How Aviation OEMs Can Use Digital Twins to Optimize Manufacturing

Digital twin technology is quietly rewriting the rules of aerospace manufacturing. Once a futuristic concept, digital twins are now critical tools for aviation original OEMs (Original Equipment Manufacturers) looking to reduce production costs, accelerate time-to-market, and enhance aircraft reliability.

Digital twins are data-driven virtual models that mirror physical systems, continuously learning from real-world inputs to help OEMs simulate outcomes, test designs, and predict maintenance needs long before any physical hardware is built or deployed. They can also test new floor layouts to see the impact on production output.

Companies like Airbus, Rolls-Royce, Siemens, and Bell are already reaping the rewards. Airbus has slashed production lead times for its A320 and A350 programs using full lifecycle digital models (Airbus Newsroom), and Siemens claims digital twins have cut engineering rework costs from 20% to just 1% for some aerospace customers (Aviation International News).

This article explores how aviation OEMs are using digital twins to design and build aircraft faster and safer, and train the next generation of aviation professionals.

What are digital twins and why do they matter?

A digital twin is a living, evolving replica of a physical object, process, or system that simulates real-world behavior through a continuous feed of real-time data. In aviation, that might mean a twin of an engine or an entire production line.

Digital twins are made possible by technologies like IoT (Internet of Thing) sensors, AI and ML (Machine Learning) algorithms, and cloud-based analytics. These allow manufacturers to virtually replicate everything from wing airflow to new beverage cart dimensions before committing to physical builds. These models are dynamic, updated constantly with operational data, and tied to simulations that run through nearly endless scenarios.

For OEMs, the value is clear. Instead of relying on costly, time-consuming trial and error, they can validate designs digitally and avoid expensive mistakes.

At Siemens, digital twin software is helping startups like JetZero aim for an aircraft certification timeline of just five years, which is significantly faster than legacy programs like the Boeing 787 or Airbus A350 (Aviation International News).

Todd Tuthill, vice president for aerospace, defense, and marine industry at Siemens Digital Industries Software, says their technology can get a 250-passenger blended-wing body aircraft built and certified “in two-thirds the amount of time it took[other OEMs] to certify their latest clean-sheet designs.”

Tuthill credits these aggressive timeframes to their superior digital twin technologies, saying they can now “fly aircraft before it’s built”—long before the factory even exists.

Digital twinning is also a central part of Rolls-Royce’s “IntelligentEngine” initiative. The company uses sensor data and real-time analytics to simulate how engines will behave under extreme conditions, pushing far beyond what traditional physical testing would allow Rolls-Royce Media).

Nick Ward, vice president of digital systems at Rolls-Royce, tells Aerospace Manufacturing and Design that it enables fewer prototypes, faster timelines, better performance, and stronger ROI (Aerospace Manufacturing Design).

Their jet engines are so well-engineered and maintained, says Ward, that the Rolls-Royce Trent engine can fly around the globe more than 1,000 times between “significant engine events.” The company’s multi-variable forecasting can map the performance of parts and provide an accurate “predictive maintenance deadline, right down to individual part numbers.”

Accelerating aircraft design with virtual confidence

Digital twins are transforming how OEMs approach the earliest stages of aircraft design. At Airbus, engineers use physics-based simulations and detailed 3D models for faster design cycles and reduced quality issues, particularly for the A320 and A350 families (Airbus Newsroom).

Airbus’s FlightLab—a modified H130 helicopter—has been used to test autonomy systems, rotor strike avoidance, and simplified fly-by-wire controls. Meanwhile, its DisruptiveLab demonstrator is focused on drag reduction and reducing CO₂ emissions. The company estimates that the DisruptiveLab could cut fuel consumption by 50% compared to current designs (Vertical).

Rolls-Royce takes a similar approach in engine development (Rolls-Royce Media). VP Nick Ward says, “New accurate information is presented to airlines daily and is seamlessly consumed by their maintenance scheduler.”

Initial Rolls-Royce reports show huge progress that data integrations are significantly extending the useful life of engines and other expensive components, even increasing the time time to first engine removal by nearly 50% (Aerospace Manufacturing and Design).

