How manufacturers can achieve digital transformation and Industry 4.0 🤔 - Connect OT systems like PLCs and MES to ERP using a unified namespace for seamless upgrades and real-time data 🕸️ - Leverage OEE metrics to increase efficiency 📈 - Embrace new technologies and build the right architecture 🎯 Watch this @WalkerDReynolds RANT on How to Future-Proof Your Manufacturing Business with Industry 4.0 and Digital Transformation. #manufacturing #RANT #digitaltransformation

Industry 4.0 Podcast W/ Fuuz xitter.cf/i/broadcasts/1RKjpzjDZ…

Industry 4.0 Podcast: Where Do We Start? xitter.cf/i/broadcasts/1dGYljbnB…

AI fatigue is real, and if we’re not careful, it’s going to turn into straight-up backlash. We could feel it at ProveIt!—talking to vendors, talking to manufacturers—everyone’s “doing AI,” but almost nobody is actually running anything meaningful in production. And meanwhile, you’ve got manufacturing executives and big consultants shoving AI down everyone’s throat like it’s the answer to everything. It’s not. Most of them aren’t even close enough to the work to understand where it actually fits. That’s the problem. AI got sold as transformation, but the market isn’t buying that anymore. Now it’s simple: show me where this actually works, show me where it fits in my workflow, and show me how it makes my people better—not how it replaces them. Because the people actually doing the work? They’re not asking for “AI strategy decks.” They’re trying to troubleshoot faster, write better code, capture knowledge before it walks out the door, reduce downtime, and improve quality. Manufacturing doesn’t need more hype—it needs outcomes. AI isn’t the product—it needs to be invisible, and that’s exactly how it should be. The companies that win aren’t going to be the loudest about AI; they’re going to be the ones quietly deploying it into real workflows and getting real results. So if you’re a vendor—or sitting in a boardroom pushing AI initiatives—here’s the real talk: stop leading with AI and start leading with the problem you solve. AI is just a tool to help you get there. #Industry40 #DigitalTransformation #Manufacturing #SmartManufacturing #IIoT #ArtificialIntelligence #AIinManufacturing #IndustrialAI #Automation #OT #WalkerReynolds #40Solutions

You know what burns you out? Not the hard problems. Hell, that’s what you signed up for. It’s the repetitive ones. The ones where you sit there thinking: “Didn’t I already build this?” Same integrations. Same data models. Same logic. Different plant, different system, same damn work. It’s not that the work is hard. It’s that it’s the same work… over and over again. And here’s the real problem: It’s not you. It’s the architecture. Every system you’re working with was built in isolation. Nothing carries forward. Nothing is reusable. Nothing actually scales. So every time the business needs something new… You start from scratch. Again. And after a while, it starts to feel like déjà vu. Are you going crazy? Nope, you're literally rebuilding the same crap over and over again. That’s what burns people out. That’s what slows companies down. That’s why “digital transformation” feels like a never-ending project. Fuuz was created by former integrators and manufacturers who got tired of living this exact problem. So they built something different. Fuuz is a manufacturing intelligence platform. An OT-native MES for ERP. A bridge between OT and IT. A model-driven system that enables enterprise-scale MES. It’s the data layer and application layer your systems never had. It connects, collects, and structures your data in a way that actually persists. So instead of rebuilding everything… • Your data model carries forward • Your integrations don’t get recreated every time • Your logic is reusable • Your work compounds instead of resets So instead of starting over… You build on what already exists. That’s the difference. Be honest—how much of your job is just rebuilding what you already built? #Industry40 #DigitalTransformation #Manufacturing #SmartManufacturing #IIoT #UnifiedNamespace #DataArchitecture #IndustrialData #Automation #OT #ITOT #MES #Engineering #ManufacturingTech

