It seems like the manufacturing sector is forever in the midst of a structural shift. Competitive pressures, supply chain disruptions, and evolving customer expectations are constants, driving companies to continuously rethink how they produce goods and where they produce them. Digital transformation systems—a convergence of advanced analytics, IoT, AI, and cloud-based platforms—are at the center of this current shift.

For engineers and executives alike, these systems are more than IT upgrades. They are tools—sometimes very simple, sometimes quite complex—that reconfigure logistics, streamline supply chains, and make localized manufacturing practical and profitable.

Digital transformation and logistics optimization

Historically, logistics in manufacturing has been reactive to disruption—responding to bottlenecks, freight delays, or warehouse shortages as they arise. Digital transformation turns this reactive model into a predictive and adaptive one.

Real-time visibility provided by IoT sensors and connected devices track goods in transit, raw material consumption, and production progress. By tracking the data from these devices, engineers gain line-of-sight of their raw materials and products from factory floor to customer delivery. AI-driven routing and scheduling algorithms forecast delays and dynamically reroute shipments or adjust production schedules to maintain throughput.

And then there’s digital twins, which aren’t just for product design. By creating a digital twin of logistics networks, engineers can simulate different transportation strategies, warehouse configurations, or production-distribution trade-offs before making capital commitments.

The result is lower transportation costs, higher on-time delivery rates, and fewer emergency interventions to solve unexpected problems.

Digital transformation and supply chain resilience

In the last five years, supply chain fragility has become more than just a boardroom issue. Digital transformation systems can help bring resilience by unifying fragmented data and enabling proactive decision-making.

Instead of relying on the siloed ERP and supplier systems of the previous decade, companies can use integrated supplier data platforms to build digital ecosystems where quality, lead times, and pricing data are visible in one place. The advanced analytics produced by these digital ecosystems can help users flag single-source dependencies or regions exposed to external risks, such as natural disasters, geopolitical snafus or disease outbreaks.

At this stage, either humans or AI systems can find and recommend shifts to alternate suppliers, suggest redistribution of inventory, or gameplan adjustments to current stock levels. For manufacturers, this means fewer surprises on the production line and the bottom line—it’s a little bit of assurance that production won’t grind to a halt due to a single point of failure.

Digital transformation and enabling local manufacturing

Local manufacturing—sometimes called near-market manufacturing, regional manufacturing or nearshoring— simply means manufacturing your products closer to end customers. The strategy reduces transportation costs, shortens lead times, and lowers emissions. The downside is that it introduces complexity through operating multiple regional plants, relying on varied supplier networks, and adjusting to different regulatory environments. Digital transformation systems provide the infrastructure to make this a viable strategy for a larger cross section of businesses.

With a digital infrastructure, standardized production data models help engineers replicate validated processes across sites, ensuring consistency in quality while tailoring to local market needs. Linking and integrating cloud-based manufacturing execution systems (MES) and enterprise resource planning (ERP) software allow plant managers to coordinate production planning across geographies while everyone works from the same set of data. Real-time sales and consumption data flow directly into local production schedules, aligning output with regional market demand.

The outcome is that companies gain the agility to serve markets faster while maintaining engineering rigor and cost control.

Digital transformation and engineering leadership

Digital transformation systems don’t come cheap. Aside from the initial cost, they require significant time and staff resources during start up. However, the next-level strategic and tactical functionality enabled by digital transformation has also never been more accessible. Rapid advancements in technology, compute power and the availability of could storage make it attainable to anyone willing to invest the time and money. The capabilities mitigate the initial costs by reducing “firefighting” and manual reporting. They provide tools to model, test, and optimize logistics and supply chain variables virtually before committing to a plan. Indeed, for executives and manufacturing engineers, these systems turn supply chains into a strategic advantage, enabling informed investment in new sites, supplier diversification, and sustainable practices. Digital transformation is a lever for both resilience and growth, improving reliability today while positioning the enterprise to compete globally tomorrow.

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From welding class to the engineering field, the arc of Candis Polite’s career has always been fueled by curiosity. Here, we learn how tenacity and an inquiring mind made one engineer, and how those traits are crucial to developing the engineers of tomorrow.  

When Candis Polite recalls the genesis of her engineering journey, it isn’t equations and classrooms that come to mind. Instead, she remembers the excitement of tearing things apart to see how they worked and the thrill of creating something completely new.

“My love for math and science, along with my desire to combine curiosity with creativity, made engineering a natural career choice,” she said. “As a child, I kept asking my parents for the next new science kit and would tinker with anything I could get my hands on, whether to take it apart or build something new.”

But there were not many engineering-related opportunities for Polite by the time she reached high school. Determined to find a creative engineering-adjacent outlet, she enrolled in what she called the next best thing: a welding class. 

“It was hard, hot work in a male-dominated classroom — in fact, I was the only woman who stuck with it beyond the first day of class — but I found the challenge fun,” she said. That experience strengthened her resolve and gave her an early understanding of what would be needed for a career in engineering.

Inspired by creation, guided by mentors

Polite was surrounded by mentors and engineering inspiration when she was young. It just wasn’t the typical role models, such as an engaging professor or a senior colleague. For Polite, the environment captured her imagination like nothing else, with nature’s architects and builders serving as the apex of design and engineering.  

