model and people & organization; (5) digital model and people & orga- nization; and (6) business intelligence and people & organization. How to enable the data transfer from a manual input and error-prone process to a streamlined and highly autonomous level is another key to the effectiveness of a Digital Twin. Numerous technologies are currently available to support these connections, such as the internet, commu- nication, interaction, collaboration technologies, as well as human- computer interaction technologies, i.e., Virtual Reality, Augmented Reality, and Mixed Reality. The connection between the physical and digital world ensures that the collected real-time data are reflected dynamically. Through the connec- tion between the digital model and business intelligence, the data can be analyzed and generated with the corresponding actions, followed by the connection between business intelligence and the physical world. The corresponding control can be sent back and executed in the physi- cal entity. The connection between people & organization and business intelligence allows business logic, processes, and services that dynami- cally reflect the generated work orders and preventive maintenance in the business intelligence system to be sent back and deployed to differ- ent actors within the organization. Given many different models and inputs among different dimensions, to ensure smooth data interaction, a standardized data exchange pro- tocol with unified communication interfaces and standards becomes more important than ever before. A standardized data exchange format reemphasizes the importance of an FM-oriented DDMS. With that as the backbone of the data management system and the foundation of the connection protocol, well-connected pathways can then be established to support a healthy Digital Twin ecosystem for managing the entire project lifecycle seamlessly. Solid Foundation + Well-Balanced Development is the Key The progression of technologies in different dimensions of a Digital Twin brings a tremendous amount of potential and possibility, which cannot advance without maintaining a well-defined DDMS as the foundation along with a well-balanced ecosystem. From the data foundation perspective, a well-functioning Digital Twin involves an increasing amount of data streams and complexity for maintaining the consistency, integrity, and interoperability of data collected from all sources, including but not limited to roll-in and roll-out of new data formats and old data formats, data from upstream BIM, as-built model, and collected IoT data. Consequently, establishing a solid DDMS as the foundation is a must for the success of a BIM lifecycle implementation or a Digital Twin system. From the technology implementation perspective, well-balanced devel- opment towards all dimensions is essential. For example, IoT technolo- gies enable the connected operation process and reshape data utilization for facilities and asset management. However, the implementation pro- cess is not just a plug-in and done scenario without challenges. First, a standardized DDMS needs to be established to ensure seamless data exchange flow. Then, all stakeholders involved in the process must work collaboratively and understand the methodology to utilize the in- novative solution effectively. This means the organizational and user
Figure 4: Digital twin ecosystem
capabilities need to increase simultaneously to maintain the balance of the ecosystem, which requires coordination between facility operations, IT, and business leaders. The learning curve of each applied technol- ogy determines the degree of the overall implementation and further affects the system performance. Lastly, from the financial perspective, a well-established Digital Twin can require high capital costs for system implementation, including DDMS deployment, various sensing and monitoring technologies installation and configuration, as-built model and data collection, business logic programing, and user training. Despite that, the Lifecycle Benefit/Cost Analysis of a Digital Twin demonstrates promising productive Benefit/Cost Ratio (BCR), ROI, and Payback Period. However, the expense is still a determining fac- tor that impedes clients from adopting or understanding the system at the outset. Moreover, associated policy modification, execution plan, adaptability, and other external influences indirectly impact system implementation positively or negatively. Nonetheless, a Digital Twin as the final objective of BIM lifecycle implementation has become a widely-adopted vision and goal to ultimately leverage available tech- nologies to their fullest potential for building lifecycle management. DR. XIFAN JEFF CHEN is the EAM Assistant Director at Microdesk, and head of EAM Strategic Advisory Service. Jeff specializes in providing strategic consulting services for clients, conducting and implementing BIM, EAM and GIS integrated solutions, and developing digital twin methodologies for lifecycle BIM implementation. GEORGE BROADBENT is Microdesk’s Vice President of Asset Management and has worked on a variety of projects including the rollout of Microdesk’s Maximo and Revit integration solution, ModelStream. George works closely with key stakeholders to identify strategies for asset management projects and manages the effort to build out new systems. DR. EVE LIN is a EAM Strategy Consultant and Sustainability Lead at Microdesk, Dr. Eve Lin specializes in providing strategic and technical solutions for clients to facilitate sustainable practices throughout the project lifecycle. Her involve- ment includes building performance simulation, design automation, BIM and GIS integration and development of digital twin solutions.
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csengineermag.com
november 2020
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