Research on supply chain optimization model based on data mining

With the rapid development of information technology, particularly the widespread application of big data, cloud computing, and the Internet of Things, supply chain management is undergoing a profound transformation. In the context of globalization and digitalization, enterprises face multiple challenges such as changes in market demand, inefficient resource allocation, and low supply chain efficiency. To address these issues, the supply chain optimization model based on data mining has gradually become an important tool for improving supply chain management and enhancing enterprise competitiveness. This study aims to construct a supply chain optimization model based on data mining, using data mining techniques to deeply analyze the massive data across various links in the supply chain, thereby providing intelligent decision support for enterprises. This research proposes a new supply chain optimization framework combining APH (Artificial Potential Field) and fuzzy membership matrix algorithms, aiming to provide more efficient and flexible supply chain solutions for enterprises in a rapidly changing market environment. The supply chain optimization model based on data mining can effectively improve supply chain operational efficiency, reduce operational costs, enhance customer experience, and strengthen the market competitiveness of enterprises.