## 2 Materials and Methods  
As previously described, Digital Twins (DTs) virtually represent a product, process, or service. This technology allows for operations to be simulated, predicted, and optimized using real-time data, making it a pivotal element within Industry 4.0 [12, 13, 14]. The integration of Digital Twins with technologies such as IoT and Big Data has shown significant improvements in product lifecycle management, reductions in downtime, and enhancements in predictive maintenance strategies [15, 16]. Comparing the current use of Digital Twins across various industries highlights both their potential and the existing gaps in their applications. In manufacturing, companies such as General Electric and Siemens have pioneered Digital Twins for real-time monitoring and predictive maintenance, achieving substantial cost savings and efficiency improvements [17, 18]. However, while these implementations have demonstrated tangible benefits, the literature often points to a lack of standardized methodologies for DT deployment, which could hinder their broader adoption [19]. Studies emphasize the challenges of creating accurate models, ensuring real-time data integration and managing the complexities of IT infrastructure required for effective DT functioning [20, 21]. In the aerospace sector, early adopters like NASA and the US Air Force have leveraged Digital Twins to improve the safety and reliability of flight operations, using DTs to simulate and predict the behaviour of critical systems [10, 16]. In recent years, advancements in Digital Twin technologies have significantly expanded their application potential across various industrial sectors. For instance, the integration of Digital Twins with Artificial Intelligence (AI) has been extensively studied to enhance predictive maintenance strategies, highlighting how AI-guided Digital Twins address challenges such as data inefficiency and model explainability, paving the way for more robust real-time decision-making systems [22]. The pharmaceutical industry, in particular, has the potential to achieve significant results by leveraging these technologies to ensure compliance with strict quality standards while optimizing production. For instance, a study in 2024 explored the transformative potential of Digital Twins in bio-pharmaceutical manufacturing, emphasizing their ability to simulate and optimize production processes, ensure consistent product quality, and predict maintenance needs [23]. These capabilities significantly improve operational efficiency and reduce time-to-market, which is critical in the highly regulated pharmaceutical sector. Another study proposed a requirement-based roadmap for standardizing predictive maintenance automation using Digital Twin technologies [24]. This is particularly relevant for pharmaceutical facilities which often operate under strict regulatory constraints, requiring advanced tools to monitor, predict, and mitigate operational risks. In environments with existing production infrastructure, such as pharmaceutical facilities, methodological advancements in Digital Twin creation are paramount. A recent article presented a com-prehensive approach to developing and validating Digital Twins, particularly in brownfield production environments [25]. The methodology demonstrated quantitative benefits in process optimization and resource efficiency, which are critical in the tightly controlled pharmaceutical sector. Furthermore, the role of service-oriented Digital Twins in empowering smart factories has recently been emphasized. The research underlines the importance of ubiquitous knowledge in improving enterprise performance and fostering human-machine collaboration within modern industrial ecosystems [26]. Although these examples underline the transformative potential of Digital Twins, many implementations remain limited in scope, often focusing on specific assets or isolated systems rather than achieving a fully integrated system-wide approach [27; 28].