## 5 Results and Discussion
Its well-structured design and flexibility make it adaptable for effective application across diverse industrial sectors, including pharmaceuticals and beyond. This approach serves as a scalable and versatile framework addressing key operational challenges and aligning with the principles of Industry 4.0. As such, it provides industries with a reliable pathway to enhance productivity, optimize resource utilization, and maintain competitiveness in an increasingly digitalized and interconnected landscape. One of the main implications of the project's success was demonstrating that the DT can serve as a decision support tool for the company. With the ability to integrate real-time data and run detailed simulations, the DT provided valuable information on production line operations, helping managers make more informed and strategic decisions. These simulations allow them to explore a variety of operational strategies and choose the most effective ones to improve production flow by testing their effectiveness without interrupting real operations, reducing risks, optimizing available resources, and reducing costs. This approach has shown how DTs can be used to monitor operations and continuously improve efficiency and productivity. To illustrate, DTs can simulate the impact of different machine configurations, changes in operating parameters, and the introduction of new technologies. This flexibility permits the formulation of well-informed decisions and the implementation of changes with greater confidence, on the basis that solutions have been tested virtually. Currently, however, the proposed methodology has its limitations, such as the following:  
* Information exchange: The exchange of information between the DT and the actual process is manual. This limits the continuity and updating of data, which depend on the frequency and accuracy of manual input operations. Automating this process would make the data update cycle continuous and more efficient, allowing for greater responsiveness and accuracy in operational decisions. * Implementation complexity: Adopting a DT requires a significant initial investment in time, resources, and technical expertise. Creating and calibrating the digital model requires detailed data collection and close collaboration between engineering and IT teams. In addition, keeping the DT updated with real process changes can be an ongoing challenge. * IT dependence: DT performance is closely linked to the IT used, including IoT sensors, communication networks, and data analysis platforms. Any technical problems or disruptions in these systems can negatively affect the effectiveness of the DT, requiring backup solutions and well-defined contingency plans. Dependence on IT also entails continuous upgrades and maintenance investments, ensuring data security and protection against cyber threats. Future developments in Digital Twin (DT) technology can help overcome its limitations. First, the reliance on manual data exchange processes introduces challenges such as delayed response times, increased risk of errors, and limited scalability. The delays caused by manual data handling compromise the Digital Twin's ability to provide real-time insights and recommendations, which is particularly problematic in applications like predictive maintenance where quick responses are critical. Additionally, the potential for human errors in manual data transfer can distort the accuracy of the Digital Twin and compromise decision-making processes. The manual process also becomes unsustainable in large-scale industrial environments that require a continuous data flow. Automating the data exchange process using automated data collection using real-time analytics sensors and Internet of Things (IoT) devices would make the data update cycle continuous and more efficient, enabling a greater synchronization between the physical system and the Digital Twin and improving its responsiveness and effectiveness in supporting real-time decision-making and adaptive responses.