Design of an enhanced fuzzy neural network-based high-dimensional information decision-making model for supply chain management in intelligent warehouses

Purpose This paper aims to optimize supply chain information decision-making systems to better manage complex, high-dimensional and uncertain information through the integration of fuzzy logic and neural network technology. Design/methodology/approach A framework based on fuzzy logic reasoning is developed to address empirical issues in traditional supply chain systems. Subsequently, an innovative radial basis function-dynamic fuzzy neural network (RBF-DFNN) model is constructed, enhancing the system’s capability to interpret uncertain information. This model retains the advantages of traditional dynamic fuzzy neural networks (DFNN) while introducing an anti-fuzzy layer and optimizing the membership function and T-paradigm layers. Findings The RBF-DFNN model leads to the creation of a high-dimensional information decision-making model for supply chains. Experimental results indicate that this model effectively utilizes the K-medoids clustering algorithm to accurately capture the high-dimensional characteristics and intrinsic correlations of supply chain data. Parameter optimization significantly improves the model’s performance, with the root mean squared error (RMSE) and mean absolute error (MAE) enhanced, resulting in coefficients of determination rising from 95.6 and 97.8–99.1% compared to STPF-AIMM and ANFIS networks. Originality/value This study contributes to the advancement of supply chain management by developing a highly intelligent and refined decision-making model, enhancing the intelligence level of intelligent storage systems and promoting more sophisticated supply chain operations.