Siemens seems to have found a home at CES over the past few years. I don’t know what it costs to give a keynote, but it’s probably well worth it, since no other major automation supplier seems to attend. I did write about a robotic exhibitor in my last post. Oh, and I’m still not likely to travel to Las Vegas for the next CES. I’ll save a ton of money and grief by receiving the news at home.
Siemens has maintained strong collaboration with Microsoft for decades—see all the Copilot news below. Recently, NVIDIA has joined the collaboration dance. Also, see news below. I think Siemens thought they’d gain penetration into the North American market through Chrysler’s acquisition by a German company plus the plants constructed by VW and BMW. That market is not so hot—see the proportion of sales into automotive by competitor Rockwell Automation, for example. Check out the customers featured by Siemens at CES this year: PepsiCo, Commonwealth Fusion Systems, Meta Ray-Ban, and Haddy.
Perhaps the best acquisition, and most successful, that Siemens ever made was with UGS years ago. While rivals have struggled with software (and competitors have nibbled at some of the Siemens applications), Siemens continues to strengthen Xcelerator and Copilot technologies. And check out the launch of Digital Twin Composer. Digital Twin technology and application seems to be finally gaining traction.
In short, Siemens announcements:
Siemens and NVIDIA expand their partnership to build the Industrial AI Operating System, reinventing the entire end-to-end industrial value chain through AI – from design and engineering to manufacturing, production, operations, and into supply chains.
Siemens launches Digital Twin Composer software, available on Siemens Xcelerator Marketplace mid-2026, to power the industrial metaverse at scale
PepsiCo using Siemens Digital Twin Composer to simulate upgrades to its facilities in the U.S. with plans to scale globally
Siemens unveils nine industrial copilots to bring intelligence across the industrial value chain
Siemens highlights new technologies for accelerating drug discovery, autonomous driving and shop floor efficiency
“Industrial AI is no longer a feature; it’s a force that will reshape the next century. Siemens is delivering AI-native capabilities, intelligence embedded end-to-end across design, engineering and operations, to help our customers anticipate issues, accelerate innovation and reduce cost,” said Roland Busch, President and CEO of Siemens AG.
“Just as electricity once revolutionized the world, industry is shifting toward elements where AI powers products, factories, buildings, grids and transportation. Industrial AI is no longer a feature; it’s a force that will reshape the next century. Siemens is delivering AI-native capabilities, intelligence embedded end-to-end across design, engineering and operations, to help our customers anticipate issues, accelerate innovation and reduce cost,” continued Busch. “From the most comprehensive digital twin and AI-powered hardware to copilots on the shop floor, we’re scaling intelligence across the physical world, so businesses realize speed, quality and efficiency all at once. This is how we scale a once-in-a-generation technology shift into measurable outcomes.”
Siemens and NVIDIA are expanding their partnership to build the Industrial AI Operating System – helping customers revolutionize how they design, engineer, and operate physical systems. They will work together to build AI-accelerated industrial solutions across the full lifecycle of products and production, enabling faster innovation, continuous optimization, and more resilient, sustainable manufacturing. The companies also aim to build the world’s first fully AI-driven, adaptive manufacturing sites globally, starting in 2026 with the Siemens Electronics Factory in Erlangen, Germany, as the first blueprint.
To support development, NVIDIA will provide AI infrastructure, simulation libraries, models, frameworks and blueprints, while Siemens will commit hundreds of industrial AI experts and leading hardware and software. The companies have identified impact areas to make this vision a reality: AI-native EDA, AI-native Simulation, AI-driven adaptive manufacturing and supply chain, and AI-factories.
Integration with Siemens software.
Siemens also announced that it will be integrating NVIDIA NIM and NVIDIA Nemotron open AI models into its electronic design automation (EDA) software offerings to advance generative and agentic workflows for semiconductor and PCB design. This will both maximize accuracy through domain specialization and significantly lower operational costs by enabling the most efficient model to handle and adapt to every specific need.
Product Launch
Siemens’ primary product launch at CES 2026 is the Digital Twin Composer, available on the Siemens Xcelerator Marketplace mid-2026. This new technology brings together Siemens’ comprehensive digital twin, simulations built using NVIDIA Omniverse libraries, and real-time, real-world engineering data.
