Siti Nur Hamidah; Moh. Ayip Fathani; Zulfadlillah Zulfadlillah; Kardita Kardita
This systematic literature review examines the evolving role of Quality Engineering (QE) in optimizing production processes within Industry 4.0 contexts. By analyzing 78 peer-reviewed studies (2010–2025), the research identifies critical shifts in QE methodologies, emphasizing integration with artificial intelligence (AI), machine learning (ML), and real-time digital twin technologies. Key findings reveal enhanced robustness through adaptive optimization algorithms (e.g., Bayesian optimization, NSGA-II), improved defect prediction via AI-driven quality control systems, and streamlined process interoperability through Manufacturing Execution Systems (MES) and Quality 4.0 frameworks. The study underscores digital integration as a catalyst for reducing variability, accelerating decision-making, and aligning quality management with Industry 4.0’s demands for agility and interconnected systems. Recommendations include adopting hybrid methodologies combining classical Six Sigma with ML-driven analytics and investing in workforce training for digital QMS adoption.