Digital twin technology is bridging the physical and digital worlds in automotive manufacturing

The adoption of digital twins is expanding beyond traditional manufacturing applications, with AI, machine learning, and edge computing driving the next phase of innovation. We are seeing AI-powered digital twins enabling more advanced predictive analytics and automated decision-making. These are using historical data and machine learning models, which enables the digital twins to more accurately predict machine failures before they happen and further avoid unplanned downtime by enhancing quality control to identifying defects at early stages. This optimisation of production line efficiency con now be based on real-time data.

Going further digital twins are evolving into cyber-physical systems (CPS), where physical assets and digital replicas interact in real-time. These systems allow for automated responses to production anomalies, so the system can self-correct manufacturing processes based on AI feedback. This can be anything, ranging from environmental variations (temperature/humidity, etc.) in the production area, where processes are sensitive to these changes, to optimising production flow in response to materials/component inventory shifts.

Drivers of digital twin development
Several factors are accelerating the development and adoption of digital twins in the automotive industry. Certainly, the push towards smart factories has been a key driver of digital twin adoption as manufacturers seek to improve the transparency of plant wide production operations. However, the advanced simulation delivery has been seized upon to plan not only large-scale plant layouts but also to develop production flows, logistics and materials handling and to trial and validate assembly processes and automation.

This, supported by AI-driven optimisations, means digital twins can deliver in two key areas for manufacturers: reducing time to market for new products and supporting the development of flexible production lines.

As an example, digital twins are a key tool in BMW’s iFactory project, and this has been demonstrated in both product and plant development with the design, engineering and production of its Neue Klasse EV and the integration of flexible production operations across its vehicle assembly network to support both ICE and EV production. The company’s new plant at Debrecen in Hungary has been designed entirely using digital tools, while digital twins have been instrumental in redeveloping BMW’s plant in Munich, Germany, enabling both ICE and Neue Klasse models to be built simultaneously while allowing for ramp-up to full EV-only production in coming years.

Powertrain manufacturer HORSE offers another good example of how digital twins can be deployed in a wider range of applications. The company has 37 digital twin projects ongoing at its Valladolid plant and says that 100% of operational areas are now digitised.

The digital twins are supporting automation, predictive analytics, and quality optimisation and are redefining process planning, real-time monitoring, and adaptive manufacturing.

Challenges in deploying digital twins
While digital twins offer clear advantages for automotive manufacturing there are challenges in deployment. The technology introduces a new layer of complexity, and organisations must navigate a set of significant technical and operational hurdles to fully realise its value.

One of the biggest barriers to digital twin adoption is the upfront cost. Creating a functional digital twin demands investment across multiple domains. First, companies must build out robust IoT infrastructure to collect real-time data from sensors embedded across equipment, products, and processes. Without this sensor layer, the digital twin lacks the raw input it needs to function.

Beyond hardware, companies need cloud computing resources and data storage capacity capable of handling vast volumes of information. Real-time monitoring and simulation generate immense data flows, which must be securely stored and quickly accessed. This drives up infrastructure and operational costs.

Finally, a functional digital twin requires integration with AI and machine learning models, which are essential for enabling predictive insights and automated responses. Developing these capabilities in-house is costly and requires access to skilled talent, while third-party solutions can also be expensive to license and scale.

Data integration complexities
For a digital twin to provide accurate, actionable insights, it must integrate data from both operational technology (OT) and information technology (IT) systems. Achieving this seamless integration can pose serious challenges.

Many manufacturers still rely on legacy systems that were never built with interoperability in mind. These older platforms often use outdated communication protocols and data structures, making it difficult to connect them to modern digital frameworks. Another issue is the lack of data standardisation. Information collected from different sensors, machines, and software tools often comes in various formats. Without consistent structuring and labelling, data becomes difficult to aggregate or analyse at scale.

On top of these technical concerns, there’s the matter of cybersecurity. Real-time data exchange between systems and cloud environments opens new attack surfaces. Companies must address vulnerabilities proactively, implementing strict access controls, encryption protocols, and continuous monitoring to mitigate security risks.

