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Abstract

Enhancing resilience through early warning is a critical strategy for mitigating disruption risks in the maritime supply chain (MSC). This study proposes a novel early warning assessment framework to determine the resilience of the MSC. It is referred to as a resilience early warning system. An evaluation index system for shipping enterprises is developed based on four dimensions: withstand capacity, adaptive capacity, learning capability, and the external environment. A resilience assessment model that uses the Bayesian best-worst method (BBWM) and the extension cloud model (ECM) is established to quantify MSC resilience. An early warning evaluation model based on a Bayesian network (BN) is constructed to dynamically link resilience indicators with risk propagation patterns. Empirical validation is conducted using the MSC of China’s new energy vehicle (NEV) exports. The proposed BBWM-ECM-BN resilience early warning model uses BNs to capture complex relationships among indicators, overcoming the limitations of traditional methods that rely on linear assumptions. Using probabilistic early warning thresholds instead of deterministic estimates enables risk prediction. The results show the following. (1) The resilience early warning level of China’s NEV export MSC is moderate. Among primary indicators, withstand capacity has a minor warning level, trending toward moderate. Adaptive capacity and External environment are at a moderate level, trending toward severe. Learning capacity is at a moderate level, trending toward minor. (2) Sensitivity analysis shows that Technology Improvement and Innovation Ability is the most influential secondary indicator (0.7360), followed by Security Risk (0.4310) and Government Risk (0.2310). This study advances theoretical understanding of MSC resilience assessment and provides data-driven tools to mitigate escalating international maritime disruption risks, supporting the stable development of China’s NEV export industry and global MSC networks.

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