AI Enabled Predictive Analytics and Autonomous Decision Systems for Resilient Supply Chain and Advanced Manufacturing under Industry 4.0 and 5.0
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Abstract
The convergence of Industry 4.0 and the emerging paradigm of Industry 5.0 is reshaping supply chain management and advanced manufacturing through the integration of Artificial Intelligence (AI), predictive analytics, and autonomous decision systems. This study explores how AI-enabled predictive analytics and autonomous decision-making frameworks enhance resilience, agility, and sustainability across interconnected production and logistics networks. By leveraging technologies such as cyber-physical systems, digital twins, Internet of Things (IoT), big data analytics, and machine learning, organizations can anticipate disruptions, optimize resource allocation, and autonomously adapt to dynamic market and operational conditions. The research proposes a comprehensive framework that integrates real-time data acquisition, predictive modeling, risk assessment, and autonomous control mechanisms to strengthen supply chain resilience under uncertain environments, including global disruptions and demand volatility. Furthermore, the study aligns technological innovation with human-centric principles emphasized in Industry 5.0, ensuring collaboration between intelligent systems and human expertise. A mixed-method research methodology combining simulation modeling, case analysis, and empirical data evaluation is adopted to validate the framework. The findings demonstrate that AI-driven predictive and autonomous systems significantly improve operational efficiency, reduce downtime, enhance responsiveness, and support sustainable manufacturing practices. The paper contributes to the evolving discourse on intelligent manufacturing ecosystems and resilient supply networks.
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