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What Is a Digital Twin, Exactly?

What Is a Digital Twin, Exactly?

In this article, we aim to clarify what a digital twin is, its essential components, and how it can benefit your organization.

  • One of the most common questions customers ask us is, “What is a digital twin, exactly?” This confusion is understandable – all you need to do is read a handful of case studies and articles before asking yourself this very question. In part, this reflects the sheer diversity of applications that are clearly digital twins, and the lack of a universally agreed definition. The overlap of digital twins with topics such as Industry 4.0 and the Industrial Internet of Things (IIoT) further contributes to a lack of clarity. In this article, we aim to clear up this confusion, or at least to share our perspective on what exactly a digital twin is, and what it isn’t.
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What is a Digital Twin?

We’ll start with the Digital Twin Consortium’s definition of a digital twin as “a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity.” This is a great minimalist definition but leaves room for interpretation. After all, simply aggregating and presenting sensor data could squeeze into this definition. So where is the dividing line between what is – and isn’t – a twin? Perhaps the more important question is what is needed to realize the maximum value from a digital twin. In our view, a system needs to meet three criteria to fulfill the potential of a digital twin: synchronization, separation, and synthesis.
"A digital twin is a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity."

Synchronization

  • Synchronization refers to the need for any digital twin to access data representing the state of the “real-world entities and processes” within its scope. Practically speaking, this often involves aggregating real-time sensor data from buildings and equipment, but frequently also includes other data such as environmental conditions, location data, and information from business systems. Synchronization can also include episodic updates, for example from manual sampling of process outputs or using reality capture to update geometry data. To support synchronization, a digital twin will usually connect to both operational technology (OT) and information technology (IT) systems within a company. This is common sense; you can’t have a digital twin without an effective way to access real-world data.

Separation

Separation describes the need to segment a digital twin apart from the system or systems it is twinned with. There are several reasons for this, ranging from the essential to the pragmatic.

Security is one essential driver of separation with permissions and authentications built into the data exchange layer.

Pragmatism is another driver, and considering future growth in digital twin applications is a pragmatic approach.

Building a digital twin within the confines of existing applications can be limiting if those applications are not explicitly designed to support digital twin development. It often makes sense to incorporate a digital twin platform adept at integrating and aggregating data from diverse sources.

Synchronization

  • Synchronization refers to the need for any digital twin to access data representing the state of the “real-world entities and processes” within its scope. Practically speaking, this often involves aggregating real-time sensor data from buildings and equipment, but frequently also includes other data such as environmental conditions, location data, and information from business systems. Synchronization can also include episodic updates, for example from manual sampling of process outputs or using reality capture to update geometry data. To support synchronization, a digital twin will usually connect to both operational technology (OT) and information technology (IT) systems within a company. This is common sense; you can’t have a digital twin without an effective way to access real-world data.

Synthesis

Synthesis is a core differentiator that helps a digital twin deliver more value than even an advanced IIoT implementation. A major benefit of a digital twin is the ability to synthesize data from multiple sources to generate novel insights and support applications to progress efficiency, quality, safety, or other business imperatives. The potential applications for this are diverse. In a physical process, a digital twin can generate estimates of internal process states based upon sensor inputs. Such insights into process states can be used to optimize system efficiencies, improve safety, quickly diagnose faults and control issues, or develop simulation-based operator XR training. In a different context, a digital twin can be used to manage complex and widely dispersed assets. Among other uses, such a digital twin for complex assets can integrate with design data, field-based IIoT, and maintenance systems to improve both the planning and execution of maintenance activities, including major overhaul projects.
"A major benefit of a digital twin is the ability to synthesize data from multiple sources to generate novel insights and support applications to progress efficiency, quality, safety, or other business imperatives."

Artificial Intelligence and Digital Twins

Artificial intelligence deserves its own mention in the context of synthesis. AI is not a cure-all, but a thoughtful choice of AI approaches and applications can generate powerful insights into opportunities and looming risks. The insights from a digital twin can support and be expanded upon to generate AI-based insights. 

Incremental Adoption of Digital Twins

The good news is that it’s completely possible to incrementally adopt a digital twin approach. The order of our three digital twin criteria – connectivity, separation, and synthesis – are a reasonable proxy for the order in which to approach such a project. 

Client IT and business organizations should actively engage in driving the adoption of modern platforms and applications for connectivity and security of IIoT and business process data as a foundation. 

The next step is to identify and adopt a digital twin platform to integrate and aggregate data from multiple sources within your IT and OT architecture. It’s important at this stage to have a business case and associated use cases mapped out and to ensure that these and other likely use cases are supported in your chosen platform(s) and related applications.

Integration with 3D Data Sources

Not every digital twin requires integration with 3D data sources, but this is useful for several applications. Organizations responsible for geographically dispersed assets can often benefit from integration with geographic information systems (GIS). 3D geometric assets – often a combination of reality capture and design data – can be useful in renovation projects, training, and for ongoing maintenance activities. Many other applications can effectively leverage 3D data for applications ranging from preventative maintenance optimization to improving customer experience for building tenants.

How TransformXD Can Help

At TransformXD, we are ready to engage with you along every step of your planning and adoption of a digital twin. We are experts in reality capture, including multiple capture technologies and model processing. We can advise and assist in the adoption and integration of digital twin platforms and associated applications. We work with a network of solution providers and studios with 3D and application development expertise, and by combining our internal resources and network, we can scale our support based on your needs. If you’re considering a digital twin solution, we encourage you to reach out to us to discuss your objectives and how we can help.
“We are ready to engage with you along every step of your planning and adoption of a digital twin.”