In a Gemini Call Live presentation, Emile Glorieux from the Manufacturing Technology Centre outlined a comprehensive vision for how factory digital twins can help automotive manufacturers navigate the unprecedented challenges of electrification—proposing not one, but six interconnected digital twin concepts working in harmony.
The Perfect Storm Facing Automotive Manufacturing
The automotive industry stands at a critical inflection point. As Emile explained in his presentation on an Aston Martin-led collaborative research project, manufacturers face a convergence of disruptive forces: electrification demanding new product types, emerging manufacturing technologies requiring different skills, and volatile market demands creating unprecedented uncertainty.
“We’re looking at new parts, new components, new techniques, new material handling requiring new skills,” Emile noted. The scale of change ahead is staggering—and the one certainty is that there will be continuous, relentless transformation over the next five to ten years.
For organisations accustomed to relatively stable production environments, this represents an existential challenge. Success will belong to those who can “rapidly adapt and evolve alongside all the other things that are changing,” as Glorieux put it. This is where factory digital twins emerge not as a nice-to-have technology showcase, but as a critical enabler of organisational agility.
Beyond Monitoring: The Predictive Imperative
Working closely with industry partners, the MTC team identified a crucial requirement: manufacturers don’t just need digital twins that monitor current operations—they need systems that predict the future. “Looking at providing future insight, predicting how next month, next week, or next year might look like” was the consistent feedback from stakeholders.
This predictive capability serves two critical functions. First, it enables proactive decision-making that avoids costly corrections later. Second, it aligns stakeholders across functional boundaries, breaking down the information silos that plague traditional manufacturing operations. The goal is “future cost avoidance”—preventing decisions that require expensive rectification whilst reducing overall development costs.
A Symphony of Six: The Interconnected Digital Twin Ecosystem
Rather than proposing a monolithic solution, the MTC’s approach recognises that different aspects of factory transformation require specialised tools. Their framework comprises six distinct but interconnected digital twin concepts:
1. The Operational Digital Twin
This foundational system assesses current factory performance and uses that data to predict near-term futures. “The most reliable way to predict the future is to look at the past,” Emile explained. By creating a closed loop between historical analysis and forward simulation, manufacturers gain continuous insight into whether they’re on track or need course corrections.
2. The Transformation Digital Twin
Acting as the conductor of change, this platform coordinates all factory modifications—from maintenance and services to new technology introduction and continuous improvement initiatives. It answers critical questions: What changes happen when? What can proceed simultaneously? Where are the interdependencies that could derail progress?
3. The Knowledge-Based Digital Twin
As the system accumulates operational data and decision history, patterns emerge. This digital twin becomes an organisational memory, identifying recurring issues and successful solutions. “Last time we solved it in this way, so here’s a recommendation to do it again,” Emile illustrated, describing how it provides insights beyond any individual stakeholder’s perspective.
4. The Adaptive Scheduling Digital Twin
Continuous change sounds excellent in theory, but someone needs to orchestrate the ballet of activities whilst minimising downtime. This digital twin determines optimal timing for all factory activities—operational and transformational—ensuring resources are available when needed and disruptions are minimised.
5. The Software-in-the-Loop Digital Twin
Innovation requires testing, but you can’t experiment on a live production line. This concept provides a virtual testing environment with synthetic data streams and synchronisation, allowing manufacturers to validate new digital twin configurations without interrupting operations.
6. The Synthetic Data Digital Twin
As AI and machine learning become integral to manufacturing, they require training data that may not yet exist for new processes or equipment. This digital twin generates representative synthetic datasets, enabling algorithm development before physical implementation.
Critically, these aren’t six independent systems but an integrated ecosystem. “The operational digital twin needs to inform the adaptive scheduling what’s happening at the moment… It’s not just four independent digital twins, there’s four digital twins that are then working together, exchanging information, exchanging data to drive this continuous change,” Glorieux emphasised.
