Back in May, Lux attended the Digital Twin Summit organized by the American Society of Mechanical Engineers (ASME). The two-day event focused on state-of-the-art digital twin developments and the future scope of asset-intensive industries. Experts from aerospace, defense, automotive, and academia pondered use cases, frameworks, standards, and business models for digital twins. Interestingly, use cases of digital twins outside asset-intensive industries also garnered significant attention, especially in healthcare. In this blog, we outline the key takeaways.
Focus on value extraction and not the nitty-gritty of definition:
"You don’t need a definition of digital twins; focus on value extraction," proclaimed one of the keynote speakers from the Florida Institute of Technology, Michael Grieves, when participants asked for a definition of a digital twin. Other speakers emphasized the use cases and pointed out the value of digital twins in the aerospace and automotive sectors. The discussions gravitated toward the importance of building a digital thread between physical and digital assets for the digital twin. Having built integrated systems over the years, aerospace companies like Lockheed Martin and Northrop Grumman are leveraging the existing infrastructure to build digital twins. Instead of a definition, the discussion on digital twins focused more on the overall digital maturity and cybersecurity, especially for the defense industry.
Look beyond efficiency for value extraction; think about services:
So far, most of the use case of digital twins have focused on reduction of operational and design costs through improved efficiency. However, experts argued that digital twins have the potential to generate new revenue streams with service-based business models like selling data to contextualize digital twins. A speaker from the Cambridge Service Alliance suggested that companies should expect shifts in business models from product-focused to service-focused. Another speaker highlighted Rolls-Royce's shift toward a platform-based business model in partnership with Iotics. Similarly, with the collection of continuous data, Tesla is offering new services to customers. However, the progress of digital twins varies greatly depending on the sector; for instance, progress is slow in the construction industry compared to the aerospace and aviation sector. Panelists pointed out that the progress has changed recently due to the emphasis on smart cities by governments around the world. Overall, service-based business model innovations are suitable for companies at advanced stages of digital maturity, and clients should assess their organization's digital maturity before embarking on new business models. A small pilot project in a controlled environment is a good start.
Don't wait for the standards to start digital twin projects:
Given the complexity and necessity of interoperability among digital twins, participants were interested in the development of standards for digital twins. Though there was some agreement on the necessity of standards for interoperability of digital twins, panelists were skeptical about near-term developments. Some panelists pointed out efforts by the ASME Model-Based Enterprise Standards Committee (MBE SC) and MTConnect. However, experts warned that technology is fast-moving, and standard development processes are slow; hence, the pragmatic approach is to embark on digital twin pilots without waiting for standards. Companies should develop digital twins for the critical assets and focus on the framework that suits their organization. For now, the best approach is to manage interoperability among digital twins and associated tools inside organizations through common information models and APIs.
Digital twins in non-asset-intensive industries like healthcare are still in early stages:
Though most of the discussions talked about digital twins in asset-intensive industries, the keynote by Philips on digital twins in healthcare received significant attention. Digital twins have the potential to transform healthcare in at least a couple of areas, namely R&D and operations. In the R&D space, digital twins can be used for system design, building virtual patients, virtual device design and testing, and even virtual hospital design. The primary aim here is to perform simulations and test solutions on these models. Similarly, for operations, digital twins can also be deployed as systems, patient, device, and hospital digital twins. One use case discussed was how digital twins for medical imaging systems like MRI scanners could be deployed with zero unplanned downtime. Another use case discussed was the patient digital twin. By developing a digital copy of the patient based on all available data (electronic health records, genomics, wearables, etc.), it is possible to take a more predictive approach to care provision, allowing more informed clinical decision-making and enabling a cutdown on follow-ups and readmissions. However, clients need to note that not all companies taking this approach may refer to it as a "digital twin" approach, as this term is not often used in the healthcare industry. Many pharmaceuticals and medical device companies build similar models but may refer to them as "simulations" or "in silico modeling."
Overall, discussions focused more on finding suitable use cases for digital twins and the necessity of digital threads for building a digital twin for value extraction. It is clear that the aerospace industry, which has developed various digital twins for design and maintenance, is now leveraging the infrastructure to build valuable digital twins for the product life cycle. Digital twins for closed-loop engineering applications are gaining momentum in the manufacturing industry.
Companies should recognize that standards for digital twin development will take years to develop, but this should not stop them from leveraging the existing information models to embark on digital twin projects. A good starting point is to start mapping processes and find a critical asset for a digital twin use case.
Moreover, those interested should monitor digital twin use cases in the supply chain, logistics, and healthcare sectors for new business models and opportunities. Expect large players like Philips and Medtronic to test these concepts first in partnerships with other companies with domain expertise, such as Sim&Cure, before they spread more broadly.