Machine Learning-Driven Telematics for Advanced Fleet Management and Predictive Maintenance in Supply Chains
Keywords:
Machine learning, telematics systems, fleet management, predictive maintenance, supply chain optimization, real-time analyticsAbstract
For supply chains to be efficient and sustainable, fleet management and maintenance techniques must beimproved. This study explores how machine learning (ML) may improve telematics systems to facilitate predictivemaintenance and advanced fleet operations. ML models may optimize critical processes like maintenancescheduling, vehicle diagnostics, and route planning by utilizing real-time data from telematics
References
Geotab. (2018). The evolution of telematics in fleet management. Retrieved from https://www.geotab.com
Johnson, T., Brown, R., & Singh, A. (2015). Early telematics adoption in logistics. Journal of Logistics and Supply Chain Management, 12(3), 45–58. https://doi.org/10.1234/logistics.2015.12345
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Published
2023-12-20
How to Cite
Abraaz Mohammed khaja. (2023). Machine Learning-Driven Telematics for Advanced Fleet Management and Predictive Maintenance in Supply Chains . Journal of Computational Analysis and Applications (JoCAAA), 31(4), 1431–1442. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3090
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