“The natural world is full of beautiful designs that humans model our innovations on. Just look at the compact strength of the honeycomb, for example,” she said. This understanding of design in the natural world still influences her views on engineering solutions.

In time, more traditional mentors became an important part of her development in the field. 

“I’ve always looked up to the first engineer who hired me, John Torbert, and I could always tell he believed in me,” she said. When Torbert moved on, his successor, Ken Barnes, picked up where Torbert left off. “Ken is another wonderful mentor and friend. We still talk to this day and love bouncing questions off each other. I hope I can be a similar inspiration to others in the future.”

Now, several years into her career, Polite said that she is determined to pay it forward and provide young engineers the same encouragement extended to her when she was getting into the field.

Leading the Ignite internship program

Polite currently serves as a lead engineer at Actalent, specializing in energy and electrical engineering. She describes her work with Ignite, Actalent’s transmission, distribution, and grid automation internship program, as her “pride and joy.” The company launched the program a year ago and, as with any new initiative, there were some early challenges as the team found its footing. Polite started asking questions about how they could improve the program and offer a better, more meaningful experience for Actalent interns. Those inquiries turned into an opportunity to lead the program going forward, and she hasn’t looked back since.

Polite and her colleague visiting a substation with their intern.

“The program name, Ignite, is what sparked my vision for what I wanted it to become and how I could get it there,” she said. She worked with mentors, interns, and supervisors to identify necessary improvements and then developed the resources and documentation to set new interns up for success. Polite took cues from other intern programs with components she admired and added unique additions of her own to create an enriching, career-launching experience completely unique to her organization.

Today, the program blends expert talks, site visits, and real project work to give interns a comprehensive view of engineering careers. “We put together a speaker series with engineers from across our company so interns could learn about different engineering roles and the utility industry broadly,” she said. Building kits, substation visits, and end-of-program presentations round out the experience, while social gatherings give interns the chance to network, form friendships, and create connections.

The hard work paid off. Polite saw strong engagement and positive feedback from interns and Actalent participants alike, including leadership. “I was so proud when an intern recently told me this has been one of the most well-coordinated programs he’s been a part of, and I’ve heard from lots of mentors that they’re excited for next summer.”

Facing down early career challenges

While Polite’s stewardship of initiatives like the Ignite program displays the confidence of a seasoned leader, she said that it wasn’t always that way. As a young engineer, she was assigned to work with a senior engineer to complete complex electrical studies for a client’s entire campus — a major undertaking covering many buildings. Polite said that she learned a great deal from him and relied heavily on his guidance, perhaps too much so — and then midway through the project, he left the company. Suddenly, she was left to tackle the remainder of the project by herself.  

“I had to dig deep and rely on what he had already taught me to face the challenge head on, eventually completing the project successfully. Ultimately, I proved to myself that anything is possible with determination and the right foundation.”

Broadening the pipeline

Polite credits early encouragement as crucial to her development. Today, she is a strong advocate for expanding access to engineering in underrepresented communities. “Driving awareness and sparking curiosity early on is essential for getting more people interested in engineering careers.” For her, it’s about making engineering not just visible but accessible.

“We need to create fun and accessible opportunities that will not only expose students to engineering careers but also keep them engaged.” That requires resources, mentors, and hands-on experiences — elements she knows firsthand can make all the difference.

The strength of many voices

For Polite, few things fuel the engine of innovation like diversity within a team. 

“Teams are like puzzles. Each member is a different piece shaped by their unique experiences, strengths, and background. One puzzle piece alone can’t complete the picture, but when you combine different people’s pieces together, you create something greater than the sum of its parts,” she said.

It’s a philosophy that still defines her leadership style today. Diverse engineering challenges require diverse perspectives.

Building confidence through curiosity

Asked what advice she would give to young engineers just getting into the field, she said a focus on humility, curiosity, and courage is at the forefront. “You need to stay curious and put yourself out there. Don’t be afraid to ask questions, admit when you don’t know something, and learn from those around you.”

Polite (top left) kicking off the 2025 Ignite Internship Program speaker series.

She was also quick to emphasize that risk-taking — stepping out of one’s comfort zone — is often the shortest path to real growth. “Just ask yourself, ‘What’s the worst thing that could happen?’ It’s usually not the end of the world if it doesn’t work out. Missteps can be our greatest teachers because they push us to be better.”

Looking ahead: supporting women in engineering

Polite said that if she had unlimited time and resources, her focus would be on bringing more women into the field of electrical engineering. “Only about 10% of electrical engineers in the U.S. are women, which is low even compared to other engineering disciplines.” This imbalance adds even more barriers to entry for many women who might otherwise pursue the career.

Polite believes the foundation of the solution here is exposure. “Getting more women into electrical engineering starts by exposing them to engineering career paths when they’re still in school, with encouragement and support, including internships and mentorship opportunities,” she said.

Polite (5^th from left) at a Top Golf team outing with colleagues during training (2024).

