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Logibot.AI
Subject area: Science,Engineering and Technology · Area of research: Technology of AI
Abstract
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.
Keywords
Artificial Intelligence (AI), Logistics Management, Supply Chain Optimization, Internet of Things (IoT), SaaS (Software as a Service).
References
[1] J. Smith et al., "Intelligent scheduling and for warehouse Al systems: A systematic literature review," Procedia CIRP, vol. 118, pp. 142-147, 2023. [Online]. Available: https://www.sciencedirect.com/journa l/procedia-cirp
[2] R. Sharma and A. Kumar, "The Gap Between Research and Production in Artificial Intelligence for Supply Chain Management," Journal of Logistics Technology, vol. 14, no. 2, pp. 45-59, 2022.
[3] M. G. Epitropakis, “Route Optimization Algorithms in Dynamic Industrial Environments,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 8, pp. 5012-5024, 2021.
[4] L. Zhang, “SaaS-Based Logistics Solutions: Architecture and Scalability using the PERN Stack,” Int. Journal of Computer Applications, vol. 183, no. 42, pp. 9-15, 2021.
[5] P. Gupta, “Real-time Cargo Monitoring and IoT Integration in Freight Management,” Sensors and Actuators A: Physical, vol. 312, Art. No. 112115, 2020.
[6] “IDEATION’25: Ideation for Social Cause,” SIES Graduate School of Technology, Nerul, Navi Mumbai, Sept. 19, 2025. [Conference Presentation].
[7] Kaushal Freight Carriers, “Project Appointment and Deployment Letter for LogiBot.Al Platform Implementation,” Internal Corporate Document, 2025.
[8] PostgreSQL Global Development Group, “PostgreSQL 16 Documentation: ACID Compliance and Transactional Integrity,” 2023. [Online]. Available: https://www.postgresql.org/docs/
[9] Facebook Open Source, “React: A JavaScript library for building user interfaces,” 2023. [Online]. Available: https://react.dev/
How to cite this paper
@article{1715401,
author = {Dipti Tiwari, Sejal Singh, Nabila Khan, Rajashree Sanas, Dr. Rajeshree Rokade},
title = {Logibot.AI},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {9},
pages = {1774-1779},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715401.pdf},
abstract = {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.},
keywords = {Artificial Intelligence (AI), Logistics Management, Supply Chain Optimization, Internet of Things (IoT), SaaS (Software as a Service).},
month = {March},
doi = {https://doi.org/10.64388/IREV9I9-1715401}
}