Will reshoring really bring back manufacturing jobs? Will there be enough engineers and trained specialists to handle the major increase American manufacturing production?  Will artificial intelligence and advanced robotics replace skilled workers?  These are all questions that everyone from manufacturing CEOs to assembly-line workers ask every day, and in the age of increasing tariffs, uncertainty is rising exponentially.

Engtechnica.com Editor-in-Chief Roopinder Tara has definite opinions about these issues, especially the impact of artificial intelligence, and he discusses them in a wide-ranging conversation with engineering.com’s Jim Anderton. 

***

Catch up on the latest engineering innovations with more Industry Insights & Trends videos and podcasts.

The post Will reshoring bring back manufacturing employment?  appeared first on Engineering.com.

Today’s top story is about Foundation EGI, yet another AI startup that emerged from stealth last week. That acronym certainly raised my eyebrows: EGI stands for Engineering General Intelligence.

“We’re building out an AI platform for all aspects of engineering,” Mok Oh, co-founder and CEO of Foundation EGI, told me.

I’ve written a lot about AI in this newsletter—particularly AI hype—and I can’t take “EGI” as anything but spin. The now-familiar term AGI, artificial general intelligence, is either impossible or imminent depending on who you ask. EGI feels similarly tenuous.

But let’s look past the marketing, because Foundation EGI does have an interesting product. It might not cover all aspects of engineering, but it covers one: documentation.

“Engineers end up spending a lot of time and money and effort and toil writing documentation, which they really don’t enjoy doing,” Oh said. (I can think of at least one engineer who enjoys writing, but enough about me.)

Oh showed me a demo of the company’s web-based platform that turns natural language prompts into part diagrams, product manuals, technical illustrations and the like. I wasn’t permitted to record the demo, but this screenshot from Foundation EGI’s website gives the flavor:

It works like this: users load their CAD models and supporting data such as bills of materials or product descriptions. Then they write text prompts describing the documentation they want, such as “generate a user manual for this product.” In that case, Foundation EGI returns a Word file that users can download and edit.

I didn’t have the chance to examine any results, but Oh is confident in the platform’s performance, going so far as to say that it “generates the right and perfect outputs for engineers.”

How can he be so sure? That’s the interesting part. Foundation EGI is partly built with commercial LLMs (Oh declined to name which), but they don’t generate the results directly. Instead, the effect of any prompt is to generate Foundation EGI’s domain specific language (DSL), a custom code that’s hidden to the user but fully describes the platform’s output (the same way HTML describes a web page).

“The reason why it always generates a correct output is because our source code here describes exactly what an engineer would want, and only exposes the right parameters so that it does not hallucinate,” Oh said.

That’s the core idea underpinning Foundation EGI’s approach. Engineering, Oh told me, is “ultimately a system that can be described by a programming language.” The company wants to write that language one domain at a time.

Speaking of domains, engineering documentation is just the start of Foundation EGI’s roadmap. The company plans to tackle four other engineering domains: design and design exploration, parts and part sourcing, simulation optimization, and manufacturing optimization and operations, according to Oh.

“We’ll have large language models, create our own DSLs, have compilers… and produce the always-correct outputs that are relevant for engineers,” Oh said.

Oh told me Foundation EGI already has paying customers, which for now are all large enterprises. The company plans to introduce self-serve options for small and mid-size companies in the next few quarters.

Despite its lean into hype, Foundation EGI has a novel approach that I’ll be watching with interest. I’ll report back when I know more. In the meantime, here’s an article that Oh recently penned for Engineering.com: Engineering’s AI opportunity is here.

Siemens Xpands Teamcenter X

Siemens Digital Industries Software is expanding Teamcenter X with two new subscription tiers: Teamcenter X Standard and Teamcenter X Advanced. The SaaS PLM platform now has four tiers, which in order of least to most functionality are Essentials, Standard, Advanced, and Premium.

