Supply Chain Risk Prediction and Management Based on Structural Equation Modeling and Random Forest

Abstract

In order to effectively solve the financial and economic problems of the modern supply chain, this paper designs a combined framework that integrates Structural Equation Modeling (SEM) model and Random Forest (RF) algorithm. In the first part, the thesis applies SEM to empirical data from various industries, directly identifying the causal relationships between risk factors. These findings are then utilized by the RF component, facilitating risk prediction. Through comparative experiments, this SEM-RF integration model has achieved better prediction accuracy, recall and F1 score than the standalone version of SEM and RF. Feature importance analysis reveals that supplier delivery latency, demand volatility and transportation timeliness have the most significant impact on risk. By optimizing management practices according to these conclusions, enterprises can reduce costs and enhance agility. Therefore, this paper provides a new theoretical perspective for supply chain risk forecasting and also offers decision-making support.