# Application of Modeling and Simulation in a Self-Reprogrammable Prototype of a Manufacturing System
## 2 Digital Twin
However, the adoption of DT poses significant challenges for small and medium-sized enterprises (SMEs), primarily due to high investment costs and the need for skilled teams in Engineering and Information Technology (IT) [29]. Among the challenges for implementation, the following stand out: security; the complexity in the implementation is proportional to the complexity of the real system operation; interoperability among different systems; data mining and control; cost of implementing some technologies without bringing clear value to the business; dependence on other technologies for operationalization; managing large amounts of data from different sources [30; 35]. The application of DT largely depends on a company's strategic objectives. These applications may be tailored to specific products or processes or implemented across entire product lines and manufacturing systems. Some researchers argue that DT offers a new perspective on simulation by advancing digitalization and enabling real-time data access [16; 36]. This advancement can be supported by data collection systems such as Programmable Logic Controllers (PLCs), Manufacturing Execution Systems (MES), and Enterprise Resource Planning (ERP) platforms, which can be integrated with simulation and optimization tools, such as Advanced Process Control (APC). Additionally, technologies like Augmented Reality (AR) can enhance simulation environments by facilitating human interaction in manufacturing operations, such as assembly tasks [35]. In general, research on DT technology has grown considerably in recent years. Approximately 47.89% of publications are related to the manufacturing sector, 12.32% pertain to the energy sector, and 7.39% focus on aerospace applications. Mechanical Engineering accounts for slightly more than 50% of studies related to DT [32]. The application of DT in human-robot collaboration (Human-Robot Collaboration--HRC) and learning systems has become essential in modern manufacturing, enabling tasks to be executed with greater efficiency and flexibility. In this context, DT serves as a powerful tool, providing high-fidelity virtual representations of physical systems, which facilitate real-time monitoring, simulation, and collaborative processes optimization [37]. Despite their transformative potential, the widespread adoption of DTs faces several critical challenges: Absence of Universal Standards: The absence of a standardized reference framework hinders interoperability and integration across different DT systems and applications; Dependence on Emerging Technologies: DTs heavily rely on rapidly evolving technologies such as IoT, big data, and machine learning, making system compatibility and updates a persistent challenge; Security and Privacy Concerns: The extensive collection and exchange of sensitive data raise concerns regarding unauthorized access and potential privacy breaches; Absence of Performance Metrics: The absence of well-defined evaluation criteria impedes the accurate assessment of DT effectiveness and limits the comparability of different implementations [16]. Furthermore, the implementation of DT presents additional challenges: Data Integration: The accurate and real-time acquisition and consolidation of data are fundamental to the effectiveness of DT; Systems Interoperability: Ensuring seamless interaction between DT and diverse platforms and devices is critical for successful deployment; Data Security and Privacy: Safeguarding the sensitive information processed by DT is essential to maintaining trust among stakeholders [38].