Tell me if you’ve encountered this before, maybe you’re living it right now. You know digital transformation matters. Leadership is pushing for it. But when it comes time to actually start… No one really knows what the first step is. So what happens? Vendors promise the complete solution, and don't deliver. The big consultants pitch promises of AI to your leadership, with no understanding of what the realities of manufacturing are. And your industry 3.0 integrator is just as lost as you. And you’re stuck in the middle trying to figure out what actually makes sense. So let me ask you— If you had to start tomorrow… what would you do? What problem do you solve first? What data actually matters? Where do you even begin in your plant? We know what you're thinking: But we're legacy brownfield, we can't rip and replace everything - you don't have to. The last consultants told us it would cost $600k just to get started - Show them the door. We want to deploy in our new Greenfield facility - doesn't matter, you treat greenfield and brownfield the same. We feel you, and we hear you. That's what this workshop is all about. Learn from Walker Reynolds, and Dylan Dufresne, two of the leading Industry 4.0 Solutions Architects, with over 40 years of real world experience of transforming manufacturers, for REAL! If you're thinking, "Holy sh*t, that's us." Then we built this for you. Where to Start in Digital Transformation for Manufacturers May 12–13 | Live Online Walker and Dylan will walk through: what this actually looks like, what are the foundations how you pick the first problem what Day 1 should be how you move without overcomplicating it Day 2 they build it out end-to-end with real demos using a simulated Value Factory: Connect → Collect & Store → Analyze → Visualize So you can actually see how this comes together. Early Bird — $100 off through April 10 Use code START-EARLYBIRD at checkout Workshop price: $497 If this sounds like you, we got your back. #DigitalTransformation #Industry40 #Manufacturing #IIoT #UnifiedNamespace #IndustrialAutomation #WalkerReynolds #40Solutions #DylanDuFresne

ICYMI: the comments on Jeff Winter’s ProveIt! keynote say a lot. When people say things like: “This is gold.” “I learnt so much.” “I will definitely be sharing this with my organization.” …that tells you the message landed. Jeff’s keynote opened the main stage at ProveIt!, and it was an absolute masterclass. That kind of response does not happen because a topic is trendy. It happens when a session gives people clarity, practical value, and a better way to think about what’s next. If you missed Jeff Winter’s Keynote Address, now’s a good time to watch it. Go to 4.0 Solutions YouTube channel and see the full session. #ICYMI #ProveIt #JeffWinter #AI #Manufacturing #DigitalTransformation #Industry40 #WalkerReynolds #40Solutions

Let’s say your boss walked in tomorrow and said: “We need to digitally transform.” What would you actually do? Most people: • start looking at tools • try to connect everything • or build something without a clear plan And that’s where things go wrong. You don't have any idea what the first step is. So, it fails on Day 1. The real questions are: • What problem do you solve first? • What data actually matters? • Where do you start in a brownfield environment? • How do you show value quickly without breaking everything? The community has asked these questions over and over again. That’s exactly what we’re focused on in this workshop. Where to Start in Digital Transformation for Manufacturers 📅 May 12–13 | Live Online Walker Reynolds (4.0 Solutions) and Dylan DuFresne (Abelara) will walk through: • what a real starting point actually looks like • how to define your first use case • what your “Day 1” should look like in a brownfield factory • and how to take the first step without overengineering it On Day 2, they’ll show it end-to-end using a simulated Virtual Factory: Connect → Collect & Store → Analyze → Visualize So you don’t just hear it—you see how it actually works. What do I Connect, and when? What protocols do I use to Collect, and how? Where the hell do I Store it all? How do we Analyze, and make sense of the data? Where and what to Visualize? Not just pretty dashboards, but who needs it, where they need it, and how to take action on it. These questions can seem overwhelming, but we are here to help you on our way. Early Bird — $100 off through April 10 Use code START-EARLYBIRD at checkout Workshop price: $497 If you had to start tomorrow—this will show you how. #DigitalTransformation #Industry40 #Manufacturing #IIoT #UnifiedNamespace #IndustrialAutomation #WalkerReynolds #40Solutions

Most manufacturers don’t fail at digital transformation because of technology. They fail because they don’t know where to start. So what happens? They: • chase tools instead of solving problems • build disconnected systems • invest time and money without clear outcomes And eventually… everything stalls. That’s the real problem. Not “digital transformation” — but the lack of a clear starting point and process. So we built a workshop specifically to solve that. Where to Start in Digital Transformation for Manufacturers May 12–13 | Live Online A 2-day live online workshop led by Walker Reynolds (4.0 Solutions) and Dylan DuFresne (Abelara) that teaches you exactly where to start—so you can move from uncertainty to building real solutions. In this workshop, Walker and Dylan will walk you through: • how to identify the right problems to solve first • how to define use cases that actually create value • how to think about architecture before buying tools • and how to take a clear, confident first step On Day 2, they’ll show it end-to-end using a simulated Value Factory: Connect → Collect & Store → Analyze → Visualize So you don’t just hear it—you see how it actually works. Early Bird is live now — $100 off through April 10 Use code START-EARLYBIRD at checkout Workshop price: $497 If you’re stuck asking “where do we start?”—this is the answer. Link in the comments below, or reach out to the team at [email protected] #DigitalTransformation #Industry40 #Manufacturing #IIoT #UnifiedNamespace #IndustrialAutomation #WalkerReynolds #40Solutions

