Integrating AI into Smart Logistics Management: A Comprehensive Review

This paper provides a comprehensive and systematic review of artificial intelligence (AI) integration in smart logistics management, evaluating seven core technology clusters: machine learning (ML), deep learning (DL), natural language processing (NLP), computer vision (CV), internet of things (IoT), blockchain, and data mining. While the prior literature reviews analyze these technologies in isolation, this study directly addresses the critical research gap of technology fragmentation and integration challenges across the supply chain. Our main contribution is a novel, three-layered conceptual framework that structures smart logistics into interdependent layers: data collection (IoT, RFID, GPS), intelligent processing (ML/DL, NLP, computer vision), and logistics decision-making (route optimization, warehouse automation, risk management). By detailing the theoretical foundations (information processing, dynamic capabilities, and Cybernetics), inter-module correlations, and a phased four-stage deployment roadmap, this review provides a unified, practical blueprint for organizations transitioning from legacy systems to fully autonomous, cognitive logistics networks.