“When you have this level of monitoring and data,” says Ward, their “previous preventative maintenance approaches are obsolete.” Failures are almost always traced to the individual part level, and well before planned maintenance cycles.

The company has a “100% success rate” in fulfilling its goal of “zero false predictions.”

In these examples, digital twins enable OEMs to build smarter, safer aircraft faster—with fewer production and performance surprises.

Smarter manufacturing: How OEMs optimize production lines

Designing an aircraft is one challenge. Building it at scale, efficiently, and with zero delays, is another. Here too, digital twins are proving invaluable—particularly in the planning and simulation of production lines.

JetZero is betting heavily on getting manufacturing right the first time. Using Siemens’ digital twin tools, the company can simulate production processes, identify bottlenecks, and optimize factory layouts before construction even begins (Aviation International News). “I can’t wait to build [digital twin] a factory to find out I designed it wrong,” Siemens VP Todd Tuthill told Aviation International News (AIN). That’s the point of this technology: fix the mistakes virtually, not physically.

Airbus is taking a similar approach with its PioneerLab and Racer demonstrators, where it tests everything from rotor strike sensors to aerodynamic efficiencies to hybrid propulsion systems. The goal is to validate components while optimizing assembly lines and maintenance protocols in parallel (Vertical).

Inside the company’s Illescas and Saint-Eloi facilities, digital twins monitor everything from machine vibrations to temperature and humidity. That data informs decisions on quality control, machine maintenance, and workflow optimization (Airbus Newsroom). Additionally, smartglasses and tablets enable virtual training for factory workers before they even step onto the shop floor.

The technology wins are staggering. The Airbus Racer helicopter saves 20% in fuel by putting one of the aircraft’s two engines on standby at cruising altitude, while still flying much faster than typical helicopters. Additionally, its wing can provide 40% of the helicopter’s lift, reducing the strain on the rotor. It also cuts vibrations to improve passenger and pilot comfort (Airbus Newsroom).

Reducing aircraft downtime

For aviation OEMs and operators alike, downtime is catastrophic. Every grounded aircraft represents lost revenue, scheduling headaches, and cascading delays. Beyond manufacturing, digital twins are increasingly central to MRO (Maintenance, Repair, and Overhaul) firms.

A digital twin of an engine or landing gear component can continuously receive data from embedded IoT sensors, track wear and tear, and model degradation under various conditions. GE, for instance, has developed digital twins for individual components like landing gear for granular insight into part lifecycles (AeroTime).

According to Deloitte, predictive maintenance programs can reduce aircraft downtime by 15%, boost labor productivity by 20%, and cut maintenance costs by 18—25% (AeroTime). McKinsey adds that this approach can also increase aircraft availability by as much as 15%.

Air France–KLM is among the major airlines leaning heavily on AI-enhanced digital twins. By combining generative AI tools from Google Cloud with fleetwide sensor data, the airline can compress maintenance data analysis from hours to minutes (AeroTime). To date, Air France-KLM has used over 900,000 views of 104 digital twins to drive these reliability wins (Matterport).

Digital twins are also used for pre-flight pilot walkarounds and shorten crew cleaning time by up to 30%, improving aircraft flight readiness (Matterport).

At Delta Air Lines, nearly 1,000 mainline aircraft are linked to the Airbus Skywise platform, which allows real-time data streams to feed their corresponding digital twins. Over 50,000 users rely on the system to predict wear, optimize maintenance schedules, and avoid AoG events. (Airbus Newsroom).

Lockheed Martin is exploring an even more futuristic application: creating “e-Pilot” digital twins that monitor not just aircraft systems but also human pilots in real time. These digital copilots could eventually assist during critical operations (AeroTime).

Simulation, training, and workforce development

As digital systems evolve, so too must the people operating and maintaining them must evolve. That’s a growing challenge in aviation, where the workforce is aging and the next generation needs an entirely new skillset—equal parts mechanical and digital.

The industry faces a shortage of digitally fluent technicians. Boeing’s 2024 forecast calls for 716,000 new maintenance professionals over the next two decades, and the Aviation Technician Education Council (ATEC) warns of a lack of instructors to train them (AeroTime).

Unlike other sectors where AI might replace workers, aviation needs skilled professionals to work with digital tools. The rise of digital twins means today’s technicians must understand data models, predictive analytics, and simulation tools alongside traditional wrench-turning. Training programs are adapting slowly, but there is momentum.

