A review of explainable AI methods and their application in manufacturing systems

In high-stakes manufacturing environments, the opacity of "black-box" Machine Learning (ML) models presents significant risks to safety, trust, and efficiency. While Explainable AI (XAI) offers a solution, its practical application landscape remains fragmented. This paper addresses this gap by providing a systematic review and novel synthesis of XAI adoption in manufacturing. Rather than merely summarizing known XAI techniques, we introduce a unique, application-centric categorization, analyzing 91 carefully selected articles across six key industrial domains: Predictive Maintenance, Additive Manufacturing, Process Optimization, Product Quality, Product Development, and Supply Chain Management. Our analysis reveals data-driven insights into publication trends and methodology choices, highlighting a definitive shift toward local, model-agnostic methods like SHAP. By structuring the review around manufacturing-specific challenges, we move beyond a general overview to provide a targeted analysis of the field’s current state. We conclude by identifying critical, domain-specific gaps and proposing actionable directions for future research to foster a more effective and trustworthy integration of advanced AI into the human workforce.