Teamcenter X Standard is “for companies collaborating on mechanical designs in a tailored environment, with built-in change management, schedule management and workflows,” while Teamcenter X Advanced adds “cross-domain collaboration on mechanical, electronic, and electrical designs,” according to the Teamcenter X plans and pricing page (which, curiously, does not include pricing).

Onshape launches Onshape Government

Last week I covered the long-expected launch of Onshape AI Advisor, a product support chatbot for the cloud CAD software. I missed that Onshape also launched Onshape Government, “a purpose-built version of Onshape, designed specifically to help meet the compliance needs of U.S. federal and state agencies, defense contractors, and organizations working on regulated projects,” according to a press release from Onshape’s parent company PTC.

Onshape Government is hosted on AWS GovCloud (US), Amazon’s cloud service for sensitive and controlled unclassified information (CUI). According to PTC, the platform enables compliance with regulations including ITAR and EAR.

ZWSoft launches beta of ZWCAD MFG 2026

China-based CAD developer ZWSoft released a beta of ZWCAD MFG 2026, the latest version of its 2D CAD software for manufacturing.

The release’s new features include greater compatibility with AutoCAD Mechanical (up to version 2024), the ability to quickly generate screw fastener assemblies, a new hole generator tool, an interface that’s optimized for 4K displays, and more. The beta is currently available for download.

One last link

On the latest episode of the Industry Insights and Trends podcast, my colleagues Jim Anderton and Michael Ouellette discuss the truth about AI in manufacturing.

Got news, tips, comments, or complaints? Send them my way: malba@wtwhmedia.com.

The post Engineering General Intelligence? We’ll see. appeared first on Engineering.com.

The demands on product development organizations have never been more intense. Faced with shrinking go-to-market timelines, competitive pressures, stringent regulations and evolving customer expectations, companies must rethink how they design, develop and deliver products. Traditional Product Lifecycle Management (PLM) systems, once seen as essential tools, are increasingly viewed as a barrier to innovation rather than a catalyst. While effective at managing data repositories, these legacy systems fail to keep pace with modern, interconnected, agile development methods.

Consequently, many organizations grapple with the challenges of siloed decision-making. When departments operate in isolation, it results in ineffective communication and collaboration, creating obstacles that complicate the already demanding landscape of product development. This disconnection can significantly extend project timelines, introduce errors and stifle creativity. Moreover, the burden of cumbersome administrative tasks often leaves little room for teams to focus on what truly matters: driving innovation and delivering high-quality products. Additionally, a lack of visibility into project statuses and accountability makes it difficult for stakeholders to track progress and make informed decisions.

Companies need to fundamentally shift toward model-based, data-driven product development, providing the tools and capabilities required to solve today’s challenges and unlock new opportunities for the future.

Several well-documented issues hamper product development. These challenges limit efficiency, stifle innovation and increase costs.

Siloed decision-making

Traditional systems promote fragmentation. Teams work in isolation, with data scattered across siloed applications. This slows collaboration and decision-making, creating bottlenecks in project timelines.

Slow innovation and burdensome administrative tasks

Traditional PLM systems are often file-based and BOM-centric, forcing teams to manage duplicate data manually and synchronize updates. This tedium reduces focus on creativity and problem-solving. 

Lack of transparency and accountability

Meeting regulations and achieving traceability remain uphill battles. Data remains locked within engineering systems, inaccessible to non-engineering disciplines that could greatly benefit from its insights.  

Barriers to adopting model-based engineering

Most legacy systems are not equipped for agile product development or model-based systems engineering (MBSE), which are critical methods for creating complex, software-driven products.

Rethinking product development: Transforming challenges into innovation

A unified innovation platform transforms how organizations approach product development. It goes beyond addressing IT or data management issues and transforms how teams collaborate, innovate and succeed.

In today’s challenging environment, the 3DEXPERIENCE platform significantly changes how companies handle product development, providing solutions designed to address key issues directly. By fostering a unified, holistic environment that enhances communication across disciplines, the platform effectively breaks down silos, enabling teams to collaborate seamlessly.

