Bridging the Gap with Digital Twins

When Dr Michael Grieves first unveiled the concept of the digital twin in 2002, it was considered revolutionary. His idea was to create digital replicas of objects, processes and systems, enabling engineers to create, test and develop in a virtual environment rather than a physical one. Grieves’ argument was that putting a digital twin through its paces was not only faster and more cost effective than working on its real-world counterpart, it would also give engineers and developers new and exciting opportunities to innovate and unlock value.

It has taken science two long decades – and the advent of Industry 4.0 – to catch up with Dr Grieve’s brainchild. Now, however, we are finally realising the full benefits of this transformative technology across sectors as diverse as defence, manufacturing, aviation and urban planning.

Thanks to recent advances in areas such as IoT connectivity, computing power, data storage and systems interoperability, digital twin simulations are significantly more detailed and dynamic than ever before, enabling engineers to achieve results that were previously beyond their reach. In many ways comparing old and modern digital twins is like comparing blurry, black-and-white photographs with modern, full-colour, high-definition digital images. The difference really is that dramatic.

Modernising the world’s leading navies

For example, the Royal Navy is using digital twin technology to virtualise the computing environments aboard the latest state-of-the-art vessels in its surface fleet. Digital twins enable its engineers to put ships’ systems through their paces, spin up new scenarios at speed, create new capabilities and patch potential issues all within a virtual space. And the Royal Navy isn’t alone. On the other side of the Atlantic, the US Department of Defense is creating digital twins of the US Navy’s four shipyards – including the iconic Pearl Harbour – as part of a $21 billion modernisation programme.

Meanwhile, in the manufacturing sector, leading companies are now using digital twins to virtualize entire production processes – equipping factories with thousands of sensors in order to measure and gather data in areas such as equipment performance, energy consumption and environmental conditions. This data is continuously fed into the digital twin simulation in real time, so that engineers and developers can optimise the way products are designed, made and improved.

Forward-thinking automakers such as Tesla are leading the digital twin revolution in the automotive sector. Tesla, for example, creates a digital twin of every car it manufactures. The firm then updates software based on individual vehicles’ sensor data, before uploading system updates to its products.

Driving unprecedented growth

This step change in capability across multiple sectors is predicted to drive unprecedented growth within the digital twin market, generating a compound annual revenue growth rate of 38%, according to the latest research, reaching a value of US$35.8 billion by 2025.

This increased adoption is boosting accessibility for large and small organizations alike. By the end of 2022, the IDC predicts that 40% of IoT platform suppliers will integrate simulation into their platforms, systems and capabilities, enabling users to create basic digital twins, with 70% of manufacturers using the technology to conduct process simulation and scenario evaluation.

Organizations looking to leverage digital twin technology must first overcome the following five major challenges if they are to optimise their return on investment:

1. Issues related to data. Digital twins are effectively built from data, so overcoming challenges related to data trust/accuracy, privacy, cybersecurity, convergence and governance are key to success.
2. The lack of standards. Implementation often suffers due to the absence of adequate frameworks and regulations for digital twins, especially in the manufacturing sector.
3. Potentially high cap-ex investment. Without careful management, the cost of IoT sensors and connectivity can become prohibitive. The key is to capture the right data to achieve the desired outcomes.
4. Connectivity-related obstacles. In large-scale projects, such as optimising smart neighbourhoods, ubiquitous high-speed connectivity such as 5G is a must. Edge computing capability may also be needed to filter data flows and reduce latency.

Choosing the right solution partner will not only help organisations address these five challenges, it will also fast-track digital twin adoption in other ways as well.

The right partner will know how to clearly define use cases and desired outcomes and they will also be able to advise on the most effective type of twin for each scenario. For example, a parts twin, (which can help analyse the characteristics of an individual part), a digital twin (which replicates a full product), or a systems digital twin, (which virtualises a range of products to understand how they operate on a system level). They will also know how to gather and deliver specific system requirements, such as generating large volumes of ‘what if’ scenarios.

For a deeper dive into digital twin technology read our case study, which reveals how LUNIQ is collaborating with BAE Systems to cut software development times and costs for new vessels within the Royal Navy’s surface fleet.