Exploring Business Models and Benefits of Digital Twins
The application of Digital Twin technology has been a hot topic often discussed with our clients, as we evaluate the most relevant problems and challenges urban development project face in workflow executions.
Digital twins are becoming increasingly popular in the world of building design and management. While inaccurate data, which often comes from BIM, as a pre-cursor, has been a challenge in developing these tools. The advancements in cloud-based technology for data accessibility and automation are helping to reduce the amount of incorrect information in project execution.
In our recent REDC.TECH Summit, we’ve explored digital twin business models and how they can impact industry growth and operational efficiencies. We’ve also discussed the benefits of combining digital twins with smart buildings to improve building management optimization, energy efficiency, and property maintenance.
Digital Twin Business Models and Industry Growth
Inaccurate data is a widespread problem in the development of digital twins. While some basic information like quantities and square footage may be correct, more complex data can be entirely wrong, which makes the construction process less reliable. However, as we work on developing these tools further, we are hoping that the accuracy of data will improve.
The major challenge comes from sourcing data from different sources, digital and analog, and coming from many stakeholders. In that process, ensuring data correctness and timely updates is critical to reflect changes in equipment specs or purchasing decisions, for example.
Ideally, the industry should have a single source of data, so that there’s no need for a manual data entry process, but this is not the case yet. Nevertheless, the advancements in cloud-based technology and automation can help reduce the amount of incorrect information inputs and make data more accurate in the long run.
It is important to realize that there is not one best or one single use for digital twins. It really depends on the use case’s end goal. For some digital twin applications, the simulation may be the key use (e.g., when wanting to test “what-if” scenarios); for others, emulation may be the key use (e.g., when checking for configuration errors or when training operators); and for others still, prediction is the key use (e.g., when estimating future performance).
Lastly, to reach desired benefits and financial return for the investments in Digital Twin applications that need to be evaluated with the opportunity cost of not implementing a digital twin for the respective use cases/proposed application.
AEC Industry Growth and Combining Digital Twin with Smart Buildings
The digital twin is the next evolution of BIM and VDC, bringing in a lot more data points, and information, and hopefully increasing the accuracy of that data over time. The industry growth for digital twins is expected to be huge, with Gartner predicting the market to reach $48 billion soon. For buildings, 75% of those surveyed want to implement a digital twin, but only 13% currently are.
Smart Buildings and Digital Twin
Smart buildings are a great example of pre-IoT or automation data that can be utilized to improve optimization, energy efficiency and retrofitting of buildings. It’s possible to combine data analytics from a smart building with a BIM/VDC model and create a 3D representation virtually to analyze it better.
Digital Twin Business Models
There are several potential business models for digital twins, including asset-based where the owner develops the tools, platform-based which involves bringing various stakeholders and data sources, and subscription-based, like companies that build websites, which maintain and service the building for maintenance or property management.
As it stands, the integration of feedback, control, and automation, which is the ability to make changes based on data from the digital twin model, isn’t far off yet.
In conclusion, the industry still faces some challenges. However, progress has been made, and with the expected growth of digital twins, it’s crucial to approach the topic carefully, understanding the benefits of different digital twin models and the long-term goal of achieving greater accuracy for data.