Polymer Chain-Inspired Resilience Assessment Framework for Electrical Equipment Supply Chains

This study addresses the limitations of conventional entropy weight method normalization in existing supply chain metrics by proposing a polymer chain-inspired computational model for supply chain resilience assessment. The methodology quantifies supply chain robustness through statistical analysis of chains with varying resilience levels, illustrated through a six-node supply chain case study modeled on three-phase distribution transformer production characteristics. Results demonstrate that supply chains with resilience values exceeding 4 exhibit optimized node assimilation rates, while those scoring 1 ~ 3 serve as holistic resilience indicators. Empirical analysis reveals distinct application scenarios: enhanced primary chain resilience proves critical for exceptional circumstances (e.g., wartime operations, critical infrastructure projects, and competitive disruptions), whereas comprehensive chain resilience optimization better suits routine operations. The paper further establishes corresponding adjustment paradigms and computational models for both operational states, supported by practical case validations.