A digital twin is a virtual replica of a physical asset, process or system that is kept up to date with real-time data. Companies use it to monitor performance, predict failures and test changes on the model before touching the real thing. The technology has moved from pilot projects to production use in manufacturing, aerospace, automotive, energy and healthcare, and the market forecasts agree that growth will be steep through the early 2030s.
This guide explains what a digital twin is, how it works, how big the market is, what companies actually report, where the risks are, and how to start with one asset instead of a platform purchase. It was last reviewed in October 2026.
What is a digital twin?
McKinsey defines a digital twin as a digital replica of a physical object, person, system or process, placed in a digital version of its environment and linked to real-time data so that it updates as the original changes. That live link is what separates a twin from a one-off simulation or a static 3D model.
Twins come in several forms. McKinsey's explainer describes these among others:
| Type | What it represents | Typical use |
|---|---|---|
| Product twin | A product from concept design through use in the field | Testing designs and tracking in-service performance |
| Systems twin | How physical and digital processes interact, such as a factory, supply chain or store | Optimizing operations and testing changes before rollout |
| Infrastructure twin | A building, highway, stadium or other physical infrastructure | Managing energy, maintenance and capacity |
| Customer twin | A model of customers and how they interact with a product | Personalization and service design |
How a digital twin works
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Every digital twin has the same five parts, whatever it models:
- The physical asset: a machine, building, production line, patient or supply chain.
- Data capture: IoT sensors, control systems and business systems (ERP, MES, SCADA, PLM) that report what the asset is doing.
- The virtual model: a physics-based or data-driven representation that mirrors the asset's behavior.
- Analytics and AI: machine learning and simulation that turn the data into predictions, such as when a part will fail.
- A feedback loop: insights go back to engineers and operators, or directly to the control system, and the next round of data shows what changed.
Cloud platforms such as Microsoft Azure Digital Twins, AWS IoT TwinMaker and NVIDIA Omniverse provide the building blocks. Industry protocols such as OPC UA and MQTT connect equipment from different vendors. The hard part is rarely the model. It is getting clean, consistent data from older equipment and systems into one place, which is why a single high-value asset is the best place to start.
Digital twin market size in 2026
Analysts agree that the market is growing fast, but they disagree on how big it is today. Two recent reports show the range:
| Source | 2025 market size | Forecast | Growth rate |
|---|---|---|---|
| Fortune Business Insights (updated 28 Sep 2026) | $24.48 billion | $384.79 billion by 2034 | 35.4% a year, 2026 to 2034 |
| Grand View Research (24 Jun 2026) | $35.8 billion | $328.5 billion by 2033 | 31.1% a year, 2026 to 2033 |
The gap comes from what each firm counts: software only, or software plus services and hardware. Treat any single figure as an estimate, and read the definition before you quote it. Both reports put North America first by share (34.0% for Fortune Business Insights and 31.3% for Grand View Research in 2025), and Grand View Research expects Asia Pacific to grow fastest.
Adoption data points the same way. McKinsey reports that about 70 percent of C-suite technology executives at large enterprises are exploring and investing in digital twins, and that almost 75 percent of companies in advanced industries had adopted them at medium complexity or higher in its 2023 survey. An Altair survey of 2,007 professionals in 10 countries (2022) found 69% of organizations already using them.
What companies report from digital twins
Results vary widely by use case, and most published figures come from the companies themselves. Use them as a guide to where the value is, not as a promise:
| Company | Use case | Reported result |
|---|---|---|
| BMW Group | Virtual Factory on NVIDIA Omniverse, covering more than 30 production sites | Vehicle fit checks that took nearly four weeks of manual testing now take three days; production planning costs projected to fall by up to 30% |
| PepsiCo with Siemens | 3D twins of selected US manufacturing and warehouse sites (announced at CES, 6 Jan 2026) | Up to 90% of potential issues found before physical changes; 20% more throughput on first deployment; 10 to 15% lower capital expenditure |
| Rolls-Royce | Engine monitoring and predictive maintenance | Time on wing for critical parts extended by over 70% in some cases (March 2024) |
| McKinsey research | Product development and consumer electronics manufacturing | Development time cut by up to 50% for some users; scrap waste reduced by roughly 20% |
McKinsey also notes that supply chain disruptions cost companies, on average, 45 percent of one year's cash profit, and that digital twins can raise decision-making speed by up to 90 percent. McKinsey gives no source or date for either figure, so read them as upper bounds.
