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.

To address the core bottlenecks in the evolutionary performance evaluation of automobile door stamping processes, namely insufficient adaptation to dynamic uncertain information and the lack of systematic methodological support in existing methods, this study establishes a complete evaluation methodological framework covering the indicator system, weight algorithm, and evaluation model. First, based on the PDCA cycle logic, a multi-stage indicator system integrating static benchmarks and dynamic evolution dimensions is designed, covering the entire process of planning, execution, inspection, and optimization. Second, an entropy weight-information cloud coupled weighting algorithm is proposed. The entropy weight method is used to extract objective data features, and the fuzzy-random modeling capability of the information cloud model is combined to achieve robust weight allocation of evolutionary indicators. Finally, to solve the problem that the traditional TOPSIS method cannot distinguish the advantages and disadvantages of schemes near the ideal solution, JS divergence is introduced to improve the traditional TOPSIS algorithm, and the distance between the process scheme and the positive/negative ideal solutions is quantified to realize accurate performance ranking. Through case validation on automobile door stamping processes, five typical door stamping process schemes are evaluated. The results show that the comprehensive performance score ranges from 0.1765 to 0.8689, and the performance ranking is highly consistent with the actual production logic, verifying the effectiveness and industrial adaptability of the proposed methodology. This study provides a systematic quantitative tool for the evolutionary performance evaluation of processes in complex manufacturing scenarios, and has important theoretical reference and engineering application value.

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.