intelligence, 2024)
AI-driven predictive analytics revolutionizes supply chain risk management by offering advanced tools for anticipating and mitigating potential disruptions. This approach leverages artificial intelligence (AI) and machine learning (ML) algorithms to analyze historical data, detect patterns, and forecast future risks more accurately than traditional methods. AI-driven predictive models enable organizations to proactively address issues like supply shortages, demand fluctuations, and operational bottlenecks by integrating real-time data from various sources, such as market trends, supplier performance, and geopolitical events. This abstract explores the methodologies and technologies underpinning AI-driven predictive analytics, discusses its impact on enhancing supply chain resilience, and presents case studies illustrating successful implementations. The benefits of improved risk visibility and proactive decision-making are highlighted, alongside challenges such as data quality and model interpretability.