If there is one trend whose impact on global manufacturing markets can’t be overstated it is that of automation. Driven by robotics, digitalization, and smart technologies, industrial automation is enabling manufacturers from virtually all sectors to streamline their processes and augment productivity and consistency, all while mitigating costs and labor requirements. Perhaps no one understands this paradigm shift better than JOT Automation, a Finnish company with nearly four decades of experience in automation that specializes in delivering modular microfactories to a variety of industries, including automotive, telecommunications, consumer electronics, and power electronics. We recently had the opportunity to speak with <a href="https://www.jotautomation.com/" target="_blank" rel="noopener noreferrer">JOT Automation</a>’s Global Key Account Manager, Kimmo Kilpeläinen, about the company’s ANT Plant Microfactory and the benefits it is bringing to end users. He also speaks more broadly on the state of automation today and how JOT Automation is facilitating the transition to efficient production and reshoring.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1790097780614-1790097780614.jpeg" width="562" height="375" class="fr-fic fr-dii" />JOT Automation’s ANT Plant Microfactory. Source: JOT Automation.<h2>Manufacturing Welcomes Microfactories </h2>When one thinks of a factory, one typically pictures a massive building fitted with large-scale machines, conveyor belts, and workers working tirelessly to achieve a target output. But this is not the only representation of a factory that exists. Increasingly, manufacturers are turning to microfactories, which are compact, highly automated production facilities designed to fulfill complex manufacturing tasks. Notably, microfactories are not just an alternative to old-school manufacturing floors; they also offer an economical alternative to customized automation systems, which can take years to implement and come with a massive upfront investment. That’s because microfactories like JOT Automation’s ANT Plant Microfactory are based on a modular, pre-designed structure that can be easily tailored to the manufacturer’s production requirements, whether it's assembly, testing, laser marking, packaging, or all of the above.In the case of the <a href="https://www.jotautomation.com/solutions/ant-plant-microfactorycrofactory?_gl=1*1kdqdm6*_up*MQ..*_ga*NDUxOTAxMTguMTc5MDA5NzgwOQ..*_ga_FBSJ8R2X7V*czE3OTAwOTc4MDkkbzEkZzAkdDE3OTAwOTc4MDkkajYwJGwwJGgxNzg3ODE4Nzk." target="_blank" rel="noopener noreferrer">ANT Plant Microfactory</a>, the system is based on modules, which integrate ceiling-mounted robots to maximize the workspace in the production cell. Each module is designed for one application, and multiple modules can be integrated into a workflow or to scale production as needed. If product design changes, end users can also adapt their ANT Plant Microfactory cells by installing new tooling. This capability is key for manufacturers that must maintain production flexibility or who have high-mix production requirements. As Kilpeläinen explains: “We can offer flexibility and modularity because when you need to update your products, you just need to change some tooling in the cell module. In that way, the modular configuration is very efficient to use.”<h2>Collaboration at the Core</h2>From the customer’s perspective, adopting JOT Automation’s microfactory solution starts with collaboration. As Kilpeläinen tells us, in most cases a customer will come to them with an idea and a Request for Information (RFI). “They introduce their case to us, and then we evaluate the customer process and how suitable it is for automation. If there is room for improvement, then we tailor our microfactory to get the best results. We also offer a proposal of how the product itself could be modified to fit better with our processes.”This collaborative approach also takes into account customer limitations, including physical space, budget, and timeline. Beyond these, Kilpeläinen suggests that the ANT Plant can accommodate a wide variety of applications, including assembly and testing of consumer electronics, EV batteries, power electronics, and more. In cases where the production requirement is too complex or too big for a microfactory — ANT Plant robots specialize in handling parts under 10kg—the company also develops custom automation solutions from scratch. When it comes to installation, the Microfactory has nothing but benefits. Compared to a traditional automation system built from the ground up, the ANT Plant Microfactory can be delivered 30% faster and be installed on site in as little as eight hours (90% faster than a custom automation infrastructure).“It's a ready platform,” Kilpeläinen says of the ANT Plant. “When we are dealing with the customer, we need only design customer-based applications. That means that on day one, when we get the purchase order from the customer, we can already order parts from the supplier because we don't need to design anything from scratch—70% of it is ready. And while those parts are coming, we can design for the applications.” This concurrent approach shortens the delivery time dramatically.<iframe width="640" height="360" src="https://www.youtube.com/embed/MnLnA6vWcEg??si=Gbp8MKcMhKfaj61w&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" class="fr-draggable">        </iframe><h2>Bringing Microfactories to the World</h2>JOT’s innovative automation solutions have not gone unnoticed and many end users have welcomed the adoption of its ANT Plant Microfactory. In fact, within one year of launching the modular microfactory, JOT Automation had made €20 million in sales from Europe and the United States.At the time of this milestone, in late 2025, Kilpeläinen stated: “There is clearly traction in the market for this type of solution. Customers appreciate the possibility to start manufacturing quickly with minimal risks and scale manufacturing as needed.”To date, the greatest interest in JOT’s microfactory solutions has come from manufacturers in the automotive and battery industries as well as power electronics and consumer electronics. For instance, a leading manufacturer of electronics in the United States installed a fully integrated ANT Plant microfactory for its battery electric vehicle electronic control unit (BEV ECU) assembly and testing line. This installation, which spans only 4.1 square meters (44.1 square feet), is designed to handle 14 high-precision tasks, including component feeding and assembly, ROM writing, laser marking, and electrical testing. Not only that, but the modular nature of the microfactory enables the customer to easily scale up or update the tooling for future product variations. Despite these tangible benefits and enthusiastic adoption by certain sectors, there are still hurdles to the broad rollout of automated microfactories. Among these challenges is awareness: many manufacturers simply don’t realize that economical, modular microfactory solutions are already on the market. “There hasn’t been a customer who has said that the system isn’t working,” Kilpeläinen explains. “We’ve proved that it really is working and that it is cost competitive. But I think awareness is a key thing. We have to push our stories and tell everybody what we can do and that the technology is here. That’s how customers will find out.”Besides a lack of awareness, he also cites organizational inertia as a key hurdle, particularly long decision timelines that can impact a manufacturer’s ability to stay competitive. Fortunately, the rapid assembly and installation of the ANT Plant Microfactory can address this particular challenge. “When we start talking with a customer, it takes time on their end to make decisions about the investment and ROIs, etc. Often, by the time a decision is made, a significant part of the lead time has elapsed. That means we have to deliver the microfactory quickly, which we can do with the ANT Plant.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1790097780621-1790097780621.jpeg" width="624" height="416" class="fr-fic fr-dii" />JOT Automation’s automation line with ABB robots. Source: JOT Automation.<h2>Production Comes Home   </h2>Another key benefit of JOT’s microfactories is that they enable manufacturers to reshore production. With much of the company’s business coming from Europe and the United States, it’s clear that establishing manufacturing at home is a key priority for many customers, as they aim to simplify and strengthen supply chains, protect themselves against tariffs, and overcome labor shortages. Microfactories like ANT Plant are facilitating reshoring by offering a pathway to rapid, modular, and low-cost automation. In one case, for example, Kilpeläinen explains how a customer was able to transition from using 50 employees at a factory in China to needing two people to run the microfactory in Europe. “Cheap labor is not a winning case anymore,” he says, “And on top of that, there is no human error in the processes because everything is automated.” The minimal labor required to keep the microfactories up and running is skilled labor, and JOT Automation provides training to operators from the beginning of the collaboration and beyond the installation process. “When we are doing the installation, we are teaching key topics to customer engineers and maintenance people,” he elaborates, “After installation, we also have a support period where we are available in case something happens.” Fortunately, the learning curve is not steep and the ANT Plant system integrates a number of safety features that, on top of not requiring much intervention, keep operators completely safe. Among these features is an enclosed cell and electrical locks that prohibit entry into the unit when production is ongoing. <h2>JOT Automation, Up for a Challenge</h2>With the manufacturing landscape undergoing a dramatic shift impacted by new technologies, supply chain challenges, and labor shortages, automation is central to remaining competitive. And while some may think the path to automated production is difficult and costly, innovative players like JOT Automation are showing that this doesn’t have to be the case. With its modular ANT Plant Microfactory, the company is enabling manufacturers in diverse sectors to implement automation rapidly, flexibly, and cost-efficiently. Presently, the company is largely serving electronics and battery markets, which <a href="https://www.jotautomation.com/solutions/ant-plant-microfactory#guide" target="_blank" rel="noopener noreferrer">benefit from the compact, highly precise capabilities of the robotic automation platform</a>, but Kilpeläinen wants to make clear that its solutions are broadly viable and can transform virtually any production application. “What I always tell the customer is to challenge us,” he says. With that said, if you have a manufacturing application or a reshoring need that is being held back by lead times and costs, JOT Automation’s microfactories might just be the answer.  <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1790097780632-1790097780632.png" width="624" height="276" class="fr-fic fr-dii" />

Kimmo Kilpeläinen, JOT Automation Global Key Account Manager, discusses the impact of automated microfactories on productivity, labor, and costs.

