An Integrated Indicator-Weight Evolution Method for Sustainable Industrial Supply Chains in Dynamic Scenarios

The resilience and stability of industrial supply chains are the core pillars of the sustainable development of the global economy, and also an important foundation for achieving the United Nations Sustainable Development Goals of “Responsible Consumption and Production” and “Industry, Innovation and Infrastructure”. To address the problem that traditional industrial supply chain risk assessment systems have fixed indicators and static weights, which are difficult to adapt to complex scenarios such as dynamic adjustments of supply chain nodes, fluctuations in market demand and changes in geopolitical patterns, and thus restrict the construction of sustainable supply chain resilience, this paper proposes a comprehensive indicator-weight dynamic evolution algorithm for industrial supply chain risk assessment systems. Firstly, the evolutionary characteristics and trigger rules of the industrial supply chain risk system are defined. Indicator-weight evolution is performed when the original indicator system can no longer meet the evaluation requirements. The Delphi method combined with the actual risk characteristics of the supply chain is adopted to determine the candidate indicator set. The candidate indicators are screened using the grey relational analysis algorithm and the entropy weight-variation coefficient method to construct the evolved risk indicator set. Secondly, an improved DEMATEL model is used to analyze the causal relationships among the evolved indicators. Evolutionary analysis of the original indicator weights is carried out according to the influence degrees between indicators, so as to obtain the evolved indicator weights and realize the dynamic linkage update of evaluation weights. This study conducts a case analysis using publicly available aggregated synthetic data on supply chain disruptions and resilience and compares the proposed method with the entropy weight–analytic hierarchy process (EW-AHP) and dynamic Bayesian network (DBN) methods. The performance of the methods is evaluated in terms of the consistency of indicator weights or relative importance, computational time, out-of-sample prediction performance, and retrospective analysis of a representative disruption event scenario. The results indicate that, under the current case and parameter settings, the proposed method can reconstruct the risk indicator system and coordinate indicator and weight updates, demonstrating certain advantages in computational efficiency and risk factor identification in the specified comparative experiments. It should be noted, however, that the current validation relies primarily on publicly available synthetic aggregated data and retrospective event-scenario analysis. Therefore, the evidence is insufficient to directly establish the risk-warning benefits or improvements in operational response efficiency that the method might deliver in real-world enterprise settings. This study provides a methodological framework for the coordinated updating of indicators and weights in dynamic industrial supply chain risk assessment. Its applicability across industries and practical effectiveness require further validation using independent data sources and actual enterprise operational data.