Belsimpel deals with large waiting times during peak times under COVID-19 circumstances compared to non-COVID-19 times. Therefore, a discrete event simulation model of the Belsimpel shops has been developed. However, the model is not validated due to data scarcity. This paper proposes a model calibration procedure based on the idea that service times decrease during high-demanded hours and increase otherwise. The results show that the proposed procedure enables the generation of realistic Key Performance Indicator values. The calibrated simulation model can be used for analyzing the performance of possible improvements. Accordingly, the calibrated model is applied to investigate the impact of an improved employee scheduling. The results show that the mean waiting time decreases by 20-33 %, the maximum waiting time decreases by 12-20 %, and the mean service level increases by 3-11 %. These improvements enhance customer satisfaction while scheduling the same number of working hours.