International Journal of Science, Engineering and TechnologyAuthors: Aayush Kumar Jha, Abhishek Kumar Rai, Karan Singh, Kartik Dhiman
Abstract: This paper presents the design and implementation of a full-stack platform for predictive logistics management. The system integrates machine learning techniques with modern web technologies to forecast logistics demand and optimize supply chain operations. By utilizing historical logistics data and real-time inputs, the platform enables accurate prediction of delivery demand, efficient resource allocation, and improved operational performance. The system architecture includes frontend interfaces, backend APIs, and a machine learning module for prediction. Experimental results demonstrate improved forecasting accuracy and reduced operational inefficiencies. This research highlights the role of predictive analytics in transforming traditional logistics systems into intelligent, data-driven platforms.
