Article by Andrew Fodor, DBM Vircon – Vancouver
Digital twins have become one of the most widely discussed concepts in the built environment. Yet despite the growing interest, many organizations still struggle to answer two fundamental questions:
“What, exactly, is a digital twin?”
“And what kind of digital twin do we actually need?”
A digital twin is generally defined as a digital representation of a physical asset, facility, system, or process that can be used to understand, monitor, simulate, and improve real-world performance. That broad definition is where the challenge lies: a digital twin can mean something entirely different from one organization to the next. It can even mean different things to people within the same company.
Not all digital twins are created equal. A static 3D model, a live operational dashboard, and an AI-driven optimization platform can all be described as digital twins, but they each offer very different capabilities. This distinction matters because organizations often pursue digital twin initiatives without first defining what they are trying to solve. The end result is an expensive visualization platform that delivers little measurable value.
Organizations should first define the use cases, expected outcomes, governance, and ownership model. Without that blueprint – which helps guide the outcome of what achieving a digital twin means – companies often create disconnected twins. The result is limited business engagement and no clear way to attribute value.
Understanding the Five Types of Digital Twins
In a previous article, we outlined the five key stages involved in delivering a digital twin. Digital twins exist along a spectrum of maturity, with each level serving different operational needs. Understanding these five types of digital twins can help owners, operators, and project teams determine what they have today, and what a future roadmap could look like on a digital twin implementation journey.
Type 1: The Descriptive Twin
A descriptive twin is the most basic form of digital twin. It provides a digital representation of a physical asset, often through BIM models, CAD models, laser scans, or asset databases. At this level, the twin serves primarily as a source of information and visualization. It documents what exists but provides little insight into performance.
While some dismiss descriptive twins as ‘just models’, they can deliver substantial value by establishing a single source of truth for asset information.
Use case: A hospital owner maintains a detailed BIM model containing room layouts, equipment locations, asset specifications, and maintenance records. When planning a new surgical wing, project teams can quickly identify existing utilities, space constraints, and equipment dependencies without relying on outdated drawings or site investigations.
For more insights on how digital engineering and digital twins can benefit healthcare facilities, refer to this article.
Type 2: The Connected Twin
A connected twin combines the digital model with live operational data from sensors, equipment, building systems, or industrial controls. Instead of simply showing what an asset looks like, the twin reflects what the asset is doing in real time.
Many of today’s digital twin deployments fall into this category. They provide improved visibility and operational awareness, but their primary role remains observational rather than predictive.
Use case: A water treatment facility integrates Supervisory Control and Data Acquisition (SCADA) and sensor data with its digital model. Operators can instantly see which pumps are running, monitor flow rates and energy consumption, and receive alerts when operating conditions deviate from normal ranges. All this can help reduce downtime and improve operational visibility. McKinsey & Company recently estimated that digital twins could boost capital and operational efficiency in public sector projects by as much as 30%.
Type 3: The Predictive Twin
A predictive twin uses historical and real-time data to forecast future conditions and performance. At this stage, the focus shifts from monitoring to anticipation. The twin helps users identify emerging risks, forecast failures, and understand how assets are likely to perform in the future.
Predictive twins allow organizations to move from reactive maintenance strategies toward proactive asset management. This can deliver measurable improvements in reliability and cost control.
Use case: A rail operator combines inspection records, track geometry measurements, weather data, and sensor inputs to identify track segments likely to require maintenance within the next twelve months. Rather than reacting to defects after they occur, maintenance crews can prioritize interventions based on predicted risk and expected service impacts.
Type 4: The Prescriptive Twin
A prescriptive twin goes one step further by recommending actions. Using simulations, operational constraints, and optimization algorithms, the twin evaluates different scenarios and identifies the most effective course of action.
At this level, the digital twin becomes a decision-support tool, helping organizations determine not only what may happen, but what they should do about it.
Use case: A steel fabrication facility uses a digital twin to simulate production schedules, shop loading, material availability, and labor constraints. When a major project accelerates its delivery schedule, the twin can evaluate multiple production scenarios and recommend the sequence that maximizes throughput while minimizing overtime costs and fabrication bottlenecks.
Type 5: The Autonomous Twin
The most advanced digital twins can make and implement decisions with limited human intervention. These systems use automation, artificial intelligence, and continuous feedback loops to optimize performance in real time.
While autonomous twins represent the ultimate vision for many organizations, they remain relatively uncommon and are typically deployed in highly controlled operational environments.
Use case: An automated container terminal monitors vessel schedules, equipment availability, yard capacity, and cargo demand. The digital twin automatically reallocates resources, adjusts equipment operating parameters, and reschedules maintenance activities in real time, optimizing throughput with minimal human intervention.
Why Digital Engineering Matters
Notice that each step in digital twin maturity requires more than additional technology. It requires increasingly reliable, structured, and connected engineering information.
Most digital twins begin as digital engineering initiatives built around Building Information Modeling (BIM). While BIM is often viewed as a design and construction tool, its real value lies in creating a structured, governed, and information-rich representation of an asset that can support decisions throughout its life.
As organizations progress from descriptive models to connected, predictive, and ultimately autonomous digital twins, the challenge becomes connecting people, systems, processes, and data around a trusted source of information. The digital twin becomes the operational layer, but BIM and digital engineering provide the foundation upon which it is built.
The geometry, asset data, equipment relationships, metadata, maintenance records, and operational information that power a digital twin must first be created, structured, connected, and governed. Without that foundation, even the most sophisticated software platform will struggle to deliver meaningful value.
And this is why many digital twin initiatives stall. The challenge is usually not the technology itself, but the quality and accessibility of the underlying information. An organization cannot build reliable predictive models if its asset data is fragmented. It cannot automate decisions if critical systems are disconnected. And it cannot trust the outputs of a digital twin if the inputs are incomplete or inaccurate.
Viewed through this lens, digital twins are not a standalone technology. They are the operational expression of digital engineering.
