## 4 Discussion  
The results obtained in this study were made possible through the application of the developed methodology in a controlled environment at a composting plant located in Cajamarca, Colombia. The conditions included the availability of historical data, technological infrastructure for integrating sensors and actuators, as well as a management system that allowed real-time operational adjustments. This context facilitated the implementation of the DTs and enabled the validation of its capability to improve both the efficiency and quality of the compost produced. The composting process is inherently sensitive to various external variables that can affect the validity and reproducibility of experimental results. These include ambient temperature and humidity, human intervention, sensor drift over time, and variability in the composition of organic matter. If unaccounted for, these factors may introduce biases or mask the actual performance of the DTs system. In this study, the system was operated under normal facility conditions, without artificial isolation of the composting beds. The decision not to cover the composting rows allowed the DT to be exposed to real environmental fluctuations, enabling the evaluation of its adaptive behavior in operationally realistic scenarios, such as changing weather and uncontrolled humidity. To mitigate human-induced variability, a standard operating procedure (SOP) was established for key manual tasks such as irrigation, pile turning, and data logging. Additionally, incoming organic material was preselected and homogenized prior to composting to reduce heterogeneity in its chemical and biological properties. While sensors were not recalibrated during the trial period, they were configured using verified factory settings and validated prior to deployment, ensuring acceptable accuracy levels throughout the experiment. Under these conditions, the DT system demonstrated stable behavior and generated valuable recommendations despite the presence of environmental noise. This highlights its operational robustness and suitability for deployment in non-laboratory, real-world environments. The decision to use one-way ANOVA and [PERSON]'s test was driven by the need to clearly and accessibly demonstrate the statistically significant difference in process efficiency before and after the implementation of the DTs. Given the applied nature of the study and the operational context in which it was conducted, interpretative clarity was prioritized over methodological complexity. Nonetheless, the collection of over 500 data points provides a robust foundation for conducting multivariate analyses and regression modeling, which are currently being developed as part of the project's next phase. This initial stage successfully fulfills the objective of validating the model's effectiveness under real-world conditions with strong statistical power. Beyond the results obtained in the composting case study, the proposed methodology stands out for its modular, scalable, and adaptable structure, which enables its application in other productive sectors with specific adjustments. This adaptability lies in the functional separation of its three layers--physical, cloud, and virtual--which can be reconfigured according to the technological and operational requirements of each new context. In precision agriculture, the methodology could incorporate sensors for soil nutrients, crop stress, or satellite imagery, alongside decision models focused on irrigation scheduling, fertilization, or pest control. The virtual layer could simulate plant growth dynamics and provide real-time recommendations. Expected benefits include greater resource efficiency, increasedagricultural productivity, and reduced environmental impact. The validation process revealed several key aspects that contributed to the success of the proposed methodology. What worked particularly well was the integration of low-cost sensors with cloud-based analytics and virtual modeling, which enabled accurate and timely operational feedback.