## 5 Results and Discussion  
The proposed method for creating a Digital Twin (DT) was successfully implemented, resulting in an accurate and functional digital model. The process comprised a series of key steps that contributed to creating the Digital Twin using Siemens Tecnomatix Plant Simulation software. The development of an accurate digital model based on real-time data collected from machines and production processes allowed for a faithful replication of the actual operational dynamics. Data validation using Siemens Tecnomatix Plant Simulation software demonstrated high accuracy, with close to 90% agreement between the digital model and real system performance. This accuracy was further confirmed by analyzing several key indicators, including overall equipment effectiveness (OEE), which showed a strong correlation between the simulated and real values. This degree of precision enabled the identification of bottlenecks and operational inefficiencies, providing a comprehensive and detailed representation of the production process. One of this analysis's key findings was identifying inefficiencies within the production line. Key areas such as machine downtime and set-up times were highlighted as the main contributors to downtime. Machine uptime, one of the main factors influencing efficiency, was divided into two categories: uptime due to downtime, and uptime due to set-up. The DT highlighted the issue of set-up times, which significantly reduced operational efficiency. Analysis showed that the availability due to set-ups was 70.9%, emphasizing the importance of optimizing these operations. Another identified challenge was frequent machine breakdowns and maintenance delays, resulting in unplanned downtime. Labelling and cartooning machines were particularly affected, with 92% and 91% availability rates, indicating the need for improved preventive maintenance. Breakdowns of the entire production line also impacted line performance, with an availability rate of 88% and a mean downtime (MDT) of nearly 23 min. Overall, the DT highlighted the need for enhanced  
Figure 4: Process machines statistics. set-up efficiencies and preventive maintenance to improve the operational performance. Moreover, the operational efficiency of the production line was 77.5%, indicating significant scope for enhancement. The DT's advanced simulation capabilities enabled the identification of the underlying cause of inefficiency, which was attributed to the sub-optimal allocation of resources, including labour and materials. This analysis underscored the potential of utilizing Digital Twin technology to address these challenges by simulating different resource allocations to optimize the operational efficiency of the process. These findings enabled targeted optimization efforts to focus on specific stages of the production process, thereby improving resource allocation and reducing unplanned downtime. The DT will also provide a robust decision support tool, allowing for managers to simulate different operational scenarios before implementing them on the shop floor. The ability to test various hypotheses, such as increasing production volumes or managing multiple simultaneous breakdowns, provided valuable data for strategic planning. The DT will deliver significant improvements regarding predictive maintenance by providing accurate data on optimal maintenance schedules. The results analysis shows that implementing the Digital Twin can improve overall operational efficiency and be a powerful tool for continuous process optimization. Simulating different operating conditions and test improvement strategies without disrupting live production provides a significant competitive advantage. In addition, statistical validation of the results, including Chi-square tests applied to the OEE metric, confirmed the reliability of the developed Digital Twin. The success of this methodology demonstrates its robustness highlighting its potential to play a central role in future Digital Twin implementations.