## 1 Introduction  
Due to a scarcity of skilled workers and technical resources, components and parts manufacturers often outsource some of their products to suppliers for machining [1]. Research shows that more than 80% of vehicle failures result from the process quality of automobile components and parts [2], which is why the manufacturing process quality of components and parts has been a hot topic in current research. Research on process quality control mainly focuses on total quality management [3], statistical process control [4], multistage manufacturing process quality control [5], supply chain quality management [6], etc. Total quality management adopts quality strategies to meet customer needs, and emphasizes that all departments of the enterprise shall ensure process quality to promote quality development and improvement, but it does not consider the coordination among various suppliers. Statistical process control focuses on product quality characteristics and applies sample statistical methods to achieve monitoring of the production process quality, but it is not suitable for small batch production stages with few data samples. Multistage manufacturing process quality control applies state-spaceto represent the analysis of the error transmission process of multiple processing units, operating equipment, etc., which is based on the stream of variation. However, it does not consider the production capabilities of various suppliers at each stage. Supply chain quality management focuses on the relationship between upstream suppliers and downstream customers, with the goal of selecting appropriate suppliers and ensuring product quality. In the face of the existing problems in traditional process quality control, [PERSON] [7] proposed a process quality improvement method, emphasizing monitoring, diagnosis, and control in the production process. [PERSON] et al. [8; 9; 10] analyzed the literature review of zero-defect manufacturing and obtained that the four strategies for zero-defect manufacturing were detection, repair, prediction, and prevention. They also proposed a hybrid decision support system based on the zero-defect manufacturing strategies of detection and repair, to help manufacturers move closer to zero-defect manufacturing. The above methods applied data-driven technologies to ensure product quality level and minimize low-quality costs, but they ignored the quality control demands at different stages in the manufacturing supply chain cycle. Therefore, the focus of this study is to propose a quality control method for the entire manufacturing supply chain process based on the demands at different stages of the manufacturing supply chain cycle. The automobile manufacturing supply chain is a hierarchical network comprising automobile manufacturers and auto parts suppliers, with the assurance of auto parts process quality as the core [11]. The process quality control oriented to the manufacturing supply chain includes three stages: quality assessment for the auto parts suppliers, quality management during production, and quality inspection and assurance [12]. Product quality is jointly completed by multiple auto parts suppliers, who play different roles in quality management at different stages of the manufacturing supply chain [13]; product process quality is an important criterion for the selection and monitoring of auto parts suppliers [14]. However, since the real-time sharing of information for process quality among parts suppliers has not been realized, problems relating to process quality may not be discovered in the first place. How to select appropriate auto parts suppliers, and enhance the coordination and exchange of process quality information among them in the entire process of manufacturing supply chain, is an issue that needs to be solved urgently. Selecting right parts suppliers is conducive to reducing product procurement costs.