Digital Twin–Based Analytics for Intelligent Manufacturing and Industrial Systems

Abstract

Digital Twin–based analytics has emerged as a transformative paradigm for intelligent manufacturing and industrial automation. By integrating physical systems with their virtual counterparts, digital twins enable real-time monitoring, predictive analytics, and enhanced decision-making across industrial environments. This study investigates the analytical capabilities, enabling technologies, and industrial impacts of digital-twin frameworks, emphasizing their role in optimizing production efficiency, reducing downtime, and enabling autonomous operations. The paper includes conceptual analyses, graphical illustrations, and a referenced literature foundation to support advancements in smart manufacturing.