## 3 Methodology  
The methodology described is summarized in Figure 1, which provides a concise representation of the main phases and key steps involved in the creation of a Digital Twin. The first phase of creating a Digital Twin involves studying the process that needs to be replicated digitally. This process generates a significant amount of data, which can be analyzed to optimize the process and make informed decisions. It is essential to identify the data types required for the Digital Twin, which can come from various sources such as sensors, enterprise databases, and IoT devices. After identifying the data sources, the next step is to determine how to collect the data, which may involve installing new sensors or using existing database systems and IoT technologies. It is also necessary to analyze the production process history to ensure the reliability of the dataset. This analysis should consider periods with no radical changes that would affect the collected data. Once all the preliminary steps are completed, data collection proceeds. Each production line has a theoretical production capacity, representing the maximum output the system can generate in a unit of time for the given input conditions. This capacity is defined in terms of ideal operating conditions and values, such as the nominal capacity of the machines. However, each production line is subject to several dynamics that inevitably affect performance [25]. Data collection focuses on understanding these dynamics, their causes, and the consequent data determining their line efficiency. These data are crucial because they make each production line unique. It is necessary to focus on these data to create a Digital Twin that can fully describe the production process and learn its behaviour. This phase is therefore the most delicate and time-consuming part of the method. Each step must be approached with the utmost care and attention to ensure the consistency and accuracy of the data, thus providing a solid basis for the subsequent steps. This phase has been identified as the indispensable starting point of the method, as the data collected form the foundation on which the entire Digital Twin development process is based. The primary indicator required to comprehend the production line and its operational efficiency is overall equipment effectiveness (OEE), which evaluates the overall efficiency. OEE considers three principal factors: machine availability (A), performance efficiency (P), and product quality rate (Q). The next stage in the production process study is creating the digital model. This model will serve as the virtual representation of the production line, which will be developed through an approach that combines in-depth inspections and detailed measurements. Initially, a complete examination of the production line must be conducted to fully understand its operations and the degree of interdependence between its components. Next, precise measurements, such as cycle times and production speeds, are taken to acquire quantitative data on the line's performance. These data are then integrated into the digital model to ensure that it reflects the line's behaviour under ideal conditions. Figure 1: Graphical representation of the method. Following this, identifying the bottleneck machine is essential as it dictates the maximum system throughput. This machine has the highest utilization rate and constrains overall production capacity. Real-time data collection and continuous analysis can help ensure that bottleneck identification remains accurate and reflects current conditions. Another critical element is the inclusion of personnel, whose roles and skills must be analyzed to ensure optimal line performance. Unlike robots, which are predictable and easier to model, humans add complexity due to their adaptability and decision-making variability, making the accurate representation of human tasks challenging.