To remain competitive, aerospace companies need to get digital twins right
As the importance of digital twins grows, most companies still have work to do to fully capitalise on their use.
By Veronica Martinez, Francisco Gómez Medina, Annika Wollermann Umpierrez and Hansjoerg Fromm
Digital twins are increasingly regarded as key components of digital transformation, particularly in asset-intensive sectors such as aerospace. As ‘living’ models that mirror the properties and actions of a physical entity, they have the potential to unlock new and innovative value propositions that can help companies achieve sustainable competitive advantage.
But while recognition of their value is growing, no one has yet emerged as an industry leader.
As part of our research, we spoke with over 50 industry experts from 13 different commercial aerospace manufacturers to understand how digital twins are being adopted and used within their companies. The results show significant variation in maturity levels across different dimensions, with no clear front-runners emerging.
For instance, only 15 per cent of companies have deployed their digital twins commercially. Almost 88 per cent of companies are using their digital twins’ diagnostic and predictive capabilities, but only a small percentage of twins have moved beyond this to where they can automatically execute actions based off of the data.
One in six aerospace companies report that they are changing their business model as a result of digital twin implementation, but the main focus of activity is still at the process level, where the digital twin enables improvements to existing processes such as maintenance scheduling, rather than wholesale changes to their approach.
In the even more critical dimension of data accessibility, a quarter of the industry currently has no access to operational data of the physical products they sell, and half of the industry has only limited access, despite data access being critical to operating the digital twin.
Interestingly, we found that many of the leaders in each of these dimensions were different, showing that most companies are advancing only across a few of the 10 dimensions we identified: analytical capability, model update frequency, data collection frequency, modelling scope, decision implementation, lifecycle integration, digital twin individualisation, business models, operational data accessibility and commercial implementation.
Success factors and challenges
In our discussions, a clear consensus emerged as to the success factors needed for digital twin implementation: a shared vision of the objectives, frequent communication within and across departments and the right set of digital and data skills in the workforce. The importance of partnerships was also highlighted by almost all companies, reflecting both the need to collaborate with specialists to provide the best possible solutions and for gaining access to data.
The most critical barriers to implementation were seen as resistance to change within the company, an inability to develop a convincing business case and lack of access to data.
This challenge of data accessibility exists both outside and within the company. For example, one department may gain access rights to external data but other departments are not covered by the contract agreement and hence cannot access it. This highlights the critical role of contractual agreements in being able to implement and exploit the digital twin to its full potential. You need to have access as a company to operational data and you must be able to share that data across departments.
But challenges go far beyond the technical. The lack of a data-driven culture is a major issue to which companies must pay particular attention. It is important to find a balance between more experienced employees who rely on intuition and younger, data-driven employees who lack field experience.
Solving these challenges and moving forward across the multiple dimensions of digital twin implementation is essential to competitiveness. Customers are increasingly looking to digital twin implementation as a point of differentiation between aerospace manufacturers, and it is likely that certain operational levels of digital twins will soon appear in regulation as well.
Digital twins are a multidisciplinary endeavour that crosses company boundaries. Therefore, they require cultural changes within the company and throughout its ecosystem, with a shift towards digital and analytical literacy, as well as data and information sharing. Those companies that can truly leverage systems-thinking and nurture stakeholder relationships will be well positioned to harness the potential of digital twin-enabled offerings.
Find out more in our full report, Digital twins in the aerospace sector: Maturity model, practices and opportunities.