# Digital Twin for Supply Chain Coordination in Modular Construction
## 2 Literature Review
### Digital Twin for Real-Time Monitoring and 'What-If' Analysis  
A digital twin is a virtual representation of a physical object or system used to understand and predict potential issues across its lifecycle. A digital twin consists of three main components: the physical object, the virtual object, and the linkage between them [27]. Together, these components allow real-time object monitoring, data visualization, data analysis, and 'what-if' simulation to head off potential issues before they occur, deducing useful insights and opportunities. The digital twin was first proposed in the context of product lifecycle management [12] and has been applied most prominently by aircraft and aerospace industries to mirror vehicle conditions, systems, or processes for simulation analytics [13; 14]. Since then, the digital twin has been applied in many different areas such as robotics [28], health monitoring [29], and manufacturing [15]. One important potential benefit of the digital twin in construction is to complement BIM and GIS for more 'live' information sharing. Digital twins can use BIM and GIS as a backbone, but also includes entire assets such as workers, materials, equipment, systems, and processes. For example, a more detailed and interactive construction process can be visually simulated using 4D BIM, human, and equipment twins. Digital twins allow for real-time asset visualization, behavior and performance monitoring, and operation simulation and optimization--all of which are important pieces of information to be shared among project participants. Particularly, the IoT sensor, a device that detects one or more physical asset conditions, converts these conditions into human and/or machine-readable signals, connects to the internet to communicate with others [30; 31], and enables digital twins to be synchronized with the status of physical assets. There are many different types of IoT sensors: GPS [32], image sensor [33], proximity sensor [34], radio frequency identification sensors [35], motion sensors [36], and biosensors [37]--all of which are widely used in construction. On the other hand, the digital twin can exploit artificial intelligence (AI) for rich data analytics--challenges of previous BIM and GIS applications [38; 39]. Recently, digital twins have been applied widely in construction for different purposes such as information sharing among project participants [11], smart city level infrastructure management through linkage with GIS [40], sustainability (i.e., plan, design, operation, and maintenance) evaluation of railway station buildings [41], bridge construction management [42], collapse assessment [43], and real-time monitoring and anomaly detection for built assets [44]. These studies have shown that the digital twin can effectively monitor assets in near real-time and test 'what-if' scenarios to detect risks and optimize operation in a dynamic construction environment. If the digital twin is applied in logistics together with BIM and GIS in modular construction, it can monitor and simulate different logistics scenarios in real-time to predict any potential logistics risks and to estimate more accurate delivery routes and arrival times for true 'just-in-time' delivery. In addition, the whole supply chain process (i.e., manufacturing, logistics, and assembly) can be simulated and integrated through the digital twin to find optimal operation strategies (e.g., optimal module ordering). Despite this potential, digital twins have mainly been used in the construction research domain for the operation management of individual products (e.g., equipment, robots, and buildings), leaving untapped areas for logistics monitoring [16]. One critical reason for this untapped potential is that BIM and GIS should be combined on one platform for logistics simulation, but they are created by different tools with different data formats.