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eISSN: 2581-9615 || CODEN (USA): WJARAI || Impact Factor: 8.2 || ISSN Approved Journal

Machine learning-powered shipment tracking: Enhancing logistics efficiency

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Milan Kumar *

Independent Researcher.

Research Article

World Journal of Advanced Research and Reviews, 2025, 27(03), 1316-1320

Article DOI: 10.30574/wjarr.2025.27.3.3246

DOI url: https://doi.org/10.30574/wjarr.2025.27.3.3246

Received on 09 August 2025; revised on 14 September 2025; accepted on 17 September 2025

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into shipment tracking is revolutionizing logistics and supply chain management by improving real-time visibility, predictive analytics, and overall operational efficiency. This article delves into how AI-powered technologies—including IoT, data analytics, and cloud computing—enhance route optimization, inventory management, and demand forecasting, leading to reduced costs and faster deliveries. While AI drives automation and predictive maintenance, challenges such as data security, regulatory compliance, and seamless system integration remain. The discussion also explores practical industry applications, underscoring AI’s pivotal role in creating a smarter, more efficient, and interconnected supply chain ecosystem.

Artificial Intelligence; Machine Learning; Business Intelligence; Predictive Analytics; Shipment Tracking

https://journalwjarr.com/sites/default/files/fulltext_pdf/WJARR-2025-3246.pdf

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Milan Kumar. Machine learning-powered shipment tracking: Enhancing logistics efficiency. World Journal of Advanced Research and Reviews, 2025, 27(03), 1316-1320. Article DOI: https://doi.org/10.30574/wjarr.2025.27.3.3246.

Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0

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