Shaoqian Ji

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.

In the context of a low-carbon economy, automotive parts supply chains face multifaceted risks, making an effective supply chain risk assessment model a crucial means of ensuring supply chain stability. Traditional evaluation methods struggle to comprehensively and accurately identify all influencing factors and their interrelationships in automotive parts supply chains. This article constructs an evaluation model based on the principle of symmetry. The “structural symmetry” is determined by the ratio of the completeness of risk dimension coverage in the indicator system to the precision of indicators, while “fusion symmetry” refers to the degree of equilibrium in information contribution during the fusion of subjective and objective weights. First, Fault Tree Analysis (FTA) and the Delphi method are adopted to establish a risk evaluation index system, whereby structural symmetry is ensured by the equilibrium between the completeness of risk dimension coverage and the accuracy of indicators in the index system. Second, drawing on the symmetric fusion principle, this study proposes a hybrid evaluation approach integrating hesitant fuzzy DEMATEL with entropy weight-coefficient of variation (HDEC), and the fusion symmetry is guaranteed by the balanced integration of subjective and objective weight information. Finally, a case study of an automotive parts supply chain enterprise quantitatively assesses and ranks risk factors, with corresponding countermeasures proposed. The symmetry-guided HDEC method achieves high accuracy, identifying indicator–causal relationships. Compared with the traditional entropy-weighted AHP algorithm, the Pearson correlation coefficient is 0.8566, and Spearman’s rank correlation coefficient is 0.88, indicating strong weight correlation and robust stability. The integration of mathematical symmetry enhances the model’s theoretical rigor, which aligns with symmetry-oriented optimization research.

The automotive industry is a key pillar of many national economies, and automotive body manufacturing is among the most complex production processes. In the automotive body manufacturing process, quality control and safety assurance are of paramount importance, directly influencing the overall safety performance, structural reliability, and comfort of vehicles. Therefore, it is crucial to analyze the primary factors that influence quality and safety during the car body manufacturing process. The study first focuses on four key processes of car body manufacturing—stamping, welding, painting, and assembly—using the man, machine, material, method, environment (4M1E) framework to analyze the factors affecting quality and safety. Subsequently, a quality and safety early-warning indicator system is established for the automotive body manufacturing process, followed by a comprehensive analysis of the constructed system. To address the issue of subjectivity in traditional technique for order of preference by similarity to an ideal solution (TOPSIS) evaluation methods, this paper employs the coefficient of variation method for objective analysis of criterion-level indicators, the trapezoidal fuzzy number method for subjective analysis of criterion-level indicators, and establishes a model for optimizing target weight that balances subjective and objective approaches. Furthermore, a relative entropy-based method is applied to comprehensively evaluate criterion-level indicators. This approach reduces the information loss associated with separate weighting schemes and overcomes a known limitation of traditional TOPSIS—its inability to distinguish alternatives that lie equidistant from ideal solutions. Finally, an evaluation model for quality and safety influencing factors in body manufacturing is developed and validated through a case study, demonstrating its feasibility. The results show that the proposed model can effectively identify the key quality and safety influencing factors in the automobile body manufacturing process, guarantee quality control and safety assurance in the body manufacturing process, and thus ensure that the automobile production process meets the quality and safety requirements.