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Siemens and IFS have entered a partnership aimed at helping manufacturers connect how products and factories are designed with how they perform in practice.
The deal will link Siemens’ digital twin capabilities with IFS’s asset and service lifecycle data, giving manufacturers a clearer picture of how equipment, production lines and assets are behaving once they move from the design phase into live operations.
A Persistent Gap
The partnership is designed to address a common challenge in manufacturing: engineering, factory operations, maintenance and supply chain systems often sit in silos, making it harder to identify performance issues, optimise maintenance schedules, reduce downtime and respond when conditions change.
Manufacturers are under pressure to get more from existing assets, whether that means producing more on the plant floor, protecting margins or extending the useful life of equipment. Yet many still operate with production, maintenance planning and supply chain management systems that do not communicate with each other, leaving engineering intent, real-world performance and service strategy disconnected.
How The Integration Works
The collaboration brings together Siemens’ industrial AI, engineering, automation and manufacturing execution capabilities with IFS’s enterprise asset management and field service platforms.
Siemens’ digital twin provides the engineering, simulation and manufacturing context. IFS contributes the service history, asset behaviour and operational lifecycle data that shows how products and assets perform once deployed.
Together, the companies plan to create a closed-loop digital twin that is grounded in both design intent and field performance. The system is intended to be secure, governed and auditable across design, simulation, service records and factory execution.
Industrial AI At The Core
Industrial AI sits at the centre of the partnership. Both companies take the view that the next phase of industrial performance will be shaped by bringing the physical and digital worlds together – translating design intent into operational outcomes and feeding real-world performance data back into the design process.
Unlike general-purpose AI models, industrial environments demand accuracy, reliability, regulatory compliance and adaptability. Even small error rates become unacceptable when decisions affect safety, compliance and physical assets. The partners’ shared approach to industrial AI is built around that constraint.
Tony Hemmelgarn, President and Chief Executive Officer of Siemens Digital Industries Software, outlined the thinking behind the integration.
“Industrial AI only delivers value when it is grounded in both engineering intent and real-world performance,” he explained. “Together with IFS, we are bringing these domains together by connecting design, manufacturing and asset lifecycle data in a secure, contextualized data fabric.”
Hemmelgarn added that the combined effort will deliver “an executable Digital Twin that will enable [customers] to accelerate innovation with confidence.”
Closing The Loop
Mark Moffat, Chief Executive Officer of IFS, pointed to the practical problem the partnership targets.
“Manufacturers need their factory floor to behave the way it was designed,” he remarked. “This partnership with Siemens brings together two companies that each own a critical piece of the puzzle.”
Moffat described agentic AI as “the critical frontier” for industrial operations, noting that “industrial leaders need solutions with closed loop models and data and a rich set of context that will not hallucinate in active operations.”
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“By combining our collective strengths in Industrial AI, we can help manufacturers close the loop between design and reality and unlock real, measurable performance gains,” he added.
What It Means For Manufacturers
The partnership is focused on giving manufacturers a single, continuous data loop that runs from initial product design through to how an asset performs in the field and how it is maintained over its lifecycle.
By connecting engineering simulation data with real-world service records and asset behaviour, the companies aim to help manufacturers make faster, better-informed decisions about production planning, maintenance scheduling and asset management.
The approach also supports adaptability. With operational data feeding back into the design process, manufacturers can identify where products or production lines are underperforming and adjust accordingly, rather than relying on disconnected systems and manual analysis.
Last Updated on June 30, 2026 by Nick Ross