ENOVIA on the 3DEXPERIENCE platform connects all disciplines throughout the product lifecycle. This foundational capability eliminates the barriers created by siloed systems and fosters cross-disciplinary collaboration. Critically, it provides compatibility with legacy systems, ensuring a smooth transition to this innovative platform.

Real-time access to 3D-configured engineering data allows users to retrieve information instantly, from anywhere and on any device. This capability enables stakeholders from engineering, design, manufacturing and marketing to interact with and make decisions based on the same 3D data in a fully interactive manner. The benefits are evident — leaders can make informed decisions using actionable insights rather than relying on outdated information.

Built-in collaboration and data science tools

Virtual Twin Experiences transform collaboration and decision-making by creating dynamic, digital replicas of physical products, processes or entire enterprises. These virtual models enable real-time simulation, analysis and optimization, bridging the gap between the physical and digital worlds. By leveraging technologies such as IoT, AI and advanced analytics, virtual twins provide actionable insights, enhance decision-making and improve efficiency across the product lifecycle or organizational operations. They empower organizations to predict outcomes, test scenarios and innovate faster, ultimately driving smarter strategies and sustainable growth.

Support for MBSE

Unlike legacy PLM solutions, the 3DEXPERIENCE platform offers native support for MBSE, a crucial differentiator in today’s complex, software-driven environments. From defining use cases to designing and validating systems, MBSE workflows are integral to the platform’s capabilities, paving the way for faster, more effective development cycles.

Capitalizing on enterprise knowledge with AI

The 3DEXPERIENCE platform helps organizations unlock and leverage their intellectual property. Using AI-powered generative experiences, the platform reveals hidden opportunities in enterprise knowledge, enabling teams to learn from the past and innovate for the future. 

Transforming product development with proven results

“This is really a big advantage for us, that we have a best-in-class system that we can use to track, manage and ultimately act on, manufacture and bring to market what started out as data and turns into the real world,” Allison said. “To create world-class technology, you have to use world-class technology.” —Eric Allison, Chief Product Officer, Joby Aviation

“A holistic digital model comprising all the attributes of each discipline invariably shortens time to market because we can work faster and more efficiently.” —Bernd Hirt, Group Manager Core Function Mechanics, Bosch Car Multimedia

Why invest in the 3DEXPERIENCE platform now 

The stakes for product development teams have never been higher. Competitive pressures, workforce dynamics and sustainability goals are reshaping industries. Resilience and adaptability are no longer merely desirable, they are essential for survival and market leadership.

The 3DEXPERIENCE platform provides a critical framework for navigating these challenges and unlocking new opportunities. By adopting a unified platform, organizations position themselves for the future of innovation, efficiency and collaborative success.

The time to act is now. Unlock a new realm of possibilities for your product development processes and shift from file-based, siloed systems to an agile, innovation-first future. Download the e-book to learn more.

About the Author

Nancy O’Flaherty is a Senior Offer Marketing Manager at Dassault Systèmes, where she has spent the past 17 years developing marketing strategies to drive product awareness, customer engagement and adoption. With over 25 years of experience in the high-tech industry, Nancy has strong expertise in market trends, customer needs and positioning technology solutions for success.

The post Unlocking innovation in product development appeared first on Engineering.com.

This video is brought to you by the Italian Trade Agency via its Machines Italia project.

Ask any engineering professional, or even a person on the street about Italy, and you’ll hear similar answers: history, high-fashion and technology, particularly automotive in aviation technology, will be the inevitable response. In manufacturing, Italy has long been known as a source for advanced production equipment in sectors such as machine tools, injection molding and automation.

A recent innovation in die casting which has attracted global attention in the electric car industry, was developed in Italy by IDRA, based in Travagliato, near Brescia. That large format innovation appears to be spreading across the automotive industry, fundamentally changing the way light vehicles are designed and built.

Engineering.com’s Jim Anderton discussed this technology and Italian engineering in general by IDRA Group’s General Manager & CEO John Stokes. 

