Research on Supply Chain Demand Forecasting and Dynamic Adjustment Model Based on Big Data and Artificial Intelligence

作者：Liubo Hu, Weiqun Han · 发表于：2024 International Conference on Interactive Intelligent Systems and Techniques (IIST) · 年份：2024 · DOI：10.1109/iist62526.2024.00100 · 被引用次数：2

This paper conducts research on supply chain demand forecasting and dynamic adjustment based on big data and artificial intelligence technologies. Firstly, it analyzes the problems and limitations of traditional demand forecasting methods, as well as the challenges of supply chain demand forecasting. Secondly, it proposes the idea and methods of establishing dynamic adjustment models using big data and artificial intelligence technologies. By analyzing and mining massive data, combined with deep learning and neural network algorithms, an efficient and accurate supply chain demand forecasting model is established. Finally, through example verification and case analysis, the paper demonstrates the advantages and value of this model in practical applications. The research results indicate that the supply chain demand forecasting and dynamic adjustment model based on big data and artificial intelligence proposed in this study can effectively enhance the intelligence level of supply chain management, optimize operational efficiency, reduce costs, and improve enterprise competitiveness.