E-Commerce Logistics and Supply Chain Network Optimization for Cross-Border

Abstract

E-commerce is a growing industry that primarily relies on websites to provide services and products to businesses and customers. As a brand-new international trade, cross-border e-commerce offers numerous benefits, including increased accessibility. Even though cross-border e-commerce has a bright future, managing the global supply chain is crucial to surviving the competitive pressure and growing steadily. Traditional purchase volume forecasting uses time-series data and a straightforward prediction methodology. Numerous customer consumption habits, including the number of products or services, product collections, and taxpayer subsidies, influence the platform's sale quantity. The use of the EC supply chain has expanded significantly in the past few years because of the economy's recent rapid growth. The proposed method develops a Short-Term Demand-based Deep Neural Network and Cold Supply Chain Optimization method for predicting commodity purchase volume. The deep neural network technique suggests a cold supply chain demand forecasting framework centred on multilayer Bayesian networks (BNN) to forecast the short-term demand for e-commerce goods. The cold supply chain (CS) optimisation method determines the optimised management inventory. The research findings demonstrate that this study considers various influencing factors and chooses an appropriate forecasting technique. The proposed method outperforms 96.35% of Accuracy, 97% of Precision and 94.89% of Recall.