Collaborative Quality Management in Industrial Engineering from a Supply Chain Perspective: AIDriven Enterprise Quality Optimization

Abstract

Amidst the intensifying digital economy and global competition, supply chain quality management is evolving from traditional linear models toward networked systems characterized by data-driven and intelligent collaboration. This paper constructs an AI-driven “Supply Chain Quality Collaborative Management” framework through system optimization and artificial intelligence analytical capabilities from a supply chain perspective. The study first analyzes core challenges in supply chain quality collaboration across three dimensions: data fragmentation, standard discrepancies, and mechanism asymmetry. It highlights that traditional static and reactive quality controls struggle to adapt to complex, dynamic supply chain ecosystems. Subsequently, through systematic literature review and theoretical synthesis, the paper elucidates AI’s role in multi-source quality data fusion, semantic alignment, standardized governance, and intelligent incentives. It proposes collaborative optimization pathways based on deep learning, blockchain, and reinforcement learning. Through case studies in the automotive and pharmaceutical industries, the research validates the feasibility of AI in predictive maintenance and cross-linkage collaborative decision-making, demonstrating AI’s ability to significantly enhance the systemic resilience and decision-response capabilities of quality management. This paper innovatively integrates industrial engineering process optimization with cross-organizational governance mechanisms for supply chain quality management, providing a new theoretical framework and practical pathway for intelligent manufacturing and sustainable supply chain development.