23 October 2023
By Livia Martinescu
In the rapidly evolving digital landscape, new concepts and terminologies emerge frequently and often cause confusion — at least in my case. I became aware of the term ‘digital twin,’ along with its complexities and intricacies when Oxford Insights worked with the Digital Twin Hub at Connected Places Catapult. For the DT Hub, OI put together a new collection of case studies that explore novel Digital Twin applications.
Before exploring the diverse uses of digital twins across industries, I needed to understand what digital twins are in the first place – and we cannot talk about digital twins without mentioning two other closely related concepts: digital model and digital shadow. In this article, I want to provide a short explanation of these concepts, highlight their applicability, emphasise the value of connecting and standardising them, and shed light on the growing eagerness to label everything as a digital twin.
A digital model is a virtual representation of a physical object, system, or process. It can have various forms, such as 3D models, computer-aided design (CAD) files, simulations, or mathematical algorithms. Digital models enable the visualisation, analysis, and manipulation of objects or systems in a digital environment, aiding in design, optimisation, and testing. Usually, a model represents a prediction or guess as to how a physical object, system, or process might operate in the future or in a particular environment.
For instance, an architectural firm might use a digital model to create a virtual walkthrough of a building design, allowing clients to visualise and make changes to the layout and aesthetics before any construction begins.
A digital shadow is an evolving digital representation that mirrors the current state and behaviour of a physical entity or system. It collects data from the asset (this could be a database, a railway system, or a banking platform) through sensors, Internet of Things (IoT) devices, or other sources and provides a feed of information that is fed into the model. This means that a digital shadow is up-to-date with the physical entity. It represents the asset to a sufficient level of detail, so it is useful to gain a good understanding of it.
Typically, digital shadows are mathematical models, but they could also be 3D representations and often focus on specific aspects (such as performance metrics, operational conditions, or environmental factors). They enable monitoring, predictive analysis, and decision-making.
Let’s use another example to better understand how digital shadows are used. A manufacturing company might create a digital shadow of their production line. The digital shadow allows the company to monitor and analyse production processes, identify bottlenecks, and make data-driven decisions for process optimisation and quality improvement. In case of any unexpected disruptions or data loss, the digital shadow can be used to restore the most recent operational state and minimise downtime.
Digital twins integrate the virtual and physical realms by creating a real-time connection between the physical entity and its digital counterpart, where the physical object gives information to the digital replica and vice versa. Digital twins simulate, monitor, and control physical objects or systems, facilitating analysis, optimisation, and predictive maintenance. They enable live feedback loops and foster insights for improving performance, efficiency, and reliability.
To put it differently, there is a two-way interaction between the physical and the digital environments, where the digital replica is able to change how the physical entity operates. For instance, this is crucial if we are talking about a critical asset that is important in terms of the value it is adding, in areas of national importance, such as safety, or competitive advantage. In these cases, it is key to ensure that there is sufficient resilience when it comes to the ways in which the asset will respond to potential deviations.
In practice, consider the example of a gearbox with a predicted 15-day lifespan, currently at day 10. However, through usage, it becomes evident that the gearbox has experienced more damage than expected and is physically equivalent to being on day 14. The digital twin detects this discrepancy and responds by imposing usage limits on the gear to minimise further wear and tear, ensuring it reaches its full intended lifespan. By using the gathered data and setting rules through the digital twin, the gear is utilised for more than one additional day. Consequently, the digital twin introduces innovative approaches to asset usage. Decisions made using the digital replica directly impact the real-life performance of the asset.
Even though I have laid out the concepts in what may look like a progressive order, it is important to note that there is no hierarchy between digital models, shadows, or twins. Rather, it is better to look at a system that requires a digital representation in terms of decisional needs and the types of desired value to make a decision on the solution that is required. Where is each of them most suitable?
Digital models find applications across various industries and disciplines. They are mostly used in architecture, engineering, manufacturing, and entertainment for design, prototyping, and visualisation purposes. In scientific research, digital models are useful in simulations, data analysis, and hypothesis testing.
