Beyond buzzwords: digital twin maturity spectrum

In the first blog of this series, I explored the basic concept of digital twins. Fundamentally, they are a digital replica of a physical thing - a ‘twin’. But depending on maturity, this replica can range from a simple representation of a local component, all the way to a fully integrated and highly accurate model of an entire asset, facility or even a country[1], with each component dynamically linked to engineering, construction, and operational data.

This broad range of what a digital twin can be has made defining and understanding them extremely difficult, with disagreement on what level of maturity or features constitute a ‘true’ digital twin. Technology and service providers promising more than is currently achievable and inflated market expectations have further complicated things.

In this second blog, I put forward a maturity spectrum in an attempt to offer more clarity and understanding. Undoubtably there will be critics, but it has been tested extensively cross-industry and seems to offer a clear framework for simply articulating what a digital twin is at each element of maturity. I welcome feedback as industry continue working to create a common definition.

Value mapping

The global digital twin market was valued at USD $3.8bn in 2019 and is expected to reach USD $35.8bn by 2025[2].. Gartner predict that half of all large companies will use some form of one by 2021 – resulting in a 10% improvement in effectiveness [3]. Irrespective of how various analysts communicate value, they all anticipate one thing – significant growth and importance of the digital twin. The infrastructure and construction sectors, along with their supply chains, are all now looking to harness that potential.

Process and methodology are key to developing and managing digital twins for the built environment, as is remembering their relevance to the entire asset lifecycle. Their creation and management are a journey, and while a twin can be developed at any point in an asset’s life, it’s most effective when deployed at an early stage, so that captured data adds value for longer.

It’s easy to be distracted by a unicorn-like concept of what a twin could achieve if fully implemented, despite this being largely unachievable and/or cost-prohibitive today. Instead we should more usefully focus on purpose, understanding the benefits of each milestone and how value is increasing along the journey to maturity.

 

Digital twin maturity spectrum

To help achieve this, we propose an industry-agnostic maturity spectrum. This defines the different elements and provides a framework to communicate progress[4] as illustrated in Figure 1.

As a digital twin develops, each element (see below) increases in complexity and connectivity, and subsequently value. It’s important to note that these elements are not necessarily linear or sequential, so a digital twin might possess features of higher-order elements before lower-order ones. However, complexity is best considered logarithmically, whereby the higher-order elements are significantly more complex than the lower-order, foundational ones (Table 1).

It’s essential that the purpose and value of increased complexity and connectedness are clearly identified, justified and realised, which relies on effective implementation and management.

The physical and digital are securely connected via a constellation of data platforms or aggregators. This enables data from asset management systems, document management systems, common data environments, data historians and so forth, to come together in support of new business scenarios.

“While a digital twin can be developed at any point in the asset lifecycle, it is most effective when deployed at an early stage of a project, so that captured data adds value along the way”

The ability to run simulations answering ‘what if’ questions, and to interrogate and analyse the data to inform management of physical assets, is a key part of the digital twin. This is a capability that’s possible across every element of the maturity spectrum.

As we move through the maturity spectrum, each of the elements further enables removing humans from hazardous processes or tasks, intrinsically improving safety.

Element

(logarithmic scale of complexity and connectedness)

Defining principle

Outline usage

Element 0

Reality capture

(e.g. point cloud, drones, photogrammetry, or drawings/sketches)

Brownfield (existing) as-built survey

Element 1

2D map/system or 3D model

(e.g. object-based, with no metadata or BIM)

Design/asset optimisation and coordination

Element 2

Connect model to persistent (static) data, metadata and UK BIM Framework
 

(e.g. documents, drawings, asset management systems)

4D / 5D simulation

Design / asset management

UK BIM Framework

Element 3

Enrich with real-time or right-time data

(e.g. from IoT, sensors)

Operational efficiency

Element 4

Two-way data integration & interaction

Remote & immersive operations

Control the physical from the digital

Element 5

Autonomous operations & maintenance

Complete autonomous operations & maintenance

Table 1 – digital twin maturity spectrum defining principles and outline usage

 

Element 0 – Reality capture (for existing physical assets)

The lowest order element to start a digital twin (relevant only on existing physical assets) is creation of an accurate, as-built data set of the asset geometry or system design. This is the foundational element, over which data is connected and overlaid.

Data is collected through a variety of survey and reality capture techniques (such as point cloud scanning, drones, photogrammetry, drawings/sketches, etc) which are more accurate, efficient and cost-effective than was possible just a few years ago, and significantly more so than traditional survey methods.

Equally, for certain situations or assets a drawing or sketch might be an appropriate method of reality capture.

Element 0 immediately provides value through having greater asset certainty, spatial context and understanding. This is particularly true in sectors where a high proportion of assets are built and ageing, or in high-hazard sectors where it reduces worker exposure to dangerous tasks. Sometimes it’s appropriate to work within these point-cloud datasets, but often there is significant value in going to the next level of maturity.

Element 1 – 2D map/system or 3D Model (object-based only)

Element 1 is the typical entry-point for new assets as an outcome of the design process and is often updated through reality capture (as per Element 0) post-construction to create the as-built model.

Models are purely object-based (surface, shapes, etc), with no metadata or associated information attached. Point-clouds from Element 0 can be proportionally converted, as and when required, into object-based 2D map/systems or 3D models. The conversion is largely a manual process today but will soon be done through semi-automated methods involving machine learning.

At this level of maturity, the twin provides significant value through design/asset optimisation and coordination.

Element 2 – Connected to persistent (static) data, metadata and UK BIM Framework

Further benefits are realised when Element 1 is connected to persistent datasets, such as design information, material specifications, inspection reports, and asset management information; and further enriched with metadata. The data is added, tagged and pulled from existing systems, not necessarily embedded or stored in the model directly.