With the Digital Twin Composer, companies can create a virtual 3D model of any product, process, or plant; put it in a 3D scene of their choosing; then move back and forth through time, precisely visualizing the effects of everything from weather changes to engineering changes. With Siemens’ software as the data backbone, the Digital Twin Composer builds Industrial Metaverse environments at scale, empowering organizations to apply industrial AI, simulation and real-time physical data to make decisions virtually, at speed and scale. Digital Twin Composer is part of Siemens Xcelerator, an industry proven portfolio of software used by companies worldwide to develop digital twins.
Customer application of digital twins
PepsiCo and Siemens are digitally transforming select U.S. manufacturing and warehouse facilities by converting them into high-fidelity 3D digital twins that simulate plant operations and the end-to-end supply chain to establish a performance baseline. Within weeks, teams optimized and validated new configurations to boost capacity and throughput, giving PepsiCo a unified, real-time view of operations with flexibility to integrate AI-driven capabilities over time.
Leveraging Siemens’ Digital Twin Composer, NVIDIA Omniverse libraries and computer vision, PepsiCo can now recreate every machine, conveyor, pallet route and operator path with physics-level accuracy, enabling AI agents to simulate, test, and refine system changes – identifying up to 90 percent of potential issues before any physical modifications occur. This approach has already delivered a 20 percent increase in throughput on initial deployment and is driving faster design cycles, nearly 100 percent design validation and 10 to 15 percent reductions in capital expenditure (Capex) by uncovering hidden capacity and validating investments in a virtual environment.
New Industrial Copilots Streamline Manufacturing Operations
Siemens also spotlighted its partnership with Microsoft highlighting co-building the industrial copilot.
Siemens also announced that it is expanding its set of AI-powered copilots across the industrial value chain. This will embed intelligence that extends from design and simulation to product lifecycle management, manufacturing, and operations.
Siemens will deploy nine new AI-powered copilots for its software offerings, this will include Teamcenter, Polarion, and Opcenter. These copilots, respectively, streamline product data navigation, reducing errors and accelerating time to market; automate compliance, helping to ensure faster regulatory approvals and lower risk; and transform manufacturing processes, driving cost savings and operational efficiency.
These copilots, along with the rest of Siemens’ expanding portfolio of industrial AI solutions, are available to companies of every size on the Siemens Xcelerator Marketplace.
AI-Driven Innovations in Life Sciences, Energy and Manufacturing
Siemens acquired Dotmatics whose Luma platform enables scientists to unify billions of data points generated across instruments and labs, creating a coherent foundation for AI-driven exploration. Combined with Siemens Simcenter simulation and digital twins, teams can rapidly test molecules, identify promising candidates, and virtually scale production to help life-changing therapies reach patients up to 50% faster and at a lower cost.
Bob Mumgaard, CEO and co-founder of Commonwealth Fusion Systems, described how the company uses Siemens’ technologies as it leads the path to commercial fusion. Commonwealth Fusion Systems uses design software and a strong data backbone to help it accelerate the development of fusion machines that promise clean, limitless energy for generations to come.
In manufacturing, Siemens announced a collaboration to bring Industrial AI to Meta Ray-Ban AI Glasses. With hands-free, real-time audio guidance, safety insights, and feedback, shop floor workers will feel empowered to solve problems efficiently and confidently.
Haddy is reshaping manufacturing through AI-powered 3D printing and localized micro factories that deliver sustainable, high-quality products faster and closer to customers. Facing challenges around supply chain disruption, sustainability, and production agility, Haddy partnered with Siemens to streamline design, optimize operations, and scale efficiently.
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User studies remain one of the primary ways software companies can gain insight and achieve some public recognition. Most of the studies emanate from cybersecurity protection developers. This one comes from a software company with which I’ve had little contact. There was a woman I knew from one company who came to SAS for a while. We had occasional conversations before she left that company.
SAS develops software applications. I’ve never had a handle on its business. It now bills itself as a global leader in data and AI. This study was conducted by the research firm IDC. And we have the acronym AIoT—or the convergence of AI and IoT. Somehow I feel that concatenating acronyms is the beginning of the end times 😉
This one should surprise no one. Everyone discusses predictive maintenance.
Predictive maintenance dominates current AIoT use. Nearly 71% of organizations use AIoT for predictive maintenance, the most widely adopted use for manufacturing/industrial and energy companies surveyed. IT automation (53%) and supply and logistics (47%) were the next most cited uses for AIoT.
Executives continue to dream of significant cost reductions from AI.
AIoT drives tangible business value. 54% of respondents anticipate major cost savings, 52% predict smarter and faster innovation and 49% expect streamlined operations from their investment in AIoT. Additionally, 63% believe AIoT will boost productivity and competitiveness.
