The sharp increase in the burden on the healthcare system during the COVID-19 pandemic revealed the ineffectiveness of traditional methods for calculating labor productivity based on mathematical formulas. These methods do not account for the dynamics of work processes or challenges in planning human resources, equipment, and space. This leads to inefficient workload distribution, especially when — as in the case of clinical laboratories — there was a need to process thousands of samples for PCR testing daily. The aim of this study is to develop and analyze a method for planning workload using simulation modeling in AnyLogic, which allows for the visualization and optimization of laboratory processes. The objectives include analyzing existing approaches, describing the methodology, applying it to a PCR laboratory case study, and evaluating its benefits in a pandemic context. The proposed approach involves timing technological processes, collecting data in tabular form, and creating a digital model of the laboratory to identify “bottlenecks,” equipment downtime, and staff downtime. Using a PCR laboratory as an example, the paper demonstrates the ability to optimize resources, calculate maximum throughput, and justify procurement decisions. This method allows for improving the efficiency of laboratory operations in situations of unpredictable demand, minimizing the risks of disruptions and financial losses.