Augmented reality (AR), virtual reality (VR), and immersive simulations are becoming critical components in upskilling programs. Similarly, solutions like AK GO and AK View provide AR-based training environments that simulate emergency procedures and maintenance tasks (AeroTime).

Leonardo has taken things a step further by creating a digital twin of its Proteus uncrewed helicopter demonstrator, which allows teams to develop and test components virtually, well before any live aircraft takes flight (Vertical). The synthetic environment serves as both a development sandbox and a training simulator.

The UK Digital Twin Centre, launched in May 2024, is another major step. Focused on aerospace, space, and maritime industries, the center promotes standardization and shared infrastructure to make twin-based training more accessible (NATO Science & Technology Organization).

Digital test environments are also key to ensuring simulation-based training aligns with regulatory requirements. New frameworks aim to integrate pilot competency programs with simulator capabilities, bridging the gap between training realism and certification standards (Royal Aeronautical Society).

Training is needed for AI tools to bring success. The future of aviation may be digital—but it still depends on highly skilled people.

AI-driven reality

Siemens is pushing the boundaries of AI and the real world with its NX Immersive Designer, which combines augmented reality, voice commands, and generative AI to let engineers interact with 3D models in a real-world context (Siemens Newsroom).

At the 2025 Paris Air Show, Siemens compared this experience to a functional holodeck (from television’s Star Trek), bringing aircraft designs to life in fully immersive environments (Aviation International News).

Sikorsky’s Matrix autonomy system is already flying real-world missions. In 2024, it enabled a Black Hawk helicopter to autonomously detect and suppress a simulated wildfire—identifying the fire, positioning the aircraft, and making a precision water drop without pilot input (Vertical).

Challenges and barriers to adoption

Despite all the promises, OEMs face significant hurdles in adopting digital-twin technology at scale.

Interoperability is one of the biggest challenges. Integrating digital twin platforms across complex, multinational supply chains is no small feat. Manufacturers work with hundreds of suppliers, each using different tools, standards, and data formats. Getting these systems to seamlessly and securely exchange data is a major hurdle.

Cost is another limiting factor. While digital twins often result in long-term savings, the upfront investment can be steep. Sophisticated sensors, cloud infrastructure, AR (Augmented Reality) headsets, software licenses, and training programs all add up. For smaller OEMs or Tier 2 and Tier 3 suppliers, the ROI could take several years to realize.

Then there’s the talent gap. As noted earlier, digital twin systems require a hybrid workforce that can blend mechanical expertise with digital fluency. Right now, our current pipeline of qualified workers isn’t keeping up. Companies are already short of traditionally trained workers, let alone those who know how to use hew AI technology effectively (AeroTime).

Finally, regulatory frameworks are still catching up. Although agencies like EASA (European Union Aviation Safety Agency) and the FAA are beginning to incorporate simulation into certification pathways, there's lingering uncertainty around how digital-only validation will be treated, particularly when it comes to safety-critical components (Royal Aeronautical Society).

What’s next: The immediate future of digital twins and AI in aviation

The current momentum behind digital twins in aviation is unmistakable, and it’s only gaining speed. Original equipment manufacturers (OEMs) are investing heavily in simulation, artificial intelligence (AI), and immersive design environments, signaling a major shift in how aircraft are conceived, built, and even certified.

Increasingly, the most important design and manufacturing decisions are being made in the digital realm—long before a single physical part is ever machined or installed.

One compelling example is JetZero, whose digital twin strategy goes beyond just the aircraft itself to include the factory that will build it.

By simulating every detail of both the product and the production environment, JetZero is setting a precedent for how digital proofs could one day replace traditional inspection and certification workflows. Instead of waiting for lengthy physical validations and costly iterative rework, companies may soon present regulators with virtual demonstrations proving that safety and quality standards have been met—even before the first tool is installed (Aviation International News).

AI is also opening new frontiers in environmental performance. Consider the Airbus Racer helicopter, which was developed under the Clean Sky 2 initiative (European Union Clean Aviation, Vertical). The push for more eco-friendly flights is what drove the development of its AI system that allows one engine to shut down during flight, and is a powerful real-world example of how AI tools are creating faster, more efficient technology that’s also better for people and the planet.

