Machine learning applications in early warning systems for supply chain disruptions: strategies for adapting to risk, pandemics and enhancing business resilience and economic stability

Kasali, Kemisola and Olawore, Abiola O. and Raji, Adeola Noheemot (2025) Machine learning applications in early warning systems for supply chain disruptions: strategies for adapting to risk, pandemics and enhancing business resilience and economic stability. International Journal of Science and Research Archive, 15 (2). pp. 1829-1845. ISSN 2582-8185

Supply chains face unprecedented disruptions from cascading challenges such as pandemics, geopolitical tensions, and natural disasters, which pose significant risks to operational continuity and economic stability. This research examines the transformative role of machine learning-driven early warning systems in enhancing business resilience through predictive capabilities while supporting economic stability. Systematic analysis of evidence from literature and industry reports reveals machine learning (ML) models achieve up to 41% improvement in forecast accuracy and 15% reduction in supply chain costs, offering crucial lead time for proactive mitigation strategies before disruptions escalate. Organizations adopting predictive analytics with automated machine learning (AutoML) experience up to 35% reduction in disruptions, strengthening resilience against future challenges. The framework presented combines real-time data processing with ensemble learning to identify risks, evaluate impacts, and deliver actionable insights to stakeholders. Strategic recommendations include investing in predictive technologies, improving data infrastructure, promoting cross-industry collaboration, and supporting policy reforms to increase ML-based EWS adoption for long-term operational stability and economic security.