The logistics and supply chain industry faces persistent challenges regarding operational efficiency, route optimization, and real-time cargo visibility. While Artificial Intelligence (AI) and the Internet of Things (IoT) offer transformative potential, a significant “production gap” remains between theoretical academic prototypes and scalable, real-world implementations. Most existing studies focus on isolated use cases or static datasets, failing to address the dynamic nature of industrial environments. This paper introduces LogiBot.AI, a comprehensive, end-to-end intelligent logistics management platform designed to bridge the gap between research and practical application. Built on the PERN stack (PostgreSQL, Express, React, Node.js), LogiBot.AI functions as a modular Software-as-a-Service (SaaS) solution that integrates ten distinct AI tools. The system moves beyond “black-box” modeling by providing a user-centric interface that facilitates real-time data processing, predictive delivery analytics, and automated decision support. By combining robust backend architecture with intuitive frontend visualization, the platform addresses critical issues such as delivery delays and resource allocation. The implementation results demonstrate that LogiBot.AI successfully translates theoretical AI capabilities into a deployed, scalable system, offering a practical framework for modernizing freight carriers and enhancing operational transparency.
Tiwari et al. (Mon,) studied this question.