## 1 Introduction  
The implementation of DTs has proven to be very useful in addressing the modern challenges of Industry 4.0, such as increasing process complexity, the need to reduce downtime, and the goal of improving product quality [1, 2, 3]. Recent studies have shown that adopting DTs can significantly improve operational efficiency and reduce costs [4, 5, 6].  
The scientific literature has shown an increasing focus on implementing DTs in the manufacturing industry. For example, an analysis by [PERSON] et al. in 2017 highlighted how using DTs optimizes manufacturing operations, improves resource management, and reduces waste [2]. The ability to monitor and analyze operations in real-time will enable companies to react quickly to changes in demand and take corrective action before problems become critical [7, 8, 9]. In addition, simulating different operating conditions provides a significant competitive advantage, allowing for best pracises to be tested and implemented without disrupting day-to-day operations. A further advantage of DTs are their ability to support predictive maintenance [10]. This approach relies on analyzing collected data to predict when faults will occur and take preventive action. This reduces unplanned downtime and improves machine reliability, helping to maintain high productivity. For example, a study by [PERSON] et al. in 2021 showed that implementing predictive maintenance strategies based on DTs can reduce downtime by 20-30% [3]. Furthermore, integrating AI and Machine Learning with DTs can improve product quality. By analyzing the data collected from the sensors, patterns and anomalies that indicate potential quality problems can be identified, enabling timely action to correct them. [PERSON] et al. demonstrated in his work that AI used for quality management can reduce product defects by up to 15%, significantly improving customer satisfaction and reducing rework costs [11].  
The main objective of this paper is to describe a rigorous method for effectively implementing Digital Twins to improve operational efficiency in the manufacturing industry. The proposed method integrates advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning to develop an accurate digital replication of manufacturing processes. This innovative approach allows for real-time monitoring, simulating, and optimizing industrial operations, providing a detailed and up-to-date view of performance and potential improvements.  
The proposed method involves the following steps:  
1. Data collection and analysis;
2. Digital model creation;
3. Data implementation;
4. Results verification and validation;
5. Analysis of inefficiency causes.  
The developed methodology aims to offer a faithful Digital Twin from which numerous benefits can be derived:  
* Improvement of operational efficiency: Optimization of production processes leads to increased productivity and reduced operating costs.
* Reduced downtime: Adopting predictive and preventive maintenance strategies reduces unplanned downtime, improving machine availability.
* Increased product quality: Advanced quality control and data analysis reduce defects and rework, improving overall product quality.
* Decision support: DTs provide data-based decision support, facilitating the implementation of targeted and timely corrective actions.  
This paper's final section will then address the implications, limitations, and prospective future developments of the methodology above.