What Is Digital Twin Technology and Where Lies Its Value?
Editor’s Note: This article is reprinted from the Internet of Things Exhibition, with authorization granted to VCBeat.
Digital twin is a simulation process that fully leverages data such as physical models, sensor updates, and operational history, integrating multidisciplinary, multi-physical, multi-scale, and multi-probability approaches. It enables physical mapping in virtual space, creating a pattern where the physical world in the physical dimension and the digital world in the information dimension coexist and intertwine. Digital twin technology bridges the physical and digital worlds. This article will introduce you to digital twin technology and some practical use cases.
In recent years, a new term has gained popularity in the Internet of Things (IoT) sector: digital twin. Moreover, Gartner recently added the term “Digital Twin Optimization (DTO)” to its list of the Top 10 Strategic Technology Trends for 2019.
With the continuous advancement of digital twin technology, it has evolved from isolated digital representations of individual projects into interconnected system models. An increasing number of enterprises are leveraging digitalization to coordinate personnel and manage project timelines, thereby delivering enhanced business outcomes. But how does digital twin technology work? Where lie its greatest opportunities?
As digital twins mature and their applications expand, this article focuses on clarifying what digital twins truly are, how to distinguish genuine digital twins from the plethora of information available, and the future trends of this technology.
Simply put, a digital twin is the digitization of IoT devices. By using sensors to collect real-time data on physical assets, digital twins serve as a bridge between the physical and digital worlds. This data is then used to create a digital replica of the asset, enabling its understanding, analysis, manipulation, and optimization. Other terms that have been used over the years to describe digital twin technology include virtual prototypes, hybrid twins, virtual twins, and digital asset management.
Although digital twins have existed for decades, it was only in recent years, with the rapid rise of the Internet of Things (IoT), that they became widely recognized as an essential tool for future development. Digital twins are gradually entering the public eye and garnering attention. By integrating technologies such as Artificial Intelligence (AI) and Machine Learning (ML), digital twins combine data, algorithms, and decision analytics to create simulations—virtual representations of physical objects. This enables the identification of issues before they occur, monitoring of changes in physical objects within the virtual model, AI-driven diagnosis through complex multidimensional data processing and anomaly analysis, prediction of potential risks, and rational and effective planning or maintenance of related equipment.
Having addressed the more elusive question—what is digital twin technology—we now explore how to leverage it to improve business processes. Traditionally, digital twins were used to enhance the performance of individual devices, such as wind turbines or jet engines. However, in recent years, digital twins have become increasingly sophisticated. They now connect not only single devices but also multi-device systems and even entire Internet of Things (IoT) ecosystems. As more devices are integrated into digital twins, device operational workflows and operator information are also incorporated, thereby enhancing the system’s ability to solve complex problems.
A typical example of digital twin applications is in healthcare. By creating a digital twin model of a hospital, administrators, doctors, and nurses can access patients’ health status in real time. Using sensors to monitor patients and coordinate equipment and personnel, digital twins provide a better approach to analyzing workflows and alerting relevant staff at the right time when immediate action is required.
This solution can improve emergency room utilization and streamline patient flow, reduce operational costs, and enhance the patient experience. According to reports, one hospital achieved a 900% reduction in comprehensive costs after implementing digital twin technology. Furthermore, digital twins can predict and prevent patient emergencies, such as cardiac or respiratory arrest, thereby saving more lives. In fact, after implementing digital twin technology, one hospital’s healthcare network reduced Code Blue (emergency) events by 61%.
Another example of digital twins making a significant impact at the application level is the real estate and construction industry. Digital twins enable building operators to integrate previously disconnected systems—ranging from security and heating, ventilation, and air conditioning (HVAC) to wayfinding systems—to facilitate new decision-making, optimize workflows, and enable remote monitoring. Digital twins can also be used to control workspace and environmental conditions within rooms, thereby enhancing the occupant experience.
By optimizing systems and connecting personnel, owners and operators can leverage digital twins to reduce costs, lower post-commissioning maintenance expenses, and enhance overall asset value while improving utilization rates. In fact, digital twins can reduce the operating costs of certain buildings to as low as 88 cents per square foot annually.
The term “digital twin” has emerged across various industries. But can it truly be applied to all of them? In fact, we are on the verge of a digital twin explosion. Gartner’s research found that in 2018, 48% of IoT projects were using or planning to use digital twin technology. Furthermore, by 2020, 50% of large manufacturers will have launched at least one digital twin use case, and solutions related to digital twin technology will triple by 2022. In other words, digital twins are here to stay.
The key lies in how to implement digital twins. Digital twin technology is being deployed in new and more mature ways, combining sensors, artificial intelligence, and machine learning to address the biggest challenges facing IoT use cases. To maximize its practicality, digital twins need to be supported by high-performance databases that can aggregate and process large datasets in real time.
In the process of building smart cities, digital twins offer greater possibilities. For instance, applying digital twin technology in hospitals or real estate buildings not only provides decision-making support for facility administrators or owners but also creates a favorable environment for occupants. Digital twins adopt a human-centric approach to problem-solving and contextual analysis, ultimately integrating connected devices into IT systems to address major challenges and generate long-term value.
In short, for IoT companies that have already adopted digital twin technology, digital twins represent the next step in their digital transformation journey. Digital twins can enhance efficiency, optimize processes, predict issues before they occur, and drive future innovation. The potential of digital twin technology is well worth exploring in depth!