## 1 Introduction  
As observations of production environments indicate, one common phenomenon is the constant pursuit of the continuous improvement of production processes [1]. There is a constant demand for the development of new solutions to improve the efficiency and harmonization of production systems [2]. In particular, this practice has intensified in the era of challenges posed by the increasingly popular idea of Industry 4.0 [3]. In practice, the use of modern optimization tools is expected to lead to an increase in efficiency while reducing costs [4]. When browsing the offers of simulation software manufacturers, it can be assumed that they have very advanced functions and extensive object libraries that enable the very precise construction of the desired model and its actual operation. In the case of production systems, these tools can be used to conduct experiments on existing production lines and to examine the possibility of undertaking optimization and harmonization projects of already operating production lines [5]. With the use of a properly selected tool, it is possible to model these processes without the need to have physical devices constituting their elements. As indicated in the literature on the subject, the obtained results can illustrate the operation of the system over a selected time course, showing its behavior in the event of a failure, and they also enable the analysis of areas defined as problematic [6]. A very common subject of research on functioning production systems is the location of the so-called bottleneck of production processes. In the available literature, many managers and engineers of manufacturing companies are looking for effective methods to eliminate this phenomenon. By addressing this issue, it is possible to streamline production processes and increase their system efficiency while reducing costs. This optimization and harmonization can be achieved with the right simulation tools. Their proper use allows for manufacturing companies to conduct a thorough process analysis and implement beneficial modifications [7]. However, it is worth bearing in mind that using the above-mentioned solution also has disadvantages. The purchase of appropriate software is associated with relatively high financial outlays, especially in the case of detailed models, where creating a correct model can be very time-consuming and cost-consuming [8; 9].There is also a risk of making a mistake at the design stage of the model due to a lack of real data, resulting in erroneous results. It is worth noting that there are also very advanced production environments for which it may be impossible to build a correct simulation model [10; 11]. However, in most cases, a correct approach to modelling and optimizing production processes based mainly on the use of these tools and real data for the analysis of production systems can bring many systemic benefits in terms of the harmonization and optimization of production. The course and logic of the simulation modelling procedure and its relationship to a real system are shown in Figure 1.  
At this stage of consideration, it is important to understand that every manufacturing company may have many sources of waste, e.g., overproduction, defective products, unnecessary inventory, failures, excessive storage, etc. Each of these phenomena has a major impact on the efficiency of the production line and, consequently, on sales revenues. This is an important argument that motivates the use of modelling and simulation tools to eliminate waste from areas at risk. The ideal tool for solving this type of problem seems to be the specialized FlexSim software. FlexSim is an analytical tool used in practice in process design and decision-making [13]. In the FlexSim simulation software, three-dimensional models can be created that map a real system, e.g., a production system. Figure 1: General diagram of simulation modelling in production processes [12].