ing, monitoring, and measuring technology along with IoT allows an abundance of real-time data to be collected and utilized. Evolving from traditionally monthly metered utility bills, real-time outdoor and indoor environments and building systems can be captured, measured, and monitored for operational and maintenance system opportuni- ties. However, the effective response path will rely on the business intelligence of the Digital Twin, which will be described in the fourth dimension of the ecosystem. In addition, the collected data quality also impacts the response results. To prevent garbage in and garbage out accurate and validated data must be obtained to ensure data integrity for multiple purposes. Digital World: Digital Model Maturity The second dimension of the Digital Twin ecosystem is related to the maturity of the digital model. With the adoption of ISO 19650 and an FM-oriented DDMS as a backbone, a streamlined process from PIM to AIM becomes possible. While several BIM performance measure- ment metrics, maturity models, and tools were developed to gauge the performance of BIM, the measurement metrics for an FM-oriented Digital Twin are still not clearly defined. However, the maturity of the data model can be roughly gauged based on the data quality (i.e., data accuracy, richness, consistency), data capability, and lifecycle support. People & Organization How people and an organization interact with and utilize a system is the determining factor of the effectiveness of a Digital Twin. No matter how advanced technologies are or how rich the data model is, a self- driving car won’t start until the driver knows where the power button is and to push it. The people & organization aspect is an often-overlooked area. On the individual level, how well is an individual equipped to interact with their necessary systems? How can you increase an indi- vidual’s competency? On the organizational level, how efficient are the processes within the organization? How do you improve the organiza- tional process from a decentralized operation to a centralized and agile process? Sometimes matching users with the tools they know how to use is more efficient than giving them a supercomputer that they don’t know how to run. Business Intelligence With the solid foundation of the collected data, data models, and clear- ly-defined business logic, this dimension focuses on how to make the system intelligent, such as applying AI, ML, and an Artificial Neural Network to replicate the human cognitive process and learning behav- ior to perform tasks and improve performance. With the foundation of real-time data and asset information, big data and advanced predic- tive analysis can be utilized to bring the unorganized data to several actionable insights to increase the accuracy of prediction and support decision making. The services involved include but are not limited to condition monitoring, function simulation, evolution simulation, dy- namic scheduling, reductive maintenance, quality control, etc. Digital Connections The final dimension includes the six digital connections that bring to- gether the previous four areas, including the relationships between the (1) physical model and digital model; (2) physical model and business intelligence; (3) digital model and business intelligence; (4) physical
Figure 3: A Sample loop of data collection and validation
periodically during the entire service life of the facility. Facility asset information, from detail to high level, can be synchronized into the Digital Twin, and perform analytics and reporting from there. Mean- while, the 3D representation of the facility can help the operation man- agers or line workers to accelerate work order response time, enhance data interoperability, and increase FM efficiency and efficacy. Being a living digital replica, the Digital Twin keeps itself updated during the facility’s lifespan, and is ready to serve as a refreshed “as-built model” for the next renovation. In this way, a Digital Twin reshapes the expectation of “Cradle to Grave” to “Cradle to Cradle” BIM lifecycle. Digital Twin Ecosystem The living virtual reality mapping mechanism enables analyses and re- ports of real-world data to handoff problems before they occur, prevents failure, and possibly develops new plans. The concept of a Digital Twin perfectly connects BIM, CMMS, Business Intelligence (BI), Artificial Intelligence (including machine learning, deep learning, etc.), data sci- ence, GIS, and the Internet of Things (IoT) naturally and logically. “Living” is a keyword in this connected process making everything possible that does not apply to a traditional “static” as-built model. An effective and functioning Digital Twin for facility management requires a well-balanced five-dimensional ecosystem, as illustrated in Figure 4, including Physical World, Digital World, People & Organi- zation, Business Intelligence, and Digital Connections. How effective and intelligent a Digital Twin will be relies on how well each dimen- sion is developed and connected. Physical World: Data Capture Capability To have a virtual replica of the physical world depends on how well the Digital Twin can capture real-time data. The advancement of sens-
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