This study provides data analysis support for the entire enterprise procurement management process, thereby improving the management effectiveness of supply chain operations. It analyzes upstream and downstream industry market status data in the supply chain and various primary data in enterprise management activities. By utilizing the Delphi method to screen and verify multimode market status data indicators, which significantly impact upstream and downstream industries in multiple rounds, 28 types of market status data were selected for analysis. This analysis aimed to investigate the effect of supply chain management on operational decisions within the company. The data reduction method based on adaptive statistics was the most effective in revealing the market status and promoting efficient operation decision-making based on supply chain management. This study also suggests a brand-new technique for measuring supply chain performance based on the Levenberg-Marquardt Back Propagation (LMBP) algorithm, offering a more impartial manner of doing so. The performance evaluation results showed a maximum error level of less than 0.4% when paired with empirical analysis. The proposed optimization model provides strategic guidance for optimizing supply chain management and improving overall performance.