## 1 Introduction
In any case, Key Performance Indicators (KPIs) may be used to assess the models' accuracy. These result from the combination of Key Result Indicators (KRIs), which are directly measurable, and Performance Indicators (PIs), that are mathematically obtainable using the latter [17]. Therefore, DTs have proven to be powerful tools when it comes to ensuring reasonable proximity between the established goals and the real outcome, enhancing production processes and quality check, as well as monitoring and making predictions related to the manufacturing systems [18]. According to [16], more than half of the analysed articles approached DTs in a conceptual way. Regarding the level of integration, 35% only achieved the Digital Shadow level, 28% dealt with Digital Models and no more than 18% were considered full Digital Twins. In [5], a semantic model was developed with the aim of tackling resource virtualisation. For that, Ontology Web Language (OWL) was used as the ontology language and Jena as the rule language for resource capability description. Describing a machine's attributes and abilities through semantic web languages allows the representation of real-world information in a comprehensive format for computing. Furthermore, ontologies promote the models' scalability through their objects' properties [19]. The practical application described in [20] consists of a high-fidelity model of a physical object focusing on its geometry. For that, ISO 10303, or simply STEP, was used. Besides the precise representation of geometrical objects through standardised terms, STEP excels at the _.stp_ files' compatibility. Nevertheless, this application only represents the physical shape of an object, disregarding its behaviour. In [21], a DT was created to monitor the energy consumption of a 14.0 laboratory. The Open Platform Communications Unified Architecture (OPC-UA) was used for communication amongst machines and data acquisition. Aside from being a step towards standardisation, this protocol has an embedded security layer, ruling out the need for further safety rules. In addition to Simulink, both MATLAB and its OPC Toolbox were vital for integrating data exchange between the servers and the simulation, given that all the Programmable Logic Controllers (PLCs) were compatible with this communication protocol. At last, in [22], the DT concept was applied to a rear lights' production system. In order to avoid the need for integration, two models were created using AnyLogic: the front-end, representing the physical system which only executed tasks, and the back-end, representing the actual DT which was responsible for giving orders to the former. After connecting both models using a Java interface, several simulations were carried out and the following conclusions were drawn:  
* Throughput time was reduced by lower failure rates and available feedback from each station and higher amounts of sensors along the railway. * The amount of lost messages increases with the number of sensors due to concurrent communications. * In spite of delaying communications, a higher number of sensors reduces throughput time, in particular when stations' failure rate increases. Overall, implementing DTs raises a number of issues related to communication speed constraints, the data acquisition method, the lack of standards that ensure scalability of existing solutions and unawareness of how to integrate human beings in the process. Therefore, most applications only support workers in the decision-making process [23].