Over the last few posts, we’ve been walking through what it actually takes to make AI work in manufacturing. Not the hype. The reality. It all starts with the foundation. There's no way around it! You have to connect your machines. You have to collect the data. You have to structure it in a way that systems can actually understand. Most manufacturers are still right there. Just trying to get connected. Trying to get visibility. Trying to make sense of what they already have. That’s where a platform like Litmus meets you. You need to connect to machines? Handle different protocols? Start building a real data foundation? That’s exactly what they’re built for. Then you start to move forward. You begin running analytics and AI at the edge. Now you’re not just looking at dashboards anymore. You’re making decisions in real time, inside the plant, where production is actually happening. Litmus supports that too. Running applications. Running models. Driving real-time operations at the edge. Then comes the next step. You want those models to improve. Not just in one plant—but across all of them. So now you’re moving data to the cloud. Training models. Refining them. Learning across sites. And again, Litmus is right there. But this is where things usually fall apart. Because proving something in one plant is one thing. Scaling it across an entire enterprise is something completely different. Here’s what typically happens. A team gets a use case working in one facility. It delivers value. Everyone’s excited. So leadership says, “Roll it out everywhere.” And that’s when the freaking wheels come off. The next plant doesn’t look the same. Different machines. Different systems. Different data. So instead of deploying what already works… They start rebuilding it. Again. And again. And again. What was supposed to scale turns into a collection of one-offs. Each plant slightly different. Each deployment harder than the last. Each one drifting further away from a standard. That’s the real problem. Scale isn’t a technology problem. It’s an architecture problem. And most architectures were never designed to scale past a single plant. Now here’s the real deal. Most manufacturers aren’t here yet. And that’s okay. But when you get there—when you move from “we proved it” to “we need this everywhere”—this is the challenge waiting for you. And this is where Litmus brings it all together. With Litmus Edge Manager, you’re not rebuilding every time. You’re managing. Models. Applications. Data. Devices. All centrally. With version control. With templates. With coordinated rollouts. So when something improves in one plant… It becomes part of the standard. And it gets deployed across the entire enterprise. Same architecture. Every site. No starting over. That’s the real prize. Not just connecting. Not just doing AI. Not just proving it works. Scaling it across the business. And having a platform that’s with you from the first connection… …all the way to enterprise scale. That’s where Litmus fits. #Industry40 #IndustrialAI #EdgeAI #CloudAI #Manufacturing #DigitalTransformation #UnifiedNamespace #WalkerReynolds #40Solutions

Over the last few posts we’ve been building a story about industrial AI. First — the foundation. Before anything intelligent happens in manufacturing, you have to connect the machines, collect the data, and build an architecture that actually scales. Then we talked about Edge AI. Running analytics and models right next to the machines so operational decisions can happen in real time. But that raises the next question. What happens next? Because AI models don’t get better sitting in one plant. They get better when they learn from all of them. One factory gives you data. Ten factories start revealing patterns. Fifty factories start uncovering insights you would never see locally. That’s where Cloud AI comes in. Edge AI is where the real-time decisions happen. But the cloud is where models are trained, improved, and governed across the entire operation. This is where a lot of AI strategies fall apart. If every plant is running its own model… You don’t have AI at scale. You have AI experiments. The real architecture looks like this: Models are trained in the cloud using data from across plants. Those models are versioned and managed centrally. Then they are deployed back out to the edge where production is actually running. Train in the cloud. Run at the edge. Improve everywhere. That’s the loop. This is exactly what Litmus Edge Manager enables. Edge data flows to the cloud for model development and monitoring. Litmus Edge Manager becomes the control plane that governs models, versions, and deployments across every facility. So improvements don’t stay stuck in one plant. They roll out across the entire operation. One architecture. Many factories. Continuous improvement across the fleet. That’s what industrial AI actually looks like when it scales. #Industry40 #IndustrialAI #EdgeAI #CloudAI #Manufacturing #DigitalTransformation #WalkerReynolds #40Solutions