* * * 

Learn more about Italian manufacturing technology by downloading the latest edition of Machines Italia magazine – Volume XVII from our sister site, Design World.

In an era of economic volatility and industry disruption, North American manufacturers are turning to partners who can help them adapt, automate, and grow. This issue of Machines Italia spotlights how Italian machine builders are meeting that call, delivering flexible, sustainable, and highly automated solutions that empower end users to navigate today’s toughest challenges.

And, visit Machines Italia to learn more about the Italian manufacturing sectors and how to connect North American buyers to Italy’s manufacturers.

The post Large format die casting showcases advanced Italian technology appeared first on Engineering.com.

The role of digital transformation in machine health monitoring

Digital transformation in manufacturing integrates Industry 4.0 technologies—IoT (Internet of Things) sensors and other edge devices, data analytics and cloud computing—to provide real-time visibility into machine performance and wear conditions.

By leveraging these technologies, manufacturers can transition from traditional preventive maintenance (also called “guessing”) to a predictive maintenance (PdM) approach, reducing downtime and extending the lifespan of equipment. Predictive maintenance relies on condition-based monitoring (CBM), which continuously collects data from machines to detect early signs of wear before they cause failures.

How machine wear impacts efficiency

Machine wear manifests in multiple ways, including:

Increased Friction and Heat Generation: Worn-out bearings and misaligned shafts increase resistance, leading to higher energy consumption.

Component Degradation: Gradual wear in belts, gears, and cutting tools reduces precision and consistency in machining operations.

Vibration and Noise: Unbalanced rotating components generate excessive vibration, potentially damaging adjacent machinery.

Fluid Leaks and Lubrication Deficiency: Worn seals and gaskets lead to leaks, affecting hydraulic and pneumatic system efficiency.

Reduced Speed and Throughput: Deteriorating drive systems slow down production cycles, directly impacting overall equipment effectiveness (OEE).

Identifying these inefficiencies early is critical to maintaining productivity. This is where digital transformation plays a key role.

Digital technologies for identifying machine wear

Modern industrial machinery can be equipped with IoT-enabled condition monitoring sensors to track parameters such as vibration levels, temperature fluctuations, ultrasonic emissions, Oil contamination levels, and electrical current anomalies.

These sensors provide real-time machine health data, which is transmitted to edge computing devices or centralized cloud platforms for analysis.

With data streaming from thousands of sensors, big data analytics is essential to extracting meaningful insights. Machine learning (ML) models can analyze historical and real-time data to detect patterns indicative of wear-related inefficiencies.

For example, if a CNC machine exhibits a gradual increase in spindle vibration amplitude beyond its normal operating baseline, analytics can flag this deviation as potential bearing degradation, allowing for timely maintenance.

Taking this a step further, the latest advances in artificial intelligence (AI) can now enhance predictive maintenance by providing automated failure prediction models based on machine learning algorithms. AI-driven PdM systems use supervised learning models, trained on historical failure data to classify wear severity levels. Unsupervised anomaly detection algorithms detect deviations from normal operating behavior without prior failure data.

Finally, deep learning techniques, such as convolutional neural networks (CNNs), can analyze acoustic and vibration signals for early fault detection.

All of these techniques and technologies create predictive maintenance systems can estimate remaining useful life (RUL) of components, enabling proactive part replacements before critical failures occur.

Digital twins for virtual machine wear simulation

A digital twin is a real-time virtual replica of a physical machine, continuously updated with sensor data. Digital twins allow engineers to accomplish a number of tasks digitally rather than testing on physical models. For machine wear, digital twins can simulate wear progression under various operating conditions to predict when and where degradation will occur. This can help optimize maintenance schedules without disrupting production.

For instance, a digital twin of an industrial robotic arm can predict how frequent joint articulation under varying loads affects lubrication breakdown, enabling precise maintenance planning.

Cloud-based monitoring and remote diagnostics

Manufacturers with several production locations can leverage cloud-based machine health dashboards to monitor wear trends across multiple facilities. Remote diagnostics allow maintenance teams to analyze machine conditions and troubleshoot issues without being physically present, significantly reducing response time.

