Cross-border e-commerce logistics management system based on artificial intelligence and multi-source data

E-commerce’s growing globalization has made it difficult for SMEs to manage complex, unpredictable cross-border logistics networks. Quick decision-making in unstable global marketplaces, nonlinear data dynamics, and demand uncertainty make conventional logistics optimization and evaluation difficult. The research introduced a quantum-powered optimization model, QuantumLogiAI, powered by AI federated learning, which addresses these restrictions in logistics optimization. The proposed distributed data framework enables predictive logistics features, including route optimization, demand forecasting, and global inventory management. Shipping companies, customs authorities, and warehouses provide this data. Federated learning enables stakeholders to train models in a safe, shared environment, eliminating the need for centralized data infrastructure. Quantum-assisted optimization improves trade flexibility by tackling large-scale combinatorial logistics challenges. According to experiments on real-world cross-border e-commerce datasets, QuantumLogiAI effectively manages international trade fluctuations, is scalable, and boosts routing flexibility, delivery speed, and accuracy.