Many assets create vast amounts of useful data simply by operating. Harnessing and utilising this data could benefit businesses ranging from asset-producers through to end-users.

Consider the following questions.

  • One truck requires maintenance much more frequently than others in the fleet. To what extent is this because of driving style, route, component degradation, weather conditions or one of many other factors?
  • How much energy will a farm of wind turbines create given historical performance, likely future environmental conditions and recent outages?

Answering these questions enables product developers and manufacturers to build better turbines and trucks as well as allowing businesses to use them more effectively and efficiently. But can we find the answers to these questions without visiting a remote offshore wind farm or taking a fleet of trucks off the road for testing?

Digital biology

Digital twins are virtual models of physical things or processes, not only in appearance but also functionality and features. The basis of virtual twins is 3D simulation and modelling technology, but the Internet of Things (IoT)1 and machine learning have made these digital replicas far more useful and cost-effective.

Data scientists create digital twins by using scanners to build a digital model which simulates the real world object. Sensors are applied to the real object (or one or more of its components) which are connected to the IoT. The sensors collect real-time data about the object and send it to a cloud-hosted system so that the data can be processed. This allows the twin to replicate the tangible object digitally, offering real-time analytics and insights on how it is functioning and any potential issues. 

In this way, digital twins can help product developers to create better products. Businesses can apply the insights obtained from these products to implement and monetise their assets more effectively.

Applying the theory

According to research by Gartner, 24% of organisations that have IoT solutions in production or in progress use digital twins, with another 42% planning to use digital twins by 2022. With the IoT market projected to be worth US$520 billion by 2023, and the digital twin market occupying around US$15 billion of that, this "product as a service" business model is set to become much more widely spread. 

In principle, any product or process can have a digital twin. This provides an almost limitless variety of potential applications. 

Technicians can easily and quickly test a proposed fix for a problem on a digital twin of a piece of equipment before applying that solution to the physical object. This can be done at no risk and with no disruption to the live environment. For example, the fleet of trucks referred to earlier can be digitally monitored and evaluated using vibration and wear sensors, allowing for preventative maintenance and an improved design process for future models. 

Virtual simulations can assist with new product development through low-risk trial and error in the digital world. It allows a manufacturer to see and understand how a product is likely to perform before it is built. Being a piece of programmable software, the twin can be set up as a kind of test environment where changes can be made without any impact in the live environment. Similarly, real-world feedback can be implemented during the product redesign process. 

Blending data

Even processes can have digital twins. Sensors placed throughout a manufacturing factory can measure critical points in the physical manufacturing process and feed into manufacturing execution, supply chain management and ERP systems. The data is then aggregated, processed and prepared for analysis. Artificial intelligence can be used to pick out key insights for human review.

There is also scope for the digital twins of patients and their organs that would allow surgeons to practise a specific procedure on a specific patient in a simulated environment. Other types of non-surgical training could also be simulated – for example, by allowing factory workers to practise aspects of a manufacturing process in a safe, digital environment. 

Operational challenges

Integration is a key challenge. In many businesses, there will be multiple systems interacting, each of which has numerous assets within it. In turn, these assets comprise many more components. If each of these systems, assets and components has a digital twin, this presents significant additional complexity and requires that issues are addressed at both a high level and a granular level of detail.

For example, a jet engine's digital twin may need to integrate with the digital twins of each of its many component parts as well as the flight system it works within. To realise the benefits of predictive maintenance, operational efficiencies and improved product design, some kind of summary will be required to be able to easily draw conclusions. 

Operating at such a level of complexity with such a potentially large number of data sets, data quality and data integrity are key issues. It will be difficult to be confident that the data comprising the digital twin is accurate and reliable. This is particularly important with an asset like a jet engine where a decision based on flawed data could be catastrophic. 

Legal issues

To address the above issue, allocation of responsibility for data accuracy between data creators/controllers and data processors will be a key contractual concern. 

Data security and data privacy will be key concerns when implementing any IoT technology, particularly one that will potentially deal with such high volumes of data. 

Data security will be essential in protecting commercially sensitive information, while protecting personal data will be paramount to ensuring compliance with regulations like the GDPR. Businesses can face heavy penalties for non-compliance with rules on the processing of (broadly defined) "personal data". While IoT data is less likely to contain personally identifiable information than many other cloud applications, there is still scope for hacking and breaches. 

These risks can be mitigated by creating roles to manage digital twin resources and compliance within an organisation. Many cloud providers also offer security and identity tools to manage these type of risks. 

Ownership of digital twins and the intellectual property associated with them will also be a key consideration. There is scope for digital twin based "product as a service" business models which could also include the selling of physical object-related performance data or physical object pricing based on performance data. 

Digital twins offer the opportunity to achieve superior insights on physical objects and drive better business decisions. However, these benefits come with operational and legal risks encompassing cost, integration, security and privacy. 

They may be too heavy-handed in certain scenarios and will require significant initial expenditure, but digital twins can offer unparalleled insights and lower both operating and capital expenses in the long term.


1. The IoT is the network of physical objects containing the technology to communicate and interact via the internet.

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