Interpretable Neural Network-Based Early Warning of Proxy-Based Supply Chain Disruption Vulnerability: Evidence from Cross-Border Equipment Manufacturing Enterprises in Shandong, China

Cross-border equipment manufacturers in Shandong are under growing pressure to maintain supply chain continuity and long-term sustainability amid geopolitical uncertainty and industrial restructuring. Using quarterly data for 149 listed firms from 2001Q1 to 2024Q3, this study develops an interpretable early-warning model for firms’ relative vulnerability. Because firm-level disruption events are not consistently observable, vulnerability is proxied by return-on-assets underperformance relative to the industry median. We compare a multilayer perceptron (MLP) with logistic regression, decision tree, random forest, XGBoost, and LightGBM, and then use Shapley additive explanations (SHAP) to interpret the selected model. Under the study’s F1-oriented early-warning objective, the multilayer perceptron achieves the highest observed F1 score (the harmonic mean of precision and recall) in our evaluation setting, whereas XGBoost performs slightly better on threshold-independent ranking metrics. The interpretation results show that stronger profitability is associated with lower predicted vulnerability, policy-backed export demand with greater stability, and India-related geopolitical risk with higher predicted vulnerability. These findings suggest that interpretable early-warning tools may help support continuity-oriented operations, resilience investment, and sustainability-oriented industrial upgrading in export-dependent manufacturing regions.