Do Digital Twins Live up to the Hype? A Q&A with Garrett Owens

Firstly, what is a digital twin? 

Fundamentally, a digital twin is a digital or virtual replica of a physical asset or system. It is developed for the purpose of intelligently connecting complex information to offer insights into the physical system in a risk-free environment. A digital twin communicates and visualizes these insights to owners and operators to better-inform decisions about how the asset or system is operated and maintained. Better decisions support positive interventions and thereby produce valuable outcomes. 

Do they live up to the hype?

Digital twins are currently one of the hot button technology trends in the industry. However, we must avoid the hype associated with specific technologies and instead focus on the benefits that digital twins bring to the industry. 

The benefits for organizations are numerous, ranging from reducing costs to increasing throughput and production capacity. Digital twins are an active agent in value creation and their applications are constantly expanding. Digital twins have been developed and applied in everything from the human body to an entire city. As organizations continue to recognize the value of digital twins, we can expect their uses to evolve and grow.

How do digital twins help teams deliver and operate infrastructure more efficiently?

In the infrastructure space, digital twins are created to improve efficiency at every stage of an asset’s lifecycle. A digital twin presents many applications to asset owners and operators, ranging from remotely intervening in the physical system to leveraging advanced simulation and data analytics. All these applications provide the opportunity for the owner or operator to realize cost savings, reduce downtime and improve asset performance, making the physical twin more resilient to large scale changes and short-term shocks. 

What are some of the unexpected challenges organizations face in developing a digital twin?

Data is the foundation of a digital twin, but not just any data will do. Data must be accessible and easily ingested by the tools integrated as part of the digital twin environment. It can be difficult to manage data complexity and ensure quality, particularly when combining data from several sources. 

Real-time data is essential for digital twins but processing it quickly so it can be cleaned with outlier detection and infilling tools and monitored for data deviations requires a strong IT (information technology) infrastructure, which presents a challenge for some businesses. 

Leveraging the insights generated by a digital twin to their full potential needs buy-in from the entire organization. Failing to secure by-in across all levels of the organization can make adopting digital twins a challenge. Changes to organizational process for making decisions can also be met with hesitation and/or resistance.

What key questions should organizations ask themselves when deciding whether to implement a digital twin?

Multiple questions spring to mind straight away! 

From an implementation perspective, questions should include: what are the drivers/business needs for implementing a digital twin? Is our digital environment mature enough to  develop a digital twin? What are the key metrics to measure the success of a digital twin? Who is the person or people to own and actively manage the digital twin through development, deployment and operations?

From a strategic perspective, asking yourself what your expectations are is key. Many asset owners/operators seek the positive outcomes of a digital twin without fully understanding the path to attaining it, creating the illusion that digital twins are a ‘unicorn’ solution. That is not the case. Developing a digital twin is a journey, and a significant portion of a digital twin’s value comes from the incremental milestones and positive organizational changes that happen along the way. People and processes within the organization have as much bearing on the success of a digital twin as the technology being deployed. 

What do rapidly evolving technologies and techniques like artificial intelligence (AI) and machine learning mean for digital twins in the future?

As data-driven technologies like machine learning and AI become more mature and proven, digital twins that incorporate these technologies will be able to provide real-time recommendations on the system’s performance and how to operate it more efficiently. These technologies will significantly enhance the capabilities of digital twins, making them more predictive, autonomous, and adaptable. The use of data-driven technologies has the potential to facilitate proactive problem-solving by enabling real-time analysis of complex data and generating actionable insights and recommendations.