Travel Time Prediction in Supply Chain Management Using Machine Learning

Abstract:The purpose of this research is to find data and methods using machine learning and deep learning to correctly predict the estimated travel time for transportation and logistics in a supply chain system. The supply chain ecosystem is very complex and heavily relies on the transportation and logistics of raw materials and finished goods. Accurate travel time estimation is critical because it helps supply chain members to improve logistics consistency and performance. This helps in planning, demand forecasting, lead time management and assembly planning. The logistics on the delivery side of the customer also plays a crucial role in customer satisfaction and voice of customer. With the collection of huge historical data and using novel techniques, the research builds an accurate model to predict travel time of inventory.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.38190 [cs.LG]

(or arXiv:2609.38190v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.38190 Focus to learn more arXiv-issued DOI via DataCite

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