Predicting the Investment Risk in Supply Chain Management Using BPNN and Machine Learning

Abstract

The present work is aimed at solving the investment risks in the supply chain management (SCM) process of enterprises. Therefore, the Back Propagation Neural Network (BPNN) algorithm, logistic regression analysis, and other related theories are used for the risk prediction analysis of supply chain samples. Firstly, 40 pieces of supply chain training data are collected as research samples. Secondly, the examples are trained using the BPNN algorithm. Meanwhile, a logistic regression model is constructed based on Principal Component Analysis (PCA). Finally, the two models conduct risk prediction on the test samples. The results indicate that the BPNN model can effectively predict various risks in the SCM process. It achieves an excellent evaluation effect of single risks, consistent with the actual results. Still, there are some deviations between the prediction results and the actual results of mixed risks. When the significance P value is more than 0.5, the sample is predicted to be of high risk. When it is less than 0.5, the sample is predicted to be of low risk. The prediction accuracy of the logistic regression model is as high as 92.8%, demonstrating brilliant applicability and popularization in the investment risk prediction of the supply chain. The BPNN algorithm and logistic regression model can precisely predict the investment risk in SCM and provide a reference for the improved SCM and the sustainable and stable development of enterprises in the supply chain.