Application of big data in forecasting logistics demand and resource allocation for cross-border E-commerce

Abstract

The cross-border e-commerce industry is experiencing unprecedented growth, and big data technology has great potential as a key tool to support its operations and management. This paper mainly studies the application and effect of big data in cross-border e-commerce logistics demand forecasting and resource allocation. First, it discusses how big data can improve the forecasting accuracy of cross-border e-commerce logistics demand and conducts empirical research by constructing different forecasting models, including time series analysis and machine learning algorithms. Secondly, it explores the application of big data in logistics resource allocation, including warehouse management, transportation route optimization and supply chain collaboration, and evaluates the effectiveness of resource allocation strategies through mathematical modeling. In addition, the methods of this study are compared with existing research, and the advantages and limitations of big data in this field are discussed, and its implications and significance for practical business are analyzed. The research results show that through big data technology, cross-border e-commerce logistics demand prediction is more accurate and resource allocation is more efficient, but at the same time, it is necessary to pay attention to the challenges in data quality and privacy protection.