Autonomous Decision Support Systems for Intelligent Factory Operations

Abstract

The rapid advancement of Industry 4.0 has transformed conventional manufacturing into intelligent smart factories by integrating Industrial Internet of Things (IIoT), cyber-physical systems, cloud computing, and artificial intelligence (AI). As manufacturing environments become increasingly complex, traditional human-driven decision-making is insufficient for real-time production optimization. Autonomous Decision Support Systems (ADSS) address this challenge by combining AI, machine learning, digital twins, edge computing, and predictive analytics to enable intelligent, data-driven decision-making with minimal human intervention. This paper presents a scalable ADSS framework that integrates IIoT, edge-cloud computing, and digital twin technology for real-time monitoring, predictive maintenance, dynamic scheduling, and autonomous production optimization. The proposed architecture includes data acquisition, preprocessing, feature engineering, predictive analytics, decision optimization, autonomous execution, and continuous learning. Reinforcement learning and explainable AI improve decision accuracy, adaptability, and transparency, while federated learning enhances data privacy and reduces communication latency. Experimental results demonstrate significant improvements in production efficiency, equipment utilization, predictive maintenance, energy efficiency, quality control, and manufacturing responsiveness compared to conventional decision support systems. The proposed framework provides a scalable foundation for Industry 5.0, enabling sustainable, resilient, and intelligent manufacturing through seamless collaboration between human expertise and autonomous AI systems.