How JOT Microfactories Are Ushering in the Age of Automation

<a href="https://www.wevolver.com/article/alex-macdougall-is-on-a-mission-to-solve-a-problem-at-the-core-of-the-electronics-industry">Six years ago</a>, Wevolver spoke with SamacSys founder <a href="https://www.wevolver.com/profile/alex.macdougall">Alex MacDougall</a> about his journey building a company around a deceptively simple problem: engineers were spending far too much of their time creating CAD models that should already exist.At the time, SamacSys had recently been acquired by Supplyframe, and MacDougall was looking ahead. He predicted a future in which engineers would find it almost unbelievable that previous generations had manually created component models. He also talked about modular engineering and the possibility of AI eventually generating designs from functional requirements.Quite a lot has happened since.Supplyframe was subsequently acquired by Siemens. Adoption has continued to grow, the platform is now used by nearly 1.4 million engineers, and MacDougall has remained involved far longer than he originally expected.He has also written Built, Not Given, a book that reveals a considerably more complicated story behind the entrepreneur we interviewed in 2020.So, six years later, we caught up with him again.<h2>1. When we spoke in 2020, SamacSys had recently been acquired by Supplyframe. Six years later, what has changed - and what hasn’t?</h2>Well, after just looking back at our conversation from then, I’ve realised two things: the watch I’m wearing hasn’t changed, but this office has - and it’s much nicer than my old one!But back to the business… that has definitely changed, mostly in scale.Our goal was always super simple: engineers shouldn’t be wasting time drawing CAD models of electronic components. If we could solve that properly, engineers would just expect the models to be there. That is what has happened and it’s great to see. As you know, today there are nearly 1.4 million registered engineers signed up with around 350,000 CAD model downloads every month. We also now have thousands of manufacturers on the platform, it’s become something engineers simply expect and I see our Green icons everywhere.On a more personal note, there are now people working on it I’ve never even met, solving problems before I know they exist. As a founder that’s both wonderful and mildly offensive!I spent years at the heart of things and then discovered that ultimately you have to make yourself less necessary so the idea can continue without you! <h2>2. Back then, you said engineers of the future wouldn’t believe their predecessors spent time building CAD models. How close is the industry to that reality now?</h2>Much closer.When I started all this, engineers spent hours creating symbols, footprints and 3D models and that was completely normal. The industry had effectively accepted this huge duplication of effort.Imagine all those engineers, at all those companies independently drawing the same models. Nobody would deliberately design a system that way, but that’s what was just happening.Today an engineer can search for a component, get the model and get on with their design very quickly. But the win isn’t somebody saying, “SamacSys is awesome.” Strangely it’s an engineer being slightly annoyed when a model isn’t immediately available. <blockquote>We’ve gone from engineers having no expectation that a CAD model would be there, to them being annoyed when it isn’t. That’s a pretty big change.</blockquote><h2>3. SamacSys started with a simple mission: has the problem you set out to solve become bigger than that?</h2>Not entirely, but a little bit.The CAD model was the obvious pain point because I’d experienced it myself. But there is also a larger issue: engineers need reliable technical information when they’re making decisions.A CAD model isn’t just a picture. It represents something real - electrical connections, physical dimensions, thermal characteristics and the relationship between a component and the product being designed around it.CAD models are part of a much bigger technical-data problem. That’s becoming even more important now with AI.If a human engineer is working with bad CAD model information, they can sometimes notice that something looks wrong. AI can potentially make the wrong decision extraordinarily quickly and with tremendous confidence.Now it’s important to have trusted, structured part data that machines as well as humans can understand.<h2>4. Supplyframe itself was subsequently acquired by Siemens. What has it been like taking something you started as an independent business through two acquisitions?</h2>It’s been fascinating, difficult and much longer than I originally expected.When SamacSys was acquired, I had a two-year earnout. My plan was to complete that properly, hand everything over and take a break for a little while.Then Siemens <a href="https://press.siemens.com/global/en/pressrelease/siemens-accelerates-digital-marketplace-strategy-acquisition-supplyframe" target="_blank" rel="noopener noreferrer">acquired</a> Supplyframe. Apparently I have a terrible habit of completing one tour of duty and accidentally signing up for another. So two years became more than five.The biggest adjustment was moving from an environment where I could make a decision and immediately execute it, into organisations where responsibilities are deliberately shared between wider teams.That’s sensible at scale, but it’s very different psychologically for a founder.You learn that building something and integrating something require different skills. Neither is necessarily easier.<h2>5. Founders often leave after an acquisition. Why did you stay, and has working inside larger organisations changed the way you think about building technology?</h2>I stayed initially because I’d committed to it.There were employees, customers and engineers relying on what we’d built. I didn’t want to sell the company, take the money and immediately walk away.Then Supplyframe was acquired and there was another commitment to fulfill.Working within larger organisations has definitely changed my perspective. A founder can get used to speed. If something needs doing, you find a way, make the decision and get on with it.At larger scale you begin to understand why structure exists. You’re balancing infrastructure, security, legal requirements, different products, countries and thousands of other considerations.I still occasionally think, “I could have done this by lunchtime in 2015.” But in 2015 we also had considerably fewer lawyers to think about!Both ways have advantages and disadvantages I think. <h2>6. In our 2020 interview, you predicted that AI could eventually generate parts of an electronic design from functional requirements. Were you right - and what did you underestimate?</h2>Even if I do say so myself, I think my estimate was right. What surprised me was how quickly AI became accessible to everyone.In 2020 I was thinking primarily about specialised engineering systems: tell the software that a product needs USB power, a display, wireless connectivity and certain physical constraints, and eventually it could help construct the design.That still makes sense.What I underestimated was that millions of people would first become comfortable having conversations with AI about everything from software to what they should cook for dinner.The AI apps arrived before the specialist engineering capability; that's been interesting to watch. Now I believe the engineering challenge is making sure AI has something trustworthy underneath it, because an AI system can produce a very convincing answer. Unfortunately, electronics don't care whether an answer sounds convincing. The board either works or it doesn’t.Physics remains annoyingly resistant to marketing! <h2>7. There’s enormous excitement around AI in engineering. Where do you think the industry is getting AI right, and where is it getting it wrong?</h2>Well we all have seen that AI is incredibly powerful when it removes repetitive work and allows engineers to spend more time solving actual engineering problems.That’s essentially the same philosophy SamacSys started with but where I become more cautious is when AI is treated almost like a magic answer to everything.Engineering has constraints. Components have dimensions and pins have specific functions. Increasingly, products have regulatory requirements, so a confident hallucination in a poem might be ok. A confident hallucination in a medical device is somewhat less charming.The opportunity is obviously enormous, but engineering AI needs strong foundations. We need to make sure we’re asking, “What information are we feeding it?” So we get more correct outcomes. <h2>8. Why do you think data is becoming more important rather than less important in an AI-driven design environment?</h2>Because automation amplifies whatever you put into it. Good inputs can create enormous efficiency. Bad inputs can create enormous efficiency at producing mistakes!If an AI system is going to recommend components, generate sections of a circuit or eventually contribute to complete product designs, it needs to understand the components it’s working with at a level that goes far beyond a part number and a paragraph from a datasheet.It needs structured, validated information, but it seems most people are looking at the clever thing happening at the interface.I’m more interested in the boring bit underneath it, I’ve made quite a good career out of boring bits underneath things! <h2>9. Is the opportunity still primarily ECAD models, or does the same problem exist across other engineering disciplines?</h2>The underlying problem absolutely exists elsewhere.Electronics was where I understood the issue well enough to attack it. ECAD was therefore the obvious starting point for me.But mechanical engineers also spend significant amounts of time finding, validating, recreating and managing technical information around mechanical parts.The broader principle is the same: highly skilled engineers shouldn’t repeatedly recreate information that could be generated once, validated properly and made available at the point of design.So there’s potential anywhere we can remove unnecessary friction from engineering, basically making it simpler to go from an idea, to a product in your hand. <h2>10. Looking back, what turned out to be harder: building SamacSys, scaling it globally, or integrating it into larger organisations?</h2>They’re all difficult in completely different ways.Starting from nothing is frightening because there is quite a risk. You’re trying to persuade people that a problem they’ve tolerated for years suddenly deserves solving.Scaling is difficult because everything can break at the same time and you have to fix things while people still rely on it.Integration is difficult because you have less direct control, to be blunt.I learned something from all three.But I like building things. There’s something very satisfying about identifying a problem, having almost no resources and just figuring it out.<h2>11. The first time we interviewed you, we concentrated mainly on the company. You’ve since written Built, Not Given. Why did you decide to tell the story behind the business now?</h2>I actually resisted writing it for a long time.Publishers approached me around the time SamacSys was sold in 2019, but I didn’t think I had anything particularly useful to say. I was still in the middle of it.Later I realised the business was almost the easy part of the story to explain. The part the writers found more interesting was why I approached things the way I did.As you know I didn’t have the best start in life, effectively without guidance from a young age. I joined the military then discovered I had a medical condition that changed that career path.The book isn’t really saying, “Look what I achieved.”It’s more: “This is what happened, this is what I tried, this is what worked, and perhaps some of it will be useful to somebody else.”That’s a much better reason to write a book, to try and help others, rather than just explain your story. <h2>12. How much did your experiences before SamacSys shape the way you built the company?</h2>Almost all parts of my life shaped it I suppose. My childhood taught me independence. Marines training taught me discipline. Engineering taught me precision. Sales taught me how to communicate value. And some badly run businesses taught me what happens when standards disappear.None of it felt useful while it was happening. I certainly wasn’t sitting in a freezing tyre-fitting workshop thinking, “Excellent, another important chapter in my future leadership philosophy.”Mostly I was thinking that it was cold and I wanted to get the grease off my hands! By the time I started SamacSys, I understood engineers because I’d been one. I understood PCB software because I’d supported and sold it. And I understood the commercial side well enough to work out who might pay for what we were building. The business came from all of those experiences I guess. <h2>13. You’ve built a career while adapting to muscular dystrophy. Has that changed your definition of ambition or success as you’ve got older?</h2>100% yes!As the book explains, when I was about twenty, a doc told me to prepare to be in a wheelchair by twenty-five.I’m now in my forties and I’m still walking and generally irritating the prognosis, but living with a degenerative condition does change your relationship with time.Most people can tell themselves they’ll do something physical in five or ten years. I don’t know what I’ll be able to do by then. So I’ve tended to do things right away.I’ve been very lucky to do a lot of fun things and sometimes the condition has limited me but I've mostly found a way around it.When I was younger, success was about proving I could do something despite the circumstances.Now it’s much more about having control of my time, being present for my family, staying physically capable for as long as possible and doing things that are actually useful.It only took me about forty years to work out I should be useful!<h2>14. Founders tend to make success look inevitable. What part of building SamacSys felt least inevitable while you were actually living through it?</h2>Most of it if I’m honest!That’s one of the reasons I wanted the book to be unpolished.Success stories become suspiciously tidy when they’re told backwards. Someone spots an opportunity, has a vision, assembles a brilliant team, overcomes adversity and eventually exits for millions.Lovely! Living through it doesn’t feel anything like that in real life!You’re worried about paying people. A customer changes direction. A server goes down. Somebody leaves. Something you were convinced would work doesn’t. Then something you thought was relatively insignificant suddenly becomes important.I obviously didn’t know it would succeed. I just kept solving the next problem and kept going. That’s probably the least exciting business advice imaginable, but unfortunately I think it’s true for me. Basically parts of success are repetitive and fairly boring while it’s happening but you have to get through it. <h2>15. Six years ago we asked what was next for SamacSys. This time: what’s next for Alex MacDougall?</h2>There are still big engineering problems I want to work on with Supplyframe and Siemens. Especially as AI is so hot at the moment and I find the topic fascinating. Outside engineering, I want to write more, work with founders and companies where my experience might be useful, and hopefully help people avoid some of the mistakes I made. I’ve accumulated quite a good collection of those!I also have my charitable ‘Built Forward Fund’, which will use proceeds from the book and anything else I build around it to provide help to people who need it. Nothing grand, just if people need equipment, food, transport or an opportunity and we can help, we will help.I have a young family, and as I said a moment ago, I’m much more conscious of time than I used to be. I spent years behaving as though every available hour needed to be converted into progress. I don’t want to do that forever, especially as my bones creak more these days!So I doubt there’ll be one giant “next thing”, just working on interesting problems, helping good people build good things, spend time with my family, do some more things that are probably slightly stupid while I’m still physically able to do them, and enjoy myself.That feels like a pretty decent plan, let’s check in again in another six years and see how that goes!  Recommended reading: <ul><li>Alex MacDougall's first Wevolver interview: "<a href="https://www.wevolver.com/article/alex-macdougall-is-on-a-mission-to-solve-a-problem-at-the-core-of-the-electronics-industry">Alex MacDougall is on a mission to solve a problem at the core of the electronics industry</a>" </li><li>Alex MacDougall's Wevolver profile with his articles <a href="https://www.wevolver.com/profile/alex.macdougall">here</a>. </li></ul>

The SamacSys founder on two acquisitions, 1.4 million engineers, and why AI makes trusted part data matter more than ever.

