by Gary Mintchell | Apr 23, 2025 | Software, Standards, Sustainability
While I am on a standards reporting kick, this news reflects the growing collaboration among formerly competitive standards development organizations. I wrote recently about how OPAF is actively taking an end user view into standards collaboration and rationalization. Working together usually brings benefits to users.
From the statement of purpose: Accurate energy consumption data is essential for companies aiming to achieve climate-neutral production. To support this goal, a consortium of organizations has recently published a groundbreaking specification for interoperable and efficient energy management in industrial and process automation.
\A key goal of the mechanical and plant engineering industry is to achieve climate-neutral production in the future. This effort is supported by the European Union’s European Green Deal, which aims to make Europe climate-neutral by 2050. In order to achieve this goal and implement many other use cases, accurate data on energy consumption in production is crucial. The consortium, consisting of the organizations ODVA, OPC Foundation, PI and VDMA, has now jointly published version 1.0.0 of their groundbreaking specification for interoperable and efficient energy management in industrial automation and process automation. This group is chaired by the VDMA.
Dietmar Bohn, Managing Director of PNO, explains: “The measurement and analysis of energy consumption in machines and systems is an extremely important topic for the future. We are pleased to make an active contribution to this important initiative to optimize energy consumption and thereby reduce the harmful effects on the environment caused by waste and surplus.”
This specification defines a standardized information model based on OPC UA that enables comprehensive energy management in industrial automation. “This Power Consumption Management collaboration ensures that end users have a highly standardized and interoperable means of achieving their environmental, social and governance (ESG) goals,” explains Dr. Al Beydoun, President and CEO of ODVA.
The introduction of this standard will make energy management in industry considerably easier: companies can now record, analyze and use precise and consistent energy data even more efficiently in order to further increase their energy efficiency. This not only helps to reduce operating costs, but also to reduce the ecological footprint. Standardization makes it possible to implement innovative technologies and best practices faster and more effectively, which contributes to more sustainable and environmentally friendly production in the long term.
The specification essentially comprises two main content fields: Firstly, monitoring, i.e. the display of all types of energy consumption, including electrical energy as well as energy from air, water or coal. Secondly, standby management, which is understood to mean the control and display of various energy-saving modes on machines and components. It is based on the results of the research project “Development of energy management interfaces for IoT technologies (IoTEnRG)”. “The aim of the IoTEnRG research project was to make the results available to industry. We were able to contribute our results directly to the Joint Working Group and thus significantly accelerate the development of the OPC UA Companion Specification,” says Prof. Dr. Niemann from the Institute for Sensor Technology and Automation at the University of Applied Sciences and Arts in Hannover.
“For digitalization, we need an agreement on a common understanding and description of data, including in the energy sector. OPC UA provides exactly that. I am proud that with this joint group, we can also contribute to the energy transition and thus promote optimized energy savings through standardized and efficient monitoring,” says Stefan Hoppe, President of the OPC Foundation.
The VDMA has defined a fundamental standard for the entire mechanical and plant engineering industry, known as “OPC UA for Machinery”. Various functional building blocks are specified in this standard. A new building block for energy management is being developed based on the publication. “The four organizations have been working hard to harmonize and standardize information on energy consumption in manufacturing. This is an excellent first step towards defining an upcoming OPC UA Building Block for mechanical engineering that will bring the machine and plant manufacturing industry a big step closer to the goal of climate-neutral production,” says Andreas Faath, director of the VDMA Machine Information Interoperability department.
by Gary Mintchell | Apr 23, 2025 | Software, Standards
I have mixed feelings toward standards organizations and consortia. Some engineers use their work to build systems. I’m never sure what the final benefit is. Some have built technology in everyday use—OPC, ODVA, FieldComm (HART, FDT), Profinet. Some publish papers that I have hear practical outcomes emanating from.
Yet, I still report on some of these. You never know how some engineers may benefit from the work while building their systems.
This news (I’m catching up on news that came my way while traveling and thinking about what I learned there) comes from The Digital Twin Consortium (DTC), a unit of the Object Management Group. My last two trips and several subsequent interviews and press events all worked in the term Digital Twin somewhere in the discussion. So, it’s relevant.
The Digital Twin Consortium (DTC) published a whitepaper titled Spatially Intelligent Digital Twin Capabilities and Characteristics to help business executives, enterprise, business, and solution architects, system designers, and developers understand the base concept of spatial information relative to the capabilities and characteristics used to describe locational intelligence in the context of digital twin capabilities. The concepts described in the whitepaper apply to a broad spectrum of digital twin use cases, industries, and disciplines.