Challenges and future trends

While digital transformation offers immense benefits, manufacturers must overcome certain challenges. Retrofitting legacy equipment with IoT sensors can be challenging, but this has been a recommendation for years now and many companies have already started swapping out old technology on the shop floor.

Also, handling massive datasets requires robust infrastructure and analytics capabilities. Sending data to the cloud will eliminate the need for in-house computational infrastructure and there are many options for manufacturing software that can handle complex analytical tasks. However, using the cloud, combined with the increased connectivity found in virtually all modern sensors, exposes machines to potential cyber threats.

Digital transformation has the power to revolutionize many aspects of manufacturing, and improving your response to machine wear detection is no different. By integrating IoT, AI, big data analytics, and digital twins, manufacturers can better predict failures, optimize maintenance schedules, and improve efficiency—ultimately reducing costs and extending machine life.

The post How digital transformation helps identify efficiency-sapping machine wear appeared first on Engineering.com.

In a vote of confidence, Heather Natal was re-elected to the position of Secretary, a role she has filled for the past two years. Presently, she serves as Chair of the Governance Committee, Co-chair of the Registration Committee, and as a member of the Executive Committee and Event & Hospitality Committee. Natal was awarded AMUG’s DINO (Distinguished INnovator Operator) Award in 2024.

Alex Roschli was elected to serve as Director of Education & Conference after four years as a member and Co-chair of the Track Leader Committee and Agenda & Program Committee member. These committees are part of AMUG’s education and conference programs, which Roschli will oversee in his new role. He received his DINO Award in 2023.

Kim Killoran is returning to the board to fill the new Director of Marketing & Events position, having previously served as Secretary from 2014 to 2019. Presently, she is Chair of the Marketing Committee, a role she has filled for five years, and a member of the Recognition and Agenda & Program Committees. Killoran was presented with a DINO Award in 2017.

Dallas Martin, the newly elected Director of Sponsors & Exhibitors, has been an active participant of AMUG since first attending 11 years ago. Currently, he is a member of two committees: Expo and Sponsors & Exhibitors. For these contributions, he received the DINO Award at this year’s AMUG Conference.

In a press release regarding the newly named AMUG Board, Shannon VanDeren, AMUG President, said:

“The board of directors that will serve AMUG for 2025-2026 is comprised of various backgrounds, skillsets, additive manufacturing disciplines, years in the industry, and personalities. Yet collectively, they are focused and committed to upholding the integrity and purpose of AMUG. We will have the opportunity to glean from the diverse perspectives and viewpoints of our board members, consisting of men and women, engineers and non-engineers, as well as people with business-oriented and technically focused careers. I look forward to healthy conversations and a forward-moving approach!”

The multi-year terms for these positions will commence on July 1, 2025. The balance of the board includes three elected and two appointed positions, each with one or more years remaining in their terms.

The AMUG Board Members for the 2025-2026 term are:

• Officers
  • President: Shannon VanDeren, Layered Manufacturing & Consulting
  • Vice President: Tim Bell, Sciath aiM Forge
  • Treasurer: Robin Van Bragt, Eagle Plastics Plus
  • Secretary: Heather Natal, GoEngineer
• Directors
  • Director at Large: Bruce LeMaster, Obsidian Solutions Group
  • Director of Education & Conference: Alex Roschli, Oak Ridge National Laboratory
  • Director of Marketing & Events: Kim Killoran, Stratasys
  • Director of Membership: Claire Belson Barnes, Markforged
  • Director of Sponsors & Exhibitors: Dallas Martin, Toyota

Beyond oversight and management of the organization, the board’s primary responsibilities will include building the program for the 2026 conference, soliciting support from businesses in the additive manufacturing industry, and overseeing the event’s day-to-day activities.

The five-day conference will be held in Reno, Nevada, from March 15 -19, 2026 and feature hands-on workshops, instructional sessions, technical presentations, the AMUGexpo, and the Special Event & Dinner.