Six Years Later: A Follow-Up Conversation with Alex MacDougall

<h2>Building Integrated Photonic Products That Scale</h2>Integrated photonics has produced some of the most remarkable engineering achievements of the past decade. Yet for every photonic innovation that reaches the market, many more stall somewhere between the laboratory and commercial reality.This gap, often called the valley of death, is not a failure of ideas but rather a failure of industrialization. A device that performs brilliantly on a handful of prototype wafers may behave very differently when manufactured at scale. Yield drops, process variations compound, timelines stretch, and the assumptions baked into a design optimized for demonstration begin to crack under the demands of production.Bridging that gap requires more than engineering excellence. It demands that manufacturing be treated as a design constraint from day one. In this conversation, Sreekanth (Sree) Chirayath, Vice President Business Development at XIVER, explains what that looks like in practice and why the most successful integrated photonics teams are the ones that think about scale before they think they need to. <a href="https://www.wevolver.com/article/global-photonics-engineering-contest-2026?utm_source=article&amp;utm_medium=xiver&amp;utm_id=Photondelta" target="_blank" rel="noopener noreferrer"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1784115387646-image1.png" style="width:100%px" class="fr-fic fr-dib" /></a><h2>Innovation to Industrialization in Integrated Photonics: Insights from XIVER</h2>XIVER is a European-owned specialty microsystems and semiconductor foundry with more than 40 years of experience and a fully qualified 200 mm wafer fab at High Tech Campus Eindhoven. XIVER helps customers transform complex innovations into manufacturable, high-performance products by combining expertise in silicon micromachining, wafer-level processing, microsystems integration, and Design for Manufacturing (DfM). Core focus areas include semiconductor equipment and metrology, integrated photonics, advanced transducer technologies, and infrared sensing. XIVER serves a diverse range of markets, including semiconductor equipment, integrated photonics, IR sensing, defense, and medtech. What differentiates XIVER is their concurrent engineering approach: the company works with customers from the earliest stages of development through industrialization and high-yield volume production. By combining agility, reliability, and manufacturing excellence, XIVER helps organizations accelerate time-to-market and successfully scale advanced technologies from concept to commercial reality.Sreekanth (Sree) Chirayath, Ph.D., is Vice President Business Development at XIVER, focused on integrated photonics. With a background in physics, photonics, and semiconductors, he helps customers bridge the gap between innovation and manufacturing, supporting the industrialization of integrated photonic technologies for optical communications, sensing, AI infrastructure, and other high-growth applications. Peter (PhotonDelta): There's an assumption in deep-tech circles that if you build something technically superior, the rest will follow. In your experience, what actually stops great technology from reaching the market?Sree (XIVER): One of the biggest misconceptions in deep tech is that technical superiority alone guarantees commercial success. Many breakthrough technologies never reach the market because they fail to cross what we call the industrialization gap. A device may work perfectly on a handful of prototypes, but when you try to manufacture hundreds or thousands of wafers, yield issues emerge. Small process variations can significantly impact performance, creating unacceptable costs or reliability risks.That is why our role as a specialty foundry extends beyond fabrication. We work closely with customers from the earliest stages to ensure that process choices, design decisions, and integration strategies are aligned with high-yield production from day one. By combining expertise in silicon micromachining, wafer-level processing, microsystems integration, and manufacturing, we help customers de-risk the transition from innovation to volume production. Great technologies reach the market not only when they perform exceptionally, but when they can be manufactured reliably, repeatedly, and economically on scale. That's where many innovations succeed or fail.Peter (PhotonDelta): What are XIVER's core areas of expertise, and what kinds of challenges are customers typically looking to solve when they come to you?Sree (XIVER): Customers generally come to us when they have a technically demanding microsystem, photonics or semiconductor challenge and need a partner that can turn an innovative concept into a reliable, manufacturable product.Our core expertise is built around micromachining, wafer-level processing, microsystems integration, and DfM. Combined with our fully qualified 200 mm wafer fab, this capability allows us to support customers all the way from concept development and process design through industrialization and high-yield production.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1784115482267-image2.png" style="width:100%px" class="fr-fic fr-dib" />Inside XIVER's 200 mm cleanroom in Eindhoven, where integrated photonic innovations are prepared for scalable production.At XIVER, we focus our business on three strategic domains where we see the strongest fit between market demand and our capabilities.The first area is semiconductor and metrology equipment, which today represents roughly two-thirds of our business. Here, customers are often developing precision measurement systems or infrared sensing solutions. Their challenge is typically not just performance, but achieving that performance with the reliability and repeatability over decades of the product lifetime required by the semiconductor industry.The second area is transducer technology, including capacitive ultrasound devices, particularly for medical applications. Here, customers face long development and qualification cycles, often involving years of validation and regulatory processes. They need manufacturing partners that can maintain process stability and product consistency throughout that journey.Our third strategic pillar is high-speed data communication, especially in integrated photonics. While it is a smaller business at the moment, it represents one of our strongest growth focus. Customers in the integrated photonics business are looking for ways to move breakthrough optical technologies out of the lab and into scalable production environments that can support rapidly growing demand for data and connectivity.A good example of our involvement in this ecosystem is the <a href="https://www.wevolver.com/article/global-photonics-engineering-contest-2026?utm_source=article&amp;utm_medium=xiver&amp;utm_id=Photondelta">Global Photonics Engineering Contest 2026</a>. XIVER is one of the industrial partners supporting the initiative alongside other leading integrated photonics organizations. The contest is designed to help engineers, startups, researchers, and innovators develop new photonic chip applications, with access to technical support, industrial expertise, and potentially up to €2 million in investment. Our role reflects something we strongly believe in: <blockquote>Great ideas need more than technical brilliance — they need a credible path to manufacturing and commercialization.</blockquote>In fact, that's a theme we see across all our markets. Whether the customer is a startup or a multinational company, the challenge is usually not just creating breakthrough technology; it's achieving yield, reliability, scalability, and cost-effective production.Ultimately, customers come to XIVER when they need help crossing the industrialization gap. We provide the process expertise, manufacturing know-how, and production infrastructure that enable advanced technologies to become successful products in the market.Peter (PhotonDelta): At what stage do most companies first engage a manufacturing partner, and at what stage should they?Sree (XIVER): In many cases, companies engage a manufacturing partner a bit later than they ideally should. Often, they first focus on proving that the technology works, building prototypes, and demonstrating performance. Only once they've achieved that milestone do they start thinking seriously about industrialization.The challenge is that by then, some of the most important design decisions have already been locked in. The technology may work brilliantly, but it wasn't necessarily designed with manufacturing in mind.We often see situations where a device performs exactly as intended in the lab, but when you try to scale it, yield becomes an issue; certain process steps prove difficult to reproduce consistently. At that point, redesigns can become expensive and time-consuming.That's why we believe manufacturing should be part of the conversation much earlier. Ideally, we prefer that the customers involve us when they are still making key architecture and design decisions. That's the stage where small changes can have a huge impact on manufacturability, reliability, and ultimately time-to-market.At XIVER, we always say that industrialization isn't something you do after innovation; it's something you build from the start. The earlier manufacturing expertise is brought in, the fewer surprises there are later.This is particularly important in emerging fields like integrated photonics, where moving from a promising concept to a scalable product can be challenging. That's one of the reasons we're involved in initiatives like the <a href="https://www.wevolver.com/article/global-photonics-engineering-contest-2026?utm_source=article&amp;utm_medium=xiver&amp;utm_id=Photondelta">Global Photonics Engineering Contest 2026</a>: helping innovators think not only about what is technically possible but also about what can be successfully manufactured and commercialized.In short, most companies engage a manufacturing partner after they've proven the technology. The most successful ones engage much earlier, when the design is still flexible, and manufacturing considerations can be built in from day one.Peter (PhotonDelta): XIVER talks about concurrent engineering from day one. How does your approach differ from a traditional manufacturing partner?Sree (XIVER): The traditional model is often quite sequential. A customer develops a technology, completes the design, proves the concept, and then hands it over to a manufacturer to figure out how to make it. By that point, many key decisions have already been made, and if manufacturing challenges emerge, the only option is often to go back and redesign.At XIVER, we believe manufacturing expertise should be part of the development process from day one. That's what we mean by concurrent engineering. Instead of designing first and industrializing later, we work on both in parallel.That means our process engineers, manufacturing specialists, and customer development teams are collaborating from the earliest stages. While the customer is optimizing performance, we're already thinking about process compatibility, yield, scalability, reliability, and cost. Those considerations influence design decisions long before the product reaches production.A good example is when a customer develops a microsystem that performs exceptionally well in a laboratory environment. In a traditional hand-off model, the manufacturer might discover later that certain structures are difficult to produce consistently at scale, resulting in yield losses or process complexity. With concurrent engineering, those manufacturability questions are addressed upfront, so the design evolves with volume production in mind from the beginning.Ultimately, we see ourselves as more than a foundry. We're a development and industrialization partner.Peter (PhotonDelta): How does DfM look different for integrated photonics versus Micro-Electro-Mechanical Systems (MEMS) applications? Are there integrated photonics-specific constraints that engineers often underestimate?Sree (XIVER): There are similarities between MEMS and integrated photonics when it comes to DfM. In both cases, success depends on translating a promising device concept into something that can be fabricated reproducibly. But the manufacturing sensitivities are often quite different.Compared to MEMS, constraints in integrated photonics can be much less visible, which is why they're sometimes underestimated. Small process variations that are barely noticeable from a manufacturing perspective can have a significant impact on optical performance. One misconception we occasionally encounter is that once the optical design has been simulated successfully, the manufacturing part will be straightforward. Photonic devices are often extremely sensitive to process variation. Designing for nominal performance is not enough, you also need to design for manufacturing tolerances.Another area that's frequently underestimated is packaging and integration. In many photonic applications, the chip itself is only part of the challenge. Efficient optical coupling, alignment tolerances, thermal management, testing, and integration into a larger system can become just as important as the photonic circuit design itself.We also see engineers focus heavily on device-level performance while underestimating questions such as scalability, testability, and production yield. A design that achieves record performance in a demonstration environment may not be the design that can be manufactured economically in larger volumes.That's why at XIVER we try to bring manufacturability considerations into the discussion very early. We don't just consider whether a photonic device can be fabricated, we consider whether it can be fabricated repeatedly, with stable performance, acceptable yield, and a realistic path to production.This is particularly relevant as integrated photonics moves into larger commercial markets such as high-speed data communications, sensing, AI infrastructure, and healthcare. The industry is increasingly shifting from proving what is technically possible to proving what can be produced reliably at scale.Suggested reading: <a href="https://www.wevolver.com/article/transforming-sensing-with-photonic-integrated-circuits">Transforming Sensing with Photonic Integrated Circuits</a>That's also one of the reasons we're involved in initiatives like the <a href="https://www.wevolver.com/article/global-photonics-engineering-contest-2026?utm_source=article&amp;utm_medium=xiver&amp;utm_id=Photondelta">Global Photonics Engineering Contest 2026</a>. Helping innovators think about manufacturability, integration, and scalability from the beginning is exactly where XIVER can add value.In short, MEMS DfM is often about controlling mechanical structures and process robustness. Integrated photonics DfM is frequently about managing extremely tight optical tolerances, packaging, integration, and performance variation; areas that many teams underestimate until they start scaling toward production.Peter (PhotonDelta): If you could give one piece of advice to teams participating in the <a href="https://www.wevolver.com/article/global-photonics-engineering-contest-2026?utm_source=article&amp;utm_medium=xiver&amp;utm_id=Photondelta">Global Photonics Engineering Contest 2026</a>, what would it be?Sree (XIVER): Don't just ask yourself, 'Can I build it?' Ask yourself, 'Can I build it a thousand times, reliably and economically?'Innovation doesn't end when the prototype works; it begins when you can manufacture it.Key Takeaways for Integrated Photonics Innovators<ul><li>Technical performance is necessary, but not sufficient. A device that works in the lab must also work on the production line, repeatedly, reliably, and economically.</li><li>Design for Manufacturing from day one. Photonic devices are highly sensitive to process variation.</li><li>Engage manufacturing partners early. The most costly mistakes in photonics industrialization happen when design decisions are locked in before manufacturing expertise enters the conversation.</li></ul>Ready to scale your photonic innovation? Learn how XIVER helps teams bridge the gap from prototype to production at <a href="http://xiver.com">xiver.com</a>.

XIVER's Sreekanth Chirayath on why photonic innovations fail on the production line and how designing for manufacturing from the start changes the outcome.

A Great Idea Is Not Yet a Product: XIVER on Industrializing Integrated Photonics Innovations

