## 4 Discussion
This focused implementation allowed for a rigorous evaluation of the methodology's effectiveness, including real-time deployment, sensor integration, operator interaction, and statistical validation of performance improvements. Nonetheless, we acknowledge that reliance on a single use case may limit the generalizability of the findings. The proposed methodology, due to its modular and scalable structure, holds significant potential for application in other sectors beyond composting. In precision agriculture, it could be adapted to monitor variables such as soil moisture, nutrient levels, and crop stress, enabling real-time decision-making for irrigation, fertilization, and pest control. In the context of wastewater treatment, the methodology could integrate parameters like pH, dissolved oxygen, and sludge retention time to optimize biological treatment processes and ensure regulatory compliance. Additionally, in decentralized manufacturing or cold chain logistics, DTs could be used to monitor equipment performance or temperature-sensitive goods, respectively. These examples illustrate the flexibility of the model to support operational efficiency, predictive maintenance, and data-driven decision-making across diverse industrial and environmental scenarios. For successful replication, certain minimum conditions must be met:  
* Basic digital infrastructure, including sensors for critical variables and connectivity (e.g., WiFi, LoRa, or 4G). * Access to cloud or edge computing platforms capable of running simulations and storing data. * Defined performance indicators (KPIs) relevant to the target process. * Operational control points that can be influenced via feedback (manually or automatically). * Willingness and capacity for operator engagement, especially in environments with human-in-the-loop systems. These foundational requirements enable the methodology to maintain its modular integrity while ensuring contextual adaptability. Future studies will focus on piloting the proposed framework in these sectors to test its scalability and robustness under different technical, environmental, and organizational conditions. The proposed methodology is particularly applicable to scenarios characterized by operational variability, limited automation, and the need for contextualized feedback systems. Suitable environments include rural composting units, decentralized agricultural operations, small-scale wastewater treatment plants, and logistics systems requiring traceability and real-time monitoring. Common enabling conditions include access to basic sensing infrastructure, connectivity, and human operators capable of interacting with digital feedback. In these contexts, the modular and scalable design of the methodology facilitates adaptation without requiring full digital maturity or high capital investment. The proposed methodology was designed to support technical interoperability across various systems and industrial contexts using standardized and modular communication protocols. At the physical and edge layers, data acquisition devices (e.g., sensors, PLCs, microcontrollers) can communicate using MQTT, Modbus RTU/TCP, or OPC-UA, depending on the sector's requirements and available infrastructure. These protocols allow for efficient, low-latency, and scalable data transfer across heterogeneous devices. At the middleware and cloud levels, RESTful APIs, WebSocket connections, and Node-RED-based orchestration layers are used to ensure flexible integration with enterprise systems, digital dashboards, and decision support tools. The virtual environment may operate on simulation engines such as AnyLogic, MATLAB R2021a, or custom Python 3.8 models, allowing bidirectional data flows between the DT and external applications, such as ERP, MES, or GIS platforms. For integration with more complex or emergent systems, such as smart factories, circular supply chains, or urban infrastructure (smart cities), the architecture can be scaled horizontally and vertically.