Managers continue to see AI as an aid to overcome the current skills gap of employees.
Skills gap emerges as the top challenge. The skills gap is the biggest barrier to AIoT success, outpacing legacy system integration and data quality issues as the most significant roadblock. Other challenges include high implementation costs, business process misalignment and cultural resistance. Addressing these issues is essential to unlocking AIoT’s full potential.
Some actually use the technology!
Heavy AIoT users see greater value. Organizations using AIoT heavily are twice as likely to report benefits that significantly exceed expectations as those that only use the technology sparingly. Strikingly, less than 3% say the value of AIoT “did not meet expectations.”
The IDC research is based on a global survey of more than 300 industrial executives in the manufacturing and energy industries.
And from the company:
SAS IoT solutions combine AI, machine learning and edge-to-cloud integration, enabling analysis of high-volume, high-velocity data. And joining AI with these IoT solutions extends the value of existing infrastructure investments and digitally transforms the workforce by shifting from manual oversight to intelligent orchestration.
Other organizations benefiting from SAS IoT and streaming analytics for improved asset reliability, enhanced product quality and increased efficiency across connected systems include:
We met in a conference room at an office in Barrington, IL. A place where sometime later a couple guys thought they’d screw me in a business deal. I came out ahead in the end, but the place has mixed memories.
This meeting involved thinking about the future of asset data and systems interoperability. We had a system diagram. The idea was to solve a huge problem for owner/operators of process manufacturing enterprises—flowing engineering data into other software systems for operations, maintenance, and enterprise. The incumbent system was a morass of paper (or pdf documents which was much the same thing).
We did trademark searches and domain name searches and eventually settled on the Open Industrial Interoperability Ecosystem—OIIE.
I plot this history for context for the conference I attended recently—the 2nd ADIF Workshop at Texas A&M University dubbed Driving Asset Data and Systems Interoperability Toward an Open and Neutral Data Ecosystem.
This workshop brought together owner/operators, EPCs, System Integrators, university researchers, standards organizations, and software vendors. Each group conducted a panel discussion of its needs and successes. I was there for a short presentation and to moderate the standards panel.
Professor David Jeong from Texas A&M and the session leader previewed the discussions. One of his colleagues later presented research his team has performed to provide a method for taking P&ID documentation into a standard format usable by other software systems.
The message that came to me from the panel of owner/operators (grossly summarized, as will be all the discussions) included two key words—collaborate and operationalize. They are impatient about solving this data interoperability problem. One panelist quipped, “We know the project is finished when the large van backs into the loading dock and disgorges mountains of paper.”
What blows my mind is that I was moved to a position called Data Manager in 1977 to tackle the (much smaller) mountain of paper our product engineering department provided to operations, accounting, and inventory management. I led a digitalization effort in 1978 to tackle the problem. The problem not only remains, but it is immensely more complicated and critical.
The EPCs basically said that their hands were tied by the owner/operators mandating which design and engineering software to use and the inflexibility of the vendors of said design and engineering software. When owner/operators had requested digital documentation, they had responded with pdfs. Hardly interoperable data.
Our standards panel included the leader of DEXPI, whose organization has developed a method of changing P&ID data into an xlsx (Excel) format. That, of course, is a good start.
An organization called CFIHOS (see-foss) presented their take on standards. I’m afraid I got a bit lost in the slides (note: more research needed). What I gathered was that they were attempting one overriding standard—and that that work was years away. Interesting that I listened to Benedict Evans’ podcast this morning. He is a long-time tech industry analyst. He remarked in another context, “It seems that where there are 10 standards and someone comes along with a standard to encompass them all, you wind up with 11 standards.”
The ISA-95 was presented. This messaging (and more) standard is incorporated with the OIIE, which was presented next. Dr. Markus Stumptner of the University of South Australia presented his research work on proof of concept of the OIIE.
If we can get enough momentum focusing on this area and find some SIs willing to take the OIIE to an owner/operator, perhaps we can finally prove the business case of asset data and systems interoperability.
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Industrial IT developers increasingly incorporate standard IT infrastructure. Rather than send platitudes about the mythical “IT/OT Convergence,” some actually just do it. Integration with Kubernetes exemplifies one such technology.
This news comes from edge orchestration developer ZEDEDA. The short list of benefits from edge orchestration solutions with Kubernetes.