Digital shadows are valuable in situations where monitoring and analysis are critical. They are commonly employed in industries such as logistics, supply chain management, energy, and transportation. By collecting and analysing data from sensors and IoT devices, digital shadows facilitate predictive maintenance, anomaly detection, and optimisation of processes and operations.
Digital twins are applicable in complex systems such as manufacturing plants, infrastructure, healthcare facilities, and smart cities. They enable real-time monitoring and control, predictive maintenance, and performance optimisation. Digital twins can also play an important role in optimising energy consumption, enhancing product development, and enabling remote monitoring and assistance.
They are applicable across industries to improve how any complex system is operating. Here, I am referring to ‘complexity’ in terms of the degree of interaction between different data types, sources, and uses. The more intricate the data, the higher the level of complexity. The benefit that can be derived from the implementation of a digital twin is directly proportional to the complexity of the system or asset. This is because complex systems usually require a holistic and dynamic understanding, which digital twins provide by integrating data, simulation, and analytics. They are used in factories, plane engines, oil and gas turbine engines, gearboxes, and even hospitals.
Moreover, digital twin technologies are becoming increasingly accessible. Sensors are becoming cheaper to produce, and artificial intelligence technologies are becoming more available and widely-used, which means that developing digital twins is time and cost efficient.
As previously explained, digital twins have gained attention as a powerful concept for optimising performance, enhancing operational efficiency, and enabling predictive maintenance. Consequently, there is a tendency to use the term ‘digital twin’ broadly to encompass any digital representation that exhibits some form of virtual-physical integration. However, it is important to distinguish between digital twins and other digital models or shadows to ensure clarity and avoid diluting the concept’s significance.
The increasing eagerness to label various digital representations as digital twins stems from the recognition of their potential benefits. As ‘digital twins’ are a fairly recent concept, companies want to project the idea that they are offering a competitive advantage through using the ‘best’ technology out there. Sometimes, this is not justified in terms of the actual maturity of what is actually developed.
Nonetheless, we should keep in mind that there are only certain cases where this sort of technology is needed. Not everything requires a digital twin, but in some situations, it can make a significant impact. To use a simple example, if a key component in a larger system breaks down, everything else fails. In that context, a digital twin of a critical part of a system would be useful. Digital models, shadows, and twins are complimentary and can be used for different parts of the same system or asset.
More and more, digital models, shadows, and twins are used in conjunction to understand the complete system or asset and enable it to achieve its core targets. Ultimately, connecting and standardising digital models, shadows, and twins can provide significant benefits:
Standardisation allows different digital representations to communicate seamlessly, enabling integration across domains, systems, and organisations. This facilitates data exchange, collaboration, and interoperability, leading to more comprehensive insights and holistic decision-making.
Connecting digital representations promotes knowledge sharing and collaboration. It allows experts from various fields to contribute their expertise and insights, enabling multidisciplinary problem-solving. Standardisation also encourages the development of common frameworks, best practices, and industry standards, fostering innovation and efficiency.
Connecting and standardising digital representations create a foundation for scalability and flexibility. Organisations can easily replicate and adapt successful models, shadows, or twins across different entities or systems, saving time and effort. This scalability allows the application of digital representations to a wide range of contexts and promotes the adoption of emerging technologies.
Digital models, digital shadows, and digital twins represent different types of digital representation and integration with the physical world. Understanding their individual characteristics, applicability, and value is crucial for harnessing their potential. Connecting and standardising these digital representations can unlock even greater benefits, such as improved collaboration, interoperability, and gathering collective insights for enhanced decision-making and efficiency across industries.
Thank you to Professor John Ahmet Erkoyuncu for taking the time to explain the differences between the three concepts to me!
If you are curious about the practical uses of digital twins, you can read the case studies Oxford Insights has written for the Digital Twin Hub here.