Improving Supply Chain Resilience with Machine Learning: A Focus on Fleet Vehicles and Telematic
Keywords:
Machine learning, supply chain resilience, fleet management, telematics data, predictive analytics, operational efficiencyAbstract
In order to handle operational uncertainties and disruptions, supply chains' resilience is becoming more and more
important. The goal of this research is to improve supply chain resilience by applying machine learning (ML)
techniques, particularly in the fields of telematics and fleet vehicle management. ML models facilitate predictive
analytics for risk mitigation, maintenance forecasts, and route optimization
References
Christopher, M., & Peck, H. (2004). Building the resilient supply chain. The International Journal of Logistics Management, 15(2), 1-14.
Sheffi, Y., & Rice, J. B. (2005). A supply chain view of the resilient enterprise. MIT Sloan Management Review, 47(1), 41-48.
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Published
2025-04-16
How to Cite
Abraaz Mohammed khaja. (2025). Improving Supply Chain Resilience with Machine Learning: A Focus on Fleet Vehicles and Telematic. Journal of Computational Analysis and Applications (JoCAAA), 34(4), 1175–1186. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3088
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