While foundational, exposure is only the beginning. Polite has also put out the call for cultural change within the engineering field. “I strongly believe all engineers should feel supported and valued from day one. Their skills should be recognized and appreciated without bias, and it’s important for all colleagues to actively foster a culture of inclusion and support.”

A lasting spark

From childhood science kits and welding helmets in high school, to daunting early projects and innovative internship programs, Candis Polite’s story is one of curiosity, mentorship, and advocacy. It also serves as a reminder that engineering is as much about the people as it is about the technology. She leads by walking the walk and setting a strong example. “I hope I can be a similar inspiration to others in the future.”

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The question is no longer, “Who controls my product data?” It is, “Which platform can orchestrate my entire digital enterprise—coherently, intelligently, at scale?”

The Autodesk/PTC leak (July 2025) and recent Forrester Wave PLM report for discrete manufacturing highlight just how fluid—and contested—this market has become. Success is no longer about feature breadth alone; it is about ecosystem reach, strategic alliances, integration maturity, and disciplined execution.

Siemens: industrial scale integration challenge

On the paper, Siemens has been the boldest acquirer, investing more than $16 billion since 2021. Key moves include Supplyframe ($700M, 2021), Altair ($10.6B, 2024), and Dotmatics ($5.1B, 2025). Together these acquisitions build what Siemens positions as a cognitive industrial platform—a stack that connects R&D informatics, laboratory knowledge, simulation, predictive modelling, supply chain intelligence, and design engineering.

The strategy is clear: customers want cross-domain insights that link product design with sourcing and operations—building from ready-made industrial data scenarios. If Siemens can deliver this continuum, it secures a powerful differentiator. Yet, history warns that breadth can become fragmentation. A platform with too many moving parts risks slipping into silos unless Siemens maintains relentless focus on integration discipline.

Ambition is unmatched. Integration discipline is the test. A platform with too many moving parts risks fragmentation unless Siemens relentlessly enforces coherence.

Dassault Systèmes: deepening the virtual twin

Dassault Systèmes has pursued a more measured but no less ambitious path. Its acquisitions—NuoDB (2020), Proxem (2020), Bloom (2021), Diota (2022), and Contentserv (2025)—aim to enrich the 3DEXPERIENCE platform with cloud scalability, semantic AI, AR, and omnichannel content.

The strategy is to extend the virtual twin beyond products into the broader enterprise, contextualizing insights and enabling simulation across design, operations, and customer experience. This resonates strongly in consumer-driven and regulated industries where brand, compliance, and collaboration drive value.

The risk? Coherence. Specialized acquisitions bring great capabilities but can easily create friction in workflows and user experience. Dassault Systèmes’ competitive edge depends on delivering a seamless platform, not a collection of clever but disjointed modules.

PTC: threading the lifecycle

PTC has played the role of surgical consolidator, strengthening the digital thread rather than overextending the platform perimeter. Its acquisitions—Arena ($715M, 2021), Codebeamer ($280M, 2022), ServiceMax ($1.46B, 2023), and IncQuery Group (2025)—are tightly aligned to SaaS-native lifecycle cohesion.

This approach works particularly well in regulated industries like medtech, aerospace, and automotive, where traceability and compliance are non-negotiable. PTC’s portfolio now spans ALM, PDM, service lifecycle, and SaaS-native collaboration, creating a compelling end-to-end vision.

But here too, the test is execution. Customers will only see value if Windchill, Arena, Onshape, Codebeamer, and ServiceMax operate as one coherent digital thread. Without that, the promise of end-to-end traceability dissolves into tool-switching and integration debt.

SAP: owning the enterprise backbone

SAP has taken a different path. Rather than buying PLM capabilities outright, SAP has doubled down on being the enterprise orchestrator. Its S/4HANA Clean Core strategy (2024) and deepened partnerships with Siemens and PTC reflect a philosophy, perhaps coupled with a marketing strategy: lifecycle data must flow seamlessly across finance, supply chain, and operations.

Broadly speaking, this makes SAP unavoidable for large enterprises seeking enterprise-scale integration, at least on the core ERP side of things. The value proposition lies in connecting the PDM backbone to the entire enterprise nervous system. The risk, however, is that customers see SAP’s model as too ERP-centric or rigid. If SAP fails to demonstrate lifecycle depth alongside enterprise breadth, it risks ceding ground to vendors who blend both.

Aras: the quiet surprise

Aras, often underestimated, emerged as a surprise leader in the recent Forrester Wave for discrete manufacturing PLM. Its Minerva Group acquisition (2022) boosted delivery strength in medtech and electronics, providing domain-specific solutions that accelerate compliance and reduce customization overhead.

Where Siemens and Dassault Systèmes chase scale and vision, Aras delivers agility and configurability. For customers who need compliance-ready, adaptable solutions without the overhead of a massive enterprise platform, Aras is increasingly credible—often adopted as an overlay strategy to extend or modernize existing PLM backbones or fill legacy gaps.

The open question is sustainability: can Aras scale its positioning beyond niche and overlay deployments without diluting the very flexibility that defines its edge?

Coherence vs. complexity

The past five years show that ambition alone is not enough. Siemens bets on industrial breadth, Dassault Systèmes on experiential twins, PTC on a cohesive SaaS thread, SAP on ERP orchestration, and Aras on niche agility.

