## 3 Results
### Propositional Stage
\begin{table}
**Component** & **Sensor Type** & **Frequency** & **Thresholds** & **Failure Model** & **Triggered Actions** \\ \hline Internal Temperature & DS18B20 (digital) & 4 times/day & \(-\)55 to 125 \({}^{\circ}\)C & Sensor drift, low battery & Suggest turning or adjusting moisture \\ \hline Moisture Level & Steel Soil Sensor + LM393 & 4 times/day & 0–100\% & Corrosion, signal degradation & Trigger irrigation \\ \hline Operator Logging & Manual with SOP template & 4 times/day & N/A & \begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \end{tabular} & \begin{tabular}{p{113.8 pt} p{113.8 pt}} \end{tabular} \\ \hline Virtual Feedback & AnyLogic simulation model & Every 24 h & \begin{tabular}{p{113.8 pt} p{113.8 pt}} Target compost profile \\ \end{tabular} & \
\begin{tabular}{p{113.8 pt} p{113.8 pt}} Model mismatch due to data gaps \\ \end{tabular} &
\begin{tabular}{p{113.8 pt} p{113.8 pt}} Recommend pile adjustment or delay \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 3: Configuration of DT components, measurement parameters, and triggered decision logic. To evaluate the impact of the DT implementation on process efficiency, a structured and sustained data collection process was conducted. In this context, \"Process Efficiency\" refers to the amount of stable compost produced per unit of raw organic material over a defined period, accounting for factors such as moisture optimization and accelerated transformation rates. Over a period of 29 consecutive weeks, trained personnel recorded four measurements per day, Monday through Friday, targeting key variables such as internal pile temperature, humidity, and material transformation indicators. This schedule, aligned with the facility's operational availability, allowed the capture of diurnal variation and gradual trends without disrupting daily workflows. The volume and temporal resolution of the resulting dataset provided sufficient statistical power for rigorous analysis. A one-way analysis of variance (ANOVA) was performed to compare the mean efficiency before and after the deployment of the DTs. The null hypothesis (H\({}_{0}\)) stated that no significant difference would be observed between both conditions, while the alternative hypothesis (H\({}_{1}\)) posited that the DTs would produce a measurable improvement. The results yielded high statistical significance (\(p<0.001\)), confirming that the implementation led to a notable increase in average efficiency. Additionally, the Levene test showed a significant reduction in performance variability (\(p<0.01\)), indicating that the process became more consistent and stable. These findings validate not only the effectiveness but also the robustness and reliability of the methodology under real-world operational conditions. During this validation, significant results were observed. First, a 10% increase in composting efficiency was achieved, significantly improving the transformation of organic matter into fertilizer. Additionally, the implementation of the DTs minimized inconsistencies between processed batches, resulting in more homogeneous and higher-quality products. Another key outcome was the optimization of resources, thanks to the DT's capabilities for facilitating predictive analysis and real-time adjustments, which improved operational control and reduced waste during the process. Despite being conducted in a real operational environment with non-specialized personnel and low-cost technology, the case study adheres to principles of ecological validity, enhancing the applicability of the model in similar contexts. The presence of external factors was not considered a limitation, but rather an intentional part of the methodological design. To ensure a reliable result, specific control and mitigation measures were implemented. Table 4 outlines the main external factors that could potentially affect the performance and consistency of the composting process during the DT validation.