Inferring Supply Chain Plasticity from a Social–Ecological Systems Perspective: A Regime-Conditioned Probabilistic Framework

Supply chain plasticity (SCP) is a critical dynamic capacity through which firms respond to disruptions by reconfiguring their supply chains. Its latent, regime-dependent nature makes SCP difficult to identify. Grounded in a social–ecological systems view, this study aims to infer SCP endogenously from longitudinal supply chain networks by developing a regime-conditioned probabilistic framework (RCPF) that integrates graph-theoretic measures with latent-variable models. The framework enables the endogenous inference of regime-transition states, the tracing of firm-level SCP dynamics, and the identification of cross-firm SCP archetypes through three sequential probabilistic modules. Specifically, a hidden Markov model is specified to decode the latent regime-transition-state trajectory from the graph edit distance and spectral distance between consecutive network snapshots. Next, a regime-conditioned hidden Markov model is formulated to trace firms’ SCP dynamics from local relational adjustments and network positional variations, with latent-state transitions conditioned on the posterior distribution over the inferred regime-transition states. Finally, a finite mixture model is applied to identify interpretable SCP archetypes from phase-specific profiles constructed from firms’ posterior plasticity-state probabilities across regime-shift phases. Applied to China’s electric vehicle supply chain network, the framework identifies regime shifts aligned with disruptive developments and shows that regime-shift conditions increase the likelihood and persistence of firms’ structural reconfiguration. The inferred SCP archetypes reveal role-specific adaptation pathways and resilience outcomes, informing firms’ differentiated strategic responses to disruptions.