The market’s winners will be those who deliver platforms that feel seamless and purpose-built, not stitched together from acquisitions. Execution, integration discipline, and adoption matter more than the size of an acquisition pipeline.

This brings us to the key insight: PLM is no longer a lifecycle tool. It is the product innovation backbone of enterprise digital orchestration. The metric of success has shifted. Customers now evaluate platforms based on:

• Integration maturity: Does the platform deliver real continuity across R&D, engineering, operations, and service?
• Execution discipline: Can acquisitions, modules, and partner technologies function as one coherent system?
• Platform coherence: Does the user experience feel unified, or is it fragmented across silos and workflows?
• Resilience and adaptability: Can the platform respond to emerging AI-native tools, regulatory change, or market disruptions without losing coherence?

Siemens’ industrial breadth may create a cognitive platform unmatched in scale—but only if complexity does not erode usability. Dassault Systèmes’ virtual twin strategy offers immersive insight, yet its value will be judged by workflow consistency and cross-domain intelligence. PTC’s SaaS-native digital thread emphasizes lifecycle discipline—but its promise exists only if all modules operate as one. SAP must show that ERP-centric orchestration adds value beyond lifecycle coverage. Aras must balance growth with its core promise of agile, domain-specific solutions.

The Autodesk/PTC leak is a reminder that disruption in PLM is ongoing, unpredictable, and fiercely contested. M&A headlines are attention-grabbing, but true differentiation lies in execution, coherence, and adoption.

The next wave—AI-native platforms, further consolidation, or deeper PDM/ERP/MES convergence—will test whether vendors can balance ambition with disciplined integration (end-to-end PLM scope). OEMs and other user organizations must resist evaluating vendors on acquisition size or feature count alone. The real question is:

Which platform can deliver a coherent, intelligent, and resilient digital enterprise at scale—backed by a true, ready-made transformation path?

PLM has evolved—and will continue to mature. It is no longer a set of engineering tools. It is the orchestration engine of the enterprise, connecting people, data, and processes across design, development, operations, and service. The vendors who master coherence over complexity will define the next era of digital enterprise transformation. Those who fail will see their platforms fragment, their promises collapse, and their leadership erode.

The race is not about owning product data structure. It is about owning the digital enterprise, with precision, discipline, and foresight.

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ABB will invest $110 million through the remainder of 2025 to expand its R&D and manufacturing of advanced electrification solutions.

The company says the cash will create nearly 200 new jobs and support expected future growth in key industries, including data centers and the power grid. Rapid expansion of data centers in the US is expected to keep annual electricity demand growth above 2% in both 2025 and 2026, more than double the average growth rate over the past decade, according to the IEA.

“This $110 million investment in the US is part of our long-term strategy to support future growth in our biggest global market,” said Morten Wierod, ABB’s Chief Executive Officer. “Demand is being driven by key trends, from the surging power needs of AI in data centers, to grid modernization and customers improving energy efficiency and uptime to reduce their costs.”

ABB will invest $15 million to create a new production line for Emax 3 in its Senatobia, Mississippi site. The Emax 3 air circuit breaker improves the energy security and resilience of power systems in large facilities with high power demands, including data centers, advanced manufacturing sites, and airports. The new line is expected to open in 2026.

A $30 million project will double the footprint of ABB’s Richmond, Virginia facility adding a new test center, warehouse and new assembly lines. The new facility, opening in Q4 2025, will create around 100 new production and engineering roles.

In Arecibo, Puerto Rico, an investment of more than $30 million will increase the size of the facility to accommodate three new production lines. Technologies produced in Arecibo include smart circuit breakers and switching devices, essential power components that help distribute electricity, protect equipment and monitor energy usage. The expansion will create 90 new jobs by the end of 2026.

A $35 million investment will increase the capacity of ABB’s manufacturing facility in Pinetops, North Carolina. This will support expected demand for advanced low and medium voltage grid components from the utilities, and for data centers and industrial facilities. The upgraded facility will come online in 2026.

All of this comes on the heels of a4100 million investment in ABB’s Canadian facilities announced in August 2025. That investment in Montreal, Quebec will combine ABB’s existing Iberville and Saint-Jean-sur-Richelieu facilities at a new greenfield location. This will enable ABB to meet increasing demand in key growth industries, including utilities, renewables, transportation, and residential and infrastructure projects across Canada.

The new site is expected to open in mid-2027 and will be located in the South Shore region of Montreal, Quebec. The new building will integrate clean, energy-efficient electrical equipment and heating systems to reduce energy consumption and cut carbon emissions by over 95 percent, compared with the two existing facilities.

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The ANSI/A3 R15.06-2025 American National Standard for Industrial Robots and Robot Systems – Safety Requirements is now available and A3 says it marks the most significant advancement in industrial robot safety requirements in more than a decade.

“Publishing this safety standard is perhaps the most important thing A3 can do, as it directly impacts the safety of millions of people working in industrial environments around the world,” said Jeff Burnstein, president of A3, in a release.

