# Digital Twin for Supply Chain Coordination in Modular Construction
## 5 Discussion  
Schedule deviation along the supply chain is a critical risk factor in modular construction. Such deviation often occurs in the logistics process because modules are vulnerable to transportation uncertainties due to their complexity and large size. To address this issue, this study suggested a digital twin that connects IoT sensors, BIM, and GIS to predict various risks that may occur in the logistics process and calculate an accurate ETA based on different what-if scenarios. One important aspect of the suggested digital twin is that it does not require any data conversion between different data formats (e.g., BIM and GIS), rather it mediates them on demand to avoid any information loss in data conversion. This aspect is particularly important in modular construction where different interdependent stakeholders use a variety of tools and data formats. For example, a manufacturer may not know how transportation will be impacted or what logistics risks will occur during the logistics phase because they normally do not use GIS-implemented vehicle routing tools. Conversely, it may be difficult for a transporter to know the geometry, schedule, cost, and weight of the module they carry because they normally do not use BIM in their work. For such reasons, supply chain simulation across different phases (e.g., production, logistics, and assembly) has not been thoroughly tried or realized. If multiple stakeholders and their different work platforms can be integrated at their application level (not the data level) through a digital twin for project monitoring and information sharing, effective supply chain simulation can be possible. Furthermore, Figure 3 shows that a digital twin can predict potential risks and find alternative routes. Figure 4a indicates that such risk prediction and alternative route searching can predict a more accurate ETA. Also, Figure 4b shows that a digital twin guarantees much less idle time and less standard deviation regardless of traffic conditions. This is because the digital twin uses IoT sensors to update the module's location in real time and re-search the route with potential risks, so it can predict the accurate schedule deviation regardless of traffic uncertainty. Sharing an accurate ETA among all project participants (e.g., manufacturer and on-site assembly teams) can improve team coordination along a modular construction supply chain. Specifically, the module production schedule in the  
Figure 4: Results of logistics simulation: (**a**) Average ETA for a module; (**b**) Total idle time for 80 modules. factory, module shipment schedule, and on-site assembly schedule can be coordinated based on an accurate ETA. For example, if an unexpected accident occurs along the planned route during module delivery and the ETA is delayed by 10 h, the manufacturer may adjust the next module's production and shipment schedules while the on-site assembly teams can perform other tasks (e.g., foundation work) first or adjust their assembly schedule to prevent worker and equipment idle time. Having such ETA information is especially important if the module's factory is far from the construction site (e.g., outside the country) or if the delivery truck has to pass through multiple warehouses or inventory centers during the logistics process. Meanwhile, based on the updated ETA, the digital twin can be used to simulate different supply chain scenarios for efficient coordination. For example, to minimize project schedule delay due to a delayed ETA, various module production, delivery, and on-site assembly scenarios can be simulated for coordinated scheduling. Overall, such flexible supply chain coordination based on a digital twin can help reduce losses from potential logistics risks, consequently maximizing the benefits of modular construction. The ability to accurately predict potential risks in a supply chain also can help widespread applications of modular construction.