top of page
  • Copperpod

Digital Twin - What is the Future?

As companies continue to aggressively pursue smart sensor technology and Internet of Things (IoT) technology, analyses and visualization of the data collected from the sensors can be combined with augmented and virtual reality technology. Because of the incoming data collected through digital twins, utilizing a virtual or augmented experience is the best way to digest the information and the results gained from digital twins are collected from the performance of simulations. AR/VR provides convenience and a sense of reality to the data obtained and compliments the benefits provided by digital twin technology.

With an estimation of 25 billion connected global sensors by 2021, digital twins will soon exist for millions of things including a jet engine, a human heart, even an entire city that mirrors the same physical and biological properties as the real thing. The implications are extreme, real-time assessments and diagnostics much more precise than currently possible, repairs executed at the moment, and innovation that is faster, cheaper, and more logical.

What is Digital Twin

It is a computer program that takes real-world data about a physical object or system as inputs and produces as outputs’ predictions of how that physical object or system will be affected by those inputs. The twin could also be designed based on a prototype of its physical counterpart, in which case the twin can provide feedback as the product is refined; a twin could even serve as a prototype itself before any physical version is built.

Capabilities of Digital Twin

Digital twinning capabilities has accelerated manifold due to a number of factors :

  • Interoperability. Over the past decade, the ability to integrate digital technology with the real world has improved exponentially. The majority of this improvement is because of enhanced industry standards for communications between IoT sensors, operational technology hardware, and vendor efforts to integrate with diverse platforms.

  • Visualization. The data required to create digital twin simulations can complicate analysis and make efforts to gain meaningful insights, challenging. Advanced data visualization can help meet this challenge by filtering and distilling the information in real time. The latest data visualization tools go far beyond basic dashboards and standard visualization capabilities to include interactive 3D, VR and AR-based visualizations, AI-enabled visualizations, and real-time streaming.

  • New sources of data. Data from real-time asset monitoring technologies such as LIDAR (light detection and ranging) and FLIR (forward-looking infrared) can now be incorporated into digital twin simulations. Similarly, IoT sensors embedded in machinery or throughout supply chains can feed operational data directly into simulations, enabling continuous real-time monitoring.

  • Instrumentation. IoT sensors, both embedded and external, are becoming more accurate, cheaper, and more powerful. With improvements in networking technology and security, traditional control systems can be leveraged to have more granular, timely, and accurate information on real-world conditions to integrate with the virtual models.

  • Simulation. The tools for building digital twins are growing in power and sophistication. It is now possible to design complex simulations, backtrack from detected real-world conditions, and perform millions of simulation processes without overwhelming systems. Further, with the number of vendors increasing, the range of options continues to grow and expand. Finally, machine learning functionality is enhancing the depth and usefulness of insights.

  • Platform. Increased availability of powerful and inexpensive computing power, network, and storage are key enablers of digital twins. Some software companies are making significant investments in cloud-based platforms, IoT, and analytics capabilities that will enable them to capitalize on the digital twin trend. Some of these investments are part of an ongoing effort to streamline the development of industry-specific digital twin use cases.

Applications of Digital Twin

  • Retail: Digital twin implementation can play a significant role in enhancing the retail customer experience by creating virtual twins for customers and modeling fashions for them on it. This technology also helps in better in store planning, security implementation and energy management in an optimized manner.

  • Healthcare: Digital Twins along with data from IoT is important in the health care sector from cost savings to patient monitoring, preventative maintenance and providing personalized health care.

  • Automobile: Digital Twins can be used in the automobile sector for creating the virtual model of a connected vehicle by capturing the behavioral and operational data of the vehicle and helping in analyzing the overall vehicle performance as well as the connected features. It also helps in delivering a truly personalized/customized service for the customers.

  • Industrial IoT: Industrial firms with digital twin implementation can now monitor, track and control industrial systems digitally. Apart from the operational data, the digital twins capture environmental data such as location, configuration, financial models, etc. which helps in predicting future operations and anomalies.

  • Manufacturing: Digital Twin is designed to change the current face of the manufacturing sector because of its significant impact on the way products are designed, manufactured and maintained making manufacturing more efficient and optimized while reducing the throughput times.

  • Smart Cities: Smart city planning and implementation with Digital Twin and IoT data help enhance economic development, efficient management of resources, reduction of ecological footprint and increase the overall quality of a citizen’s life. The digital twin model can help city planners and policymakers in smart city planning by gaining insights from various sensor networks and intelligent systems.

Startling Examples

Companies use digital twin technology for a multitude of reasons including improvement of ongoing operations, training employees and to test new products or procedures before launching them to the real world. Often, Artificial Intelligence and machine learning are used to analyze the model of operations represented by the digital twin no matter where the real facility is located.

  • NASA used the precursor to digital twin technology called pairing technology from the earliest days of space exploration to solve the issue of operating, maintaining and repairing systems when you aren’t near them physically. This was how engineers and astronauts on Earth determined how to rescue the Apollo 13 mission. Today, digital twins are used at NASA to explore next-generation vehicles and aircraft.

  • Digital twins can transform healthcare operations as well as patient care. A digital twin of a patient or organs allows surgeons and health professionals to practice procedures in a simulated environment rather than on a real patient. Sensors, the size of bandages can monitor patients and produce digital models that can be monitored by AI and used to improve care.

  • Digital twin technology has even been sited to refine Formula 1 car racing. In a sport where every second counts, a simulation can help the driver and the car team know what adjustments can improve performance.

  • There is also a digital twin of Singapore including all the variables that go into the management of a city. Digital twin technology helps city planners understand and improve the efficiency of energy consumption as well as many applications that can improve life for its citizens.

  • Digital twin technology can help personnel get comfortable with Internet of Things (IoT) implementation and automation because they have the opportunity to simulate the application prior to it going live. The lessons learned and opportunities uncovered through a digital twin can then be applied to the physical environment.

Top 10 Players

The above chart shows the total number of Digital Twin patents assigned to top market players. With 163 patents, Siemens is the top player in the Digital Twin industry. General Electric is closely behind with 149 patents. Desktop Metal, Guangdong University of Technology, ABB Schweiz, Beijing University of Technology and Johnson Controls Technology have almost an equal number of Digital Twin patents. The graph shows that digital twin technology is not limited to the computer technology field, but medical, civil engineering, electrical machinery, and digital communication industries are also exploring the technology area.


The global digital twin market was estimated to be worth USD 5.1 billion in 2020 and this value is projected to reach USD 115.1 billion by 2035, growing at an estimated CAGR of 23.2%.

Creating a digital simulation of the complete customer life cycle or of a supply chain that includes not only first-tier suppliers but their suppliers, may provide an insight-rich macro view of operations, but it would also require incorporating external entities into internal digital ecosystems. Today, few organizations seem congenial with external integration beyond point-to-point connections. Overcoming this roadblock could be an ongoing challenge but, surely one that is worth the effort. In the future, expect to see companies use blockchain to break down information silos, and then validate and feed that information into digital twin simulations. This could free up previously inaccessible data in volumes sufficient to make simulations more detailed, dynamic, and potentially valuable than ever.


Related Posts

See All


Recent Insights
bottom of page