The next agricultural revolution is here. Climate volatility, shrinking labor availability, and the digitization of farms are reshaping how growers operate, but they’re also redefining the technologies that are needed to sustain global food production. What was once a largely mechanical industry is now a distributed network of sensors, edge devices, wireless communication systems, and automated decision‑making tools.For <a href="https://components.omron.com/us-en/" target="_blank" rel="noopener noreferrer">Aratas</a>, this shift is more than a market trend. It’s an engineering opportunity that aligns directly with the company’s core strengths in sensing, connectivity, and ruggedized component design. Drawing on decades of experience across industrial automation, the Internet of Things (IoT), and environmental monitoring, Aratas is building the foundational technologies that will enable growers to operate with greater precision, resilience, and autonomy.This article explores the major technology shifts shaping smart agriculture, the impact of climate change and workforce constraints, the evolution of communication and data management architectures, and the growing role of long range (LoRa), low‑power sensing. It also highlights how Aratas is applying its cross‑industry expertise to deliver reliable, field‑ready solutions for the next generation of climate‑smart agriculture.<h2>Smart Agriculture: Sensors are the New Infrastructure</h2>Agricultural advances need to maximize yield and efficiency while minimizing resource use and operational risk. From planting to harvesting to machine monitoring, growers want solutions that will help them optimize workflows. That means automation is no longer optional. Instead, it’s the backbone of modern farm management. Sensors make intelligent, flexible automation possible by measuring physical quantities and turning them into digital signals.  <a href="https://components.omron.com/us-en/products/sensors" target="_blank" rel="noopener noreferrer">Smart agriculture uses different types of sensors,</a> but there’s a common thread. They’re all related to environmental monitoring. Growers who leverage data about soil temperature, rainfall, wind, humidity, and soil radiation can make better decisions about irrigation, fertilization, pest management, and equipment scheduling. This environmental data provides the contextual intelligence that’s needed to interpret soil conditions, crop stress, and machine health.Aratas’s Weather Sensor platform isn’t designed to be a standalone device. Instead, it’s a core building block for distributed agriculture sensing networks. This <a href="https://components.omron.com/us-en/solutions/sensor/weather-sensor" target="_blank" rel="noopener noreferrer">all-in-one environmental sensing unit</a> provides real-time measurements of key environmental variables. Moreover, it’s designed for remote, harsh, and low-maintenance deployments such as rural locations and large agricultural fields.   <h2>Climate Volatility Is Redefining Agricultural Requirements</h2>Climate change isn’t a future threat. Growers are already facing climate volatility that’s disrupting regular operations. Extreme heat, unpredictable rainfall, and severe year-to-year variability are forcing farms to abandon long-standing assumptions about seasonal patterns. Some are still using climate models that are decades old, but they need devices that use live weather data to predict weather conditions with greater accuracy.This is a profound change. Historically, agricultural planning depended on historical averages. Climate instability has rendered those models insufficient, however, and growers now require:<ul><li>Real‑time, hyperlocal environmental data</li><li>Continuous monitoring rather than periodic sampling</li><li>Automated alerts and decision support</li><li>Predictive analytics based on live sensor inputs</li></ul>These requirements place new demands on sensor accuracy, durability, and communication reliability. Devices must withstand extreme weather, operate autonomously for long periods, and deliver actionable data without requiring constant maintenance. The Aratas Weather Sensor was engineered for this new reality and provides real-time measurements of temperature, humidity, barometric pressure, wind speed, wind direction, and rainfall.  <h2>Ruggedization, Low Power, and Zero‑Maintenance Design</h2>Agricultural sensors must withstand conditions that would degrade conventional instrumentation. Dust, mud, sunlight, temperature swings, and mechanical vibrations are everyday realities in the field. There are other challenges as well. Remote locations often lack grid power, and cable installation is time-consuming. That’s why the Aratas Weather Sensor doesn’t require nearby grid connections or cables. It can also transmit data over long distances and consumes just 1.5W.  Developing this sensing technology meant tackling multiple challenges head-on. To enable deployment in remote locations without access to grid power, the Weather Sensor is optimized to run on small, inexpensive solar panels. This wireless device is also built for rapid deployment, minimizing installation time and the need for specialized technicians. In addition, it overcomes challenges associated with traditional anemometers and tip buckets that are used to measure weather conditions.Anemometers measure wind speed or velocity. Tip buckets are a type of rain gauge that measure the amount of precipitation. Both are subject to degradation from dirt buildup and mechanical fatigue. In designing a non-contact weather sensor, Aratas’s engineers chose ultrasonic sensing for wind speed and wind direction. They also implemented optical rainfall detection with a proprietary calculation algorithm. This reduces maintenance requirements and saves precious labor.<h2>Labor Shortages are Driving Automation and Remote Monitoring</h2>Agriculture is facing a labor shortage. An aging workforce, rising labor costs, and the physical demands of farm work are pushing growers toward automation. Farmers who understand what’s happening in their fields have a business advantage, but making the rounds is time-consuming and labor-intensive. Remote monitoring reduces the need for manual field checks and enables:<ul><li>Fewer site visits</li><li>Faster response to environmental changes</li><li>Automated irrigation and fertigation</li><li>Better allocation of limited labor resources</li></ul>Despite its advantages, remote monitoring only works if the underlying sensors are reliable, autonomous, and capable of long-range communication. This highlights one of the most important technology shifts in modern agriculture: the evolution of communication protocols. As farms become digitized, the challenge is not just about collecting data. It’s about transmitting, aggregating, and managing this data across large, distributed environments.   <h2>From Short-Range Serial to Long‑Range Wireless Communications</h2>RS‑232 and RS‑485 are serial communications standards that are used commonly in agricultural environments.  RS-232 is limited to about 15 meters. RS-485 can cover up to 1200 meters but still requires cables. Because many farms are in rural areas, there are limited opportunities for network connections. Plus, Narrowband Internet of Things (NB-IoT) and other cellular technologies are expensive. They also require high-voltage power.These realities create a set of related constraints. There are long distances between sensors and gateways, limited power availability, and sparse or unreliable cellular coverage. To address these challenges, the Aratas Weather Sensor supports a range of communication options – including LoRa. This long-range, low-power wireless standard is increasingly used on large-acreage farms because of its ability to transmit data over kilometers with only milliwatts of power.Originally, Aratas’s engineering team explored Bluetooth, Long Term Evolution (LTE), and Category M1 (Cat M1, LTE-M) cellular connectivity. The Weather Sensor supports all these technologies, but it’s not limited to a single standard. This flexibility ensures that Aratas sensors can operate in agricultural environments ranging from small research plots to large commercial farms that span thousands of acres. <h2>Why LoRa Is Foundational for Smart Agriculture</h2>LoRa’s growing popularity isn’t accidental. It’s the result of strong alignment between the protocol’s strengths and agriculture’s needs. Among its advantages, LoRa supports:<ul><li>Long‑range communication of up to 10–15 km in rural environments</li><li>Ultra‑low power consumption that’s suitable for solar‑powered sensors</li><li>High penetration, even with vegetation and rough terrain</li><li>Low infrastructure cost compared to cellular or satellite networks</li><li>Scalability for hundreds or thousands of nodes</li></ul>As a result, LoRa is used in agricultural applications ranging from weather stations to soil moisture networks. LoRa-enabled sensors are also used in livestock trading, irrigation automation, pest monitoring systems, and distributed machine-health monitoring. To expand the capabilities of its weather sensor platform, Aratas partnered with KS Technologies, an engineering firm that specializes in IoT hardware and firmware.The partnership with KS Technologies enabled Aratas to deliver pre-validated LoRa connectivity, seamless integration with Long Range Wide Area Network (LoRaWAN) gateways, optimized power management, and robust long-range performance. However, LoRa isn’t just a communications option. It’s a strategic enabler for Aratas’s broader automation roadmap in agriculture, and the innovations keep coming.<h2>Edge Computing: The Next Frontier for Smart Agriculture</h2>As sensor networks grow, the amount of raw data becomes too great to transmit continuously. By processing data locally at the sensor or the gateway, edge computing reduces bandwidth requirements and enables real-time decision-making. Today, edge computing technologies that work with climate data are essential for optimizing operations. The benefits of edge computing for agriculture include:<ul><li>Real‑time alerts for frost, heat stress, or irrigation needs</li><li>Local decision loops for automated irrigation or ventilation</li><li>Reduced data transmission costs</li><li>Improved reliability in low‑bandwidth environments</li></ul>When combined with LoRa, edge computing enables a hybrid architecture where sensors collect and pre-process data, gateways aggregate and analyze trends, and cloud platforms handle long-term analytics and machine learning. For scalable, resilient agricultural automation, this layered approach is essential. So is Aratas’s ability to transfer its engineering insights across industries.<h2>Cross‑Industry Innovation: How a Broad Portfolio Informs Agricultural Solutions</h2>The weather sensor platform isn’t an isolated development. Instead, it’s the result of years of innovation in IoT, industrial sensing, and environmental monitoring. The basis for this device was an IoT evaluation board that Aratas had developed. This board was powered by universal serial bus (USB) technology and used similar functionality and algorithms.  Aratas’s cross-industry approach accelerates development and ensures that its agricultural solutions benefit from proven sensing algorithms, mature component libraries, and deep expertise in low-power design. The company also uses well-established reliability testing frameworks and leverages its experience in relays, switches, connectors, and industrial sensors. This informs its approach to ruggedization, sealing, and long‑term reliability—critical attributes for agricultural environments.<h2>The Future of Smart Agriculture: Autonomous, Connected, and Climate‑Adaptive</h2>The agricultural sector is moving toward a future where sensors operate autonomously for years, wireless networks cover entire farms, and edge devices make real-time decisions. Cloud platforms will optimize long-term strategy, automation will compensate for labor shortages, and climate-adaptive systems will respond dynamically to environmental change.      Aratas is positioning itself at the center of this transformation by delivering foundational technologies that make climate‑smart agriculture possible. Sensing, connectivity, power management, and ruggedized components are all critical. The company’s engineering‑driven approach, combined with its cross‑industry expertise, ensures that its solutions are not just technologically advanced but practical, reliable, and ready for deployment.Smart agriculture is no longer a niche. It’s the operating system of modern farming. As climate pressures intensify and labor shortages deepen, the need for robust, autonomous sensing and communication systems will only grow. Aratas’s low‑power architectures, long‑range communication, zero‑maintenance sensing, and flexible interoperability are helping growers build more resilient, efficient, and sustainable operations.The <a href="https://components.omron.com/us-en/solutions/sensor/weather-sensor#cont02" target="_blank" rel="noopener noreferrer">future of agriculture will be defined by data, automation, and connectivity</a>. Aratas is engineering the components that make that future possible.

How Aratas Is Enabling Climate Smart Data Driven Farming

Engineering the Future of Smart Agriculture

A Q&A with Epiphany Design and Bright Photonics on transforming PIC concepts into manufacturable systems, with practical guidance for teams participating in the Global Photonics Engineering Contest 2026.

Inside Photonic Chip Design: Perspectives from Epiphany Design and Bright Photonics

<h3>Interview with Mario Mauerer</h3>Robotics is entering a new phase. The industry conversation is no longer centred on whether robots can perform impressive demonstrations in controlled environments, but whether they can operate reliably, safely, and efficiently in the real world.Across industrial automation, logistics, humanoids, service robotics, surgical systems, and autonomous mobility, robotics teams are facing the same reality: success depends on far more than intelligence alone. Motion systems, actuator integration, thermal management, reliability, safety, and system architecture all play a critical role in determining whether a robot becomes a scalable product or remains a promising prototype.As the Global Business Development Manager, Robotics, at maxon, Mario Mauerer works closely with robotics developers across a broad range of applications. In this interview, he shares his perspective on what separates successful robotic deployments from demos, why motion-system design must happen early, and what robotics teams should prioritize as the industry scales into increasingly complex real-world environments.<h2>Robotics Beyond the Demo</h2>Robotics is advancing quickly, but real-world deployment still looks very different from the lab. In your view, what separates robots that demo well from robots that succeed in the field?Robots that work well in the field typically show one or both of two traits. First, they are designed for a very narrow and often relatively simple task, such as transporting materials, moving carts, or performing repeatable inspections. Second, they are deployed in controlled environments, with close collaboration between the robotics company and the customer.According to Mauerer, successful deployment is only partially about the robot itself.“An equally important aspect is the business approach of the deploying company,” he explains. “Tasks must be sharply defined, product understanding must be consciously nurtured, and growth has to be sensible.”He believes one of the biggest risks for robotics companies is scaling too aggressively before systems are truly ready for deployment.“Companies cannot sell robots that barely work too aggressively,” he says. “Otherwise, their hardware will be overtaken by real-world disappointment.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1781112100914-1781112100914.jpeg" width="602" height="348.914572808456" class="fr-fic fr-dii" />Across today’s robotics landscape, industrial robots, mobile robots, humanoids, service robots, and others, what engineering challenges keep showing up regardless of application?Mauerer points out that robotics remains one of the most multidisciplinary engineering fields today.“The challenges span the entire software stack, locomotion, perception, autonomy, manipulation, as well as hardware, including actuators, power electronics, compute, communication, batteries, mechanics, polymers, fabrication, and supply chain,” he says.Among those challenges, motion systems remain especially important.“Actuators are a very critical aspect because they are everywhere in modern robots,” Mauerer explains. “They need to operate reliably and with high performance while still remaining cost-effective.”That balance between torque, velocity, efficiency, controllability, robustness, and cost continues to define many of the industry’s toughest engineering trade-offs.There is a lot of attention on AI and autonomy right now. What motion-related realities do teams still underestimate when turning robotic intelligence into physical performance?For Mauerer, safety remains one of robotics’ most underestimated challenges.“Robots are terribly strong, while humans and surrounding equipment are often comparatively soft and weak,” he says.As robots move into broader deployment over the next several years, he believes functional safety and safe-motion architectures will become increasingly important.“There are not yet many complete solutions on the market today,” he notes. “But a lot of strong ideas are emerging, and they will become highly relevant very quickly.”While AI continues to improve perception and autonomy, translating intelligence into safe and dependable physical behavior remains one of the industry’s biggest engineering hurdles.From your perspective, what are the clearest signs that robotics as an industry is maturing from exciting prototypes into dependable deployed systems?Mauerer believes the industry is still in the early stages of a larger transition.“Robotics is moving from controlled and repeatable settings toward unstructured environments and more general-use applications,” he explains.He points to examples such as autonomous cleaning systems, boardwalk delivery robots, and autonomous vehicles as early indicators of that shift.“But there is still so much more to come,” he says. “Robots will increasingly become part of everyday environments, powered by autonomous machine-learning systems.”Even with the momentum around robotics and AI, Mauerer emphasizes that widespread deployment remains a long-term engineering challenge rather than a solved problem.“It’s an exciting time,” he says, “but there is still a tremendous amount of work ahead.”<h2>Motion-System Design in Practice</h2>From a drive-system perspective, what are the first questions robotics engineers should ask when moving from concept to system architecture?Mauerer believes robotics teams should begin by defining the broader system architecture before selecting individual components.“What gearing technologies will be used? What communication architecture? What power supply structure? What thermal-management concept? What peak and continuous performance levels are required?” he asks.Only after those system-level decisions are made should teams begin selecting actuators.“At maxon, we focus heavily on highly compact and cost-effective actuator integration,” he explains. “Combined with flexible firmware, that allows robotics teams to build systems that truly fit their architectural needs.”Because maxon works across many robotics sectors, Mauerer says the company often serves as a close engineering partner during actuator-system integration.“We have a lot of experience helping customers create and integrate robotics actuation systems effectively.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1781112100921-1781112100921.jpeg" width="602" height="339" class="fr-fic fr-dii" />Once those requirements are defined, how should robotics teams think about the trade-offs between velocity, precision, energy efficiency, thermal limits, packaging constraints, and overall system performance?According to Mauerer, those trade-offs must be considered as part of the complete robot architecture rather than solved independently.“Teams first need to understand the trade-offs themselves,” he says, “and then select actuators that align with the intended design philosophy.”Simulation has become an increasingly valuable tool in that process.“Our simulation models help customers make these decisions in a data-driven way,” Mauerer explains. “One of our core ideas is: ‘Simulate a lot, build once.’”By improving early-stage modeling and reducing the sim-to-real gap, robotics teams can minimize redesign cycles while extracting significantly more performance from final hardware.What tends to go wrong when motion-system decisions are made too late in the design process, or when they are treated as a component choice rather than a system-level design decision?Mauerer’s response is immediate.“Everything,” he says.Because actuators influence nearly every aspect of robot behavior, including packaging, thermal management, structural dynamics, power distribution, and controllability, late-stage changes often require extensive redesigns.“It’s absolutely critical to consider actuators from the beginning,” he explains. “Once hardware is locked in, changing these decisions becomes extremely difficult.”How important is it to evaluate the full drive system motor, gearhead, encoder, controller, and software behavior together, and how has Maxon’s system-level work shaped the way you think about that integration?Mauerer sees robotics steadily moving away from isolated subsystem components and toward tightly integrated actuator platforms.“This greatly simplifies system-level discussions and supply-chain complexity,” he says. “It also allows robotics companies to move faster with development and deployment.”One of maxon’s goals, he explains, is to abstract much of the underlying motion-system complexity away from customers.“That allows robotics developers to focus more directly on their actual value proposition instead of dealing with every low-level motion detail,” Mauerer says.<h2>Reliability and Real-World Constraints</h2>Many robots today are expected to operate for long hours, in tight spaces, and under demanding duty cycles. What design considerations matter most when reliability and continuous operation are essential?Mauerer believes actuator robustness is one of the defining factors behind reliable robotics deployment.He points specifically to:<ul><li>Overload Tolerance</li><li>Thermal  Resilience</li><li>Ingress Protection</li></ul>“If one actuator fails, the entire robot often goes down,” he explains.That challenge becomes especially significant given the scale of modern robotic systems.“Many robots today contain between 12 and 30 high-torque actuators operating continuously under mechanical and environmental stress.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1781112100925-1781112100925.jpeg" width="602" height="339" class="fr-fic fr-dii" />How do environmental realities shock, vibration, contamination, temperature, or limited installation space, change the way you think about actuator and joint design?According to Mauerer, those environmental realities must be considered during the earliest stages of actuator development.“These factors need to be engineered into the actuator itself,” he says.At maxon, many drive systems undergo testing for shock, vibration, ingress protection, and temperature performance during development.“That allows customers to avoid having to solve many of those challenges independently,” Mauerer explains.For engineers building robots that interact closely with people, equipment, or sensitive processes, how do precision and controllability influence overall system safety and usability?Interestingly, Mauerer believes overall safety architecture matters far more than precision alone.“The broader system-level safety architecture is significantly more important than the precision or controllability of the drive itself,” he explains.What matters most at the actuator level is whether drives can support functional safety concepts such as safe motion.“That requires very careful hardware and software decisions,” he says.How valuable are simulation and early system modelling today in reducing integration risk before hardware is finalized?Mauerer sees simulation as one of the industry’s most important development accelerators.“Teams should iterate much less on hardware and much more in simulation,” he says.Again, he returns to the same guiding principle:“Simulate often, build once.”Accurate actuator models, he explains, are especially important for reducing the gap between simulation and real-world robotic performance.“That’s how teams extract the final 20 percent of performance from a robot.”<h2>Cross-Industry Lessons and What Comes Next</h2>When you look across different robotics sectors, what lessons has maxon been able to carry from one application area to another, and where do requirements become fundamentally different?maxon supports robotics applications ranging from humanoids and AGVs to surgical robots and space systems.“Every application has unique requirements,” Mauerer says. “What makes maxon special is the ability to engineer and manufacture systems that can support such diverse robotics needs.”That broad exposure allows the company to transfer lessons between industries while adapting actuator technologies to highly specialized environments.Logistics and mobile robotics demand high uptime, compactness, and precise positioning. What does that application area reveal about broader robotics design priorities?Mauerer sees logistics and mobile robotics as reinforcing many of the same reliability priorities discussed earlier.High uptime, robust actuator design, thermal resilience, and compact integration all become increasingly important when robots must operate continuously in demanding environments.As robotics deployments scale, those requirements are becoming common across many application categories not just logistics.Looking ahead, what do you think will matter most for robotics teams over the next few years as systems move from impressive prototypes to dependable, scalable deployments?Mauerer believes long-term robotics success will depend heavily on disciplined execution rather than technology hype alone.“Teams need to build strong supply chains, make smart make-versus-buy decisions, and focus on the actual value generated by the robot,” he says.He also emphasizes the importance of selecting dependable engineering partners that can scale from prototyping into high-volume production.“maxon aims to be that type of global actuation partner,” Mauerer explains, “supporting robotics companies from early development through scalable manufacturing.”As robotics systems continue moving into less structured and more dynamic environments, Mauerer believes the industry’s next phase will be defined not by demonstrations, but by dependable real-world performance.<a href="https://www.maxongroup.com/en-us/market-solutions/mobility-solutions/robotics">See what maxon can do.</a>

