Digital twins are becoming essential in automotive manufacturing, helping OEMs and suppliers optimise efficiency, adapt to electrification, and enhance real-time decision-making. We look at how BMW, HORSE, Stellantis, Webasto and Cintoo are pushing the technology’s boundaries.
The automotive sector has long been at the vanguard of industrial innovation, embracing automation, robotics, and now digitalisation. Digital twins, once confined to the realm of theoretical engineering, are now a core component of modern manufacturing. These virtual replicas of physical systems are driving efficiency, reducing costs, and improving flexibility across global production networks.
The latest AMS Livestream, ‘Digital twins: faster, more transparent production planning’ brought together industry experts to dissect the evolving role of digital twins, revealing how they are reshaping vehicle production at major OEMs and suppliers. AMS Editor and livestream host, Nick Holt, outlined that the process of digitalising automotive manufacturing is advancing, and the industry seeing it across various companies, tier suppliers, and carmakers. The adoption of digital twins is increasing, driven by improved data capture and increasingly sophisticated analytics, supported by AI.
”There’s a strong need to digitalise processes before implementing physical changes to avoid wasted time, materials, and resources”
- Leonardo Fontanelles, Co-founder and CTO, Cintoo
The scope of digital twins has evolved from large, plant-wide projects that digitally replicate entire facilities to smaller, localised projects focused on specific lines, products, or processes. Another major benefit is faster time to market, a critical issue for carmakers in a competitive industry. Digital twins are making an impact by streamlining design iteration, engineering, and production validation, ultimately reducing costs by minimising physical iterations and optimising process steps.
For vehicle manufacturers juggling the complexity of multi-powertrain production, digital twins offer a powerful tool for planning and adaptability. BMW’s Munich plant provides a compelling case study, where digitalisation has enabled the restructuring necessary to accommodate the production of Neue Klasse EVs alongside internal combustion engine (ICE) and hybrid models. With the industry’s transition towards electrification, digital twins are emerging as an essential technology for flexible manufacturing.
The technology’s potential is further illustrated by powertrain manufacturer HORSE, which has embarked on an ambitious digital twin rollout at its Valladolid plant in Spain. With 37 separate digital twin projects in progress, the company claims full digitalisation of its operations, a level of adoption that remains aspirational for many manufacturers.
The different forms and functions of digital twins
The speakers kicked-off with a nuance that many automotive manufacturers would do well to note: Digital twins are not a monolithic concept but exist on a spectrum of complexity with different iterations - and for different use-cases across production. Keenan O’Brien, Head of Digital at Stellantis’ North American Vehicle Processes Engineering division, outlined three distinct levels of digital twins that manufacturers are implementing.
“First, we have the ‘simple digital twin’, which is essentially a 3D model representation of a physical object - this could be a product, tool, or a full assembly plant. It’s a static digital version used for planning purposes,” he explained. “Next is the ‘simulation digital twin’, which incorporates discrete events, robotic automation, and operator simulations to model real-world processes more accurately. Finally, the ‘advanced digital twin’ is connected to equipment and the plant floor, using real-time data from sensors and devices to create a dynamic representation of factory operations.”
Data accuracy and reality capture: the key to precision
One of the foundational elements of digital twin development is data accuracy. Traditional CAD models, while useful, provide only an idealised representation of production environments. Leonardo Fontanelles, Co-founder and CTO at Cintoo, highlighted the importance of integrating real-world data into digital models.
“There’s a strong need to digitalise processes before implementing physical changes to avoid wasted time, materials, and resources,” Fontanelles stated. “At Cintoo, we focus on reality capture as a starting point. Relying solely on CAD files provides only a partial representation, whereas capturing real-world data with full colour and precision is essential.”
“The biggest challenge isn’t the technology - it’s organisational transformation”
- Dr Walter Huber, Director Process Methods and Tools for ME and Production, Webasto
The ability to share and process vast amounts of reality capture data - often measuring in the hundreds of gigabytes - has historically been a challenge. Advances in cloud computing and AI are making this data more accessible to engineers, architects, and operators across multiple locations, ensuring greater collaboration in the planning and execution phases of manufacturing.