Simplifies edge operations by delivering consistent app management across distributed locations, minimizing expensive manual intervention at each site
Automates large-scale edge infrastructure and application deployment—enforcing desired state for thousands of edge clusters, even with unreliable network connectivity
Optimized AI processing close to operational data sources reduces expensive data backhaul to the cloud
ZEDEDA announced the first full-stack edge Kubernetes-as-a-Service solution that extends a cloud-native deployment experience to distributed edge environments. This new solution, ZEDEDA Edge Kubernetes App Flows, automates the edge application lifecycle—from packaging and configuration to delivery and observability—eliminating the need to manage cluster and application orchestration infrastructure. Edge Kubernetes App Flows supports the bare-metal and GPU compute required for edge AI applications, such as automated detection of manufacturing flaws and predictive maintenance.
Built on ZEDEDA’s proven edge platform, the new integrated Kubernetes solution extends the platform’s zero-trust architecture and offline resilience—keeping tens of thousands of devices and Kubernetes instances running continuously, even in demanding field environments with physical vulnerabilities and intermittent connectivity.
ZEDEDA Edge Kubernetes App Flows combines GitOps-based delivery with ZEDEDA’s zero-trust edge platform—letting organizations focus on applications, not infrastructure.
Key capabilities include:
Application Definition and Marketplace: Deploys customizable application definitions consistently across distributed edge locations.
Application Packaging and Distribution: Builds and distributes manifests tailored for edge requirements.
GitOps-Based Continuous Delivery: Automates deployments through approved Git workflows for full auditability.
Adaptive Observability: Monitors deployment and performance, even with intermittent connectivity and limited bandwidth.
ZEDEDA Edge Kubernetes App Flows is built on and integrates with all the leading security and scalability capabilities of ZEDEDA’s proven edge platform, including:
Zero-Trust Security: Continuous validation of edge devices, applications, and communications.
Offline Resilience: Graceful handling of intermittent connectivity and disconnected operations.
Edge Scale: Support for tens of thousands of clusters and unattended edge devices.
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Inductive Automation unleashed Ignition 8.3 during its Ignition Community Conference in September. It included many significant updates. Trying to be “IT-friendly” since it founding, this tagline of “The OT-IT Bridge of the Future” draws ever closer. They call Ignition 8.3 a unified industrial integration platform that makes it easy to bring your OT and IT environments into one system.
I’ll bullet a few examples of new tools and end with a link to a cool use case for Ignition from the Ukraine.
Centralized Events Management
Use the new Event Streams Module to handle and manage tag changes, database events, alarms, and more from a central location.
Event Streams
Easy Enterprise Orchestration
Connect or manage configuration across enterprises using standard IT technologies with Ignition’s self-documenting REST Web API.
World-Class Security
Protect enterprise data with first-class security features like Secrets Management, with extensibility to integrate with third-party secrets management platforms like HashiCorp Vault coming soon.
Secrets Management
A Stable, Long-Term Foundation
Since 8.3 is a Long-Term Support release, you’ll get improvements and fixes for a minimum of five years.
Git Compatibility
Use Ignition 8.3 with Git to collaborate better on big projects and gain complete version control.
Check out the Hebron Project presented at the Ignition Community Conference. This application should get you thinking outside the box searching for cool applications of your own.
(Note: Inductive Automation is a long time sponsor of The Manufacturing Connection. This post is purely my own writing/editing with no additional compensation.)
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I find it sort of amazing that the head of product development at the manufacturing company where I was working in a position sort of like a combination of manufacturing engineering and materials management plucked me out of the factory to assume a role a manager of data.
That was 1976. The problems I attempted to solve 50 years ago are the same problems (albeit on a much larger scale) that executives face today. Multiple silos of proprietary data. Insufficient insight into the company’s operational health. Buried risks to enterprise decision-making.
ADIF (Asset Data Interoperability Framework) working group is a dedicated research group of industry experts and academia that is committed to fostering open, vendor-neutral, and standards-based solutions for achieving data and systems interoperability for assets intensive industries.
I will be in College Station next week to moderate a panel discussion on standards—perhaps discussing how so many standards can work together. The panel includes luminaries Markus Stumptner, University South Australia, Alan Johnston, MIMOSA, Micheal Wiedau and Reiner Meyer-Rossl, DEXPI, Peter Townson, CHIFOS, and Chris Monchinski, ISA 95.
There is still time to register and come. I will probably have some live reports for those who cannot make it.
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