Mario Mauerer, maxon's Global Business Development Manager, Robotics, discusses what it takes for robotic deployments to be successful in complex real-world environments.

Robots in the Real World: What It Takes to Build Motion Systems That Deliver

<h2 id="isPasted">Introduction</h2>The demand for high-performance integrated photonic systems continues to grow, driven by applications such as AI, datacom, and sensing. These requirements place increasing emphasis on integration, performance, and efficiency at platform level.Indium phosphide (InP) is widely used in integrated photonics, enabling compact and high-speed systems. To explore how such systems are designed and brought from concept to fabrication, we sat down with Erik Luyk from SMART Photonics for a Q&amp;A session on working with InP-based photonic integrated circuits (PICs). SMART Photonics operates as an independent foundry specializing in InP integrated photonics.This article is brought to you by PhotonDelta, a foundation  focused on accelerating the growth and the maturity of the Integrated Photonics industry Through initiatives such as the <a href="https://www.wevolver.com/article/global-photonics-engineering-contest-2026?utm_source=article&amp;utm_medium=smart&amp;utm_id=Photondelta">Global Photonics Engineering Contest 2026</a>, PhotonDelta creates opportunities for engineers and researchers to translate ideas into real photonic hardware.<h2>Why InP Is the Platform of Choice</h2><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzc4NTEyMTE0MTU2LTE3Nzg1MTIxMTQxNTYuanBlZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" width="740" height="185" class="fr-fic fr-dii" />Peter (PhotonDelta): What makes InP fundamentally different from other photonic platforms?Erik (SMART Photonics): InP is the only material that has the physical properties to generate, modulate, and transfer light signals, allowing the integration of all photonic components on a single chip. This way we can make smaller, faster, and energy-efficient chips for our customers.<iframe width="640" height="360" src="https://www.youtube.com/embed/gagV1LjOCMU?&amp;wmode=opaque" frameborder="0" class="fr-draggable">       </iframe>This capability simplifies system architectures significantly. Instead of combining multiple materials, designers can implement complete integrated photonic systems monolithically. This means fewer constraints when translating a concept into a manufacturable design.Peter (PhotonDelta): Which applications require InP (not just benefit from it)?Erik (SMART Photonics): Quite a few, like AI, data &amp; telecom, and structural health monitoring &amp; sensing. But especially datacenters, of course, due to the rapid rise of AI.In data center environments, the need for high bandwidth and low energy consumption is pushing conventional electronic interconnects to their limits. InP-based PICs provide a path forward by enabling high-speed optical communication with improved efficiency. Similarly, sensing applications benefit from the ability to integrate sources, detectors, and modulation elements on a single platform.Peter (PhotonDelta): Where does InP deliver the biggest performance gains today?Erik (SMART Photonics): Full integration of all active and passive components on a chip and therefore efficiency in energy and cost. This level of integration directly impacts system-level performance by reducing losses, improving reliability, and lowering overall system complexity. <a href="https://www.wevolver.com/article/global-photonics-engineering-contest-2026?utm_source=article&amp;utm_medium=smart&amp;utm_id=Photondelta" target="_blank" rel="noopener noreferrer"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzc4NTEyMTc5NTMyLWltYWdlMS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:100%px" class="fr-fic fr-dib" /></a>For contest participants, this highlights an important design consideration: choosing a platform that aligns with both performance goals and integration requirements.<a href="https://www.wevolver.com/article/global-photonics-engineering-contest-2026?utm_source=article&amp;utm_medium=smart&amp;utm_id=Photondelta" class="button-main-action">PARTICIPATE NOW</a><h2>Designing with InP: From Idea to First Chip</h2>Designing a PIC involves multiple stages, from initial concept to fabrication and testing. While the specifications vary depending on the application, the general workflow reflects a structured approach that is increasingly accessible to a broader range of engineers.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzc4NTEyMjI1MTYwLWltYWdlLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width:100%px" class="fr-fic fr-dib" />A typical design cycle includes:<ul><li>Defining the application and performance targets</li><li>Designing the circuit using available software tools and building blocks</li><li>Validating the design through simulation</li><li>Preparing the layout for fabrication</li><li>Manufacturing through a foundry</li><li>Packaging and testing the final chip</li></ul><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzc4NTEyMjM0MDk3LWltYWdlNC5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:100%px" class="fr-fic fr-dib" />Cleanroom environment at SMART Photonics for PIC fabrication. Source: SMART PhotonicsSMART Photonics operates within an ecosystem that includes software providers, design houses, and packaging partners, enabling a collaborative approach to PIC development.Peter (PhotonDelta): How do your Process Design Kits (PDKs) simplify PIC design?Erik (SMART Photonics): PDKs provide customers with a set of standardized ‘building blocks’ that perform certain functions on a chip and can be combined in multiple ways, leading to very different functionalities across a broad range of end-user markets. We have 50+ proprietary building blocks in our PDK with 10+ years of experience and knowledge.When we work with our customers to create photonic chips, we make the process quicker, cost-effective, and more efficient by reusing these proven building blocks, photonic components that have already been tried and tested. By abstracting the complexity of fabrication, PDKs allow designers to focus on system-level functionality rather than low-level process details. This is particularly valuable for engineers who may not have deep expertise in semiconductor manufacturing but want to develop working integrated photonic systems.<h2>From Prototype to Real-World Deployment</h2>Moving from a prototype to a deployable product introduces additional challenges related to manufacturing, quality, and scalability. This is where the role of a foundry becomes critical.Peter (PhotonDelta): What changes when moving from prototypes to volume production?Erik (SMART Photonics): Rather than new breakthroughs, scaling depends on a couple of things: experience, effective feedback loops, and close cooperation across the ecosystem.We have the experience and expertise to scale production to larger wafer sizes—from 2-inch to 3-inch, and later from 3-inch to 4-inch. In the near future, we will take the same step towards 6-inch wafers, together with TNO, all while enhancing yield and performance.On completion in 2028, SMART Photonics’ 6-inch pilot line is expected to produce around 10,000 wafers per year, corresponding to millions of photonic chips.The pilot line will be hosted by TNO and built at the High Tech Campus Eindhoven. It is part of the EU’s PIXEurope project. It marks an important step towards industrial-scale production – and shows how scaling is not driven by technology alone, but by the experience, feedback loops and ecosystem cooperation behind it.Peter (PhotonDelta): How do you ensure consistency and quality?Erik (SMART Photonics): Producing a wafer with photonic chips requires a series of about 450 complex steps. Ramping up production means optimising each of these steps. The challenge of ensuring all chips on a wafer function flawless becomes greater as the wafer size increases. A lot hinges on finding the right settings.When depositing plasma on a wafer as part of the lithography process, for instance, a specific voltage is applied to the wafer. This voltage determines the speed with which molecules are pulled to the wafer to create a layer of plasma. The challenge is to find the optimal voltage level. Spreading the layer equally over the wafer is another challenge. One needs a keen sense of touch for that, something our engineers gathered over the years.Peter (PhotonDelta): What role does a pure-play foundry model play here?Erik (SMART Photonics): As an independent foundry, SMART Photonics focuses on manufacturing photonic components and integrated circuits for its customers. This model allows customers to retain their intellectual property while leveraging shared infrastructure and expertise.A mature platform with proven processes and building blocks reduces the number of design iterations required and enables faster transition from concept to production.In addition, scaling capabilities are continuously evolving. Currently, SMART Photonics produces on 4-inch wafers (max capacity 5000 wafers) and is expanding to 6-inch wafer production in collaboration with TNO, with a pilot line expected to be finalized around the end of 2027.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzc4NTEyMzA4Njk2LWltYWdlMi5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:100%px" class="fr-fic fr-dib" />InP wafer with multiple PIC designs patterned for fabrication. Source: SMART PhotonicsFor early-stage designs emerging from events like the <a href="https://www.wevolver.com/article/global-photonics-engineering-contest-2026?utm_source=article&amp;utm_medium=smart&amp;utm_id=Photondelta">Global Photonics Engineering Contest</a>, this kind of infrastructure provides a pathway from prototyping to eventual scaling.Together we can bring your innovation to life!<h2>Conclusion</h2>InP continues to play a critical role in advancing integrated photonics, offering the ability to generate, modulate, and transfer light signals within a single platform. This capability enables the development of compact, high-performance, and energy-efficient integrated photonic systems across applications ranging from AI-driven data centers to sensing.Foundries such as SMART Photonics occupy a key position in this ecosystem, supporting the transition from design to fabrication and ultimately to scalable production.Initiatives like the <a href="https://www.wevolver.com/article/global-photonics-engineering-contest-2026?utm_source=article&amp;utm_medium=smart&amp;utm_id=Photondelta">Global Photonics Engineering Contest 2026</a> are lowering the barrier for engineers to engage with these technologies. By providing a platform to develop and test ideas, such efforts are helping bring new innovations closer to reality.As access to advanced photonic platforms continues to expand, the combination of design ecosystems, foundry capabilities, and global initiatives is expected to play an important role in shaping the next generation of photonic systems.

Indium phosphide (InP) enables fully integrated photonic chips with high performance and efficiency. As an InP foundry, SMART Photonics supports the transition from design to scalable production through mature platforms and proven design flows.

