Current and future applications of AI in logistics FIGURE 6 Current applications: Digital twins Demand prediction for inventory allocation Route optimization Predictive maintenance Supply chain resilience testing Identification of bottlenecks Future applications: Autonomous decision-making Dynamic rerouting Adaptive live restructuring of supply chains Optimized inventory levels across networks Mitigation of bottlenecksCurrent applications: Real-time tracking Condition monitoring ETA prediction Facility-level Scope 3 emission measurement Future applications: Real-time shipment visibility Augmented smart containers (IoT) Global Scope 3 emission measurement Current applications: Autonomous vessels Smart warehouses Customer service chatbots Future applications: Autonomous fleets for seamless delivery Connected smart warehouses Intelligent virtual assistants Predictive analytics and simulation End-to-end visibility Autonomous operations End-to-end visibility: AI is enhancing supply chain transparency through real-time tracking and monitoring of shipments. By integrating AI with GPS and IoT sensors, logistics providers can determine the exact locations and conditions of goods, reducing transit losses. Improved end-to-end visibility can also pave the way for new services, ranging from trade compliance to supply chain resilience testing. Automation and autonomous operations: AI can streamline routine tasks, from document automation to autonomous vessel navigation. Adopting automation in targeted sectors can reduce human error and free up human resources for more strategic activities. Picking, packing and inventory management represent tasks that AI- powered robots have mastered to produce overall gains in efficiency and accuracy. An emerging frontier involves the use of AI to power chatbots and virtual assistants for routine inquiries and shipment updates. Additionally, AI is enabling the development of self-navigating vessels that use a combination of sensors, GPS and ML algorithms to plot optimal routes, avoid collisions and adapt to changing weather conditions. Overall, the successful integration of AI in logistics will require a collaborative effort among all stakeholders in the supply chain, including shipping companies, technology providers, regulators and standard-setting bodies. Artificial Intelligence for Efficiency, Sustainability and Inclusivity in TradeTech 18

Artificial Intelligence for Efficiency Sustainability and Inclusivity in TradeTech 2025