## 1 Introduction  
Although a considerable amount of research has already been conducted in the field of production management, manufacturing companies are still facing major challenges as a result of unexpected failures. Due to the increase in technical maturity, today's manufacturing industry is also confronted with a large amount of data. In this context, production systems need to introduce sustainable alternatives based on new technologies to identify disturbances within the production system at an early stage and even prevent them before they occur [1]. Data mining technology brings new breakthroughs to failure prediction toward reducing machine downtime, optimizing resource utilization, and increasing production volume with a potential reduction in maintenance costs [2]. Most of the machine processes that we know today are very specialized, depending on their parts and interrelationships. In this context, a type of system that deserves much more attention is the large systems built of parts that have many states of equilibrium [3]. The demand for adaptable manufacturing systems has increased in the past years due to the constant market changes [4]. Moreover, the joint impacts of decisions belonging to different production areas that are traditionally considered in isolation, such as logistics, maintenance, and quality, are difficult to predict [5]. In this regard, digital twins (DT) are a proven tool to support the evaluation and control of manufacturing systems [5], helping to increase the flexibility and robustness of unexpected conditions [6] of the manufacturing system. At the same time, if it is combined with computing capabilities, networking, and physical processes, it can provide new functionalities to the management processes and support systems that will help organizations to have better and more automated architectures [7]. Moreover, a DT model allows one to represent the current status of the manufacturing system and to perform real-time optimizations, decision making, and predictive maintenance according to the actual condition of the system. Research on digital twins is still at the initial stage, and there is a need for future research [6] that requires real-time synchronized simulations of the production system operations to be fully proven [8]. Whereas an independent simulation of certain aspects of product and manufacturing planning is possible, the integral simulation of complex manufacturing processes involves high costs as well as a high time expenditure, as, in general, an interoperability between heterogeneous IT (information technology) tools is not given [9]. Moreover, current manufacturing has not achieved the level of Industry 4.0, although many researchers and companies are working on this topic. Every current manufacturing system is able to cover some of the concepts of Industry 4.0 [10]. There are several models pursuing Industry 4.0's integration into a company using different maturity models such as web-based, self-assessment tools, etc. A well-known architectural model is The Reference Architectural Model Industry 4.0 (RAMI 4.0) [11]. The RAMI 4.0 was developed in 2015 consisting of several layers, hierarchical levels, and the product lifecycle representing the value stream [12]. RAMI 4.0 provides a holistic view for all the important aspects of Industry 4.0 that are needed by different stakeholders and gives general guidelines for the whole organization but does not prescribe a specific modeling approach [13]. Therefore, this research pursues to apply a novel methodology for modeling and assessing manufacturing systems and processes suitable to consider all relevant elements of any system and process under Industry 4.0 environments [14].