Maier, FlorianFlorianMaierPuppala, SivaphaniSivaphaniPuppalaOberle, MichaelMichaelOberle2024-04-192024-04-192024https://publica.fraunhofer.de/handle/publica/46621810.1109/ICAIIC60209.2024.104632342-s2.0-85189942952This review provides a structured literature analysis of Artificial Intelligence (AI) applications in enhancing manufacturing resilience. The research is guided by three primary questions addressing the use cases, technologies, and benefits of AI across the five resilience phases: Prepare, Prevent, Protect, Respond, and Recover. Findings from 78 papers reveal that AI significantly contributes to predictive maintenance, risk mitigation, and quality control, with machine learning and deep learning being the predominant technologies. The study highlights the pivotal role of AI in advancing manufacturing towards proactive, resilient, and adaptable operations. The insights gleaned offer a roadmap for future research and practical AI integration in manufacturing, underscoring the value of AI in driving industrial innovation and efficiency.enArtificial IntelligenceDeep LearningFault DetectionMachine LearningManufacturing ResiliencePredictive MaintenanceSmart ManufacturingSystematic Literature ReviewZero-Defect Manufacturingconference paper