A IoT-Based Framework for Cross-Border E-Commerce Supply Chain Using Machine Learning and Optimization

With the expansion of communication technologies and their impact on trade patterns, e-commerce strategies have also undergone significant changes. Adapting to these changes necessitates the utilization of artificial intelligence techniques to automate a wide range of processes and further develop e-commerce. This article employs a combination of machine learning techniques and multi-objective optimization to enhance the supply chain performance in Cross-Border E-Commerce (CBEC). To achieve this, a framework for intelligent CBEC based on Internet of Things (IoT) technology is proposed. By deploying machine learning models within this framework, efforts are made to improve supply chain performance through demand volume prediction. The predictive model used in the proposed method is an ensemble system based on Adaptive Neuro-Fuzzy Inference System (ANFIS), which employs weighted averaging to predict demand volume for each retail unit. The configuration of this prediction model is done at two levels, utilizing Particle Swarm Optimization. At the first level, the hyperparameters of each ANFIS model are optimized, and at the second level, the weight values of each learning component are optimized using this algorithm. The performance of this predictive model in enhancing the CBEC supply chain structure is evaluated using real-world data. Based on the results, the proposed predictive model achieves an average absolute error of 2.54 in demand volume prediction, showcasing a minimum reduction of 8.58% compared to previous research. Moreover, the improvement in supply chain performance through this model will lead to reduced delays and increased efficiency in CBEC, demonstrating the effectiveness of the proposed model.