Applying Machine Learning for Fleet Transportation Optimization and Trailer IoT Insights in Supply Chains
Keywords:
Fleet Optimization, Trailer IoT, Supply Chain Management, Machine Learning, Predictive Maintenance, Telematics SystemsAbstract
For contemporary supply chain operations, effective fleet mobility and trailer management are essential. There are many chances to improve decision-making, save expenses, and increase performance by incorporating Machine Learning (ML) approaches into various fields. In order to solve issues including route planning, fuel efficiency, and predictive maintenance, this study investigates the use of machine learning (ML) models in fleet transportation optimization and trailer IoT data analysis. The study demonstrates how machine learning algorithms analyze real-time Internet of Things data to produce insights that can be put to use, allowing for preventive steps to reduce operational disturbances and downtime. Additionally, the study looks at how data-driven solutions affect supply chain effectiveness, highlighting how telematics systems can provide accurate tracking and monitoring. The results show that by optimizing resource use and minimizing environmental effects, ML-based techniques not only simplify fleet and trailer operations but also support the sustainability of supply chain networks. For industries looking to use sophisticated analytics to modernize their logistics processes, the suggested architecture provides a scalable solution.
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