American Journal of Advanced Multidisciplinary Research and Innovation

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Artificial Intelligence-Enabled Smart Logistics and Last-Mile Delivery Systems

Author(s) Gary P. Pisano
Country United States
Abstract The rapid growth of e-commerce, urbanisation, global supply chains, and customer expectations for faster deliveries has transformed logistics and last-mile distribution into strategic components of modern supply chain management. Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), Big Data Analytics, Cloud Computing, Robotics, Autonomous Vehicles, Digital Twins, Geographic Information Systems (GIS), and Blockchain have emerged as key technologies enabling intelligent logistics operations. AI-driven smart logistics systems optimise transportation routes, warehouse operations, fleet management, demand forecasting, inventory control, and last-mile delivery while improving operational efficiency, sustainability, and customer satisfaction.
This study presents a comprehensive analysis of Artificial Intelligence-enabled smart logistics and last-mile delivery systems. A qualitative analytical research methodology based on secondary data is employed to examine AI-based route optimisation, intelligent warehouse automation, predictive logistics analytics, autonomous delivery technologies, drone-assisted distribution, real-time fleet management, and smart supply chain ecosystems. The research evaluates how AI improves delivery accuracy, cost efficiency, resource utilisation, environmental sustainability, and customer experience.
The findings indicate that AI-powered logistics significantly enhances delivery speed, route optimisation, warehouse productivity, inventory visibility, predictive maintenance, and real-time operational decision-making. Deep learning algorithms support demand forecasting, traffic prediction, vehicle scheduling, package tracking, and anomaly detection, while IoT-enabled sensors provide continuous monitoring of vehicles, cargo, and environmental conditions. Furthermore, Digital Twins and cloud-based logistics platforms enable simulation, optimisation, and resilient supply chain management.
Despite these opportunities, challenges remain concerning cybersecurity, data privacy, infrastructure investment, interoperability, regulatory constraints, workforce adaptation, algorithm transparency, and environmental impacts associated with increasing delivery volumes. Future research should investigate explainable AI for logistics planning, federated logistics data platforms, collaborative autonomous delivery systems, green logistics optimisation, and AI-driven urban freight management.
The study concludes that Artificial Intelligence-enabled smart logistics represents a transformative approach for achieving intelligent, sustainable, and customer-centric supply chain operations by integrating advanced digital technologies with data-driven decision-making and automated logistics processes.
Keywords Artificial Intelligence, Smart Logistics, Last-Mile Delivery, Supply Chain Management, Internet of Things, Machine Learning, Digital Twin, Warehouse Automation, Intelligent Transportation.
Field Engineering
Published In Volume 1, Issue 6, November-December 2019
Published On 2019-12-02
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