What these two players bring to the table undoubtedly provides a wealth of capability, lending concrete weight to the ideas around closing the loop between design and manufacturing—even within more disparate IT environments:
- Siemens contributes its hardware, manufacturing data, and advanced Digital Twin technology, showing exactly how a machine or factory is designed to operate.
- IFS provides operational data, service history, and lifecycle management via its cloud-based IFS Cloud platform.
Will it work? Clearly, the partners seem to have aligned most of the collaborative aspects of this venture. Tony Hemmelgarn, head of Siemens PLM, is not one to bring empty promises to the table. By weaving together the capabilities of both companies, a closed data loop is created. This allows AI models to understand both the original engineering design and the machine’s actual behavior in real time, opening up staggering possibilities for optimizing production loops.
Optimizing the Product Lifecycle Through Industrial AI
According to the two executives, this promise holds immense potential to boost the value of customers’ products—primarily by optimizing production assets across the entire product lifecycle using industrial AI.
It is also easy to see the excellent opportunities this collaboration brings. It merges Siemens’ leadership in industrial AI, engineering, automation, and manufacturing execution with IFS’s strengths in industrial AI, enterprise asset management, and field service. Together, they can help manufacturers bridge a persistent divide: the gap between how a factory operation is designed and how it functions in reality. This is a reality where unplanned downtime, disjointed maintenance schedules, siloed production data, and supply chain disruptions continue to erode throughput, flexibility, and margins.
”Production planning, maintenance, and the supply chain still operate in disconnected systems. When disruptions occur, companies always react, but they never predict. Siemens has spent decades building the engineering and manufacturing side of this story: the digital twin, automation, the factory itself. IFS gives us what happens next: asset performance, service history, and lifecycle data from the field. Together, we close the loop. We connect design intent and real-world performance into a secure, governed data structure, so that what happens in operations feeds directly back into how the next asset is designed and built. This partnership delivers the missing piece. Design meets reality, reality feeds design,” summarized Mark Moffat.
A Common Mission for Manufacturers
Manufacturers are under intense pressure to achieve more with their existing assets—increasing factory floor production, protecting margins, and extending equipment value across its entire lifecycle, while responding to changes with greater flexibility and adaptability. Yet, many still operate with production, maintenance planning, and supply chain systems that do not communicate with one another. This leaves engineering intent, actual performance, and service strategy completely isolated.
Industrial AI is central to the partnership’s ambition. Siemens and IFS share the conviction that the next era of industrial performance will be defined by bringing together the physical and digital worlds to help manufacturers translate design intent into operational reality, and loop that operational reality back into better design to accelerate innovation.
A Closed-Loop Digital Twin
Siemens’ comprehensive digital twin brings together the engineering, simulation, and manufacturing context, while IFS aggregates service history, asset behavior, and operational lifecycle data that shows how these products and assets perform in the real world. Together, they plan to create a closed-loop digital twin based on both design intent and field performance—a digital twin that is secure, governed, and auditable across design, simulation, service records, and factory execution, and can be deployed at an industrial scale.
Unlike generic AI models, industrial environments demand accuracy, reliability, regulatory compliance, and adaptability to drive optimization and flexibility. Even minor error rates are unacceptable when decisions impact safety, compliance, and costly physical assets.
The partners’ shared strategy for industrial AI is purpose-built for this reality.