Building InP-Based Photonic Integrated Circuits with SMART Photonics

A hardware team I once spent time with believed they were ahead of schedule.They had moved quickly. Decisions were made fast. The design came together in record time.On the schedule, everything looked right.So they pushed forward to prototype.When the boards came back, the first signs were subtle. A few components didn’t sit quite flat. Nothing dramatic – just slightly off.Then power was applied.One section of the board worked, but only under certain conditions no one could fully explain.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzc2OTQxNjM2ODI0LWRvd25sb2FkLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width:100%px" class="fr-fic fr-dib" />At first, they assumed it was a manufacturing issue. Then a component issue. Then a problem with the test setup.Days passed. Then weeks.Eventually, they traced it back to something almost invisible: a footprint had been rebuilt last minute – not incorrectly, just slightly differently.A pad spacing deviation small enough to pass visual checks. A tolerance shift that didn’t trigger warnings. A variation introduced by someone trying to move quickly and “get it done.”That single inconsistency had quietly propagated through the design.By the time it surfaced, it had already cost a full prototype cycle, weeks of engineering time, and a significant loss of confidence.Not because the team lacked capability.Because their system allowed “almost correct” to exist.Suggested reading: <a href="https://www.wevolver.com/article/when-thats-just-how-its-done-becomes-the-real-problem" target="_blank" rel="noopener noreferrer">When ''That's Just How it's Done'' Becomes The Real Problem</a><h2>Speed creates the illusion of progress</h2>Moving quickly feels productive.There is visible output. Momentum continues. Teams feel like they are advancing.But speed has a blind spot: it hides the accumulation of small, unverified decisions.Each one individually harmless. Together, a source of instability.In technical products, that instability doesn’t always show up immediately. It appears later – when the cost of fixing it is highest.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzc2OTQxNjU0NTk2LWRvd25sb2FkICgxKS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:100%px" class="fr-fic fr-dib" /><h2 id="isPasted">The real danger isn’t failure – it’s doubt</h2>When something breaks, teams can fix it.What is far more damaging is what follows: uncertainty.<ul><li>Can we trust this design?</li><li>Was that model actually correct?</li><li>Is this version the right one, or just the latest one?</li></ul>That hesitation slows everything down.People stop reusing work. They double-check everything. They rebuild things they no longer trust.What started as a small quality issue becomes a systemic drag on the entire team.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzc2OTQxNjY5NDEwLWRvd25sb2FkICgyKS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:100%px" class="fr-fic fr-dib" /><h2 id="isPasted">Standards are built through discipline, not speed</h2>High-performing engineering environments do not rely on individuals being careful.They rely on systems that make inconsistency difficult.<ul><li>Clear ownership</li><li>Repeatable processes</li><li>Defined expectations of what “correct” means</li><li>Relentless removal of ambiguity</li></ul>This often feels like friction – especially to teams used to moving quickly.But that friction serves a purpose.It prevents problems from entering the system in the first place.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzc2OTQxNjg1MDUxLWRvd25sb2FkICgzKS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:100%px" class="fr-fic fr-dib" /><h2 id="isPasted">Quality compounds over time</h2>When something is built correctly once, it can be reused with confidence.When data is trusted, decisions are made faster.When systems are consistent, teams stop second-guessing themselves or each other.Over time, this produces a different kind of speed.Not reactive speed – fixing problems under pressure.But sustained speed – where complexity increases without things breaking.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzc2OTQxNjk5ODE2LWRvd25sb2FkICg0KS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:100%px" class="fr-fic fr-dib" /><h2 id="isPasted">Where AI fits – and where it doesn’t</h2>AI is increasingly being positioned as the solution to engineering bottlenecks.Generating designs. Automate workflows. Accelerate development.And it will – in the right conditions.But AI does not introduce discipline. It reflects the system it operates within.If inputs are inconsistent, AI will scale that inconsistency.If standards are unclear, AI will reproduce that ambiguity – faster than any human could.AI is a multiplier.It strengthens well-structured systems. It destabilises weak ones.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzc2OTQxNzE0OTA2LWRvd25sb2FkICg1KS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:100%px" class="fr-fic fr-dib" /><h2 id="isPasted">The real decision</h2>This is not a choice between speed and quality.It is a decision about where the cost is paid.<ul><li>Upfront – through discipline, structure, and consistency</li><li>Or later – through delays, rework, and loss of trust</li></ul>The first feels slower.The second is slower.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzc2OTQxNzI3MDg0LWRvd25sb2FkICg2KS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:100%px" class="fr-fic fr-dib" /><h2 id="isPasted">Final thought</h2>The teams that build the most reliable technical products are not the ones who move fastest at the start.They are the ones who remove doubt from their system.Because once a team trusts what it has already built, everything that follows becomes easier.And in engineering, that trust is where real speed comes from. Having the confidence your products will work first time. <img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzc2OTQxNzM5NDg4LWRvd25sb2FkICg3KS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:100%px" class="fr-fic fr-dib" /><hr />Alex MacDougall is the founder of SamacSys and author of <a href="https://alexmacdougall.com/built-not-given-book/" target="_blank">Built, Not Given</a>, a forthcoming book about building a global engineering technology business from nothing—and the setbacks, decisions and lessons that shaped the journey. Learn more about Built, Not Given at: <a href="https://alexmacdougall.com/built-not-given-book/" target="_blank" class="button-main-action">AlexMacDougall.com</a>

Standards aren't formed by how fast you move. They're formed by what you refuse to let slip.

Why Quality Systems Matter More Than Speed in Technical Products

For most of the past decade, embedded system design operated within a reasonably stable set of assumptions. Engineers optimized for performance, cost, and power consumption. The bill of materials (BOM) was a technical document rather than a strategic one. Procurement happened downstream, often after the architecture was locked. In that era, the supply chain was frequently viewed as an external operational factor rather than a core engineering concern.That model is now under significant pressure. Today, the component supply landscape has become structurally more complex. Price adjustments are rippling through analog devices, capacitors, connectors, and industrial control products. Memory markets are being pulled in competing directions by AI-driven demand. Lifecycle changes are occurring earlier and with less predictability. The feedback loops between these pressures are often non-linear and difficult to track.Engineers who navigate this environment most effectively are those who treat supply as a design variable. This shift represents a transition from a static BOM to a dynamic risk model.<h2>The Market Reality: Convergence of Overlapping Pressures</h2>A persistent misconception in engineering circles is that supply disruptions are purely cyclical. This view suggests that after a period of volatility, the market simply stabilizes and returns to a familiar pattern. The current environment challenges that assumption.What is occurring now is a convergence of overlapping pressures. According to Enrico Hu, Regional Sales Director at WIN SOURCE, the supply landscape is becoming less about isolated events and more about a combination of factors that are difficult to anticipate. These pressures include pricing volatility in categories historically treated as stable, structural shifts in manufacturing capacity allocation, and accelerated lifecycle changes.The component categories showing the most pronounced instability are not always those that receive the most design attention.<ul><li>Memory Components: These react quickly to demand allocation shifts, particularly as AI applications absorb increasing shares of production capacity.</li><li>MCUs and PMICs: Microcontrollers and power management ICs exhibit a different kind of variability. These shifts are often slower to manifest but remain more persistent once they appear.</li><li>Connectors: Frequently treated as a low-risk commodity, connectors can have broad BOM and schedule impacts when availability tightens or pricing structures adjust.</li></ul>These categories share an exposure to supply chains that serve multiple industries simultaneously. When demand patterns across those industries diverge, the resulting imbalances create effects that are hard to predict from a single industry vantage point.<h2>Supply as a Primary Design Constraint</h2>The traditional model positioned supply considerations as a downstream concern. Once a design was finalized, procurement would source the specified parts. If a part was unavailable, the response was reactive: find an alternative, re-validate, and absorb the schedule impact.Today, supply dynamics influence design decisions at stages where they were previously invisible, such as during architecture planning. Teams are factoring supply-related questions into which architectural approaches they pursue. They are choosing to standardize on platforms with strong multi-source availability and designing around component families with stable lifecycle trajectories.<h3>Redefining the "Optimal" Component</h3>Component selection now carries a longer time horizon. Engineers are choosing parts not only for how they perform today, but for how reliably they will support the system over time. A part offering best-in-class performance that is single-sourced or approaching end-of-life carries a risk profile that was often ignored in earlier frameworks. Today, that risk profile is a central part of the selection calculus.An optimal component is now defined by three specific pillars:<ol><li>Availability Awareness: The probability that a part can be sourced through multiple channels and geographical regions over a five to ten year horizon.</li><li>Lifecycle Stability: Clear visibility into the manufacturer’s roadmap to ensure the part is not nearing Not Recommended for New Designs (NRND) or End of Life (EOL) status during peak production.</li><li>Substitution Readiness: The ease of replacing a component without requiring a complete PCB redesign or a significant firmware rewrite.</li></ol><h2>Designing for Supply Resilience</h2>In practical engineering terms, supply resilience refers to the degree to which a design can absorb component-level disruptions without requiring extensive rework. Achieving this does not require a wholesale change in design philosophy. Instead, it results from incremental decisions that collectively reduce sensitivity to supply-side changes.<h3>Footprint and Interface Compatibility</h3>When alternative components are considered at the pin-compatible or footprint-compatible level, the scope of validation work for a substitution is dramatically reduced. Selecting components where viable alternatives exist with compatible pinouts provides a meaningful buffer against single-source risk.<h3>Modular Architectures and Abstraction</h3>Modular design approaches, combined with firmware abstraction layers, allow substitutions to be managed at a lower level of the system. A robust Hardware Abstraction Layer (HAL) ensures that application logic remains decoupled from hardware specifics. This is particularly relevant for MCU and memory substitutions, where peripheral differences would otherwise require significant firmware rework.<h3>Proactive Substitution Planning</h3>The most common failure mode in substitution strategies is late introduction. When an alternative is identified only after the primary component becomes unavailable, schedule pressure is high and the risk of system-level issues is elevated. High-performing teams evaluate alternatives during initial component selection. Understanding how an alternative performs within the full system context requires validation work that is better performed proactively.<h2>Lifecycle Awareness as an Engineering Discipline</h2>EOL and NRND designations have historically been treated as procurement problems to be resolved after the design is complete. In long-lifecycle industries, this creates compounding risk.In automotive and industrial applications, where products remain in service for a decade or more, a component entering NRND status triggers a chain of consequences involving sourcing challenges, lifetime buy decisions, and redesign costs. The earlier that lifecycle risk is identified, the more options are available for management.Lifecycle considerations are becoming an ongoing process. Engineers are increasingly asking how a component’s status is likely to evolve over the full project horizon. In long-lifecycle environments, visibility expands beyond tracking status in real time towards understanding how decisions made today will unfold over a much longer horizon.<h2>AI and the Structural Market Shift</h2>The impact of AI on the component supply landscape extends well beyond high-performance computing hardware. It is creating secondary effects felt across the embedded systems market.As AI training and inference workloads drive concentrated demand for specific memory types, manufacturing capacity and allocation priorities shift. This shift affects the availability and pricing of general-purpose DRAM and NAND that embedded applications rely on. The impact is indirect but significant.The broader structural dynamic is one of divergence. High-performance computing hardware is attracting concentrated investment and capacity expansion. General embedded applications follow different, more fragmented demand patterns. These patterns do not always align with the supply infrastructure being built for AI workloads. For embedded engineers, this means that supply dynamics in adjacent markets now have meaningful implications for their own component availability.<h2>Engineering and Procurement: An Integrated Operating Model</h2>The separation between engineering and procurement functions was always somewhat artificial. Both teams make decisions that affect the same product on the same timeline with interdependent constraints. The current supply environment makes that interdependence visible.In organizations where collaboration is working effectively, the process is continuous.<ul><li>Early Engagement: Procurement is engaged during the architecture phase rather than the purchasing phase.</li><li>Shared Intelligence: Engineering teams are equipped with market signals that go beyond standard datasheets.</li><li>Collective Trade-offs: Decisions regarding cost, performance, and risk are evaluated by both functions simultaneously.</li></ul>This integrated model ensures that supply intelligence informs architecture decisions at a stage where those decisions can still be made with flexibility.<h2>The Role of Supply Intelligence</h2>With supply dynamics now influencing architecture and component selection, access to contextual market intelligence becomes critical during the design process. Availability trajectories, lifecycle signals, and emerging constraints are frequently fragmented across different sources or delayed.This gap is where design risk accumulates. It exists in the space between engineering confidence in a component's technical characteristics and incomplete visibility into how that component behaves in the market.The <a href="https://www.win-source.net/nexus-solution.html?srsltid=AfmBOoplo3_UhSd7RJDCTJPSz0nzVuldx6pMx2097PoelkQ4xCT1MR58">WIN SOURCE NEXUS™ Solution</a> addresses this gap by providing visibility into market conditions. By integrating availability and lifecycle insights with alternative component identification, it provides engineering and procurement teams with a shared view of the market. Such intelligence allows teams to move from reactive sourcing to proactive risk management, ensuring that technical decisions are made with a broader context in mind.Looking more closely, the NEXUS™ solution functions as an integrated ecosystem that modularizes supply chain resilience into five core pillars: <ol><li>INSIGHT™ for deep market analytics</li><li>SMARTBUY™ for optimized procurement</li><li>FLOWSYNC™ for streamlined logistics</li><li>FLEXCARE™ for customized service</li><li>TRUSTLINK™ for verified quality assurance.</li></ol>By utilizing big data and real-time forecasting tools, the platform allows engineers to transcend simple part-number searching. Instead, it provides a predictive environment where teams can evaluate "cross-reference" alternatives and analyze the historical volatility of a specific process node before committing to an architecture. This structural approach ensures that supply chain security is a proactive design strategy that secures the entire project lifecycle from the first prototype to long-term mass production.<h2>What Good Engineering Looks Like Now</h2>The engineering teams that navigate this environment most effectively have internalized a different set of operating assumptions. They recognize that supply variability is a design parameter and that the BOM is a risk surface requiring the same analytical attention as a thermal or power budget.Success in 2026 and beyond will come less from avoiding constraints and more from working with them in a structured way. Teams that stay flexible in their design approach, incorporate external signals into decision-making, and coordinate effectively across functions will maintain a structural advantage.The BOM has always been a technical artifact. It is now also a risk model. Treating it as such is a requirement of competent embedded system design. By acknowledging that the supply chain is an integral part of system logic, engineers can build products that are both high-performing and robust enough to withstand the shifts of a global market. Resilience is no longer just a safety net; it is a competitive edge.For more information about WIN SOURCE’s approach to embedded system design, visit <a href="https://www.win-source.net/">win-source.net/</a>.

