Dassault Systèmes

DELMIA CEO: “AI Is Bringing the Digital Twin to the Factory Floor”

Posted on by Techtime

[Photo: Guillaume Vendroux, DELMIA CEO]

Generative AI is beginning to seep into the worlds of manufacturing and industrial operations, bringing with it a level of accessibility and simplicity that was previously unimaginable. Just as AI models now allow anyone to generate a realistic video clip, image, or audio sequence within seconds, the same principles are starting to apply to one of the most complex layers of industry – the creation of virtual twins. What once demanded long engineering cycles, data modeling, manual simulations, and expertise across multiple systems is now becoming faster, cheaper, and increasingly available to manufacturers of all sizes.

At the center of this shift is DELMIA, the manufacturing and operations brand of Dassault Systèmes. The company is positioning generative AI as the engine enabling a new kind of virtual twin: modular, dynamic, built quickly from existing plant layouts or physical environments, and extended over time as production evolves. “We’re entering a moment where generative AI makes the virtual twin accessible to every factory, not just the giants,” says Guillaume Vendroux (גיום וונדרו), CEO of DELMIA. “What used to require teams of specialists can now be done simply, quickly, and at a much lower cost. It’s a real democratization of industrial innovation.”

Virtual twins did not start this way. Two decades ago, they were primarily used to represent a single engineered object – an engine, a gearbox, a wing assembly. Over time, as sensors, MES platforms, and simulation engines matured, the concept evolved into something far more dynamic. Today a digital twin is no longer just a model of a product but a behavioral model of an entire system: the production line, the flow of materials, the workforce, the supply chain, and the way all these elements respond to change in real time. “We moved from modeling objects to modeling entire operations,” says Vendroux. “It’s a profound shift in mindset.”

This shift is not a matter of technological fascination but of necessity. The manufacturing world is facing severe labor shortages, rapidly rising product complexity, and a supply chain landscape riddled with disruptions. Factories of every size are being forced to re-evaluate how they plan, execute, and adapt. “The complexity and speed of manufacturing today simply don’t allow us to work the way we used to,” says Vendroux. “The variety of products, the frequency of changeovers, and the constant supply chain pressure make traditional management almost impossible. A factory without a virtual twin is basically operating blind.”

The COVID-19 pandemic exposed this fragility with unprecedented clarity. Lockdowns, logistical delays, energy price spikes, geopolitical tensions, and sudden shifts in demand turned supply chains volatile. For many factories the need to simulate scenarios, predict failures, and maintain continuity became existential. “COVID revealed the real vulnerability of global supply chains,” he explains. “Since then the ability to simulate, anticipate, and prepare has become fundamental.”

Traditionally, creating a virtual twin required gathering detailed engineering data, creating 3D models, manually connecting operational parameters, and running discrete simulations. Generative AI alters the process from the ground up. Modern systems can interpret images, videos, CAD files, or existing plant documentation and automatically build much of the required structure. “We start from an existing view of the shop floor,” Vendroux explains. “AI can recognize equipment, convert it into rich models, and begin running simulations within minutes. A few years ago that would have been science fiction.”

And the impact is anything but incremental. “We’re not talking about five percent improvement,” he says. “With generative AI we’re talking about fifty to eighty percent additional efficiency. These are magnitudes that fundamentally change a factory.”

DELMIA’s own history illustrates how the industry arrived at this point. The brand emerged in the late 1990s from Dassault Systèmes’ acquisition of Deneb Robotics, one of the early pioneers of virtual factory simulation. Initially focused on discrete manufacturing – aerospace, automotive, trucks, heavy machinery – DELMIA quickly grew to dominate these sectors. Over time it expanded into continuous manufacturing such as chemicals and metals, batch processing like cosmetics, and hybrid industries including batteries and advanced materials. Today the company serves around 2,500 customers and about 1.8 million users worldwide. Remarkably, more than one million of them are shop-floor workers. “That’s the strongest indication that these tools are not reserved for executives,” says Vendroux. “They reach the people doing the actual work.”

Credit: DELMIA

Labor shortages remain one of the industry’s most pressing challenges. Younger generations are less inclined to work on the shop floor, and manufacturers are struggling to maintain expertise as older workers retire. “If we don’t make the work more intelligent, there simply won’t be enough people to run our factories,” says Vendroux. “Workers today want to use their minds, not just their hands.” This is where AI-powered assistants come in. DELMIA is developing language-based companions capable of guiding operators, generating step-by-step instructions, analyzing issues, and providing safety or technical guidance through natural conversation. “An operator will be able to ask: ‘How do I carry out this specific weld?’ and receive complete instructions, safety guidelines, and a simulation within seconds.”

For small and midsize manufacturers the implications are transformative. Building a digital twin was once a privilege of giant corporations with vast engineering resources. Now DELMIA offers twin-building automation as well as Twin-as-a-Service for factories that lack the time or expertise to build one themselves. “You bring us the problem, and we bring you the twin,” Vendroux says. “We simulate, run scenarios, and give you the insights. It’s fast and accessible.”

The most significant innovation is the unification of three layers of virtual twins: the product twin, the process twin, and the supply chain twin. Only when these layers interact coherently can a factory achieve true operational stability. “We connect all of it,” he explains. “When the product, the production system, and the supply chain all speak the same language, the factory can finally breathe.”

Looking ahead, DELMIA envisions a future in which factories are defined largely through natural language: an engineer describes a desired line, and the system builds, validates, and optimizes it automatically. “Generative AI will allow entire production lines to be created from textual descriptions,” Vendroux says. “The role of humans will shift from model-building to decision-making. It will change everything we know about manufacturing.”