AI’s role in digital factory planning and baking-in efficiencies from inception
Artificial intelligence has long been heralded as a game-changer for product design, but its application in factory planning remains a complex challenge. Stellantis, for example, is cautiously integrating AI into its digital twin ecosystem. This is a synthesis likely to have a powerful impact across automotive production ecosystems.
“AI is becoming integral to factory planning, but it’s still a complex challenge,” O’Brien admitted. “While AI has been used extensively in product design, applying it to factory planning involves managing a vast array of variables, from logistics, automation and maintenance, to quality, and operators. We’re making progress, but scaling AI across global manufacturing operations takes time.”
Despite these challenges, AI is already streamlining several key functions. Fontanelles pointed out that AI enhances digital twins by automating equipment classification, enabling predictive maintenance, and ensuring that digital replicas remain up to date. “By automating repetitive tasks and linking data across platforms, we can enhance efficiency. AI helps ensure digital twins stay up-to-date, reducing manual workload and improving accuracy,” he said.
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Overcoming implementation challenges through organisational transformation
The technological infrastructure for digital twins is maturing, but scaling these solutions across global manufacturing networks remains a formidable task. As Dr Walter Huber, Director of Process Methods and Tools at Webasto, noted, the greatest hurdle is not the deployment of digital twin tech itself, but the structural - and infrastructural, change it requires
“The biggest challenge isn’t the technology - it’s organisational transformation,” Huber remarked. “Tools exist, but deploying them across multiple plants with different legacy systems, equipment, and varying levels of digital maturity is complex. Change management, training, and demonstrating clear ROI are critical.”
”Carmakers that successfully integrate digital twins into their operations must also ensure that their employees - from factory floor operators to plant managers - are equipped to interpret and act on the insights these systems provide”
Standardisation (and ironically, inter-technological communication) is a persistent concern. As companies seek to unify digital twin applications across diverse manufacturing environments, discrepancies in legacy documentation, data formats, and operational workflows add further complexity. For older production facilities, such as Stellantis’ North American assembly plants, some of which date back to the 1960s, digitalisation efforts are often hampered by outdated documentation, necessitating a shift towards reality capture to create more precise digital twins.
The future of digital twins in automotive manufacturing
The pandemic, alongside other persistent disruptions, starkly illuminated the importance of digital transformation, accelerating the adoption of digital twins for remote collaboration and crisis management. As automotive manufacturers continue to grapple with supply chain disruptions and shifting production strategies, digital twins are emerging as a vital tool for resilience and agility.
Looking ahead, the challenge for manufacturers to both implement and integrate digital twins into daily operations in a way that delivers tangible efficiencies. Cross-functional collaboration, AI-enhanced automation, and refined data management strategies will be crucial in unlocking the full potential of digital twins in automotive production.
Read more Digital Twin stories
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One key area where digital twins are expected to expand is in supply chain synchronisation. The ability to create a virtual replica of the entire production and logistics ecosystem enables manufacturers to model disruptions, reroute resources, and optimise material flows. This level of insight is increasingly vital as the industry contends with geopolitical instability, regulatory shifts, and the complexities of multi-regional manufacturing.
”Digital twins demand a new level of digital literacy, necessitating upskilling efforts across the manufacturing workforce”
Another crucial development is the push towards standardisation. As Dr Huber noted, manufacturers must balance flexibility with consistency, ensuring that digital twin applications align across multiple sites. Achieving this level of coordination requires not only technological investment but also a shift in corporate culture - embracing digital-first decision-making and fostering cross-disciplinary collaboration.
Equally significant is the role of workforce development. Digital twins demand a new level of digital literacy, necessitating upskilling efforts across the manufacturing workforce. Carmakers that successfully integrate digital twins into their operations must also ensure that their employees - from factory floor operators to plant managers - are equipped to interpret and act on the insights these systems provide.
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