The post AMUG announces new board of directors appeared first on Engineering.com.

Engineering.com senior editor Michael Ouellette covers global manufacturing, including the hottest topic today, artificial intelligence. But is it all it’s cracked up to be? Ouellete is skeptical, and pulls no punches in conversation with host of the Industry Insights & Trends podcast edition, Jim Anderton.

***

Catch up on the latest engineering innovations with more Industry Insights & Trends videos and podcasts.

The post The truth about AI in manufacturing appeared first on Engineering.com.

AI maturity

It’s been more than two years since ChatGPT burst onto the scene and in that time, we’ve witnessed the incredibly accelerated maturing and democratizing of the technology. What started out as hype has evolved into a viable enterprise technology. Businesses today are rapidly moving beyond mere experimentation to nascent or wide-scale AI deployments and there are multiple use cases with proven results. In fact, C-Suite leaders today are not only curious about how AI might transform their organizations, they’re also actively mandating that staff seek out AI tools so that they can tap into efficiencies and uplevel in their roles, ultimately benefiting the bottom lines.

However, it’s become increasingly clear that the broad, generic open-source AI technologies are not a good match for narrow or domain-specific use cases; they are often unreliable and prone to hallucinations. Specialized use cases require specialized, high-quality data to feed and train their LLMs (Large Language Models) and Machine Learning (ML) engines. The good news is that vertically-specialized AI technologies are on the rise – in law, marketing, medicine, autonomous vehicles – and soon, engineering. And, if you haven’t already read about agentic AI, get ready. Agentic AI is rapidly transforming and organizations are catching on to how it automates tedious error-prone tasks all while adapting and collaborating with humans.

Engineering is ready for its AI makeover

Manufacturing is incredibly complex. The multi-step processes needed at every stage – from R&D to design, production and documentation is intense. While agile, lean, smart and just-in-time processes have been deployed to attempt to streamline and eke out efficiencies, today there’s plenty of opportunity to harness the power of AI to digitally reshape this centuries-old industry. Siloed teams and data sets can be unified through AI-powered agents that take advantage of natural language processing, transforming imprecise or unstructured processes into precise actionable code – resulting in faster, more accurate processes, and a more intelligent digital foundation for the entire production lifecycle.

It’s been reported that 40-50% of the design manufacturing industry’s $40T market value is being eroded due to legacy processes, causing production delays and stagnant revenues — equivalent to $8T of economic waste. Surely that huge amount is incentive enough to harness the power of AI to modernize its processes and bring observability, auditability, and transparency – not to mention business continuity – to this vital market?

Now let’s consider the demographics of the design engineering workforce and the stark fact that by 2030, all Baby Boomers will reach retirement age – a phenomenon some call the Silver Tsunami. Of course, only a portion of those will be engineers, but decades of valuable engineering experience are often locked in the minds of long-serving employees, instead of being documented and accessible to all. This means vital institutional knowledge will be lost  when they retire. In their place will come a new generation of younger, AI-literate engineers, skilled and eager for innovation.

This means now is the time to modernize and digitally codify all that foundational engineering intelligence – or risk getting left behind. If we can use AI to supercharge the automation, accuracy and efficiency of every stage of product lifecycle management, engineering teams will be able to not only cut costs but also be more nimble, productive and creative. Ultimately, they’ll be able to build better products faster, driving healthier revenues for the world’s leading industrial brands.

Changes and challenges in manufacturing

Geopolitical and economic forces are recasting global manufacturing and supply chains. Shifts are underway that will inevitably impact where things are made, who makes them and how much they will cost.  Time will tell how this plays out, but the writing is certainly on the wall. Industrial brands would be smart to invest now in foundational AI technologies to ensure their people, operations and ecosystems of supplierS, partners and customers are agile, resilient and ready.