The overall infrastructure supporting digital twins is evolving rapidly. Digital twin environments are becoming interconnected systems that serve as shared sources of truth across entire organizations.

Airbus’s Skywise and Dassault Systèmes’ 3DEXPERIENCE platforms exemplify this evolution. Both are emerging as foundational hubs for real-time, company-wide collaboration (Airbus Newsroom). These platforms enable simulation-driven decision-making at every business level.

The numbers speak to their adoption. Over 370,000 customers (12.5 million end users) currently use 3DEXPERIENCE as their computer-aided design (CAD) platform. Customers span 12 industries across 159 countries (Dassaults Systèmes).

The platform’s widespread integration signals that AI usage, for at least some business workflows, is now mainstream. The same generative technologies transforming aerospace are also reshaping other industries such as automotive, space, and energy. AI tools are accelerating prototyping, reducing material waste, and allowing teams to model complex systems and test assumptions.

As more organizations embrace simulation, data modeling, and AI-powered insights, the case for broader deployment only grows. For any company that designs products, manages systems, analyzes performance data, or enforces governance protocols, AI offers immediate, measurable value.

AI’s digital twin technology is at the foundation of aviation. What began as a tool for visualization and design has become a cornerstone of innovation for nearly every aspect of flight and aircraft management. The technology will soon be guiding real-time airspace management and customized passenger experiences. The line between virtual and physical will only get blurrier.

Reinforce your aviation supply chain with AI built for real-world uptime.

Talk to ePlaneAI to cut AOG risk, streamline inventory, and turn maintenance data into decisive action. Talk to an ePlaneAI rep today and find out why our customers see real-world ROI from mont one. Let’s connect now.

FAQs

How long has digital twin technology existed?

The concept behind digital twins can be traced back to NASA’s Apollo program in the 1960s, when engineers built exact terrestrial replicas of spacecraft to simulate failures and test mission-critical scenarios before flight (NASA Technical Reports Server). The foundational theoretical roots go even further—to David Gelernter’s 1991 book Mirror Worlds, which envisioned digital reflections of complex systems (Simio).

Manufacturing applications emerged in 2002, when Dr. Michael Grieves at the University of Michigan introduced the mirrored‑spaces model, placing the physical asset, virtual counterpart, and data link at the heart of what we now call digital twins (MDPI).

But the term “digital twin” itself was first coined in 2010 by NASA technologist John Vickers, who formally defined it in the agency’s roadmap for spacecraft replication, simulation, and maintenance (NASA).

How long has digital twin technology been used for aviation?

From the technology’s inception, digital twins have been used for the aerospace industry. Digital twins were first used for NASA’s Apollo program in the 1960s (NASA Technical Reports Server).

Other industries, including manufacturing, automotive, health care and construction and architecture (AEC) did not begin meaningful adoption of digital twin technology until the 2010s, as it enabled tech such as IoT sensors and cloud computing (Deloitte Insights).

In the early 2020s, cities like Singapore, Shanghai, and Helsinki adopted digital twins to simulate urban infrastructure, traffic patterns, and environmental impacts. At the same time, utility companies began adopting digital twins to monitor electric grids, pipelines, and water systems in real time (Deloitte Insights, PricewaterhouseCoopers).

Are digital twins the final endpoint for AI technology evolution?

Digital twins are a midpoint, not the end of the road. Digital twins are foundational for the next chapter—cognitive twins and digital threads (École de Technologie Supérieure, ​​Pontifícia Universidade Católica do Paraná).

Cognitive twins go beyond mirroring and simulating systems; they use AI and machine learning to make autonomous decisions in real time. These next-gen models can optimize production, reroute logistics, and even self-correct without human intervention. Siemens is already developing systems that fuse AI with real-time data streams, enabling smarter responses across industrial networks (Siemens Xcelerator).

The second frontier is the digital thread, which connects individual twins across an entire product lifecycle. Unlike standalone models, digital threads integrate data from design to decommission, enabling true end-to-end traceability and system-level optimization. In aviation and defense, this could mean regulators certifying aircraft systems virtually, using simulations that replace many physical tests (Deloitte Insights).