Scalability issues
Even if a digital twin functions effectively in a pilot environment, scaling it across an enterprise is a major challenge. Doing so requires not just duplicating the technology but establishing standardised data protocols that work across multiple facilities and geographies.

To ensure consistent performance, manufacturers need high-speed data processing systems that can deliver real-time insights across large-scale operations. This demands not only powerful computing infrastructure but also network reliability and low-latency connectivity.

Finally, digital twins must be interoperable. A company may use different digital twin models for design, production, and logistics, but these systems must interact seamlessly to provide a unified view. Aligning different models requires careful planning, standardised architecture, and often, custom integration work.

Key steps in creating and deploying a digital twin
Deploying a digital twin in automotive manufacturing requires a structured, strategic approach. It’s not just about creating a virtual model – it’s about building a responsive, data-driven system that can optimise production, reduce downtime, and improve overall product quality. Below are the four essential steps to creating and deploying an effective digital twin.

Define the digital twin objectives

The first step is to establish clear objectives. What problems is the digital twin meant to solve? In automotive manufacturing, the goals usually fall into three main categories:

• Optimising production: Enhancing throughput, reducing waste and improving process flow.
• Enhancing predictive maintenance: Digital twins can help detect early signs of equipment failure by simulating operational conditions and comparing them to real-time performance.
• Improving quality control: By simulating how vehicles or components are produced and used, manufacturers can identify where defects are likely to occur and adjust accordingly.

Without defined objectives, the project risks becoming a costly exercise with limited value. The clearer the goals, the more focused and effective the digital twin implementation will be.

Collect and integrate data (OT-IT integration)
A digital twin is only as good as the data it receives. The next step is to integrate data from both operational technology (OT) – the systems that run the factory – and information technology (IT) – the systems that manage business operations.

Key data sources include:

• IoT sensor data: Devices embedded in machines and products collect real-time data on temperature, pressure, vibration, and other critical parameters.
• ERP and MES systems: These enterprise platforms provide information on inventory, production schedules, quality metrics, and supply chain logistics.
• Machine learning models: Historical and real-time data are fed into predictive models to forecast maintenance needs, production inefficiencies, or quality issues.

Integrating these diverse data streams into a unified digital twin architecture is a complex but crucial step. It forms the foundation for simulation and real-time responsiveness.

Create the digital twin model
With data in place, the next step is to develop the actual digital twin model. This involves combining simulation tools, machine learning algorithms, and cloud infrastructure to create a real-time, virtual replica of the physical system.

• 3D modelling tools are used to create the visual representation of machines, products, or entire production lines.
• AI algorithms enable predictive maintenance and adaptive control by identifying patterns and anomalies in operational data.
• Cloud computing provides the scalability to process and store large volumes of data while enabling remote access and real-time updates.

This model becomes the interactive core of the digital twin, simulating behaviour, diagnosing issues, and guiding decision-making.

Implement and continuously improve
Once deployed, a digital twin isn’t static – it needs to evolve. Continuous improvement is critical to keep the model aligned with physical changes and operational realities. Using AI-driven optimisation ensures the system adapts to performance trends and environmental shifts. Machine learning updates refine predictive accuracy as more data is collected. Importantly, regular security updates are essential to protect sensitive operational data and prevent breaches in connected environments.

By maintaining and iterating the digital twin, manufacturers can ensure it remains a powerful tool for driving efficiency, reducing downtime, and maintaining quality.

Reshaping automotive manufacturing
The advanced development and application of digital twins are reshaping the automotive manufacturing landscape. As companies like BMW and HORSE demonstrate, digital twins can improve efficiency, enhance predictive maintenance, and drive innovation.

With advancements in AI, IoT, and cyber-physical systems, digital twins are becoming more sophisticated and integral to smart manufacturing and vehicle development. While challenges such as cost, data integration, and scalability remain, the benefits far outweigh the obstacles.

As the industry moves towards electrification, automation, and sustainability, digital twins will continue to drive efficiency, cost reduction, and innovation in automotive manufacturing.