The meteoric rise of modern computing technology powers today’s simulation, high-performance computing (HPC), artificial intelligence (AI), and data analytics technology. These technologies have given rise to amazing breakthroughs separately, but the real story of the present and future will be how these technologies converge. Today, no technology better encapsulates this convergence than digital twin technology.

The concept of digital twin is simple—in essence, it’s the process of using data streams to create a digital representation of a real-world asset to improve collaboration, information access, and decision-making. Through these digital representations (twins), teams and organizations can gather data and predict outcomes that better inform the twin’s real-world counterpart. As such, digital twins eliminate the need for physical prototyping, give teams more testing flexibility, and generate time and cost savings. They also establish a working twin that teams can use to optimize their products as they evolve. Through digital twin technology, the virtual twin informs the physical product and vice versa, creating a virtuous cycle of refinement and optimization.

But though it seems simple, there’s a staggering amount of confusion around digital twin; in other words, it’s easier said than done. Executing a successful digital twin strategy requires a comprehensive collection of personnel, infrastructure, data, and more. And nowhere is the disconnect around digital twin more pronounced than between employees of different organizational levels, namely between C-suite and leadership employees and user-level engineers and developers.

CROSSING THE DIVIDE BETWEEN HIGHER-LEVEL AND LOWER-LEVEL USERS

To dive deeper into digital twin, Altair surveyed more than 2,000 international professionals. Some key findings:

• 54% of respondents felt they had limited knowledge about digital twin technology, lacked a succinct, consistent definition of what the technology is, or found understanding digital twin solutions to be confusing.
• While 64% of upper management respondents said that they were “highly knowledgeable” about digital twin technology, just 35% of user-level respondents said they were “highly knowledgeable” about it.
• While 70% of upper management employees said that digital twin solutions were “very important” to their organization, only 52% of user-level employees said the same.

Of course, some separation between higher-level and user-level employees is natural and inevitable—but these statistics displayed the most acute differences between the two groups, one that indicated a more serious problem is at hand.

This disconnect doesn’t bode well for organizations looking to invest in and implement digital twin technology. Teams operating with different definitions of digital twin are prone to miscommunication, operational hitches, and internal tension over a fundamental misunderstanding of what different teams are looking for out of their digital twin strategy. Building internal alignment around digital twin is critical, especially considering that our survey found that 69% of respondents currently leverage digital twin technology; 23% of respondents said their organization began to invest in digital twin technology in the last six months or sooner; and of respondents who said their organization isn’t currently employing digital twin technology, 58% think their organization will adopt it within the next two years or sooner. But where to start?

DEFINING AND COMMINICATING

First, teams and organizations should develop a definition that works for them. As mentioned before, any digital twin will be a virtual representation of a real-world counterpart that’s connected via circular data streams. But the exact shape of different digital twins – what the twins model – can be almost anything, from a design concept sketch, to a robotic crane arm, to a model of a consumer’s financial spending behavior. But the underlying principles will always remain the same. So while twins may look different, it’s vital for everyone within an organization to understand that the more important aspect is the process of building a digital twin rather than the twin itself.

But once everyone is on the same page about what digital twin is and how it works, teams—especially those in leadership roles like presidents, directors, and executives—must communicate why digital twin is crucial not just to the organization broadly, but to the individuals who will be responsible for bringing it to life, namely engineers, programmers, data scientists, and developers. Because, undoubtedly, digital twin strategies create streamlined workflows, better communication, and clearer, more accessible data that can build bridges between teams, enable unforeseen innovations, and save time and costs.

It’s clear that digital twin is growing and will only play a more crucial role in the years to come. But while it can seem daunting, the most difficult step is always the first one. The best thing organizations can do is to make the leap and simply try: decide what you want to model, gain some experience and metrics, and identify where the pieces are coming together and where they can grow. If everyone is aligned and understands the strategy’s goals and vision, the leap will be well worth the payoff.

To learn more about digital twin technology and how Altair is utilizing its three-decade digital twin experience to drive industry-leading digital twin solutions, click here to read the 2022 Altair Digital Twin Global Survey Report.