After reading the comments on our last post about AI fatigue in manufacturing, one thing became crystal clear. A lot of manufacturers feel like they’re behind. Let us be blunt. You’re not behind. You’re being told to run a race that most factories haven’t even reached the starting line for yet. Right now executives everywhere are being told by consulting firms and analysts: “AI is transforming manufacturing.” “You need an AI roadmap.” “Your competitors are investing in AI.” Sounds great. The problem is most of the people writing those reports have never actually worked in a factory. They’ve never dealt with: Legacy PLCs from three different decades. Machines that aren’t connected to anything. Data stuck inside proprietary systems. Operations teams that still rely on spreadsheets. But they’re telling leadership to jump straight to AI. That’s like telling someone to install autopilot in a car that doesn’t even have an engine yet. Here’s the reality inside most plants. The real work still looks like this: Connect machines and systems Collect the data they generate Store it somewhere that actually scales Analyze what’s happening in operations Visualize it so humans can make decisions Do that well and AI becomes powerful. Skip those steps and AI becomes an expensive science experiment. And this is exactly why so many manufacturers feel stuck. They’re being sold the destination without anyone explaining the road. So if you’re sitting there thinking: "Alright… but where do we actually start?" Start with the fundamentals. That’s exactly why we built the IIoT Mini-Course at IIoT university. No hype. No pitches. No consulting jargon. Just the real starting point for digital transformation in manufacturing. And I’m curious about something after reading all the comments. Inside your organization, what’s the real blocker right now? Is it: • Leadership pushing AI before the foundation exists • Budget approval for infrastructure • Legacy machines that aren’t connected • Data scattered across systems • Too many vendors selling “solutions” Drop it in the comments. Let’s talk about the real problems manufacturers are dealing with. #DigitalTransformation #Industry40 #Manufacturing #IIoT #SmartManufacturing #IndustrialAutomation #WalkerReynolds #40Solutions

Everyone is talking about AI in manufacturing right now. Edge AI. Agentic AI. Autonomous operations. But there’s a problem most people skip right past. AI doesn’t run on ideas. It runs on data infrastructure. Machines have to be connected. Data has to be collected. Systems have to understand the data before anything intelligent can happen. This is where a lot of manufacturers get stuck. And it’s exactly where Litmus is strong. Litmus is one of the best platforms in the industry when it comes to connecting and collecting industrial data at the edge. They connect directly to PLCs, machines, and industrial systems. And when it comes to writing industrial drivers, they move faster than anyone else in the industry — it’s honestly not even close. That matters because every plant has different equipment, protocols, and legacy systems. If you can’t connect the machines, nothing else matters. Once the foundation is in place, something powerful becomes possible. Edge AI. Instead of sending data to the cloud, waiting for analysis, and then sending a response back… AI models can run right where production is happening. At the edge. Right next to the machines. That means: Real-time inference. Real-time alerts. Real-time operational decisions. Even if the cloud connection drops. That’s the real promise of Edge AI. Not dashboards. Closed-loop operations. Data → Insight → Action All happening inside the plant. Litmus provides the platform that makes that possible — from the foundational connectivity layer all the way to running applications and AI models at the edge. Start with the foundation. When you’re ready for AI, the architecture is already there. #Industry40 #IndustrialAI #EdgeAI #Manufacturing #DigitalTransformation #WalkerReynolds #40Solutions

At ProveIt! 2026, one thing was impossible to miss. AI was everywhere. Every vendor booth. Every demo. Every presentation. And honestly, a lot of the vendors really impressed us. The technology they showed was genuinely cool. But when we stepped away from the booths and talked with manufacturers in the hallways, we kept hearing something very different. Over and over again: “This is cool… but we’re not there yet.” “We still don’t know where to start.” “We can’t even get all our systems connected.” And if we’re being real for a second… There’s some serious AI fatigue out there right now. Not because people don’t believe in AI. But because a lot of manufacturers are still trying to solve much more basic problems. Hell, a lot of facilities still aren’t fully connected to their own network yet. So jumping straight to AI feels like skipping about ten steps. Here’s the truth. AI is important. It is part of the future. It’s part of reaching the manufacturing holy grail. But it’s not the starting point. The real starting point looks like this: • Connect your machines and systems • Collect the data • Store it in an architecture that actually scales • Analyze what’s happening in your operations • Visualize it so people can make decisions When you do that well, then you can start to: • Find patterns • Predict outcomes • Use AI/ML to recommend operational improvements At ProveIt!, a lot of you told us the same thing: “We just don’t know where to start.” Alright. We heard you loud and clear. So we’re putting together a live online workshop with Walker Reynolds focused on exactly that. Where manufacturers should actually start with digital transformation. This will be a 2-day workshop. Day 1 We walk through the process and strategy behind digital transformation. Day 2 We get technical and show you how to digitally transform a real virtual factory. We’ll walk through the architecture step-by-step: • Connect • Collect & Store • Analyze • Visualize And yes, we’ll have a special guest educator joining Walker as well. More on that soon. But before we announce the rest of the details, we want to hear from you. Where are you in your digital transformation journey right now? Are you still trying to get systems connected? Sitting on data but not really using it? Getting pressure from leadership to “do AI” when the foundation isn’t even there yet? And if you’re a systems integrator, what’s the biggest hurdle you face getting manufacturers moving? Drop a comment and let us know. #DigitalTransformation #Industry40 #Manufacturing #IIoT #SmartManufacturing #IndustrialAutomation #UnifiedNamespace

