## 2 Literature Review of the Analyzed Problem
The effect of modelling processes, objects and phenomena is to obtain results in the form of temporal dependencies of statistical data [34]. In serial production, simulation modelling can be used to determine the most important parameters that will make the technological process more efficient and effective [35]. Another researcher emphasizes that the modelling of production processes is possible by using computer software to conduct experiments and tests. The author's research focuses primarily on modelling operations, information visualization and data analysis, thus allowing for the analysis of various scenarios of production activities without the need to disrupt them in reality [36]. Design is conducted by learning and using knowledge about past events. Analysis of available solutions may result from analogous cases or known external potential improvements [37]. At this stage of consideration, it should be emphasized that the production process analyzed by the authors is a complicated one, which may involve many unnecessary or improper activities, leading to production stoppages. Production analysis using a simulation model allows for reducing costs and optimizing the production time. Popular tools for managing and controlling production are known as Manufacturing Execution Systems (MES) [38], which is used in the operational area. However, these systems have significant drawbacks compared to the possibilities of material flow simulation. MES do not allow, for example, production planning or storage based on designated areas, which is important in the process of manufacturing large-size steel structures [39]. Therefore, the identification of complex relationships between material flows in the long term can only be achieved through simulation modelling. FEM allows for short and reasonable process cycle planning whilst only basing the simulation of the material flow on cooperation with MES to optimize production planning [40]. The authors in their works [41; 42; 43] show how the fourth industrial revolution (Industry 4.0) contributes to the development of the process of the technological and organizational transformation of enterprises, which includes the integration of the value chain, the introduction of new business models and the digitization of products and services. The implementation of these solutions is possible by using new digital technologies and data resources and ensuring communication in the cooperation network of machines, devices and people. The factors driving the transformation towards Industry 4.0 are the increasingly individualized needs of customers and the growing trend of the personalization of products and services. The transformation towards Industry 4.0, according to the authors of works [41; 42; 43], includes seven stages. The first concerns technological advancement, which takes into account flexible production systems that facilitate quick adaptation to changes in the number or category of products. The next step is to share information about the manufacturing process by people, machines and products. The third phase is about taking into account the principles of the circular economy to fully use raw materials and reduce emissions. The process of the comprehensive implementation of customer expectations towards products, i.e., end-to-end customer-focused engineering, is the fourth stage towards Industry 4.0. Next, it is crucial to focus on people, including using individual differences to strengthen the organization and build a meaningful work environment. The sixth stage, smart manufacturing, involves the use of integrated systems that respond to changing conditions in real time. In this context, storing and sharing large data sets (big data) is of great importance. The final step is an open factory that understands the needs of all participants in the value chain. All these principles make it possible to achieve the most effective production and quality of the final products [44].