The whitepaper provides organizations guidance to:
- Document the capabilities and resulting value streams provided through the ability to visualize, understand, and analyze the geospatial locational characteristics of real-world entities and conditions.
- Understand the distinction between different forms of locational representations, including geometric (3D models), spatial, and geospatial models.
- Document the key characteristics of locational representations in a digital twin so organizations can consistently capture locational attributes, enabling digital twin system-to-system integration.
- Capture the Spatially Intelligent Digital Twin’s locational characteristics in the context of capabilities using the DTC’s Capabilities Periodic Table (CPT).
By completing the steps outlined in the white paper, organizations can define locational capabilities and data requirements for their digital twins. They can design, develop, and operate digital twins that meet organizational needs and provide business value.
The Digital Twin Consortium Architecture, Engineering, Construction, and Operations (AECO) Working Group prepared the whitepaper. Download the DTC website’s Spatially Intelligent Digital Twin Capabilities and Characteristics whitepaper. Become a DTC member and join the global leaders in driving digital twin evolution and enabling technology. DTC is a program of Object Management Group.
by Gary Mintchell | Apr 21, 2025 | Manufacturing IT, Software
The media relations person found me somehow tempting with this teaser:
- Converting natural language into code – Transform vague or unstructured instructions into precise, actionable code through AI-driven natural language processing.
- Enhancing automation, accuracy, and efficiency – Minimize manual input and errors, optimizing every stage of the design-to-production lifecycle with intelligent automation.
- Seamlessly integrating with existing tools – Leverage AI to integrate effortlessly with existing design and manufacturing software, ensuring full compatibility with established engineering tech stacks.
OK, this is general, full of the latest buzz words. Reading between the lines of this and her emails, I thought I detected the seeds of an answer to problems I had working in engineering data management in the late 70s. So, I consented to an interview (I do few of these anymore) with Foundation EGI co-founder Wojciech Matusik, Professor of Electrical Engineering and Computer Science at the Computer Science and Artificial Intelligence Laboratory at MIT.
This decision was one of my best lately. Matusik laid out a coherent, logical, precise definition of what this company (that launched last week) defines as the problem and the solution it intends.
These are my words—I see what they are doing as the crucial step between PLM and MES. That’s a bit to crude, but bear with me.
Early in my career, the VP of Product Development for a manufacturing company picked me for the role of data manager. From the foundation of managing the bills of materials and specifications generated by product design communicating with other departments such as manufacturing operations, cost accounting, and purchasing. These placed me at the center of cost control and reduction efforts (Hello, Elon, I could have helped you do a better job!).
Matusik began with the premise that engineering is programming, that is, it generates a set of rules in a domain-specific language. Look at assembly, for example, it is a small set of operations from a small domain. From this, one could construct a domain-specific program for any set of tasks. (I call this workflow—something I’ve seen MES developers trying to perfect for more than a decade.)
A coding assistant would be a great help developing these task programs. AI helps today’s software engineering become more efficient. Why not use the same tools for this problem? Enter Foundation EGI. Natural language programming of these “rules” for building from the engineering design data.
- Construct domain
- Program compilers
- Data Sets
- Then use LLMs and so forth
They are building a platform. Engineering change notices built-in. Focus on the most boring of engineer’s work, leave to the engineer the most creative. They hate the add-on administrative work. They integrate with a variety of CAD files and works either with PLM or not. Also fits well with robotics with automated coding assistant.
Like I have told a few founders whom I’ve interviewed, I am enthusiastic about the potential of your platform—I’m interested in seeing how the technology actually works out.
On Thursday, April 17, Foundation EGI launched, announcing the availability of the first domain-specific, agentic AI platform — engineering general intelligence (EGI) — designed to supercharge automation, accuracy, and efficiency for every stage of product lifecycle management. With EGI, design and manufacturing engineers will be able to build better products faster, driving healthier revenues for the world’s leading industrial brands. To sign up to be part of the beta, interested customers can sign up here.
Foundation EGI was co-founded by MIT academics Mok Oh, Ph.D, Professor Wojciech Matusik, and Michael Foshey, and has assembled a seasoned team with deep engineering, industrial, startup and AI experience. Backed by an over-subscribed $7.6M seed round from early investors including E14 Fund, Union Lab Ventures, Stata Venture Partners, Samsung Next, GRIDS Capital, and Henry Ford III, Foundation’s EGI platform is already in testing at leading Fortune 500 industrial brands, which are witnessing its transformative and revenue-driving potential.