Here’s another thing that quietly kills digital transformation projects: Everything gets built site by site. A dashboard for one line. Then another for the next line. Then another for the next plant. Before long you’re not building a system anymore. You’re maintaining dozens of slightly different versions of the same thing. Every site tweaks it. Every engineer modifies it. Every upgrade breaks something somewhere. Scaling becomes maintenance. This is where the ProveIt! demo from Canary made something very clear. Once your data has a real home — and the context layer exists — you stop building solutions site by site. You start building models. Define the asset structure once. Define the calculations once. Define the dashboards once. Now the architecture does the work. Bring a new site online and nothing has to be rebuilt. The historian already understands what a site is. What an area is. What a line is. What a tank or reactor represents inside the model. So the system doesn't start over. It iterates. The moment in the demo when they switched sites on the same dashboard was the real takeaway. No rebuild. No redesign. No new project. The architecture simply scaled. That’s the difference between a historian acting like storage… …and a historian acting like infrastructure. Most industrial systems were built assuming you’d manage every site separately. Very few were built assuming the architecture itself should scale. That’s the difference, and it's a BIG one. #Industry40 #DigitalTransformation #IndustrialData #UnifiedNamespace #CanaryLabs #Historian #WalkerReynolds #40Solutions

Most people still think Litmus is a connectivity platform. That’s where the story started. But that was never the real goal. Litmus wasn’t built to move data. It was built to liberate it. About ten years ago, Vatsal Shah and John Younes started Litmus with a simple idea: Manufacturing data is trapped. Locked inside PLCs. Locked inside proprietary systems. Locked inside vendor-defined silos that have existed since the 1980s. If applications can’t access the data… nothing else matters. In the early days, Litmus wasn’t a polished platform. It was two founders, a handful of engineers, and a mission to break those silos. Vatsal has said before that he slept on couches in Silicon Valley while building the company. That’s how serious they were about solving this problem. So they started where every industrial architecture has to start: Connectivity. The first challenge was drivers. Not just a few. Hundreds. Years before AI became the buzzword it is today, the Litmus team was already using machine learning techniques to reverse engineer industrial protocols and accelerate driver development. Instead of taking months to build a new driver, they could get most of the work done in hours and let engineers finish the last mile. That’s how they went from zero connectors to hundreds faster than anyone expected. But connectivity was never the destination. It was step one. Once manufacturers started connecting systems, a new problem appeared. Cloud applications didn’t want raw OT data. They didn’t understand it. It was expensive to move. And it lacked context. So Litmus evolved. First into a platform that processes and contextualizes data at the edge. Then into a system that could model assets, organize data, and support digital twins. Then manufacturers asked a new question: “If our applications are running in the cloud… why can’t they run back at the plant?” So Litmus added containerized runtimes to bring applications back to the edge — closer to the machines, lower latency, air-gapped, secure. And then 2023 happened. Generative AI. Suddenly everyone wanted to run AI in manufacturing. But most of those initiatives stalled for the same reason. AI doesn’t fail because the models are bad. It fails because the data foundation doesn’t exist. Unstructured data. No context. No consistent pipeline from OT to IT. That’s the problem Litmus has been solving for the last decade. Today the platform isn’t just connecting machines. It’s building the data foundation industrial AI actually needs. Because AI doesn’t run on raw PLC tags. It runs on structured, contextualized information. The data has to be collected. The data has to be normalized. The data has to be contextualized. And it has to happen before the data ever reaches the cloud. That’s the architecture Litmus has been building. If you want to understand what they actually built, the best way isn’t reading about it. It’s getting your hands on it. The Litmus Edge Developer Edition gives engineers access to the full platform — the same one running in production environments around the world. If you want to kick the tires, check the comments below. #Industry40 #IndustrialAI #DigitalTransformation #SmartManufacturing #UnifiedNamespace #Manufacturing #WalkerReynolds #40Solutions

