This study investigates the impact of selected automation scenarios on occupational safety, ergonomics, and operational performance in a plastic crate production workstation. The research focuses on a specific case from the discrete manufacturing sector and aims to develop an integrated analytical framework combining ergonomic assessment with process simulation for the evaluation of organizational and technological improvements in manual handling operations. This study applies a simulation-based production model developed in the DBR77 discrete-event simulation environment to analyze alternative workstation configurations. The assessment framework integrates Ishikawa analysis for root-cause identification and the RULA and REBA methods for ergonomic risk evaluation. The investigated workstation was characterized by repetitive manual handling activities, awkward working postures, and increased physical workload associated with palletizing and transport operations. Several organizational and technological variants were analyzed, including additional operator support, robot-assisted palletizing, conveyor integration, and automated guided vehicle (AGV) transport. The simulation results indicated that the AGV-supported configuration achieved the shortest cycle time (1270 s per batch of 30 units), whereas the robot-assisted variant resulted in the longest cycle time (1520 s). Ergonomic assessment showed a reduction in RULA scores from 6–7 to 3–4 and REBA scores from 8–10 to 4–5 in the automated scenarios. The contribution of this study lies in the integration of ergonomic risk assessment and discrete-event simulation within a unified evaluation framework for workstation redesign in discrete manufacturing environments. The findings demonstrate how simulation-supported analysis can support decision-making regarding the balance between manual labor and automation under specific operational conditions. Due to the single-case-study design, the results should be interpreted as context-specific and exploratory rather than directly generalizable to all manufacturing systems.
Pawełczyk et al. (Fri,) studied this question.