# Digital Twin for Supply Chain Coordination in Modular Construction
## 4 Case Study
### Test Scenarios  
To find logistics risks and alternative routes based on the proposed digital twin framework, the authors performed a logistics simulation with the case project. First, we created a digital twin that represents the case project. The project's BIM data (e.g., module geometry, weight, and assembly schedule) was input to the back-end system in the digital twin. GIS data (e.g., traffic, geographic data, road accessibility, regulation, curfew, and accident area) was updated in real-time in the Bing Maps application. One important point to note is that BIM and GIS are stored in different platforms and they do not communicate at the data level. This scenario was thus intended to demonstrate logistics simulation without data conversion between BIM and GIS. IoT sensor data should be collected from the truck and module to create a virtual asset, but such a sensor has not yet been implemented. Thus, we created a virtual server that generates hypothetical IoT sensor data including the location of modules in real-time. The IoT sensor data was delivered to the digital twin platform which created a virtual asset in a virtual space based on BIM. The necessary information for logistics simulation in a virtual asset is the geometry and weight of the truck loading a module. These data were selected by Unity and delivered to the Bing Maps API through the application gateway in JSON format. Bing Maps then used input data and GIS to find an appropriate route requested by the simulator. The simulator requested two routes for the case project: one was the route used in the actual case project (baseline-route)--explored without considering logistics risks--and the other was accurately calculated by considering all logistics risks (DT-route). Then, the alternative route provided by the digital twin was found by comparing these two routes. The loss (idle) time that would have occurred in the actual logistics process for 80 modules was quantitatively estimated by comparing the ETA values of the baseline-route and DT-route.  
On the other hand, in the case of DT-route, the module delivery route is continuously re-searched in near real-time until it leaves the factory and arrives at the installation site. If potential logistics risks occur along the expected route, the digital twin finds and presents an alternative route and continuously updates the ETA. Such near real-time logistics simulation is particularly important because unplanned events (e.g., accidents and road construction) may occur during transportation and, accordingly, the module's optimal delivery route and potential logistics risks change over time. It may take significant effort and time to search for a new route by reviewing module information, traffic conditions, and road regulations from scratch. In this regard, the digital twin can help to search for alternative routes and update the ETA in near real-time when such vulnerable transportation conditions occur. This capability is enabled by data from connected IoT sensors which collect information such as the real-time status (e.g., quality and location) of the module. This capability ultimately supports rapid decision-making for project stakeholders if and when unexpected issues occur during the logistics process. For example, if the digital twin detects module damage, such information can be shared in near real-time among stakeholders to set up an onsite repair schedule or to request that the manufacturer ship the damaged part for quick repair.