Overview
What it is: A Digital Twin is a software-based model of any physical entity or system, from an appliance to a human heart, to an entire building. Once created, a Digital Twin offers a way to monitor a physical system, or subject it to predictive calculations, or otherwise leverage data. Applications are widespread: sectors that could be affected include the automotive industry, the medical industry, manufacturing, and many others.
What it does: A Digital Twin outputs the functioning of the physical system it models and can be reconfigured remotely, in (almost) real-time. For example, a Digital Twin of a tractor could report that one of its parts is malfunctioning; or a Digital Twin of an organization’s department could instantly report bottlenecks in production. The focus is on detailed, real-time logging of integrated data.
Why it matters: Data can be useless unless contextualized. By modeling a physical system, a Digital Twin can take individual pieces of data and place them within representative models. This is already valuable for monitoring purposes, and such data can inform machine learning algorithms to produce detailed predictive models. For example, a Digital Twin of a malfunctioning vehicle could be used to feed a model that predicts the circumstances in which another such vehicle will fail.
What to do about it: Consider whether your enterprise could benefit from more detailed modeling of specific assets or processes. Look into how feasible it would be to collect large amounts of data about this asset or process, to create a Digital Twin. Note that the creation of a Digital Twin is often costly, for example requiring management tools and processes, though the long-term advantages can be striking.
Business Advantages
- Reveals hidden patterns in the functioning of physical systems
- Allows for more detailed observation of assets, providing more prompt reporting of anomalies
- Can create predictive models that enhance the usability of future measurements
- Allows for virtual testing of various kinds
Case Study
Kaeser, a manufacturer of compressed air products, incorporated Digital Twin technology, producing virtual copies of its equipment. These virtual copies allow the company to monitor customers’ assets on an ongoing basis, which then allows the company to offer ongoing maintenance as a service. Moreover, Kaeser can now easily charge customers based on an accurate measurement of their consumption, instead of using a fixed rate. Reportedly, the efficiency gains created by this move have cut their commodity costs by 30 percent.
Digital Twin of an Organization
Digital Twins aren’t just for individual assets. Firms can potentially map all of their functioning onto a Digital Twin of an Organization, or DTO, for enhanced governance, reporting, and modeling ability, among other potential gains. However, this requires extensive data mining of business processes. Firms such as Mavim offer DTO creation as a service.
The Future of Digital Twins
The broad applicability of Digital Twins has generated much speculation about how they could affect many industries. For example:
- Predictive models of body composition generated by Digital Twins could lead to better policing by generating true height and weight from CCTV footage
- Predictive models of organ function could lead to enhanced surgical techniques, among other medical advances
- Predictive models of traffic flow could lead to more informed urban planning decisions
The list goes on. Whether Digital Twin technology will deliver on its promise, however, remains to be seen. It’s worth noting that, depending on the process being modeled, a Digital Twin can be more or less elaborate to construct, which means that Digital Twin technology could be more viable in some sectors than others.