Perhaps the simplest explanation for digital twin technology is as a ‘flight simulator’ for business. Sophisticated flight simulators have been in use in the aircraft industry for some time, and anyone who’s seen the movie Sully saw them in action, with members of the National Transportation Safety Board recreating alternate scenarios for the pilot’s famous controlled crash landing on the Hudson which saved 155 lives.
The concept of a flight simulator for business is emerging, enabling managers and professionals to look across the systems and facilities within their enterprises, planning what-if scenarios, and viewing the impacts of real-time events. This simulation could involve digital twins of a technology infrastructure, an entire building, or a supply chain network.
Also: Digital twins are optimizing supply chains and more. Here’s why enterprises should care
“We are seeing increasing adoption of digital twin technology across industries, but there are a few that are experiencing particularly rapid growth,” Bill Quinn, futurist with TCS, told ZDNET. “For example, manufacturing and production is an area showing strong growth. The need for demand forecasting, inventory management, and real-time visibility into manufacturing processes make digital twins particularly attractive to this segment.”
Quinn said the highest level of adoption of digital twin technology is still ahead of us: “Healthcare, mobility, and retail are the top areas expected to see the greatest adoption within the next three years.”
A leading sector in digital twin technology has been the automotive industry, “which has been using digital twin technology for some time to simulate vehicle hardware, such as to run crash tests virtually or to optimize the aerodynamics of a car,” said Tom De Schutter, vice president of engineering at Synopsys.
“As we move towards software-defined vehicles and more software is used in today’s vehicles for features like 360-degree cameras, heads-up displays, and advanced driver assistance systems, the automotive industry is increasingly adopting electronics digital twins, which models the main electronics components in a vehicle through virtual electronic control units to enable the validation of the software which will be deployed in the car.”
Also: 6 digital twin building blocks businesses need – and how AI fits in
While those developments are significant, the challenge is that implementing a business digital twin is not as quick and easy as implementing and taking off with a piece of software like Microsoft Flight Simulator.
These challenges were described in a recent paper published by Elsevier, in which the team of co-authors, led by Akram Hakiri of the University of Carthage, pointed out that “existing work on DT focuses primarily on the modeling perspective, and pays less attention to simplifying the control and management of industrial IoT networks.”
Additional barriers include “prohibitive complexity with network deployments, security risks, and need for new business models and practices,” the researchers stated. The researchers said security and privacy are the main concerns “for sharing IoT data among distributed digital-twin infrastructures, which depends on data being fed back between physical objects and virtual models.” In addition, there are inconsistent standards within networks and software implementations.
The bottom line: digital twins will evolve gradually as standards and business cases coalesce. You can’t go from here to there without building foundational digital competencies.
Also: XR, digital twins, and spatial computing: An enterprise guide on reshaping user experience
“At a basic level, digital twins require IoT sensors, connectivity, modeling software, compute, and reporting tools,” Quinn said.
“The sensors measure the real-world person, or object, for which the twin is being created; the connectivity transmits the data collected by sensors to a central computer; the modeling software, aided by processing power, creates the digital twin within the central computer; and the reporting tools provide actionable outputs to the owners of the digital twin.”
Availability of “virtual models at the right abstraction level and with the right performance is the most typical hurdle,” said De Schutter. The skill set to develop these types of models is specific. The developers need to understand the exact use case for which the models will be used to create a model accurately while optimizing performance to run long software workloads.
This requirement means that model developers need to understand the hardware/electronic control units and consider how software developers will use the models for their software development and testing tasks.
Also: How digital twins and XR will transform product development in virtually every industry
In addition, Quinn said adding other technologies can enhance modeling and usability: “For example, AI will make it possible to run thousands or even millions of simulations on the digital twin to identify novel designs, use cases, or optimizations of the physical object. Virtual and augmented reality will create more realistic and immersive experiences of the digital twin. This is critical for users, such as maintenance technicians, surgeons and product designers. 5G/6G and other advanced connectivity technology will allow digital twins to be leveraged in remote locations.”
To map out the route to digital twin development, the Digital Twin Consortium recently published a maturity model that identifies the stages of progress toward well-functioning digital twins:
1. Passive
- Vision and digital ambition: Lacking. “The need for a digital vision and strategy isn’t clearly understood at a senior level,” the report states. There is “little or no awareness of digital technologies.”
- UX and modeling: The authors suggest there is “post-reality monitoring and capturing,” likely involving “sketched maps for design, no models of behaviors or dynamics.”
- Technology integration: Are you kidding?
2. Starter
- Vision and digital ambition: “Some awareness of the need for a digital vision and of the major technologies that shape the industry.”
- UX and modeling: “Physical entities modeled to have a similar visual appearance and rendered in 2D or 3D drawings or models. Processes modeled but only within silos and without any consistency across the business.”
- Technology integration: There is “some integration between systems such as enterprise systems or collaboration platforms.”
3. Progressive
- Vision and digital ambition: “Aware of the broad technologies that shape the industry including digital twins but not clear on the business outcomes.”
- UX and modeling: “Quasi-real-time monitoring and capture — only within the constraints of how real-time the data is modeling of behaviors and dynamics.”
- Technology integration: “Linked interactive data, especially common data: GIS, BIM, IoT data, Systems data, etc. Flow of data unidirectional and bidirectional with real-time analytics.”
4. Mature
- Vision and digital ambition: “Understand the impact and importance of digital twin technology with defined business outcomes but not making full use of its potential.”
- UX and modeling: “Near real-time synchronized, federated, and interactive operations using digital thread (two-way integration and interaction). Visualization and simulation are incorporated into the models.”
- Technology integration: “Frequency of synchronization between systems are predictable and deterministic. Connected and interoperable systems using System of Systems.”
5. Master
- Vision and digital ambition: “Digital twin technology is used to shape and continue to update and communicate the vision and achieve business outcomes.”
- UX and modeling: “Autonomous operations and maintenance. Real-time synchronization — that is defined by the use case.”
- Technology integration: “Data in the business context is linked throughout the lifecycle — upstream and downstream. Communication protocols allow for interchangeable systems — exchange between a simulation and real system or between different systems.”
Another challenge to using digital twins effectively is getting organizations on board financially. “Building and maintaining digital twins can be expensive,” said TCS’ Quinn.
Also: Deploying digital twins: 7 challenges businesses can face and how to navigate them
“Companies need to understand the upfront and ongoing investment and be able to demonstrate a clear ROI to secure and maintain budget approval. That said, the cost of digital twin technologies is coming down rapidly and there is also a cost to avoiding implementing digital twins, including falling behind competitively, delaying the skills advancement of your workers, and lost efficiencies and innovation that comes from digital twins.”