This article explores why component availability, lifecycle, and substitution are becoming core design variables

From BOM to Risk Model: How Component Supply Trends Are Reshaping Embedded System Design

For most electronics engineers, the situation is familiar. You’ve selected the component you want to use, opened your CAD tool, and discovered that the symbol, footprint, or 3D model either doesn’t exist, doesn’t match the datasheet, or can’t be trusted. What happens next is rarely discussed. The engineer quietly builds or fixes the model themselves, checks it, and moves on. For decades, this work was accepted as part of the job. That quiet acceptance is exactly why the problem persisted for so long and created an opportunity for us to help the industry. <h2>Why The Problem Was Normalised</h2>The electronics industry didn’t ignore CAD model creation out of negligence. It persisted because responsibility was fragmented. EDA vendors focused on tools and features. Component manufacturers focused on silicon and datasheets. Distributors treated CAD data as supplementary collateral. Engineers were left to bridge the gap. Because each engineer solved the problem locally — building libraries one part at a time — the cost was never visible at a system level. It didn’t appear in project plans or budgets. It lived in evenings, workarounds, and quiet rework. When everyone carries a small burden individually, the industry never feels the weight collectively.<h2>The Real Cost Was Not Just Time</h2>It’s tempting to describe the issue as wasted hours, but that understates the impact. Manually created models introduce inconsistency across teams. They increase risk under schedule pressure. They surface errors late, when changes are expensive. In complex design environments, small inaccuracies compound. A footprint error discovered after layout can ripple into procurement delays, manufacturing issues, or late-stage redesigns. The downstream cost far exceeds the original effort of “just building the model.” The most damaging consequence was not inefficiency, but unpredictability. <h2>Seeing The Problem From The Inside</h2>For many engineers, including myself earlier in my career as a PCB designer, building CAD libraries was routine. Symbols and footprints were created manually, dimensions were checked repeatedly, and there was always the lingering concern that something subtle might have been missed. The frustration was not that the work was difficult. It was that it was repetitive, unscalable, and disconnected from the value engineers are employed to deliver. At the time, this felt unavoidable. It was simply how things were done. <h2>When a Workaround Stops Being An Edge Case</h2>Later, working closely with engineering teams across many different organisations, a pattern became impossible to ignore. <ul><li>Different companies. </li><li>Different CAD tools. </li><li>Different products. </li></ul>The same workaround appeared everywhere. Once the same behaviour repeats across dozens of teams, it stops being an isolated inconvenience. It becomes a structural issue. This was not a tooling gap. It was a data problem embedded deep within the workflow. <h2>Why Early Attempts Didn't Go Far Enough</h2>There were attempts to address parts of the problem. Some were well funded and technically competent. But most approaches treated CAD models as static files to be catalogued rather than as living, verified data inside engineering workflows. Engineers don’t want files.    They want accuracy.  They want speed.  They want trust.Without automation, standardisation, and quality control at scale, the same friction simply reappears in a different form. Suggested reading: <a href="https://www.wevolver.com/article/why-quality-systems-matter-more-than-speed-in-technical-products" target="_blank" rel="noopener noreferrer">Why Quality Systems Matter More Than Speed in Technical Products</a><h2>What Changed</h2>The shift only became possible when the problem was broken down to first principles. What does an engineer actually need at the moment a part is placed? <ul><li>Correct geometry.</li><li>Verified data.</li><li>Immediate availability. </li><li>Native compatibility with their CAD environment.</li></ul>Anything less reintroduces risk. Over time, the industry has moved toward treating CAD models as structured, managed data rather than one-off artifacts. Instead of being built repeatedly from scratch, models are now increasingly generated, verified, and delivered directly inside design workflows, aligned with manufacturer intent and maintained at scale. Platforms such as <a href="https://www.samacsys.com/" target="_blank" rel="noopener noreferrer">SamacSys</a> helped drive this shift by treating CAD models as a first-class part of the engineering data ecosystem rather than an afterthought. <h2>The Broader Lesson </h2>Many industries carry long-standing inefficiencies simply because ownership is unclear and workarounds become normalised. The most valuable problems are often the ones people stop questioning, not because they are unsolvable, but because they are familiar. Progress rarely begins with better tools alone. It begins when someone decides that a long-accepted frustration deserves to be taken seriously. This pattern is not unique to electronics. Every large organisation has its own version of the thing everyone just deals with. It might be a spreadsheet no one fully trusts, a manual approval step that exists only because it always has, or a process so fragile that only one person knows how it actually works — and everyone is quietly relieved when they are not on holiday. These issues survive not because they are difficult to solve, but because they are rarely visible at the right level. By the time leadership hears about them, they have already been normalised. My advice is simple. Pay close attention to the phrases people use when describing friction. Everyone does it this way. Its quicker if I just fix it myself. Its not ideal, but it works. Those sentences are early warning signs. They usually indicate hidden cost, unowned responsibility, and a system that relies on goodwill rather than design. Left unchallenged, they scale remarkably well — just not in a way you would ever choose if you were starting again. And a note of caution. If a problem has been around for ten or twenty years, it does not automatically mean it is hard. Sometimes it just means it has never been important enough to the right person at the right time. If you ever want a reliable source of valuable opportunities, start by asking your most capable people what they spend time on that they believe should not be their job. You will not hear about innovation or strategy. You will hear about workarounds. That is usually where the real leverage is hiding. <h2>Final Thought</h2>The real cost of “just building the CAD model” was never the time spent creating it. It was the decades spent assuming the problem couldn’t be solved. Sometimes the most meaningful improvements come not from working harder, but from finally challenging the assumptions everyone else has learned to live with. <hr />Alex MacDougall is the founder of SamacSys and author of <a href="https://alexmacdougall.com/built-not-given-book/" target="_blank">Built, Not Given</a>, a forthcoming book about building a global engineering technology business from nothing—and the setbacks, decisions and lessons that shaped the journey. Learn more about Built, Not Given at: <a href="https://alexmacdougall.com/built-not-given-book/" target="_blank" class="button-main-action">AlexMacDougall.com</a>

The hidden cost of engineers fixing what tools should provide.

When ''That's Just How it's Done'' Becomes The Real Problem

I don’t need more Dev Kits. My house is piling out with STM boards, Nordic dev kits, Arduinos (both green and black), ESP32s, Raspberry Pis, and plenty of LoRaWAN development boards I don’t even remember ordering.Prototyping has never been easier with the pletoria of available devkits, free developer accounts, and publicly available documentation. Then why don’t we focus on the real challenge of turning these prototypes into real-world products? This remains one of the most painful parts of building connected products. According to SG Wireless, <a href="https://sgwireless.com/" target="_blank">83% of IoT projects fail</a> “because they can’t handle real-world complexity.”One of the biggest complexities is crossing the gap between a POC and a scalable product.<h3>Why do we keep starting from scratch?</h3>“I think embedded development has a bad habit of wanting to build everything from scratch. And that is proven to be a pretty difficult approach” as stated by Joe Schneider during the latest<a href="https://iotstars.com/blog/modernizing-the-embedded-stack-lessons-from-iot-stars-at-ewna25" target="_blank"> IoT Stars show in Anaheim</a>.It’s a pretty difficult approach I can say, having to figure out the processes around firmware updates, secure boot, or general low-power performances blocks me from turning my many projects into a real-world product.The tooling ecosystem doesn’t help either. Embedded development remains heavily vendor-locked. Developers are nudged into STM32Cube or Microchip’s MPLAB, and each ecosystem comes with its own SDKs, HALs and build systems. Firmware written for one chipset doesn't transfer to another. Switching hardware usually means rebuilding your codebase from the ground up.Then there’s the hardware development process. From PCB design, to RF optimization, certification, to manufacturing. The upfront investment easily reaches 6+ months and hundreds of thousands of USDs.No wonder so many projects die along the way.<h3>The Rise of Software-Defined Everything</h3>Slowly but steadily a shift is happening, though. In a previous article,<a href="https://www.wevolver.com/article/the-reprogrammable-machine-the-rise-of-software-defined-iot" target="_blank"> The Reprogrammable Machine</a>, I questioned the industry’s fixation on hardware. Success in IoT rarely depends on the hardware itself; it depends on the software that powers it. The market is slowly waking up to that idea, and you can see it in the momentum behind Real-Time Operating Systems (RTOSs) like <a href="https://www.freertos.org/" target="_blank">FreeRTOS</a> or <a href="https://www.zephyrproject.org/" target="_blank">Zephyr</a>, but also on <a href="https://www.atym.io/" target="_blank">Atym</a>’s platform which is primarily based on the WebAssembly/Wasm to make binaries portable from one platform to the other.For the industry to scale, we need hardware that behaves more like software: flexible, reusable, extensible, and not tied to a single vendor’s ecosystem. That means treating the hardware not as a static asset, but as a platform layer on which software sits.This is where semi off-the-shelf hardware comes in.<h3>What Semi Off-the-Shelf Hardware Actually Means</h3>Semi off-the-shelf hardware is not yet another dev kit. And it’s not a fully custom board that kills your business case either.It’s the middle ground we’ve been missing: a field-ready hardware foundation that works for prototyping and production.It’s small and power-efficient enough to deploy in hundreds of early devices without redesigning anything. It’s open, both in terms of hardware and software, in a way your team can build upon it.This kind of hardware lets prototypes grow into products, instead of being discarded the moment they work and having to start all over to build the production-ready product.<h3>Examples of Hardware Moving in This Direction</h3>A few companies are already leaning into this idea.The <a href="https://www.quickspot.io" target="_blank">Walter module</a> from <a href="https://www.dptechnics.com/" target="_blank">DPTechnics</a> is a good example. It is fully open-source, both hardware and software and supports a wide variety of development environments, including Zephyr, ESP-IDF, MicroPython, and Arduino. The size is production-ready; the power profile works both on the bench and in the field. You can build a prototype on Walter and then deploy the same module in the real-world without restarting the entire engineering process. Packed with multiple radios (LTE-M, NB-IoT, WiFi, BLE and GNSS) and certifications, the end-product certification efforts and costs are also minimized.Others are operating with similar principles.<a href="https://www.actinius.com/" target="_blank">Actinius</a> builds Nordic-based products designed to be cost-effective, pre-certified and low-power.<a href="https://blues.com/" target="_blank">Blues</a> offers the Notecard: a cellular module that’s meant to be used directly in products, not just prototypes.<a href="https://www.rakwireless.com/en-us" target="_blank">RAK Wireless</a> provides lego-like Wisblocks which can be industrialized in a later stage. These companies aren’t selling dev kits, they’re offering foundations for developers to build upon.<h3>Where the Industry Needs To Go Next</h3>A healthy IoT ecosystem should look more like the modern software world, with hardware-agnostic firmware, development environments that don’t lock you in, tools for easy scaling, and a more collaborative mindset where work is openly shared.The real antidote to the 83% failure rate isn’t more devkits. It’s making the economics of hardware development work by closing the POC-to-Production gap.I am eager to <a href="https://www.linkedin.com/pulse/we-dont-need-more-devkits-make-economics-hardware-development-u0uqf/" target="_blank" rel="noopener noreferrer">hear your thoughts</a>.