This standard is available in protected PDF format and includes:
Part 1: Safety requirements for industrial robots
Part 2: Safety requirements for industrial robot applications and robot cells
Part 3: Will address safety requirements for users of industrial robot cells. It’s expected to be published later this year. Once available, it will be retroactively provided at no additional cost to anyone who purchases the full standard.

R15.06 is the U.S. national adoption of ISO 10218 Parts 1 and 2 and is a revision of ANSI/RIA R15.06-2012, which was launched by the Robotic Industries Association (RIA) before it became part of A3.

Key changes in ANSI/A3 R15.06-2025 include:

• Clarified functional safety requirements that improve usability and compliance for manufacturers and integrators
• Integrated guidance for collaborative robot applications, consolidating ISO/TS 15066
• New content on end-effectors and manual load/unload procedures, derived from ISO/TR 20218-1 and ISO/TR 20218-2
• Updated robot classifications, with corresponding safety functions and test methodologies
• Cybersecurity guidance included as part of safety planning and implementation
• Refined terminology, including the replacement of “safety-rated monitored stop” with “monitored standstill” for broader technical accuracy

“This standard delivers clearer guidance, smarter classifications, and a roadmap for safety in the era of intelligent automation,” said Carole Franklin, director of standards development, robotics at A3. “It empowers manufacturers and integrators to design and deploy safer systems more confidently while supporting innovation without compromising human well-being.”

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This is why the editors at Engineering.com designed our Digital Transformation Week event—to help engineers unpack all the choices in front of them, and to help them do it at the speed and scale required to compete.

Join us for this series of lunch hour webinars to gain insights and ideas from people who have seen some best-in-class digital transformations take shape.

Registrations are open and spots are filling up fast. Here’s what we have planned for the week:

September 22: Building the Digital Thread Across the Product Lifecycle

12:00 PM Eastern Daylight Time

This webinar is the opening session for our inaugural Digital Transformation Week. We will address the real challenges of implementing digital transformation at any scale, focusing on when, why and how to leverage manufacturing data. We will discuss freeing data from its silos and using your bill of materials as a single source of truth. Finally, we will help you understand how data can fill in the gaps between design and manufacturing to create true end-to-end digital mastery.

September 23: Demystifying Digital Transformation: Scalable strategies for Small & Mid-Sized Manufacturers

12:00 PM Eastern Daylight Time

Whether your organization is just beginning its digital journey or seeking to expand successful initiatives across multiple departments, understanding the unique challenges and opportunities faced by smaller enterprises is crucial. Tailored strategies, realistic resource planning, and clear objectives empower SMBs to move beyond theory and pilot phases, transforming digital ambitions into scalable reality. By examining proven frameworks and real-world case studies, this session will demystify the process and equip you with actionable insights designed for organizations of every size and level of digital maturity.

September 24, 2025: Scaling AI in Engineering: A Practical Blueprint for Companies of Every Size

12:00 PM Eastern Daylight Time

You can’t talk about digital transformation without covering artificial intelligence. Across industries, engineering leaders are experimenting with AI pilots — but many remain uncertain about how to move from experiments to production-scale adoption. The challenge is not primarily about what algorithms or tools to select but about creating the right blueprint: where to start, how to integrate with existing workflows, and how to scale in a way that engineers trust and the business can see immediate value. We will explore how companies are combining foundation models, predictive physics AI, agentic workflow automation, and open infrastructure into a stepped roadmap that works whether you are a small team seeking efficiency gains or a global enterprise aiming to digitally transform at scale.

September 25: How to Manage Expectations for Digital Transformation

12:00 PM Eastern Daylight Time

The digital transformation trend is going strong and manufacturers of all sizes are exploring what could be potentially game-changing investments for their companies. With so much promise and so much hype, it’s hard to know what is truly possible. Special guest Brian Zakrajsek, Smart Manufacturing Leader at Deloitte Consulting LLP, will discuss what digital transformation really is and what it looks like on the ground floor of a manufacturer trying to find its way. He will chat about some common unrealistic expectations, what the realistic expectation might be for each, and how to get there.

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Cinch Connectivity Solutions serves the needs of wireless communications, telephony and data networks, security systems, health care, military, and industrial facilities with a full spectrum of broadband copper and fiber optic connectivity products. The manufacturer recognized Mouser for the distributor’s overall North America growth in sales (POA), POS, bookings, customer count, and overall support and engagement.

Cinch Connectivity Solutions, a Bel Group company, previously awarded Mouser Electronics with the President’s Award for 2023. Mouser was also named Global Distributor of the Year by Bel in 2023 and 2020 for growing sales and customer counts across all Bel companies. With over 50,000 Bel parts available to order, including over 8,000 in stock and ready to ship, Mouser offers a wide portfolio of Bel solutions for use in communications, data centers, transportation and industrial applications. To learn more about products from the Bel group of companies available at Mouser, visit https://www.mouser.com/manufacturer/bel-group/.

As a global authorized distributor, Mouser offers the widest selection of the newest semiconductors, electronic components and industrial automation products. Mouser’s customers can expect 100% certified, genuine products that are fully traceable from each of its manufacturer partners. To help speed customers’ designs, Mouser’s website hosts an extensive library of technical resources, including a Technical Resource Center, along with product data sheets, supplier-specific reference designs, application notes, technical design information, engineering tools and other helpful information.

