In recent years, Industry 4.0 has provided many tools to replicate, monitor, and control physical systems. The purpose is to connect production assets to build cyber-physical systems that ensure the safety, quality, and efficiency of production processes. Particularly, the concept of digital twins has been introduced to create the virtual representation of physical systems where both elements are connected to exchange information. This general definition encompasses a series of major challenges for the developers of those functionalities. Among them is how to introduce the human perspective into the virtual replica. Therefore, this paper presents an approach for incorporating human factors in digital twins. This approach introduces a methodology to offer suggestions about employee rotations based on their previous performance during a shift. Afterward, this method is integrated into a digital twin to perform human performance assessments to manage workers’ jobs. Furthermore, the presented approach is mainly comprised of a human skills modelling engine and a human scheduling engine. Finally, for demonstrating the approach, a simulated serial single-product manufacturing assembly line has been introduced.

As manufacturing systems increasingly integrate human-centric strategies with smart technologies, understanding and modelling operator performance becomes essential. However, one of the major bottlenecks in this research area is the lack of publicly available, high-quality datasets capturing real operator performance under authentic production constraints. This data descriptor introduces a dataset collected from a semi-automated assembly line featuring two workstations, one manual and one collaborative, where four workers executed standardized tasks in a real factory belonging to Silverline Endustri Ve Ticaret A.S. (Silverline). The dataset comprises detailed, time-stamped operational durations, with over 180 task-level records per worker. By making this dataset openly accessible, the aim is to provide a critical resource for validating human performance models and fostering reproducible research in the emerging field of human-centric industrial analytics. Paper type: Data descriptor