by Gary Mintchell | May 5, 2025 | Software
Ah, competition for Adobe. And finding good, small application for AI beyond all the hype we hear. Foxit has upgraded its AI Assistant within the Foxit PDF Editor.
The enhanced AI Assistant includes features such as chat with image, multiple document analysis, automatic bookmarking, and additional read-aloud options. A new tab in the ribbon toolbar also provides users with streamlined access to the powerful suite of AI-driven tools designed to simplify and supercharge document management workflows.
The dedicated ribbon tab refers to a specific, clearly labeled section within the top toolbar (aka the “ribbon”) of the Foxit PDF Editor interface that is solely focused on AI tools. Think of the ribbon as the strip of tabs at the top of applications like Microsoft Word or Excel – where you see tabs like “Home,” “Insert,” “Review,” etc. A dedicated ribbon tab means Foxit has created a new, standalone tab specifically for its AI Assistant, placing AI-related tools – like AI Chat, Document Translation, and AI Bookmark – in one easily accessible spot. This kind of integration signals a shift from AI being a hidden or secondary feature to becoming a core part of the user interface and user workflow.
What’s New in Foxit’s AI Assistant:
- AI Chat Enhancements
- Chat with Images – Users can now upload images to extract the text in the image, translate the text, or even describe the image.
- Analyze & Compare Multiple Documents
- Easily extract key details and compare content across multiple PDFs simultaneously.
- AI Bookmark
- Automatically generate bookmarks based on document structure or specific page ranges, making navigation and organization easier than ever.
- Intelligent Read
- Documents can now be read aloud with improved voice options, enabling hands-free review and improved accessibility.
by Gary Mintchell | Apr 25, 2025 | Generative AI, Manufacturing IT
AVEVA held its AVEVA World event a couple of weeks ago in San Francisco. I was not in attendance. I also didn’t see a bunch of news. There is this one piece I saw. Partnerships being all the major trend lately, several partnerships were announced.
- AVEVA is partnering with Databricks to revolutionize industrial operations with a secure and open approach to data and AI.
- AVEVA is also announcing a strategic partnership with Track’em, a cutting-edge material tracking and mobility solution provider, to deliver real time visibility and cost control in capital projects.
Parsing through the marketing speak, the company is using generative AI for piping design. I’ve seen a few companies finding a use for the new hot tech assisting design engineers.
by Gary Mintchell | Apr 24, 2025 | Manufacturing IT
Catching up on some older news items from earlier this month. This news from Yokogawa shows how process automation companies have had to expand their offerings and problem sets to solve in this era of process automation and control market maturity. This new product is said to deliver holistic lifecycle management and operational excellence.
Yokogawa Electric Corporation announced the launch of OpreX Plant Stewardship, the most comprehensive lifecycle service program in the company’s OpreX Sustainable Maintenance family, to support customers in achieving and sustaining operational excellence.
OpreX Plant Stewardship offers a tailored, performance-based approach that strategically addresses the ever-evolving challenges faced by customers, enabling them to mitigate risks, effectively manage operational challenges, and achieve key performance indicators across all levels of their organization. OpreX Plant Stewardship is available in all regions outside of Japan.
With the acceleration of plant complexity, IT/OT integration, cybersecurity threats, and a shortage of skilled resources, traditional product-centric maintenance methods are no longer sufficient. In response to these issues, Yokogawa has expanded its lifecycle services portfolio with a program that ensures service performance across systems, field instruments, analyzers, software, and applications.
Main Features
1. A customer-centric lifecycle approach
OpreX Plant Stewardship is a service program designed through a strategic framework to drive long-term operational excellence. By proactively and systematically addressing risks and challenges, organizations can align their operational strategies with business objectives. Through this approach, Yokogawa works closely with various stakeholders across different levels of the customer’s organization, helping customers navigate complex demands and ultimately find the economic optimum that balances performance, cost, and sustainability.
2. Comprehensive coverage of five dimensions
Leveraging decades of domain expertise, Yokogawa’s service approach is built to provide coverage on five essential dimensions that drive a well-operated, efficient, and resilient business throughout the plant lifecycle:
- Safety & security: Ensuring robust operational safety and cybersecurity measures are in place
- Reliability & availability: Eliminating plant disruptions and improving equipment reliability
- Regulatory compliance: Ensuring compliance with evolving industry regulations while supporting relevant Sustainable Development Goals
- Operational efficiency: Enhancing process efficiency and reducing waste
- Investment efficiency: Optimizing asset investments to maximize long-term value
3. Four-step process for continuous engagement
The four-step engagement model ensures effective collaboration with customers:
- Identify: Understanding customer challenges, priorities, and operational risks through an assessment model
- Assess: Evaluating which services and solutions are best tailored to address customer pain points and achieve their operational goals, and crafting a tailor-fitted, long-term collaboration proposal
- Control: Ensuring seamless and effective global delivery of the services and commitment to high-quality output
- Review: Continuously supporting customers through long-term engagement and improvements, utilizing a structured feedback loop to ensure ongoing performance alignment and adaptation to evolving operational needs
Major Target Markets
Oil and gas, petrochemicals, chemicals, renewable energy, power, pulp and paper, pharmaceuticals, food, mining, iron and steel, water distribution, and wastewater treatment.
Applications
- Risk-based performance assessment and improvement
- Lifecycle management and mitigation strategies
- Maintenance and reliability enhancement
- Compliance support and regulatory alignment
- Operational performance optimization
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.”
