Full-Chain Intelligent Supply Decision Model and Optimization Algorithm based on Multi-Source Data Fusion

With the rapid development of information technology, enterprise supply chain management has ushered in new challenges and opportunities. In response to the above challenges, this paper designs and implements a full-chain intelligent supply decision model and optimization algorithm based on multi-source data fusion(MSDF). Through data preprocessing and feature extraction technology, effective integration of multi-source data is achieved. On this basis, this paper constructs a full-chain intelligent supply decision model, uses machine learning algorithms to analyze the historical data of supplier delivery time and current production capacity data, reasonably arranges inventory levels, reduces losses caused by delayed replenishment or inventory backlogs, and thus accurately predicts demand and optimizes inventory management. Experimental data show that compared with traditional methods, the demand forecast accuracy of MSDF is about 0.98, while the inventory turnover rate is about 1%-5.3%, and the overall performance is more balanced and robust.