Manufacturers should pay attention to these prevailing tailwinds: advancements in AI technologies, the need for digital transformation in engineering to overcome economic waste, and geopolitical forces. Together, they create both urgency and opportunity—signaling an AI revolution in engineering.

The post Engineering’s AI opportunity is here appeared first on Engineering.com.

The deal is an all-cash transaction of $350 million, subject to closing adjustments.

“From its earliest days, Misumi’s culture of ingenuity and innovation has made us a leader and pioneer in making it easier for customers to procure manufacturing components that fit their needs,” said Ryusei Ono, representative Director and president of Misumi Group Inc.

Misumi says the acquisition will accelerate the development of its manufacturing and supply chain solutions that “deploy the power of AI through digitally native tools built on top of robust physical infrastructure to build real-world products in an efficient, scalable way.”

“Fictiv and Misumi share a joint vision to make world-class manufacturing and supply chain capabilities easier, more accessible, more intelligent, and democratized. Giving more teams the tools to take their ideas from concept to reality will unlock innovation to fuel the advancements we want to see in the world,” says Dave Evans, Fictiv Co-Founder and CEO.

Fictiv’s Global Manufacturing Supply Chain brings together AI-driven technology workflows, a global manufacturing network, and teams of local manufacturing experts to simplify sourcing for custom mechanical components. Fictiv says customer adoption of its solution has grown at a rapid pace, accelerated further by the recent launch of production manufacturing solutions.

“This acquisition will enable us to turbocharge our mission to simplify sourcing. Fictiv and Misumi bring together digital and physical infrastructure to support a full bill-of-materials, and reduce the time, cost, and risk associated with building and scaling products,” said Nate Evans, Co-Founder and CXO for Fictiv.

Misumi supplies mechanical components, tools, consumables, and other products to more than 318,000 companies worldwide. Combining with Fictiv will help make their solution even better and more scalable, benefitting customers by offering a single global platform capable of building an entire bill-of-materials with world-class speed, price, and quality.

The deal is subject to customary closing conditions including regulatory approvals.

The post Misumi Group acquires Fictiv in all-cash deal appeared first on Engineering.com.

“The board believes listing ABB Robotics as a separate company will optimize both companies’ ability to create customer value, grow and attract talent,” said ABB Chairman Peter Voser. He says both companies will benefit from a more focused governance and capital allocation. “ABB will continue to focus on its long-term strategy, building on its leading positions in electrification and automation,” Voser said.

ABB Robotics provides intelligent automation solutions to a global customer base to solve operational challenges including labor shortages and the need to operate more sustainably. It’s robotics platforms include autonomous mobile robots, software and AI combined with proven domain expertise to a broad range of traditional and new industry segments. More than 80 percent of the company’s products are software or AI enabled.

Morten Wierod, CEO of ABB, says there are limited business and technology synergies between the ABB Robotics business and other ABB divisions, with different demand and market characteristics. “We believe this change will support value creation in both the ABB Group and in the separately listed pure play robotics business,” he said.

ABB said in a press release that the new robotics company will be listed with a strong capital structure, is well invested with a solid cash flow profile and operates through its local-for-local set-up with regional manufacturing hubs in Sweden, China and the U.S.

The robotics division has about 7,000 employees, with 2024 revenues of $2.3 billion, which made up about 7% of ABB Group revenues.

If shareholders vote in favor of the proposal, the spin-off is planned to be done through a share distribution. ABB Ltd.’s shareholders will receive shares in the company to be listed (its working name is ABB Robotics) as a dividend in-kind in proportion to their existing shareholding.

In the first quarter of 2026, the Machine Automation division—which is currently combined with ABB Robotics to form the Robotics & Discrete Automation business—will become a part of the Process Automation business, where ABB says its other divisions will benefit from technology synergies for software and control technologies. The Machine Automation business holds a leading position in the high-end segment for solutions based on PLCs, IPCs, servo motion, industrial transport systems and vision and software.

The intention is for the new robotics company to start trading as a separately listed company during the second quarter of 2026.

The post ABB to spin off robotics division appeared first on Engineering.com.