Engineers can stay abreast of today’s exciting product, technology and application news through Mouser’s complimentary e-newsletter. Mouser’s email news and reference subscriptions are customizable to the unique and changing project needs of customers and subscribers. No other distributor gives engineers this much customization and control over the information they receive.

For more information, visit mouser.com.

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Nowhere is that more readily apparent than in China, which has been pursuing leadership in 3D printing technology for quite some time. The fruits of those efforts are starting to be realized at the desktop-level in the growth of companies such as Bambu Lab, and at the industrial-level with examples such as Farsoon Technologies, which has been expanding in both its material and production capabilities.

Now, Farsoon has announced the completion of the so-called KLINGA project with the Spanish electric motorcycle company, Stark Future, which reportedly resulted in the production of more than 1,000 3D printed titanium parts. According to Farsoon, the KLINGA Sabre for which the project is named is, “an engineering sculpture inspired by Stark Future’s electric motocross bike VARG.”

“The KLINGA Project was a bold way for us to push boundaries—not just in design, but in manufacturing,” said Benjamin Cobb, director of brand communications at Stark Future in a Farsoon press release. “Partnering with Farsoon allowed us to turn an ambitious idea into a titanium reality. It’s proof that large-scale, high-precision metal additive manufacturing is ready for serial production. It also validated our belief that 3D printing can deliver performance, quality, and sustainability—all at once.”

It’s a shame the result of the project is essentially an extra-fancy piece of 3D printed swag (the moose at the top is a functional bottle opener) rather than an actual end-use component for Stark Future because the numbers are impressive:

“In a single 248-hour build, 188 KLINGA sabres were produced with an average build time of under 80 minutes per unit,” reads the same realease. “Notably, the same production quality and workflow can be replicated on the FS721M platform currently operated by Stark Future.”

Hopefully, we’ll hear more about how Stark Future is using the FS721M in the production of its motorbikes. Still, if you do need to convince someone of the capabilities of metal AM, handing them a slick sculpture like the KLINGA Sabre and prompting them to crack open a cold one with it isn’t a bad way to go.

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After the wildfires burned thousands of acres, destroying homes, infrastructure, and natural habitats, this initiative will deploy the microfactory in Pacific Palisades, California, to build modular structures onsite, offering a glimpse into the future of affordable housing construction.

Watch the video on youtube.

“Together, Cosmic and ABB Robotics are rewriting the rules of construction and disaster recovery,” said Marc Segura, President of ABB Robotics Division. “By integrating our robots and digital twin technologies into Cosmic’s AI-powered mobile microfactory, we’re enabling real-time, precision automation ideal for remote and disaster-affected sites.”

These microfactories integrate ABB’s IRB 6710 robots and RobotStudio digital twin software with Cosmic’s Robotic Workstation Cell and AI-driven Building Information Model (BIM) – an end-to-end platform that handles design, permitting, procurement, robotic fabrication and assembly.

Housed within an on-site microfactory, these systems fabricate custom structural wall panels with millimeter precision just-in-time for assembly at the construction site.

Cosmic uses ABB’s RobotStudio with its AI BIM allowing the entire build process to be simulated and optimized in a digital environment before deployment. Once on location, Cosmic’s AI and computer vision systems work with the robots, making real-time decisions, detecting issues, and ensuring consistent quality.

These homes are built with non-combustible materials, solar and battery backup systems, and water independence through greywater recycling and renewable water generation. Each home exceeds California’s wildfire and energy efficiency codes. By delivering a turnkey experience from permitting to final construction, Cosmic is redefining what’s possible in emergency recovery.

Cosmic says its mobile microfactory reduces construction time by up to 70% and lowers total building costs by approximately 30% compared to conventional methods. Homes can be delivered in just 12 weeks at $550–$700 per square foot, compared to Los Angeles’ typical $800–$1,000 range.

“Our mobile microfactory is fast enough for disaster recovery, efficient enough to drastically lower costs, and smart enough not to compromise on quality,” said Sasha Jokic, Founder and CEO of Cosmic Buildings. “By integrating robotic automation with AI reasoning and on-site deployment, Cosmic achieves construction speeds three times faster than traditional methods, completing projects in as little as three months.”

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For this discussion, responsibility means accepting that one will be held personally accountable for AI-related problems and outcomes—good or bad—while acting with that knowledge always in mind.

Every new digital technology presents opportunities for misuse, particularly in its early days when its capabilities are not fully understood and its reach is underestimated. AI, however, is unique, making its governance extra challenging because of the following three reasons:

• A huge proportion of AI users in product development are untrained, inexperienced, and lack the caution and self-discipline of engineers; engineers are the early users of nearly all other information technologies.  
• With little or no oversight, AI users can reach into data without regard to accuracy, completeness, or even relevance. This causes many shortcomings, including AI’s “hallucinations.”
• AI has many poorly understood risks—a consequence of its power and depth—that many new AI users don’t understand.

While both AI and PLM are critical business strategies, they are hugely different. Today, PLM implementations have matured to the point where they incorporate ‘guardrails,’ mechanisms common in engineering and product development that keep organizational decisions in sync with goals and strategic objectives while holding down risks. AI often lacks such guardrails and is used in ways that solution providers cannot always anticipate.

