Human-AI collaboration and Explainability in Manufacturing Operations: A Systematic Literature Review

Recent advances in artificial intelligence (AI) are accelerating digitalization in manufacturingoperations, yet many solutions remain difficult to scrutinize and operate in human-centeredways, which can hinder adoption. This study presents a systematic review to map primaryresearch on AI in manufacturing operations where human-AI collaboration is explicit orexplainable artificial intelligence (XAI) and human factors are addressed. Searches in Scopus andIEEE Xplore (2020 - 2026) identified 1.240 publications. After a rigorous screening, 177 studieswere included. All studies were coded through an operational lens combining decision domains(scheduling, maintenance, inspection-quality, process control, shop floor) with AI roles andevidence strength. The primary domains are maintenance and inspection-quality, where thedecision support is the most explored role. Furthermore, most of the studies lack a specific XAImodality, while lab and simulation environments are the most included studies. The reviewhighlights that a large share of AI research in operations is still maturing in its human-centeredoperationalization. We provide structured matrices to support evidence-based design andadoption of human-centered AI in manufacturing operations.