AI-Powered Quality Management Systems and Smart Factory Architecture for Manufacturing Excellence: Big Data Analytics, Real-Time Monitoring, and Integrated Quality Intelligence

Abstract

Quality management is the cornerstone of modern manufacturing competitiveness, yet traditional quality management systems (QMS) — built on manual inspection, retrospective reporting, and fragmented data silos — are fundamentally inadequate for the complexity, speed, and precision demands of contemporary production environments. The proliferation of industrial IoT sensors, edge computing platforms, and artificial intelligence has catalyzed a transformation in quality management from a reactive, post-hoc function to a proactive, real-time, and predictive discipline. This review provides a comprehensive synthesis of AI-powered quality management systems (AI-QMS) and smart factory architecture for manufacturing excellence, examining big data analytics for manufacturing quality, machine learning algorithms for quality assurance and defect classification, real-time quality monitoring through edge computing and digital integration, predictive quality analytics and early warning systems, and the architectural frameworks that integrate AI-QMS into the broader smart factory ecosystem. We further connect these advances to industrial optical sensing technologies — precision 3D surface metrology and four-dimensional thermal imaging — demonstrating their roles as foundational data sources for AI-driven quality intelligence. A central contribution is the articulation of an integrated Quality Intelligence Architecture (QIA) that unifies real-time sensing, edge analytics, cloud-scale ML, and human decision support into a coherent quality management platform for the smart manufacturing era.