Semi Off-The-Shelf Hardware: The Missing Link in IoT

We don't need more devkits, we need to make the economics of hardware development work

﻿This article is based on a conversation with Fabio Rosa, CEO of Tago.io.Pilots are not to prove the technology, it's to prove your business caseIn today’s IoT landscape, the technology has proven itself. Sensors are accurate. Devices are reliable. Connectivity is everywhere; from 4G and 5G to LoRaWAN and non-terrestrial networks. And IoT platforms are powerful, flexible, and customizable.Yet, despite this maturity, too many IoT projects don't make it past the pilot phase.Why?Because companies often treat pilots as technical exercises, instead of what they truly are: business case validations.<h2>5 questions to ask yourself during the pilot phase</h2>A real pilot should force you, and your team, to face some hard truths. Here are five critical questions to ask yourself:<ol><li>What is the minimum required scale for this project to succeed? Companies often over-estimate the scale and speed of hardware rollouts. Take an honest look at the numbers and define the required scale of the project to make the numbers work. </li><li>If I would get free access to hardware / connectivity / IoT platforms, would the business case look significantly different? There’s an obsession with costs, leading to tough negotiations to scrape off cents per device. If these negotiations have a significant impact on the business case of your IoT project, something is awfully off.  </li><li>Am I ready to change the roles and responsibilities of my personnel if the pilot succeeds?  IoT solutions streamline operations and automate tasks previously handled manually. If your project delivers the expected efficiencies, it will impact job roles and performance metrics. Are you ready to adjust how you evaluate, reward, and organize your team? Are you truly ready for the consequences of success? </li><li>What transformation does my company need to go through when implementing this IoT project? New processes, new skills, new departments. A real pilot highlights the organizational changes you must be willing to make, and the likelihood of these changes to succeed. The metrics used to define the success of projects and performance need to be realigned. </li><li>What additional resources do I need for installing and maintaining the solution?  Every connected device requires maintenance, support, and updates. Factor those into your business case at this phase, not during the production phase. Leaders and managers should be prepared to run a marathon, not a sprint. Therefore, additional resources and budget are necessary to achieve the long-term goals.</li></ol><h2>Focus where it matters</h2>A successful pilot is not one where everything "works" from a technical perspective. A successful pilot is one that proves your business case, that shows you can scale economically, create real value, and transform your operations sustainably.When you start treating pilots this way, your entire IoT strategy will change for the better. If you want a partner who understands this mindset, and who can help you build IoT projects that go beyond the pilot phase, talk to the team of <a href="https://tago.io/" target="_blank">TagoIO</a>.  <h2>About TagoIO</h2>TagoIO is an industry-leading IoT platform designed to empower businesses and individuals to connect, manage, and analyze the growing amounts of data IoT devices generate. The all-in-one platform streamlines data processing and visualization by eliminating complexity, saving businesses time, and freeing up resources so they can innovate their products and services.The platform is trusted by thousands of businesses that customize and deploy as the backbone of their solutions to connect IoT devices to hundreds of thousands of users worldwide. TagoIO has become a go-to solution for many IoT applications across many verticals, such as Agriculture, Buildings, Energy, Industrial, and Logistics.From its headquarters in Raleigh, NC, USA, TagoIO supports domestic and international businesses in more than 130 countries to build the best IoT solution. For more information, visit <a href="http://www.tago.io" target="_blank">www.tago.io</a>

IoT projects do not fail because of the technology, they fail because of bad business cases.

5 questions to ask yourself during the pilot phase

As aerospace and defense manufacturing targets higher precision and efficiency, digital twin and digital thread technologies are quietly reshaping how products move from design to the real world. These advancements enable companies to bridge the gap between design and production, driving down cycle times while improving innovation, safety, and adaptability. With the aerospace and defense (A&amp;D) sector transitioning to digital-first methodologies, the need for seamless integration of these technologies has never been more critical.A&amp;D manufacturers today must meet tighter production schedules, ensure greater accuracy, and create systems that can evolve in real time to changing conditions. Digital twins and digital threads serve as a single source of truth, ensuring interoperability across the lifecycle of each asset—from requirements capture to assembly and certification. In this context, these technologies are pulling the strings to improve operational efficiency and deliver data-driven insights. Companies that embrace these tools are positioning themselves to lead the next generation of A&amp;D engineering.<h2>Digital Twin and Digital Thread in Aerospace and Defense</h2>The A&amp;D sector stands to benefit greatly from innovations like digital twins and digital threads, especially with its demand for precision and safety. As A&amp;D products shifts towards mechatronics, digital twins allow system integration across mechanical, electronic, and software components, capturing real-time data to provide actionable insights into performance and condition. This helps implement smart manufacturing, remote asset monitoring, robust configuration &amp; traceability, predictive maintenance, reducing unplanned downtime and extending equipment lifespan.Model-Based Systems Engineering (MBSE) frameworks combined with digital threads help to standardize workflows from CAD/CAE to CAM/CMM, supporting both new product introduction and industrialization. In defense, digital twins allow for real-time operational readiness assessments of critical assets like fighter jets, naval vessels, and unmanned systems, while digital threads ensure system integrity and a continuous data flow across the entire lifecycle—from mission planning to post-deployment sustainment.These technologies are applicable across multiple domains—land, air, maritime, and space—to optimize design, improve maintenance, and ensure peak performance in all environments. They also support the global nature of defense contracts, with embedded Product Manufacturing Information (PMI) and annotations overcoming language barriers in multinational operations.As companies look to optimize performance and reduce cycle times, adopting these technologies is helping them achieve significant operational improvements, thus creating more cohesive, data-driven systems for better decision-making and long-term sustainability.<h2 id="isPasted">Watch It On-Demand: Unlocking the Power of Digital Twins for Aerospace Manufacturing</h2>Now available on-demand, “Unlocking the Power of Digital Twins for Aerospace Manufacturing” provides valuable insights into how these transformative technologies are reshaping the industry. Access now:<iframe width="540" height="395" src="https://c8056756.sibforms.com/serve/MUIFAK7CEV4CytTlOyQicZlewnw1wz4LEO3ekIzsaKSbfUMtK3CClXox6QoG6z0CW2D2jtbbCdyz4HrF9-TzaRIw6IcL6-TrIJhCJYOnGaQD4yGYWnj_UBjJRBEC7emto0QmypG8bO0JqhLn8YC8mFXVhdedNAYaFJB4HyeGU_EWLjPQbu_40_5stFkaMMKyTayYMiWg5KXdkrmG" frameborder="0" scrolling="auto" style="display:block;margin-left:auto;margin-right:auto;max-width:100%" class="fr-draggable"></iframe> <h2>Industry Insights: Driving Innovation through Digital Integration</h2>Quest Global has worked across various A&amp;D projects, helping companies integrate digital thread strategies to accelerate timelines and enhance efficiency. In the defense space, this integration enables complex systems, such as military aircraft and missile defense platforms, to move seamlessly from concept to deployment while maintaining mission-critical performance and security. By digitizing Standard Operating Procedures (SoPs) and augmenting them with simulations through Virtual Reality (VR) and Augmented Reality (AR), digital twins streamline production and enable maintenance, repair, and overhaul (MRO) with enhanced accuracy.In a project with a leading aeroengine OEM for the US Department of Defense (DoD), Quest Global’s design thread expertise, particularly in MBSE, was critical in connecting systems architecture and accelerating the design process. In another project, they helped reduce manufacturing design cycle times by 30% using adaptive closed-loop manufacturing, predictive analytics, and supply chain connectivity. This digital twin initiative improved the overall efficiency by 44%, demonstrating the power of real-time simulations and predictive analytics in reducing non-conformance and optimizing manufacturing.<h2>The Industry-Wide Impact of Digital Twin and Digital Thread Technologies</h2>Digital twins and digital threads enable companies to create continuous, data-driven feedback loops that enhance decision-making, streamline operations, and reduce inefficiencies and complexities. By embedding real-time insights into their processes, A&amp;D manufacturers can optimize performance at every stage of the product lifecycle, from design to production and maintenance.Digital twins, in particular, allow manufacturers to simulate different scenarios and test outcomes without needing physical prototypes. This helps reduce risk, accelerate time-to-market, and improve overall system reliability. With digital twins, we’re seeing a shift from reactive to proactive management in aerospace manufacturing. It goes beyond preventing failures toward continuously improving processes, shortening design cycles, and enabling adaptive manufacturing that responds in real time to operational data.Meanwhile, digital threads ensure that data remains connected and accessible throughout the entire lifecycle, bridging gaps between traditionally siloed systems. This interconnectedness fosters collaboration and allows for more informed decision-making, helping companies meet the growing demands for efficiency, sustainability, and speed. In A&amp;D, real-time data integration supports faster response times and optimizes product performance under changing geopolitical scenarios, empowering OEM’s &amp; Global Supply chain to maintain readiness in dynamic environments. Digital manufacturing and Industry 4.0 technologies also foster increased awareness and improvement toward Health and Social care (HSC).As the A&amp;D industry embraces digital technologies, manufacturers are empowered to optimize their operations and properly position themselves to innovate and adapt to future challenges, laying the groundwork for smarter, more responsive manufacturing ecosystems.

Bridging Design and Production: Why Digital Twin and Thread Are Vital for Aerospace Innovation

Harnessing the Power of Digital Twins for Seamless Digital Thread Integration

Perhaps IoT businesses are just IT businesses. So why the fixation on hardware? Hardware has long been considered the cornerstone of the IoT industry, but we're increasingly recognizing that success hinges not on the physical devices themselves but on the software that drives them. This shift mirrors patterns we've seen in the world of cloud computing, where software is being written without considering the hardware it runs on. Let's dive into this transformation.<h3>Commoditization: Scaling Through Common Hardware</h3>At the heart of the transformation from hardware-centric to software-focused solutions is commoditization. Jason Kridner, founder of <a href="http://beagleboard.org/" target="_blank">BeagleBoard.org</a>, aptly stated:"If you make a reprogrammable machine, then that machine can be used in a whole lot of other applications. We can pretty much put everything into that box. Then you drive the hardware to commodity because that’s what scales."By commoditizing hardware, companies can focus their resources on software development, where true differentiation occurs. Kridner emphasizes that it's the software engineers who ultimately "win" because “that’s where you put your differentiation”. Hardware becomes the scalable foundation upon which innovative software solutions are built.The saying: “hardware is hard” still resonates with many in the industry. Hardware development is associated with complexities, high investments, lengthy development cycles and significant security risks. By treating hardware as a common, reliable platform, businesses may find ways to work around these obstacles and focus on their core competencies.<h3>Software Defined Everything</h3>Hardware lacks flexibility, until it doesn’t. The move from hardware commoditization to software-defined solutions feels like a natural progression. “Software gets abstracted from the hardware layer”, as Gregory Roth from <a href="https://monogoto.io/" target="_blank">Monogoto</a> explains, “leading to more flexible solutions”.This abstraction enables agility and it liberates companies from the complexities of hardware-specific constraints. Francisco Xavier Garcia (<a href="https://www.picmg.org/" target="_blank">PICMG</a>) reinforces this point:"You want a modular solution that is quickly scalable and quickly deployable because you want to respond swiftly to the rapidly changing industry. When you have embedded software and hardware tightly coupled, your flexibility is limited, your adaptability to respond to threats is limited. Extensibility is needed, where you can extend your capability and grow what your system can do. Software gives you all that ability."By decoupling software from hardware, businesses enhance their ability to innovate, adapt, and scale, which is essential in a market characterized by constant change.<h3>Adopting Cloud Practices: Stealing from the Best</h3>The IoT industry's shift mirrors the evolution seen in cloud computing over the past decade. In 2011, Adam Wiggins, one of Heroku’s founders, published the <a href="https://12factor.net/" target="_blank">Twelve-Factor App</a>, outlining twelve principles of modern application development. While many of these principles seem obvious today, they weren’t at the time, highlighting how far we've come in embracing common cloud practices.Brandon Satrom from <a href="https://blues.com/" target="_blank">Blues</a>, drew inspiration from these principles to create a similar set for the connectivity landscape called the <a href="https://12factor.io/" target="_blank">Twelve Factor Thing</a>. This initiative aims to enhance developer efficiency and product reliability, increasing market trust in connected devices that adhere to these principles. Satrom doesn’t want to impose these 12 factors to anyone, but merely initiate the conversation on how to grow the market in terms of efficiency, security and reliability.Consider the common practices such as automated testing, DevOps, continuous integration, you name it. By applying these practices to the world of IoT, reliance on specific hardware components is significantly reduced – a lesson learned during recent chip shortages that exposed the vulnerabilities of tying software too closely to specific hardware.Eventually, there are a couple of things – or principles – we should be able to agree upon, already now. Think of the way we should approach <a href="https://12factor.io/factors/firmware/" target="_blank">firmware as a collection of dynamic assets</a> which change frequently through standardized firmware update processes. The necessity of testing, the 12 Factor Thing specifically mentions&nbsp;<a href="https://12factor.io/factors/hardware-in-the-loop/" target="_blank">Hardware-in-the-Loop test</a> (HIL). And, one of Satrom’s Things he’s most passionate about: <a href="https://12factor.io/factors/transport/" target="_blank">Harmonization of Radio Access Technologies</a> “you don’t want developers having to learn everything from soup to nuts” if they add an additional radio technology. This will “slow them down and increase the likelihood of failures”.<h3>Conclusion</h3>The IoT industry's future lies in embracing the fact that software people ultimately take all the credits. Hardware gets commoditized to better suit software-defined solutions, minimizing the learning curve for developers who can build their solutions using familiar interfaces and APIs, regardless of the underlying hardware.This approach not only streamlines development and deployment but also empowers businesses to swiftly respond to market changes and emerging threats. By focusing on software, companies can enhance flexibility, scalability, and adaptability. It's not just about building better machines; it's about redefining how we manage those machines and how we, as an industry, can better serve the needs of a constantly changing world.<blockquote>This article is based on the discussion that took place at <a href="https://iotstars.com/" target="_blank">IoT Stars</a>, held during Embedded World North America on October 8, 2024. Thanks to Brandon Satrom (Blues), Jason Kridner (<a href="http://BeagleBoard.org" target="_blank">BeagleBoard.org</a>), Francisco Xavier Garcia (PICMG) and Gregory Roth (Monogoto) for sharing their take on Software Defined IoT. To join future discussions on the developments in IoT, visit <a href="https://iotstars.com/" target="_blank">iotstars.com</a>.</blockquote>

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