A Software-Driven AI Approach to Logistics Process Automation
Article Sidebar
Main Article Content
This paper presents the design and development of an AI-enabled, software-based system for automating logistics op- erations, aiming to upgrade conventional supply chain processes. The solution includes two dedicated mobile applications—one for customers and the other for delivery personnel—along with a comprehensive, web-based admin dashboard. All components are integrated to function cohesively, ensuring efficient management and real-time synchronization across the logistics chain.
The system provides complete lifecycle support for shipments, from initial booking to final delivery confirmation. Key features include user-friendly shipment request forms, automatic identi- fication of pickup and drop-off points via the Google Maps API, and optimized routing through Dijkstra’s algorithm to reduce travel time. Real-time location tracking, powered by continuous GPS updates, enables both clients and administrators to monitor deliveries with accuracy and transparency.
For administrative users, the platform offers a powerful control panel to manage shipment approvals, assign delivery fees, allocate drivers, and oversee real-time progress. To minimize manual intervention, an AI-driven chatbot is integrated to re- spond to common customer queries, including delivery tracking, estimated arrival times, and issue reporting.
A built-in analytics module further enhances decision-making by visualizing key metrics such as delivery volume by location, commonly used routes, cost breakdowns, and preliminary rev- enue estimates. By merging automation, live data processing, and AI-driven insights, the platform delivers a scalable, efficient solution for optimizing logistics operations and modernizing traditional supply chain systems.
Downloads
References
Google Maps API Documentation. [Online]. Available: https://developers.google.com/maps/
Firebase Realtime Database. [Online]. Available: https://firebase.google.com/docs/database
S. Sahni, “Introduction to Dijkstra’s Algorithm,” GeeksforGeeks. [On- line]. Available: https://www.geeksforgeeks.org/dijkstras-shortest-path- algorithm-graph-data-structure/
React Native Documentation. [Online]. Available: https://reactnative.dev/
T. K. Satyanarayana and M. R. Reddy, ”Artificial Intelligence in Supply Chain Logistics,” International Journal of Logistics Systems and Management, vol. 35, no. 1, pp. 29–45, 2022.
S. Kumar and A. Sharma, ”Real-Time Vehicle Routing with Dynamic Traffic Data Using Dijkstra’s Algorithm,” in Proc. IEEE Int. Conf. Intelligent Transport Systems, pp. 340–345, 2021.
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.