The fusion of AI, simulation, and industrial operations marks the beginning of a new era. What happened to video creation and image generation is now happening to manufacturing itself. The world of production is moving into a phase where every factory can have a virtual replica, every operator can access knowledge instantly, and every process can be optimized continuously. For DELMIA and for the manufacturers adopting these tools, the implications are enormous: more flexibility, greater resilience, and a path toward an industrial ecosystem that is finally as intelligent as the products it creates.

 

Dassault Systèmes Ushers in the Next Generation of Digital Twin Technology

Posted on by Techtime

By: Yochai Schwieger

French company Dassault Systèmes unveiled at the Paris Air Show a new capability to develop a complete aircraft — from initial design through manufacturing and maintenance — using its newly launched 3D UNIV+RSES platform. In a demo featuring experts from Airbus, the U.S.-based NIAR aviation research institute, and Belgian aerospace manufacturer SABCA, Dassault showcased an end-to-end development and maintenance process conducted entirely in a digital environment — without building a single physical component at the early stage.

The development process — from engineering design to maintenance simulations and failure prediction — was executed entirely in a virtual environment using a precise digital twin of the aircraft. This twin allows for testing design ideas, detecting faults, performing aerodynamic simulations, structural analyses, wear predictions, usage scenarios, maintenance planning, and even technician training and drills. The digital model also incorporates the production process itself — simulating assembly lines, shop floor logistics, parts flow, and quality control.

Assembly line configuration, division of labor between robots and humans, bottleneck analysis, and production timelines — all of these are evaluated in advance within the virtual environment. This enables optimization not only of the aircraft itself, but also of how to manufacture it — even before a factory is built, a budget is approved, or a single part is produced. The approach dramatically reduces risk, cuts costs, and shortens the time from concept to deployment. The result: production can begin only after every system has already been thoroughly optimized — without wasting resources on trial and error. It’s a paradigm shift: from engineering that is tested in the field, to engineering that is implemented only after it has been proven.

Dassault Systèmes placed strong emphasis on its presence at this year’s air show, aiming to position itself as a key enabler of the European defense industry’s upcoming growth. “Europe’s defense systems are on the verge of an industrial leap forward, and we see digital twins as a critical component for accelerating innovation and maintaining operational readiness,” said a company representative during a press briefing. Dassault aims to play a central role in Europe’s rapid defense buildup. The company says its platform supports the creation of digital twins for aircraft, satellites, drones, and even ground combat systems such as armored vehicles — integrating design, manufacturing, training, and maintenance.

The Next Generation of Digital PLM Platforms

The UNIV+RSES platform, launched in early 2025, marks a new evolution in the company’s longstanding 3DEXPERIENCE system. While 3DEXPERIENCE has served for over a decade as a PLM (Product Lifecycle Management) platform in the aerospace, engineering, and automotive industries, UNIV+RSES adds a new layer: real-time data analytics, generative AI, and immersive XR experience. It’s an integrative layer that expands the use of digital twins beyond just design and engineering — into maintenance, training, diagnostics, supply chain optimization, and global team collaboration.

A Digital Twin That Evolves with the Product

The main difference in 3DEXPERIENCE UNIV+RSES lies in its intelligent, living, and real-time-updated digital twin. Whereas traditional digital twins reflected static models, the UNIV+RSES twin evolves alongside the physical system, continuously learning and updating via sensor data, usage patterns, and predictive maintenance algorithms. The twin not only mirrors reality — it predicts failures, recommends solutions, simulates future scenarios, and presents actionable data to teams.

For example, a hydraulic systems technician can put on XR glasses and view the digital twin of an aircraft’s landing gear. During training, they’ll see visual highlights of high-wear areas and rehearse recurring malfunction scenarios. But when the aircraft returns from a flight and sensors detect unusual vibrations in the right wheel, the digital twin updates in real-time, highlights the suspected area, and guides the technician to the precise location — before they even touch the aircraft.

Collaboration with Apple

During a Techtime visit to Dassault Systèmes’ headquarters in Paris, the company showcased a new system born out of collaboration with Apple Vision Pro. In summer 2025, the 3DLive app will launch, allowing users to project a digital twin from UNIV+RSES into their real-world space — in high resolution and with multi-user interaction. The advantage of Vision Pro over other XR devices lies in its accurate eye-tracking, hands-free control, and natural experience, enabling engineers, technicians, and designers to “step into” the product and interact with it as if it were real.

Vector-Based Digital Analysis

UNIV+RSES integrates a Generative AI suite that not only analyzes existing models, but actively generates new content — designs, scenarios, maintenance procedures, and operational suggestions. One of its key tools, VecAssess, allows for the analysis of digital twins in a vector space, identifying anomalies, optimization opportunities, or emerging failures before they become evident in the physical world.

VecAssess is based on the concept of vector space analysis — a mathematical approach that represents component and process attributes as numeric arrays. Each engineering component, such as a valve or piston, is represented as a vector: a data sequence including properties such as weight, material, operating temperature, maintenance frequency, usage time, and more. This creates a digital fingerprint for each part, enabling the AI to compare hundreds of thousands of vectors to detect anomalies, identify recurring patterns, or predict failures. This is similar to the vector space used in large language models (LLMs), where ideas and words are also represented as vectors to uncover semantic relationships. Here, the vector space serves not only as a representation tool, but as a foundation for inference, forecasting, and autonomous decision-making within the system.