Digital Twin Empowers Electric Vehicle Supply Chain Resilience

To reveal how digital twin empowers electric vehicle supply chain resilience, this study first proposes a novel “Human–Machine–Material–Environment” system architecture. Then, it employs dynamic fsQCA on data from 27 electric vehicle companies to explore the underlying configurational mechanisms. The results reveal that digital twin empowers electric vehicle supply chain resilience not through singular factors, but through multiple, equally effective configurations of its core dimensions. This study identifies six types of high-resilience pathways, such as “dual-driven by twin and safety” and “comprehensive upgrade digital twin”. This demonstrates that no universal best pathway exists. This finding of equifinality is complemented by causal asymmetry, as the paths leading to non-high resilience are not mere opposites of the successful ones. Across time periods, data security management, human–machine collaboration, and digital twin applications consistently emerge as core prerequisites for improving supply chain resilience. By introducing digital twin, this study expands the theoretical boundaries of electric vehicle supply chain resilience research and provides new analytical perspectives and frameworks.