This provides the basis for engineering, project planning, operations, maintenance and decommissioning. It creates a single reference point from which all data can be viewed and interrogated, reducing errors, uncertainties and costs. It enables faster decision making and collaboration; answering questions such as: Are we on target with our schedule and budget? Where are the highest risk items?

Having an information model of this maturity also allows integrated multi-physics, multi-scale, probabilistic simulations to be run against the asset, either directly in the twin or through connected simulation applications; answering ‘what if’ questions such as: If I change X how will it impact Y?

Adoption of these elements is not necessarily linear or sequential, a digital twin might possess features of higher order, more complex elements, before lower ones.

Element 3 – Enrich with real-time or right-time (dynamic) data

Facilitated by sensors, connected devices and the Internet of Things (IoT), dynamic or operational data is obtained and displayed in real-time (or more appropriately right-time) through one-directional flow from the physical to the digital asset. This data can be analysed to inform and predict the behaviour of the physical asset, and facilitate decision making, with the output or results fed back and updated into the organisation’s existing systems.

This element of maturity is what many technology and service providers would identify as the starting point of a ‘true’ digital twin, though getting to this level of maturity requires several previous steps that are often not detailed.

Developing Element 3 requires sensors and connected devices to actively or passively capture and collect data. This is often the first significant investment.

Element 4 – Two-way integration and interaction

The state and condition of the physical asset can be changed via the digital twin, with output and results fed back and updated into the digital twin. For example, an operator could manipulate a physical valve or activate machinery by initiating the action from the digital twin. This level of integration requires additional sensor and mechanical augmentation of the physical asset.

This integration can also apply between the digital twin and other digital assets, such as other digital twins or even engineering systems and applications. For example, a designer using immersive technology modifies the design, the change is pushed to all connected applications, including the engineering design and process simulation package. The connected applications calculate the impact of the change and update the geometry and data accordingly, with these updates and their impact reflected live into the twin for the designer to see.

This full integration demonstrates the two methods of interacting with digital twins; human-to-machine and machine-to-machine.

Element 5 – Autonomous operations and maintenance

In the future it’s not hard to imagine that the digital twin learns and evolves as a living repository for institutional knowledge, absorbing enough experience about the behaviour of the physical asset that it could become completely autonomous in its operations, able to react to anomalies and upsets and can take the necessary corrective action with little or no human interaction.

Achieving this level of maturity is purely aspirational at present, with only small facets of it for discrete situations possible now. The full ramifications of what Element 5 maturity means, and the quantifiable benefits it will bring, are yet to be fully understood.

Figure I – digital twin maturity spectrum

 

Analytics and simulation engine

Around the digital twin, wherever it sits on the maturity spectrum, is a data ‘analytics and simulation engine’. This interrogates the data to surface patterns and relationships, and enables trainable models based on AI and ML methodologies. It also allows simulations to be run against the physical asset, using any of the data available across the maturity spectrum.

For example, simple simulations could be run using just the reality capture data (Element 0); or multi-physics, multi-scale, probabilistic simulations from higher-order elements. These simulations can run either directly in the twin or through connected simulation applications, answering important ‘what if’ questions such as: If I change X how will it impact Y?

There are many consumers of the data within a twin, each of whom will be securely presented a different view – dependent on their requirements and access permissions – to the constellation or ecosystem of technologies that create this truth.

Digital ecosystem

Each digital twin will fit into an organisation’s overall digital ecosystem like a node in a network, alongside potentially many other digital twins for different assets or systems. These digital twins can be ‘federated’ or connected via securely shared data, and will become an embedded part of the enterprise, as intrinsic in management of the organisation as any other functions such as finance or human resources.

Although organisations strive to achieve the higher-order Elements 3 and 4, the reality is that most are only ready for the Elements 0, 1 and 2. This shouldn’t be of concern, as each milestone provides significant incremental value.

It’s also possible that higher-order elements are not necessary to achieve the organisation’s objectives, and a digital twin should always be created and developed with a specific purpose in mind.

The digital twin maturity spectrum has been published as part of the ‘Digital Twins for the built environment’ whitepaper by the Institution for Engineering and Technology (IET). The maturity spectrum, and Simon individually, were shortlisted for the Management Consultancies Association (MCA) Awards 2020 for ‘Best Use of Thought Leadership’ and ‘Thought Leader Consultant of the Year’

 

Simon is the Digital Energy Leader at Arup, Fellow of the Institution of Mechanical Engineers, and the Delivery Team Lead for the Centre for Digital Built Britain (CDBB) National Digital Twin programme.

A technology developer and chartered mechanical engineer, Simon has a passion for the transformational applications of digital engineering, and regularly speaks on the subject at events around the world. He is the author of the Digital Twin Maturity Spectrum, and has received numerous awards for contributions to the engineering profession.

Simon's background is in the structural/mechanical design and analysis of offshore structures for oil & gas and renewables, and he has experience of living and working internationally. He was elected to Council for the Institution of Mechanical Engineers (IMechE) in 2017, and is an advisor to the IMechE Trustee Board for digital transformation.

Prior to this, Simon was Director of Digital Engineering for the SNC-Lavalin Group, where he is part of the leadership team responsible for the global transformation of the +$5bn consultancy and construction group, and formed and led their Digital Twin Services business.

To find out more about techUK's work on digital twins, get in touch with Tom Henderson (Tom.Henderson@techUK.org) today! You can also visit techUK's #DigitalTwinFuture campaign here!

  • Tom Henderson

    Tom Henderson

    Programme Manager | Smart Cities and IoT
    T 020 7331 2043

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