## 1 Introduction  
The implementation of Industry 4.0 solutions, currently observed in various branches of the manufacturing industry, is expected to result in a transition from traditional production systems to smart, more flexible, autonomous, and reconfigurable cyber-physical systems. It is related to the transition from intelligent production based on knowledge to smart production based on data and knowledge, where the term \"smart\" refers to the creation, acquisition, and use of data, which use both advanced information and communication technologies, as well as advanced data analysis. As a result, future production systems and their management methods will be supported by real-time data transmission, exchange, and analysis technologies along with simulation and optimization based on digital models [1, 2, 3]. Scientific and industrial research in this area is supported by developing key technologies related to the concept of Industry 4.0, such as the Internet of Things, big data, cloud computing, Embedded Systems, and artificial intelligence. The creation of a communication interface between the digital and physical world through the integration of computations, networks, and physical resources is called cyber-physical systems (CPSs), and, concerning production systems, Cyber-Physical Production Systems (CPPSs) [2, 4, 5]. Apart from physical elements, a cyber-physical system is characterized by a cyber layer, the main element of which is a virtual component--a digital twin (DT) of the production system. DTs are taking a central position in new-generation intelligent production through integration with a CPPS, and their architecture should allow simultaneous access to production data and their processing [5, 6]. A CPS, therefore, is a system that aggregates available information to predict conditions in production plants and demonstrates the advantages of additional optimization through data collection, analysis, and processing, and also helps to make decisions through control or prediction [7; 8; 9]. In this context, the search for easily industrially applicable and effective methods for creating DTs is particularly topical. It is a challenge currently faced by researchers and engineers of production systems. As recent research shows, the creation of digital twins, which are the central element of a CPPS, can be successfully implemented using discrete-event computer simulation models [10; 11; 12; 13; 14]. The very concept of a twin, in the sense of a prototype/copy of an existing object that mirrors actual operating conditions in order to simulate its behavior in real time, appeared during NASA's Apollo program [15]. Since then, computer simulation methods and tools have undergone a process of continuous development, passing through phases from individual applications of simulation models to supporting the solution of specific problems and areas through simulation-based system design support, today's digital twin concepts, where simulation models constitute the center of the functionality of the CPS [14; 15]. A simplified view of the system architecture is shown in Figure 1.  
The approaches to using simulation models as a DT proposed in the literature, including, in particular, the references indicated above, obviously differ in scope and functionality, both with respect to the model itself and the scope and direction of data exchange between data sources and the simulation model.