# Integrated Digital-Twin-Based Decision Support System for
## 7 Discussions
### Limitations and Future Research Directions  
While the proposed DT-based support system for a relocatable module allocation plan offers significant advantages and potential, it is essential to acknowledge its limitations and identify avenues for future research and improvement. One primary limitation is scalability, as the system has only been tested on a limited case study. Expanding the system for multi-regional or nationwide applications may present computational and operational challenges, requiring enhanced cloud computing and system optimization strategies [70]. Future research should explore scalable DT architectures that accommodate larger datasets and higher processing demands, ensuring system efficiency at a broader scale.  
Another limitation lies in the adaptability of the system to evolving client requirements. The current implementation assumes static project schedules and requirements, which may not accurately reflect the dynamic nature of real-world scenarios. Incorporating mechanisms to capture and adapt to evolving client needs would enhance the system's versatility and responsiveness, ensuring that module allocation strategies align with the latest project requirements. Moreover, the current system does not provide comprehensive lifecycle tracking of modular units beyond their immediate usage period. Once a module completes its designated schedule, the system does not consider whether it should be reassigned to a new project or returned to storage for maintenance and refurbishment. This gap could lead to inefficient resource allocation and potential conflicts with ongoing projects. Integrating a comprehensive module lifecycle management system would address this limitation and ensure the optimal utilization of modular assets throughout their lifespan.  
The lack of enhanced visualization tools is another limitation. The current system provides tabular data outputs; it does not include interactive geospatial mapping for real-time module allocation visualization. Integrating GIS-based visual analytics tools can significantly improve decision making and stakeholder engagement, enabling an intuitive understanding of spatial relationships and logistical constraints. Additionally, while the system's functionality and performance have been evaluated through simulations and test cases, it has not yet integrated real-time analysis data such as module status and condition ratings. Incorporating user feedback, condition ratings, and real-world deployment scenarios would be crucial in refining the system's usability, identifying potential issues, and ensuring its practical applicability in diverse MC projects.  
Finally, although this study addresses module allocation and logistics, energy efficiency and material recyclability are not explicitly incorporated into the DT framework. While these aspects are essential in comprehensive lifecycle management, they are beyond the scope of this study. Future research could expand the DT framework to integrate sustainability metrics, such as carbon footprint tracking, energy performance optimization, and end-of-life material recovery analysis, ensuring that RMB projects align with circular economy principles. By addressing these risks, challenges, and limitations, future advancements in DT-based decision support systems for MC can enhance their scalability, adaptability, and sustainability, ensuring widespread applicability and long-term impact in the industry.