<![CDATA[The advancement of Industry 4.0 demands production systems to operate more efficiently, adaptively, and securely in facing global challenges. One promising technology that addresses these needs is the Digital Twin (DT), a digital representation of physical systems that enables integration between the real and virtual environments. Through DT, production processes can be modeled, monitored, and tested in real time, allowing for evaluation and optimization before implementation in actual systems. This study aims to explore the effectiveness of DT in modeling automated industrial systems, particularly in relation to improving production efficiency, quality control, energy savings, and operational safety. The research employed an experimental approach based on simulation within a robotic production line consisting of machines, sensors, actuators, and conveyors. The research stages included identifying system components and workflows, developing a DT model that integrates physical and virtual layers with Internet of Things–based data connectivity, and conducting simulations representing diverse operational scenarios. The findings indicate that DT implementation enhances operational efficiency, reduces production errors, and optimizes energy utilization. Furthermore, DT proves effective in strengthening safety aspects by enabling early detection of potential disruptions and providing preventive recommendations before significant impacts occur. Compared to conventional simulations, DT offers a more realistic, adaptive, and relevant approach to the needs of modern industry. The implications of this study highlight DT’s strong potential to become a new standard in the development and control of automation-based production systems, driving the creation of smarter, more efficient, and sustainable industries.]]>
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