Predicted in-sync Digital Twin for fast decision making

The paper presents a data-driven approach to improving Digital Twin synchronization in industrial environments, enabling timely decision-making even when data availability is delayed.

The solution employs predictive mechanisms to keep the Digital Twin’s state up to date and support faster, more reliable decisions, thereby contributing to improved production process resilience.

The approach is validated through an industrial use case involving a packaging line and a KUKA LBR iiwa collaborative robot, laying the groundwork for the development of Always In-Sync Digital Twins capable of supporting proactive decision-making in dynamic industrial environments.