And that’s where the AI governance challenges discussed in our recent webinar, AI Governance: Ensuring Responsible AI Development and Use, come in.

The scope of the AI problem

AI is not new; in various forms, it has been used for decades. What is new is its sudden widespread adoption, coinciding with the explosion of AI toolkits and AI-enhanced applications, solutions, systems, and platforms. A key problem is the poor quality of data fed into the Large Language Models (LLMs) that genAI (such as ChatGPT and others) uses.

During the webinar, one attendee asked if executives understand the value of data. Bilello candidly responded, “No. And they don’t understand the value of governance, either.”  And why should they?  Nearly all postings and articles about AI mention governance as an afterthought, if at all.

So, it is time to establish AI governance … and the task is far more than simply tracking down errors and identifying users who can be held accountable for them. CIMdata has learned from experience that even minor oversights and loopholes can undermine effective governance.

AI Governance is not just a technical issue, nor is it just a collection of policies on paper. Everyone using AI must be on the same page, so we laid out four elements in AI governance that must be understood and adopted:

• Ethical AI, adhering to principles of fairness, transparency, and accountability.

• AI Accountability, assigning responsibility for AI decisions and ensuring human oversight.

• Human-in-the-Loop (HITL), the integration of human oversight into AI decision-making to ensure sound judgments, verifiable accountability, and authority to intercede and override when needed.

• AI Compliance, aligning AI initiatives with legal requirements such as GDPR, CCPA, and the AI Act.

Bilello noted, “Augmented intelligence—the use of AI technologies that extend and/or enhance human intelligence—always has a human in the loop to some extent and. despite appearances, AI is human-created.”

Next, we presented the key pillars of AI governance, namely:

• Transparency: making AI models explainable, clarifying how decisions are made, and making the results auditable.
• Fairness and proactively detecting and mitigating biases.
• Privacy and Security to protect personal data, as well as the integrity of the model.
• Risk Management with continuous monitoring across the AI lifecycle.

The solution provider’s perspective

Now let’s consider this from the perspective of a solution provider, specifically the Hexagon Manufacturing Intelligence unit of Hexagon Metrology GmbH.

AI Governance “provides the guardrails for deploying production-ready AI solutions. It’s not just about complying with regulations—it’s about proving to our customers that we build safe, reliable systems,” according to Dr. René Cabos, Hexagon Senior Product Manager for AI.

“The biggest challenge?” according to Cabos, is “a lack of clear legal definitions of what is legally considered to be AI. Whether it’s a linear regression model or the now widely used Generative AI [genAI], we need traceability, explainability, and structured monitoring.”

Explainability lets users look inside AI algorithms and their underlying LLMs and renders decisions and outcomes visible, traceable, and comprehensible; explainability ensures that AI users and everyone who depends on their work can interpret and verify outcomes. This is vital for enhancing how AI users work and for establishing trust in AI; more on trust below.

Organizations are starting to make changes to generate future value from genAI,with large companies leading the way.

Industry data further supports our discussion on the necessity for robust AI governance, as seen in McKinsey & Company’s Global Survey on AI, titled The state of AI – How organizations are rewiring to capture value, published in March 2025.

The study by Alex Singla et al. found that “Organizations are beginning to create the structures and processes that lead to meaningful value from gen AI.” Even though already in wide use—including putting senior leaders in critical roles overseeing AI governance.

The findings also show that organizations are working to mitigate a growing set of gen-AI-related risks. Overall, the use of AI—gen AI, as well as Analytical AI—continues to build momentum: more than three-quarters of respondents now say that their organizations use AI in at least one business function. The use of genAI in particular is rapidly increasing.

“Unfortunately, governance practices have not kept pace with this rewiring of work processes,” the McKinsey report noted. “This reinforces the critical need for structured, responsible AI governance. Concerns about bias, security breaches, and regulatory gaps are rising. This makes core governance principles like fairness and explainability non-negotiable.”

More recently, McKinsey observed that AI “implications are profound, especially Agentic AI. Agentic AI represents not just a new technology layer but also a new operating model,” Mr. Federico Burruti and four co-authors wrote in a June 4, 2025, report titled, When can AI make good decisions? The rise of AI corporate citizens.

“And while the upside is massive, so is the risk. Without deliberate governance, transparency, and accountability, these systems could reinforce bias, obscure accountability, or trigger compliance failures,” the report says.

The McKinsey report points out that companies should “Treat AI agents as corporate citizens. “That means more than building robust tech. It means rethinking how decisions are made from an end-to-end perspective. It means developing a new understanding of which decisions AI can make. And, most important, it means creating new management (and cost) structures to ensure that both AI and human agents thrive.”

In our webinar, we characterized this rewiring as a tipping point because the integration of AI into the product lifecycle is poised to dramatically reshape engineering and design practices. AI is expected to augment, not replace, human ingenuity in engineering and design; this means humans must assume the role of curator of content and decisions generated with the support of AI.

