Quantifying the Impact of Digital Transformation on Supply Chain Resilience: A Machine Learning-Based SEM Study in China's High-End Manufacturing Sector

In the context of a global business environment marked by volatility and uncertainty, enhancing the resilience of supply chains in high-end manufacturing has become both an urgent necessity and a complex challenge. This study focuses on how digital transformation can improve supply chain resilience (SCR) in high-end manufacturing enterprises. Based on panel data from 2,434 A-share listed high-end manufacturing firms in mainland China from 2014 to 2023, the study constructs and applies Structural Equation Modeling (SEM)—a path modeling approach under the machine learning paradigm—to systematically analyze the mechanisms and causal pathways through which digital transformation influences resilience. Bootstrap resampling is also introduced to verify the robustness of the mediation effects. The empirical results indicate that digital transformation has a significant positive impact on supply chain resilience, primarily through two key mediating pathways: (1) optimizing supply-demand alignment and (2) enhancing suppliers’ innovation capability. Furthermore, the heterogeneity analysis reveals that the impact of digital transformation on SCR is more pronounced in small and medium-sized enterprises and state-owned enterprises, which may be attributed to differences in resource endowments and policy support. These findings not only enrich the theoretical understanding of supply chain resilience in the digital era but also provide empirical support for high-end manufacturing firms to design differentiated digital strategies and strengthen their ability to cope with external disruptions.