Research on Supply Chain Optimization Strategy Based on Big Data Technology

This study focuses on supply chain optimization strategies based on big data technology, aiming to address issues such as low efficiency and slow response in supply chain management by integrating advanced data processing techniques and machine learning algorithms. The study aims to construct an integrated system architecture that effectively collects, processes, and analyzes large amounts of data in the supply chain, and uses BP neural network and random forest algorithm for profit prediction, providing scientific basis for supply chain adjustment and optimization. By performing detailed preprocessing on the data, including data cleaning and feature engineering, the quality of the model training data is ensured. Strict cross validation strategies were adopted during the model training and validation process, enhancing the model's generalization ability and prediction accuracy. The experimental results showed that the BP neural network and random forest algorithm achieved prediction accuracies of 88.4% and 85.8%, respectively, verifying the effectiveness and reliability of the framework in supply chain optimization, and providing strong support and guidance for the intelligent transformation of supply chain management. Future research will further explore measures to ensure data quality and security, deepen technological integration, and explore the possibility of cross disciplinary cooperation to respond to constantly changing market demands and technological advancements.