Supply chain for customized production research on collaborative optimization modeling of project quality

As market demand becomes increasingly personalized, customized production has emerged as a critical pathway for the transformation and upgrading of the manufacturing sector. However, this production paradigm introduces multifaceted challenges to supply chain systems and project quality management, particularly regarding the timeliness of demand response, the adaptability of production system configurations, and the consistency of quality control. This study develops a collaborative optimization framework for supply chain resource allocation and quality management, grounded in multi-objective optimization theory. The framework integrates a quality prediction mechanism based on machine learning algorithms and enables the dynamic optimization of decision-making across three dimensions within customized production scenarios. Finally, using empirical data from a domestic university, it compares and verifies the performance differences between the benchmark model and the system model in key data aspects, ultimately finding that the collaborative model not only achieves the goal of improving supply cha in operational efficiency but also contributes to an increase in quality compliance rates, providing a practical theoretical model and engineering practice method system for the collaborative optimization of customized supply chains and quality management in universities.