This study systematically examines the integration of Artificial Intelligence (AI) technologies into logistics and supply chain processes from a holistic, process-oriented perspective within the framework of Smart Logistics. A Systematic Literature Review (SLR) was conducted on 142 peer-reviewed studies indexed in the Web of Science (WoS) and Scopus databases and published between 2020 and 2026. The selected studies were analyzed using thematic content analysis to identify the principal operational benefits, implementation challenges, and governance requirements associated with AI adoption in logistics. The synthesized evidence indicates that AI applications are associated with reductions of 10%–30% in logistics operating costs, improvements of 15%–40% in delivery lead times, and increases of 20%–50% in inventory accuracy. The reviewed studies further report improvements of 20%–35% in demand forecasting accuracy, fuel-efficiency gains of 10%–25% through AI-supported vehicle-routing approaches, and increases of up to 30% in warehouse operational throughput. Despite these benefits, AI adoption is constrained by substantial initial investment requirements, inadequate data quality and standardization, cybersecurity vulnerabilities, fragmented and poorly integrated software systems, and shortages of specialized human expertise. Based on the synthesized findings, this study proposes a strategic roadmap and a hybrid Human-in-the-Loop (HITL) governance framework intended to support more coordinated, transparent, and responsible AI integration across logistics and supply chain operations.
This study systematically examines the integration of Artificial Intelligence (AI) technologies into logistics and supply chain processes from a holistic, process-oriented perspective within the framework of Smart Logistics. A Systematic Literature Review (SLR) was conducted on 142 peer-reviewed studies indexed in the Web of Science (WoS) and Scopus databases and published between 2020 and 2026. The selected studies were analyzed using thematic content analysis to identify the principal operational benefits, implementation challenges, and governance requirements associated with AI adoption in logistics. The synthesized evidence indicates that AI applications are associated with reductions of 10%–30% in logistics operating costs, improvements of 15%–40% in delivery lead times, and increases of 20%–50% in inventory accuracy. The reviewed studies further report improvements of 20%–35% in demand forecasting accuracy, fuel-efficiency gains of 10%–25% through AI-supported vehicle-routing approaches, and increases of up to 30% in warehouse operational throughput. Despite these benefits, AI adoption is constrained by substantial initial investment requirements, inadequate data quality and standardization, cybersecurity vulnerabilities, fragmented and poorly integrated software systems, and shortages of specialized human expertise. Based on the synthesized findings, this study proposes a strategic roadmap and a hybrid Human-in-the-Loop (HITL) governance framework intended to support more coordinated, transparent, and responsible AI integration across logistics and supply chain operations.