Lifecycle Management: Ensuring Digital Twins Evolve with Reality
A static digital twin of a dynamic factory quickly becomes worthless. Recognising this, the MTC proposes a comprehensive lifecycle management workflow ensuring digital twins evolve alongside their physical counterparts.
The workflow encompasses four stages:
- Subsystem analysis and integration
- Digital twin configuration and development
- Virtual prototyping for testing and verification
- Training and deployment
This structured approach addresses a critical challenge raised during Q&A about future-proofing data models. As Glorieux explained, when manufacturing equipment or processes change, the synthetic data digital twin can generate preliminary datasets from first-principles simulations, kickstarting data-driven models without waiting for real-world data accumulation.
From Vision to Reality: The Adoption Challenge
Perhaps the most pragmatic aspect of the MTC’s work is their industrial adoption roadmap. Recognising that organisations can’t leap from current state to advanced digital twin ecosystems overnight, they’ve developed both a generic five-year roadmap and a methodology for customisation.
“Each organisation has already initiatives going on, most likely around digital manufacturing, training, or virtual engineering,” Emile acknowledged. The adoption methodology aligns new digital twin initiatives with existing programmes, ensuring coherent progress rather than competing initiatives.
The challenge of adoption emerged clearly in the Q&A. When asked about the top barriers to digital twin ecosystem adoption, Emile also highlighted the front-loaded investment problem: “There’s a lot of effort needed at the beginning of the adoption journey—installing sensors, buying software, developing databases—and you don’t necessarily get that much value from it at that very initial stage.”
However, once that foundation exists, the value equation shifts dramatically: “Once you’ve got a lot of those tools and infrastructure developed, you then really get more and more value and benefits from them with much less effort.”
Standards and Interoperability: Building on Solid Foundations
The MTC’s approach isn’t occurring in a vacuum. As confirmed during the session, they’re adopting ISO 23247, the international standard for digital twins in manufacturing. This commitment to standards ensures their proposals can integrate with existing digital manufacturing initiatives whilst maintaining interoperability with future developments.
The architecture leverages existing digital manufacturing investments, connecting with enterprise resource planning (ERP), product lifecycle management (PLM), and manufacturing execution systems (MES) that many manufacturers have already deployed. This pragmatic approach transforms digital twins from isolated experiments into integral components of the digital thread.
Key Insights for Manufacturing Leaders
The MTC’s comprehensive framework offers several critical lessons for organisations embarking on digital twin initiatives:
- Think Ecosystem, Not Monolith: Multiple specialised digital twins working together provide more value than a single, all-encompassing system.
- Prioritise Predictive Capabilities: Historical monitoring alone isn’t sufficient; the real value comes from predicting and preventing future problems.
- Plan for Evolution: Both factories and their digital twins will change continuously; lifecycle management must be built in from the start.
- Leverage Existing Investments: Digital twins should enhance, not replace, existing digital manufacturing infrastructure.
- Accept the Investment Curve: Initial efforts may show limited returns, but the value accelerates dramatically once foundational elements are in place.
The Road Ahead
As the automotive industry navigates its most significant transformation since the assembly line, factory digital twins emerge as essential navigation tools. The MTC’s six-twin framework provides a comprehensive blueprint for manufacturers seeking to build adaptive, intelligent production systems capable of continuous evolution.
With a technical insights report forthcoming and proof-of-concept demonstrators under development, the MTC is bridging the gap between digital twin theory and practical implementation. For automotive manufacturers facing the dual challenges of electrification and market uncertainty, their work offers both a vision of what’s possible and a pragmatic path to get there.
The message is clear: in an industry where change is the only constant, static factories will become obsolete. Those who embrace interconnected, evolving digital twin ecosystems will possess the agility to thrive amidst transformation.
The Digital Twin Hub’s weekly Gemini Calls continue every Tuesday at 10:30am BST, bringing together practitioners and innovators shaping the future of digital twin technology. All sessions are recorded and available to community members.
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