Design of supply chain risk prediction and early warning model based on deep neural network

In this paper, a supply chain risk prediction and early warning model based on deep neural network (DNN) is designed to solve the problem that traditional risk management methods can't cope with the nonlinear risk evolution in a dynamic environment. Supply chain risks are divided into three categories: supply interruption, demand fluctuation and logistics delay, and a multi-task learning framework is constructed. In terms of data construction, 12 types of data sources such as enterprise ERP, economic indicators and social media are integrated to form a tens of millions of sample data sets. In terms of model design, a hybrid neural network based on spatio-temporal attention mechanism (STAN-SCM) is proposed. This network combines Convolutional Neural Networks (CNN) to extract topological features, Bi-LSTM to capture temporal dependencies, and Graph Attention Networks (GAT) to model dynamic node correlations, thereby achieving efficient modeling and prediction of supply chain risks. Experimental results show that STAN-SCM performs the best in terms of average prediction accuracy for the three types of supply chain risks, with a Mean Absolute Error (MAE) of 0.183, a Root Mean Squared Error (RMSE) of 0.236, and an F1-Score of 0.826, significantly outperforming baseline models such as LSTM (Long Short-Term Memory), GraphSAGE, and T-GCN (Spatio-Temporal Graph Convolution). In addition, through the spatio-temporal attention mechanism, the model can accurately identify the key risk propagation paths in the supply chain, and the accuracy of identifying high-risk paths reaches 88.7%, and there is a high positive correlation between attention weight and logistics delay. The experimental results of multi-level early warning mechanism show that the early warning of different risk levels performs well in accuracy and response delay, with the accuracy of emergency level reaching 92.7%, the response delay being 8.3s, and the false alarm rate being only 7.3%. Through the model explanatory analysis of supply interruption risk, it is found that geopolitics, logistics capacity, public opinion and market fluctuation play a key role in supply chain interruption.