Unlike other digitally-transformed industries, manufacturing and engineering processes and instructions remain manual and disorganized, causing inefficiencies, production delays and stagnant revenues — to the tune of $8T in economic waste. Using Foundation EGI’s purpose-built large language model (LLM) and EGI agentic AI platform, engineers can now transform natural language inputs, including vague and messy instructions, into codified programming that is accurate and structured, optimizing automation, accuracy and efficiency at every stage of the design to production lifecycle. Foundation EGI’s web-based technology platform seamlessly integrates with the major design and manufacturing software applications and tech stacks already used by engineering teams.
“Engineering is primed for an AI revolution, but generic LLMs won’t cut it: they lack vital domain-specificity and are prone to inaccuracies,” said Foundation EGI co-founder and CEO, Mok Oh. “Our first-of-its-kind technology is purpose-built for engineering and will supercharge every stage of product lifecycle management — starting with documentation. EGI transforms what is traditionally error-prone, manual and inconsistent into structured, sustained and accurate information and processes, so that engineering teams can not only achieve significant cost-savings but also be more nimble, productive and creative.”
Dennis Hodges, CIO at Inteva Products, a global automotive supplier of engineered components and systems, commented: “We have high expectations from Foundation’s EGI platform. It’s clear it will help us eliminate unnecessary costs and automate disorganized processes, bringing observability, auditability, transparency and business continuity to our engineering operations.”
Said Habib Haddad, founding Managing Partner of the E14 Fund, the MIT Media Lab affiliated venture fund: “The timing and market conditions are perfect for a company like Foundation EGI to solve what has long been a large and expensive challenge for America’s industrial manufacturing leaders. The combination of Foundation EGI’s vision, its world-class team, the widespread industry appetite for enterprise AI solutions, plus the uptick in manufacturing demand makes this a rich opportunity.”
Co-founder Wojciech Matusik, Professor of Electrical Engineering and Computer Science at the Computer Science and Artificial Intelligence Laboratory elaborated on EGI’s potential, “Engineering general intelligence transforms natural language prompts into engineering-specific language using real-world atoms, spatial awareness and physics. It will unleash the creative might of a new generation of engineers. Expect leaps and bounds in agility, innovation and problem-solving.”
Foundation EGI’s mission was inspired by research conducted by Professor Matusik, Michael Foshey, and others at MIT and other academic institutions, published in a March 2024 paper titled “Large Language Models for Design and Manufacturing.”
by Gary Mintchell | Apr 14, 2025 | Services, Software
Many years ago in a galaxy far away, I actually sold an MRO software solution to one of my clients. Fortunately, I left that position to become a senior editor at Control Engineering magazine never learning how it all worked out.
This news concerns a partnership between an MRO software supplier and a services provider said to deliver “technology technology-drive solutions.”
- Verusen and Advanced Technology Services (ATS) Expand Partnership
- Companies leverage expertise in MRO Supply Chain optimization technology to drive a more efficient, data-driven future
- Partnership empowers tire & rubber company to achieve significant MRO inventory optimization across its North American Plants
Verusen, the industry leader driving AI MRO (maintenance, repair, and operations) supply chain and inventory optimization, announced it has expanded its strategic partnership with Advanced Technology Services, Inc. (ATS), a leading industrial maintenance, technology and parts services provider. The collaboration integrates Verusen’s AI platform with ATS’s deep industrial maintenance and reliability expertise to deliver technology-driven solutions for manufacturers. Today’s news builds on the recent announcement that ATS is now a strategic partner of Verusen’s AI-driven platform in North America. The partnership expands the companies’ reach and ability to assist manufacturers in optimizing demand forecasting and inventory management across their MRO supply chains.
The collaboration’s success is exemplified by its impact on a joint tire and rubber customer, which has realized $10M+ cost reductions through MRO inventory optimization across its North American Plants. This achievement underscores the practical value and significant opportunity the Verusen and ATS offering provides.
The Verusen and ATS Partnership Addresses Key Elements of MRO to drive cost savings and inventory optimization through the following:
- Supplier Tail-spend Management: Negotiating favorable pricing and lead times with reliable strategic suppliers.
- Network Inventory Visibility: Using more robust, AI-driven inventory tracking to monitor real-time stock levels.
- Predictive Maintenance: Using data analytics to predict potential equipment failures and proactively order spare parts.
Verusen’s platform is designed to optimize Maintenance, Repair, and Operations (MRO) inventory management using artificial intelligence, helping businesses streamline their supply chain by providing insights and recommendations based on their MRO data across different systems, ultimately ensuring the right materials are available when needed to minimize downtime and operational disruptions.
by Gary Mintchell | Mar 28, 2025 | Business, News, Software
The last of four news items this week from Siemens concerns a large software acquisition. A high level Siemens executive told me years ago that the company had learned from earlier mistakes in order to more successfully integrate acquisitions. Events have proved him correct. This acquisition should be very interesting for their customers.