Where digital twins are used
- Manufacturing: line and factory twins to plan layouts, validate designs and find bottlenecks before spending on equipment.
- Aerospace and automotive: engine and vehicle twins for predictive maintenance and design testing.
- Energy and utilities: asset and grid twins; see our guide to digital twins in solar energy management.
- Healthcare: organ, patient and hospital-operations twins; see digital twins in healthcare.
- Maintenance, construction and supply chain: predictive maintenance and digital twin approaches compared.
- Buildings and cities: energy, traffic and capacity planning.
AI and digital twins in 2026
The biggest change since this guide was first published is how closely twins and AI now work together. At CES in January 2026, Siemens announced Digital Twin Composer, which combines Siemens' twin tools, NVIDIA Omniverse simulation and real-world engineering data, and an expanded partnership with NVIDIA on an "Industrial AI Operating System". Siemens also announced nine new AI copilots across its engineering software.
In practice, AI does three things for a twin: it builds and updates models faster, it predicts failures and performance from live data, and it lets non-specialists ask questions of the model in plain language. Our post on how AI and digital twins are changing business operations goes deeper, and the link between factory floor and IT systems is covered in IT/OT convergence in manufacturing.
Challenges to plan for
- Data integration: connecting older equipment and enterprise systems usually needs middleware and protocol translation. This is a common cause of delay.
- Skills: a twin needs people who understand IoT, data science, cybersecurity and the operation being modeled. Many firms have to train or partner.
- Cost and ROI: sensors, connectivity, platforms and licences are paid up front, while the savings arrive over time. A narrow first use case with a measurable baseline makes the business case far easier to defend.
- Security and privacy: a twin connects many devices and data flows, which widens the attack surface. Build in device authentication, network segmentation, encryption and access control from the start, as part of a proactive cybersecurity strategy, and check data rules such as GDPR and HIPAA where they apply.
- Change management: twins change who decides what and when. Operations, IT, engineering and finance need to agree on ownership before launch.
How to start: a 5-step roadmap
| Step | What to do | Output |
|---|---|---|
| 1. Pick one use case | Choose an asset where data already exists and downtime or waste has a known cost | A problem statement with a baseline number |
| 2. Check the data | List sensors, systems and gaps; agree data quality and ownership rules | A data readiness assessment |
| 3. Design security first | Define access, segmentation and monitoring before connecting devices | A security design signed off by IT and OT |
| 4. Build a pilot | Model the asset, connect live data, and test predictions against what actually happens | A working twin and measured results |
| 5. Scale deliberately | Reuse the data and integration work on the next asset; add skills and governance | A roadmap with owners and budget |
Frequently asked questions
What is a digital twin in simple terms?
A digital twin is a live virtual copy of a physical asset or process. Sensors feed it real-time data, so you can see how the asset is performing and test changes on the copy first.
How is a digital twin different from a simulation?
A simulation models a scenario with fixed inputs. A digital twin stays connected to the real asset and updates continuously, so it reflects the asset's current condition.
How big is the digital twin market?
Estimates for 2025 range from $24.48 billion (Fortune Business Insights) to $35.8 billion (Grand View Research), with forecasts above $300 billion by the early 2030s. The gap reflects different definitions of the market.
What industries use digital twins most?
Manufacturing, automotive and aerospace lead, followed by energy, healthcare, construction and smart buildings. Grand View Research lists automotive and transportation among the largest end-use segments.
How much does a digital twin cost?
It depends on the number of assets, sensors and integrations. Scoping one asset first lets you price the work and measure the result before you commit to more.
Key takeaways
- A digital twin is a live virtual replica of a physical asset or process, linked to real-time data.
- Market estimates differ by vendor ($24.48 billion to $35.8 billion in 2025), but both forecasts show growth above 30% a year.
- Reported results are strongest in manufacturing planning, predictive maintenance and development speed; most come from the companies themselves, so verify against your own baseline.
- AI is the main change in 2026: faster model building, better predictions and plain-language access.
- Data integration, skills and security are common causes of delay.
- Start with one asset, prove the value, then scale.
Sources
- McKinsey: What is digital twin technology?
- Fortune Business Insights: Digital twin market (updated 28 September 2026)
- Grand View Research digital twin market release (24 June 2026, vendor-issued)
- Siemens at CES 2026 (6 January 2026)
- Process Excellence Network: BMW Group digital twins
- Aerospace Tech Review: Rolls-Royce digital twin (March 2024)
- Digital Engineering 24/7: Altair digital twin survey (September 2022)
