Bringing digital twins down to earth: Cutting through the hype

The term "Digital Twin" often evokes a sense of a comprehensive virtual replica updating in real-time, offering seamless collaboration and predictive foresight. However, its very generality has led to a wide spectrum of interpretations and considerable confusion regarding its true definition, practical applications, and real-world value. This broadness results in a polarised view. On one end of the spectrum some remain skeptical of their utility, while the other end harbors unrealistic expectations that a single piece of software will serve as an all-encompassing solution to every challenge within their industry.

Like many transformative technologies before it, the concept of the digital twin has navigated the familiar curve of the hype cycle. Almost a decade ago, the world witnessed the peak of inflated expectations surrounding its potential. Since then, the general understanding and application of digital twins have evolved unevenly across various industries. Their application across different sectors has matured at different rates, and the use of the term has grown to describe vastly different systems. Early adopters within the AECO (Architecture, Engineering, Construction, and Operations) sector are now demonstrating tangible and substantial returns on their investments, signaling a move onto the more grounded "slope of enlightenment".

However, driving this industry forward is not only about technological maturity. It also demands a collective effort to cultivate a broader understanding of what can be achieved and what changes to business processes and best practices need to occur in parallel.

Cutting through the noise

Digital twins are often broadly defined as “virtual models of real-world physical products, assets, environments, or processes, serving as digital counterparts for purposes like simulation, integration, testing, monitoring, and maintenance”. It promises transformation but also breeds confusion.

A fully integrated, live, smart digital twin is undoubtedly a worthy long-term goal. However, framing it as the immediate starting point can be counterproductive, especially given the diverse levels of digital maturity and tooling currently present across the AECO sector. Drawing on insights from industry experts and innovators, we've identified three key pitfalls with this framing:

• Discouraging early adoption: New buyers and stakeholders can be deterred by the sheer scale and complexity of an all-encompassing system.
• Unrealistic expectations: The promise of a fully integrated, real-time twin often overshadows the practical hurdles of data and system integration.
• Delaying tangible benefits: Trying to run before you can walk obscures opportunities for faster ROI through targeted applications addressing specific AECO challenges.

The solution lies not in the pursuit of a fully integrated, real time digital twin from day one, but in a fundamental shift in perspective. Instead of asking “how can we build the ultimate digital twin?”, we should be asking “what are the specific problems we are trying to solve, and where can digital twins deliver measurable value today?”. This approach, grounded in clearly defined use cases, allows us to strip away abstraction and focus on tangible outputs. Let the problem guide the technology.

Across the AECO sector, Sensat has already seen digital twins driving real results. Each use case exposes a distinct set of data, collaboration, and automation requirements, all shaped by the problem in hand and the return on investment it can deliver.

Optioneering

Planning new infrastructure, such as a power grid expansion, involves navigating a multitude of geospatial and social constraints to narrow down options. Digital solutions range from automated assessment of 1000s of scenarios against defined criteria, through to interactive environments where planners can manually move objects around like a high-stakes SimCity – the crucial outcome being quick and pertinent feedback on what-if scenarios, saving significant time and resources, and ultimately leading to more sustainable and socially responsible infrastructure.

Federated design validation

Major AECO projects often have several contractors coordinating multiple designs and managing complex interfaces. The goal is to identify clashes as early as possible. Requirements can range from automated analyses on micro elements of detailed BIM models, up to professionals eyeballing broad design compatibility issues with the natural environment. In both cases errors can be caught in the digital realm well before action on site, saving time and money on rework.

Logistics and site management

Preconstruction digital rehearsals, often performed months in advance, use a static snapshot of data to identify potential hazards and scheduling optimisations. Daily activity briefings, by contrast, necessitate continuously up-to-date site information to coordinate personnel, equipment and materials effectively. Both approaches aim for proactive risk mitigation and efficient resource allocation but different levels of detail and timeliness of data are suited to each stage of the construction lifecycle.

Asset management

Combining as-built, operational, and maintenance data enables the optimisation of asset performance over time, be that short-term fine-tuning or longer-term portfolio-wide enhancements. This can involve the analysis of high frequency sensor data to, for example, track flow rates to prioritise wastewater management interventions. Alternatively higher level statistics such as aggregated traffic volumes can be used to proactively schedule maintenance and minimise disruptions. This again highlights the variety in technical capabilities needed to deliver on similar goals.

‍

Ask yourself - “For this specific problem or opportunity, how similar to the physical world does our digital twin truly need to be to deliver meaningful value?”

‍

In our next post, we'll introduce a framework to help you answer that critical question by mapping the specific requirements of your Digital Twin use cases.