A Digital Twin–Based Approach for Risk Monitoring in Automotive Repair Workshops

In the context of the accelerated digitalization of industrial environments and the transition toward the Industry 4.0 paradigm, occupational risk management requires integrated approaches capable of overcoming the limitations of traditional, predominantly reactive methods. Automotive repair workshops represent complex socio-technical systems characterized by dynamic interactions between workers, vehicles, and equipment, where risks manifest spatially and temporally in ways that are difficult to anticipate using conventional approaches.
This paper proposes an innovative framework based on the Digital Twin concept for real-time occupational risk assessment through the development of a Dynamic Risk Heat Map at the automotive workshop level. The model integrates a digital representation of the workspace, including safe zones and exclusion zones, the positions of workers and vehicles, and the operational status of equipment.
The main novelty lies in defining and implementing a mathematical risk quantification model based on spatial and operational parameters, enabling real-time computation of risk distribution R(x,y,t)R(x,y,t)R(x,y,t). The proposed methodology is based on discretizing the workspace into a grid of cells, where the risk level is determined through weighted aggregation of relevant factors such as proximity to hazard sources, activity density, and movement dynamics.
The results are visualized as a heat map highlighting low-, medium-, and high-risk zones, providing decision support for accident prevention and activity optimization. By integrating digital components, mathematical modeling, and spatial analysis, the study contributes to the development of a proactive risk management tool capable of transforming traditional risk assessment into a continuous, predictive, and adaptive process.