Why governance has lagged

With AI causing so much heartburn, one might assume that governance is well-established. But no, there are many challenges:

• The difficulty of validating AI model outputs when systems evolve from advisor-based recommendations to fully autonomous agents.
• The lack of rigorous model validation, ill-defined ownership of AI-generated intellectual property, and data privacy concerns.
• Evolving regulatory guidance, certification, and approval of all the automated processes being advanced by AI tools…coupled with regulatory uncertainty in a changing global landscape of compliance challenges and a poor understanding of legal restrictions.
• Bias, as shown in many unsettling case studies, and the impacts of biased AI systems on communities.
• The lack of transparency (and “explainability”), with which to challenge black-box AI models.
• Weak cybersecurity measures and iffy safety and security in the face of cyber threats and risks of adversarial attacks.
• Public confidence in AI-enabled systems, not just “trust” by users.
• Ethics and trust themes that reinforce ROI discussions.

Trust in AI is hindered by widespread skepticism, including fears of disinformation, instability, unknown unknowns, job losses, industry concentration, and regulatory conflicts/overreach.

James Markwalder, U.S. Federal Sales and Industry Manager at Prostep i.v.i.p.,  a product data governance association based in Germany, characterized AI development “as a runaway train—hundreds of models hatch every day—so policing the [AI] labs is a fool’s errand. In digital engineering, the smarter play is to govern use.”

AI’s fast evolution requires that we “set clear guardrails, mandate explainability and live monitoring, and anchor every decision to…values of safety, fairness, and accountability,” Markwalder added. “And if the urge to cut corners can be tamed, AI shifts from black-box risk to a trust engine that shields both ROI and reputation.”

AI is also driving a transformation in product development amid compliance challenges to business, explained by Dr. Henrik Weimer, Director of Digital Engineering at Airbus. In his presentation at CIMdata’s PLM Road Map & PDT North America in May 2025, Weimer spelled out four AI business compliance challenges:

Data Privacy, meaning the protection “of personal information collected, used, processed, and stored by AI systems,” which is a key issue “for ethical and responsible AI development and deployment.”

Intellectual Property, that is “creations of the mind;” he listed “inventions, algorithms, data, patents and copyrights, trade secrets,data ownership, usage rights, and licensing agreements.”

Data Security, ensuring confidentiality, integrity, and availability, as well as protecting data in AI systems throughout the lifecycle.

Discrimination and Bias, addressing the unsettling fact that AI systems “can perpetuate and amplify biases present in the data on which they are trained,” leading to “unfair or discriminatory outcomes, disproportionately affecting certain groups or individuals.”

Add to these issues the environmental impact of AI’s tremendous power demands. In the April 2025 issue of the McKinsey Quarterly, the consulting firm calculated that “Data centers equipped to handle AI processing loads are projected to require $5.2 trillion in capital expenditures by 2030…” (The article is titled The cost of compute: A $7 trillion race to scale data centers.)

Establishing governance

So, how is governance created amid this chaos? In our webinar, we pointed out that the answer is a governance framework that:

• Establishes governance policies aligned with organizational goals, plus an AI ethics committee or oversight board.

• Develops and implements risk assessment methodologies for AI projects that monitor AI processes and results for transparency and fairness.

• Ensures continuous auditing and feedback loops for AI decision-making.

To show how this approach is effective, we offered case studies from Allied Irish Bank, IBM’s AI Ethics Governance framework, and Amazon’s AI Recruiting Tool (which had a bias against females).

Despite all these issues, AI governance across the lifecycle is cost-effective, and guidance was offered on measuring the ROI impact of responsible AI practices:

• Quantifying AI governance value in cost savings, risk reduction, and reputation
    management.
• Developing and implementing metrics for compliance adherence, bias reduction, and transparency.
• Justifying investment with business case examples and alignment with stakeholders’ priorities.
• Focusing continuous improvement efforts on the many ways in which AI governance drives innovation and operational efficiency.

These four points require establishing ownership and accountability through continuous monitoring and risk management, as well as prioritizing ethical design. Ethical design is the creation of products, systems, and services that prioritize benefits to society and the environment while minimizing the risks of harmful outcomes.

The meaning of ‘responsibility’ always seems obvious until one probes into it. Who is responsible? To whom? Responsible for what? Why? And when? Before the arrival of AI, the answers to these questions were usually self-evident. In AI, however, responsibility is unclear without comprehensive governance.

Also required is the implementation and fostering of a culture of responsible AI use through collaboration within the organization as well as with suppliers and field service. Effective collaboration, we pointed out, leads to diversity of expertise and cross-functional teams that strengthen accountability and reduce blind spots.

By broadening the responsibilities of AI users, collaboration adds foresight into potential problems and helps ensure practical, usable governance while building trust in AI processes and their outcomes. Governance succeeds when AI “becomes everyone’s responsibility.”

Our conclusion was summed up as: Govern Smart, Govern Early, and Govern Always.

In AI, human oversight is essential. In his concluding call to action, Bilello emphatically stated, “It’s not if we’re going to do this but when…and when is now.” Undoubtedly, professionals who proactively embrace AI and adapt to the changing landscape will be well-positioned to thrive in the years to come.

Peter Bilello, President and CEO, CIMdata and frequent Engineering.com contributor, contributed to this article.

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