## 5 Conclusions
### Practical Contributions  
The methodology was successfully validated in a real composting facility located in Cajamarca, Colombia, under operational conditions and using non-specialized personnel. This validation generated several concrete and replicable outcomes:  
* A 10% increase in efficiency and reduction in process variability. * A monthly gain of 1200 kg of compost, increasing total production without changes to the physical process, solely through digital optimization. Figure 7: Overview of the proposed DT methodology. * A Return on Investment (ROI) of 18,957.6%, derived from minimal capital investment (\(\prec\)$50 USD), showcasing the feasibility of DT adoption in low-budget contexts. The DT enabled dynamic adjustments to aeration and irrigation, improving resource use and reducing waste. The case study confirmed that real-time decision support, even with basic connectivity and computing infrastructure, can significantly enhance operational outcomes. Importantly, the implementation demonstrated ecological validity: results were obtained in an uncontrolled, real-world setting with external factors such as climate and operator variability, reinforcing the robustness and practical reliability of the model. Theoretical Contributions  
This work advances the theoretical foundations of DTs by proposing a context-sensitive definition that incorporates predictive intelligence, dynamic feedback, and actionable virtual environments. Unlike purely conceptual frameworks, this definition is grounded in measurable outcomes and operational metrics. The research addresses an evident gap in DT literature by providing a domain-specific implementation framework for organic waste treatment, especially composite, which has traditionally been excluded from industrial digital transformation efforts. While most existing methodologies focus on manufacturing, this work contributes a novel application schema that supports real-time adaptation in biologically variable processes. In addition, the study provides a quantitative comparison with established methodologies, such as the model by [24], using a multi-criteria evaluation that includes scalability, interoperability, cost, and replicability. The results of this comparative analysis, supported by a doctoral research framework, highlight the superiority of the proposed model in terms of domain adaptation, economic impact, and practical relevance. Finally, this work introduces the concept of minimum replicability conditions--a set of foundational requirements (e.g., sensor access, KPIs, human engagement) that allow organizations to assess their readiness for DT adoption. This theoretical contribution bridges the gap between abstract frameworks and operational feasibility. Long-Term Effects  
The results suggest that DTs can act as catalysts for systemic transformation in production systems by enabling continuous improvement and real-time optimization. In the long term, the methodology can support transitions toward circular economy models, improving sustainability through data-driven decisions and feedback-controlled operations. Future expansions include:  
* Multi-case validations across sectors like precision agriculture, wastewater treatment, and smart logistics. * Integration with AI, blockchain, and XAI (explainable artificial intelligence) to enhance transparency, traceability, and adaptive learning. The framework's modularity and extensibility allow for continuous evolution, keeping pace with technological advances without requiring disruptive infrastructure overhauls. Implementation Costs and Scalability  
Despite the initial perception of high investment for DT projects, this study demonstrates that costs can be dramatically reduced through strategic design choices and open technologies. The extremely high ROI observed in the case study confirms that even low-resource organizations can benefit from DT adoption.