# Integrated Digital-Twin-Based Decision Support System for
## 7 Discussions
### Broader Implications of the Proposed System
[PERSON], [PERSON] [39] introduced an integrated DT system for monitoring modular building performance, demonstrating its capability to enhance long-term facility management through data-driven assessments. Similarly, [PERSON], [PERSON] [38] applied DT techniques to assess geometric accuracy and quality control in prefabricated facades. While these studies confirmed the benefits of DT in tracking building conditions and optimizing post-occupancy efficiency, they did not address the critical issue of modular reallocation and lifecycle tracking. This research expands on these findings by introducing a decision support system that not only monitors module conditions but also optimizes their reuse, relocation, and integration into future projects. While this study focused on its implementation within RMS systems, the framework can be extended to other RMB applications, such as temporary workforce accommodations, military installations, healthcare facilities, and emergency shelters. Effective module allocation is a crucial challenge in these contexts, as it directly impacts resource utilization, cost efficiency, and logistical planning [65]. By leveraging real-time data integration and automated module selection, the system offers a scalable and data-driven approach to managing RMBs across various sectors. For instance, in the context of student housing or workforce accommodation, the system can facilitate the effective allocation and utilization of modular units, ensuring timely deployment based on fluctuating demand. In military applications, where modular structures are frequently relocated and repurposed for training camps, command centers, or operational bases, the system can enhance logistics planning, reduce transportation inefficiencies, and enable comprehensive asset tracking. Similarly, the healthcare sector, which increasingly relies on MC for pandemic response units, field hospitals, and emergency care centers, can benefit from the system's ability to allocate modules efficiently based on demand, patient surge forecasts, and geographic accessibility [66]. In addition to expanding its applications, the module allocation decision support system can be further enhanced by integrating it with cutting-edge technologies, such as clash detection, risk prediction, and cost estimation tools. The incorporation of AR technologies can provide immersive visualizations and interactive experiences for stakeholders, enabling them to virtually explore modular units, visualize their placement in intended locations, and simulate various scenarios before deployment. In addition, the integration of clash detection tools can preemptively identify conflicts in module placement, minimizing rework, delays, and cost overruns. Furthermore, by incorporating machine-learning-drivenrisk assessment tools, the system can analyze historical data, environmental variables, and logistics constraints to provide predictive risk mitigation strategies, improving operational resilience and sustainability. Financial decision making in MC is another area where the system can add value. By incorporating real-time cost estimation functionalities, the DT framework could analyze material, labor, and transportation expenses, providing stakeholders with accurate budget forecasts and cost-benefit analyses. The system's predictive capabilities could also support long-term planning by recognizing demand patterns and optimizing resource allocation, leading to more sustainable, cost effective, and responsive MC practices.