- Siemens extends leadership in simulation and industrial AI as it closes acquisition of Altair Engineering Inc.
- Acquisition strengthens position of Siemens as a leading technology company and expands its industrial software portfolio
- Addition of Altair technology to the Siemens Xcelerator open digital business platform will create the world’s most complete AI-powered portfolio of industrial software and further enhance the most comprehensive Digital Twin
- Acquisition is a cornerstone of Siemens’ ONE Tech Company program Siemens announced today that it has completed the acquisition of Altair Engineering Inc., a leading provider of software in the industrial simulation and analysis market, for an enterprise value of approximately USD 10 billion. With this acquisition, Siemens extends its leadership in simulation and industrial artificial intelligence (AI) by adding new capabilities in mechanical and electromagnetic simulation, high-performance computing (HPC), data science and AI. The addition of the Altair team and technology to Siemens will further enhance the most comprehensive Digital Twin and make simulation more accessible, so companies of any size can bring complex products to market faster.
“We welcome the Altair community of customers, partners and colleagues to Siemens. Adding Altair’s groundbreaking innovations to the Siemens Xcelerator platform will create the world’s most complete AI-powered design, engineering and simulation portfolio. Together, we will help our customers to innovate at the scale and speed that today’s complexity-driven world demands,” said Roland Busch, President Siemens AG and CEO of Siemens AG. “Through the ONE Tech Company program, we will extend our leadership in industrial software. This enables all industries to benefit from the revolution driven by data and AI.”
Integrating Altair’s capabilities in the areas of simulation, HPC, data science, and AI enhances the ability of Siemens to drive more efficient and sustainable products and processes. Now, all Siemens customers, from engineers to generalists, will have access to new simulation expertise, can optimize their high-performance computing processes, create new AI tools and perform data analytics to help accelerate innovation and digital transformation for companies of all sizes.
The acquisition of Altair is part of Siemens’ ONE Tech Company program and will meaningfully increase Siemens’ digital revenue share. This growth program enables Siemens to further expand its strong market position and reach the next level of performance and value creation. Through acquisitions like this, as well as R&D investments into areas including software, AI-enabled products, connected hardware and sustainability, Siemens is clearly prioritizing capital allocation to strategic growth fields.
With the completion of the acquisition of Altair as well as the recent expansions of Siemens’ factories in California and Texas, Siemens has now invested over USD 100 billion into the United States in the past 20 years.
by Gary Mintchell | Mar 28, 2025 | Generative AI, Software
This is the second of four Siemens news items. In the vein of everyone in industrial software is Microsoft’s best friend, Copilot headlines this news. And no news today is complete without mentioning generative AI.
- The Siemens Industrial Copilot, a generative AI-based assistant, is empowering customers across the entire value chain – from design and planning to engineering, operations, and services
- Siemens expands its Industrial Copilot offering with extended capabilities for Senseye Predictive Maintenance
- The generative AI-powered solution will support every stage of the maintenance cycle, from repair and prevention to prediction and optimization
A glimpse of Siemens’ AI strategy:
The Siemens Industrial Copilot is revolutionizing industry by enabling customers to leverage generative AI across the entire value chain – from design and planning to engineering, operations, and services. For example, the generative AI-powered assistant empowers engineering teams to generate code for programmable logic controllers using their native language, speeding-up SCL code generation by an estimated 60% while minimizing errors and reducing the need for specialized knowledge. This in turn reduces development time and boosts quality and productivity over the long term.
Siemens is developing a full suite of copilots to industrial-grade standards for the discrete and process manufacturing industries – and is now strengthening its Industrial Copilot offerings with the launch of an advanced maintenance solution, designed to redefine industrial maintenance strategies.
Bringing it to maintenance
The Senseye Predictive Maintenance solution powered by Microsoft Azure will be extended with two new offerings:
- Entry Package: This predictive maintenance solution combines AI-powered repair guidance with basic predictive capabilities. It helps businesses transition from reactive to condition-based maintenance by offering limited connectivity for sensor data collection and real-time condition monitoring. With AI-assisted troubleshooting and minimal infrastructure requirements, companies can reduce downtime, improve maintenance efficiency, and lay the foundation for full predictive maintenance.
- Scale Package: Designed for enterprises looking to fully transform their maintenance strategy, this package integrates Senseye Predictive Maintenance with the full Maintenance Copilot functionality. It enables customers to predict failures before they happen, maximize uptime, and reduce costs with AI-driven insights. Offering enterprise-wide scalability, automated diagnostics, and sustainable business outcomes, this solution helps companies move beyond traditional maintenance, optimizing operations across multiple sites while supporting long-term efficiency and resilience.
