Industry 5.0 in Manufacturing: Enhancing Resilience and Responsibility through AI-Driven Predictive Maintenance, Quality Control, and Supply Chain Optimization

This integrative literature review investigates the transformative impact of artificial intelligence (AI) on manufacturing, focusing on AI-driven predictive maintenance, machine learning-based quality control, and AI-driven supply chain optimization. By examining current literature, the study highlights AI's potential to automate and revolutionize manufacturing operations, enhancing efficiency, resilience, and transparency. The study's conceptual framework is grounded in three primary pillars: AI-driven supply chain optimization, predictive analytics, and machine learning-based quality control, each contributing to the overall enhancement of manufacturing efficiency, resilience, and transparency. The methodology involves a comprehensive review of scholarly articles, reports, and academic publications, focusing on AI applications in predictive maintenance, quality control, and supply chain optimization. The analysis reveals significant improvements in operational efficiency and resilience due to AI, alongside concerns about biases, transparency, and implementation issues. The findings confirm AI's transformative potential in manufacturing but emphasize the necessity for ongoing supervision, regular audits, and the development of AI models capable of detecting and rectifying operational anomalies. The study proposes creating jobs such as AI Manufacturing Oversight Officer (AIMOO), AI Manufacturing Compliance Officer (AIMCO), and AI Manufacturing Quality Assurance Officer (AIMQAO) to ensure responsible AI utilization, maintaining the integrity and efficiency of manufacturing operations while addressing implementation challenges. The review concludes that AI is promising for transforming manufacturing; however, careful implementation is crucial to uphold operational integrity and resilience. Future research should prioritize longitudinal studies to evaluate AI's long-term impact, focus on addressing implementation concerns, and ensure fair and transparent integration of AI technologies. These findings have significant implications for practice and policy, underscoring the need for robust frameworks and regulatory measures to guide the effective use of AI in manufacturing.