## 1 Introduction
### The Literature Review  
Industry 4.0 is based on cyber-physical system technologies and includes many digital technologies such as augmented reality, virtual reality, cyber-physical infrastructure, cloud computing, Internet of Things, artificial intelligence, big data analytics, additive manufacturing, smart sensors, autonomous robots and systems, and mobile technologies. The main areas of implementation of I4.0 technologies are production resources, transport, and storage systems [1; 2; 3; 4; 5; 6; 7; 8]. Accordingly, an increasing use of automation and robotization can be observed, which is replacing human work. Industrial robots have a mobility similar to the human arm and, like humans, can perform a variety of complex tasks. In addition, they do not experience exhaustion or boredom and can work in harmful conditions. That is why, nowadays, more and more robots work in industry, especially performing repetitive and very precise tasks (e.g., welding) or monotonous activities requiring physical effort (e.g., machine handling, palletization, etc.) [13; 14; 15; 16]. In addition, production and logistics processes are becoming more complex and more difficult to analyze due to many technical and organizational limitations, which determine the different performances of workstations and the formation of so-called bottlenecks during the production flow [17]. Industry 4.0 depends on the digital integration of technical and network aspects of manufacturing systems [18]. One part of Industry 4.0 is computer modeling and simulation, which are involved in managing the increasing complexity of manufacturing systems and can act as an integrator of solutions obtained in the process of improving existing or newly designed subsystems [19; 20]. From the system designer's point of view, the simulation of the cyber-physical approach (digital objects, with their structure, connections, and existing meta-information and semantics) is the next step in the development and systematic approach to the design of multi-level systems. (Figure 1). The Theory of Constraints (TOC) is a management paradigm that views any manageable system as being limited in achieving more of its goals by several constraints. The TOC gives a methodology for identifying the most important limiting factor (i.e., equipment, people, policy) that stands in the way of achieving a goal and then systematically improving the aforementioned constraint until it is no longer the limiting factor. The improvement of the manufacturing process and removal of one constraint is often referred to as shifting the bottleneck [21, 22]. Tests on real production systems are usually not possible due to production being in process and the high costs associated with each delay. Mathematical analysis of complex production processes is also difficult due to the large number of interrelated parameters. Therefore, computer simulation methods are often used, i.e., the simulation of discrete events (DES--Discrete Event Simulation), which is utilized in many programs for the analysis of discrete production processes, such as Arena, Enterprise Dynamics, FlexSim, Plant Simulation, and others [23, 24, 25]. To evaluate the performance of a production system various Key Performance Indicators (KPIs) can be used [26, 27]. There are financial KPIs, customer-focused KPIs, and process-focused KPIs that aim to measure and monitor operational performance across the organization. The most popular KPIs used in manufacturing include production throughput, utilization rate, number of finished and unfinished products, Work in Process (WIP), quality rate, availability, performance, etc. [28]. The term WIP is broadly used in production and supply chain management and means partially finished goods waiting for completion [29]. There is also the similar term of Work in Progress, used in a slightly broader sense and meaning the value of those unfinished goods, which is used in accounting and financial management or project management [30].