Here’s another truth for you: data needs a home. Everyone says AI needs data. Great. You’ve got it. Years of historian data across multiple sites. Thousands of tags. You want it? It’s there. But here’s what rarely gets talked about: having data scattered across facilities isn’t the same thing as having a foundation. We’ve all seen what happens next. Someone opens the historian and sees cryptic PLC tags, different naming conventions at every site, and zero alignment with how the business actually thinks about assets. So the conclusion feels obvious: “We need to clean up the naming before we move forward.” Sounds responsible. It’s also how projects stall. Because renaming tags at Level 2 across multiple plants isn’t a small initiative. It’s expensive. It’s disruptive. And in most cases, it turns a modernization effort into a multi-year cleanup project. What stood out in Canary's ProveIt! session wasn’t just performance or dashboards. It was discipline at the architectural level. They start by giving the data a home. A reliable, scalable historian that can ingest thousands of tags, compress massive volumes of time-series data into flat files, and retrieve years of high-fidelity history in seconds. That foundation comes first. Then they model it. Instead of forcing a naming war at the PLC layer, they build virtual views on top of the stored data. They restructure tag hierarchies inside the historian layer. They attach metadata. They define assets after the data is already secure and accessible. That changes the equation. Now “TC-100” at one site and “Reactor_Temp_01” at another don’t require a plant shutdown to reconcile. They become part of the same enterprise asset model. You build a dashboard once and iterate it across two sites… or twenty. You compare performance across facilities without redesigning the control system. The transformation moves forward because the foundation is solid. Most historians were built to log what happened. Store it. Archive it. Trend it. Very few were built to become the home that modern architecture grows from. That’s the difference. #Industry40 #DigitalTransformation #IndustrialData #UnifiedNamespace #CanaryLabs #WalkerReynolds #40Solutions

ProveIt! 2026 Schedule Update – And This Is a Big One We’re excited to announce a change to Wednesday morning at ProveIt! Wednesday, 2/18 | 8:00–8:45 AM • ProveIt! Main Stage • Live Q&A + Whiteboard Session with Walker Reynolds and Dylan DuFresne, Co-Founder & Solutions Architect at Abelara. This is the kind of session that started it all. Walker in front of a whiteboard — answering and diagramming out your questions in real time. Now he’ll be joined by one of the most gifted technologists in the industry. This opportunity gives ProveIt! attendees a chance to: • Ask Walker & Dylan hard architecture questions • See Unified Namespace mapped out from scratch • Watch two practitioners work through problems • Go deeper than surface-level digital transformation talk Wednesday morning on the ProveIt! Main Stage is going to feel like the early days of Industry 4.0 — raw, technical, and built around the community. Come ready with questions. #ProveIt2026 #Industry40 #UnifiedNamespace #DigitalTransformation #ManufacturingLeadership #WalkerReynolds #40Solutions #Abelara

A manufacturer has several plants. Nothing unusual there. Each plant runs well on its own. The SCADA systems work. Operators know their lines. Engineers can troubleshoot problems locally. But as the company grows, something subtle starts to happen. Every new plant made it harder to answer simple questions. Which plant is actually performing best today? Are we seeing the same downtime patterns everywhere? Why does the same issue get solved three different ways depending on the site? The data exists. The people exist. But the answers don’t — because every plant is effectively its own island. This is where traditional SCADA starts to show its limits. It was never designed to help an organization operate as one business. It was designed to help a single plant run safely and reliably. So growth becomes a penalty. More plants mean more systems, more licenses, more one-off decisions, and more technical debt. Instead of scaling insight, you scale complexity. What stood out in our conversations with AVEVA is how clearly they understand this problem — and how AVEVA Operations Control is positioned to address it. The goal isn’t to rip out what’s working. It’s to create a single operations layer that sits above individual sites, giving teams a consistent way to see performance, issues, and opportunities across the business. You can start with one plant. You can solve one real problem. But you’re not painting yourself into a corner when the next plant comes online. That’s a fundamentally different way to think about operations systems — and it’s why their Bronze session at ProveIt! last year stood out. Walker saw a team focused on helping manufacturers grow without breaking their operational backbone and said they needed to be on stage. If you’re trying to scale your operations without losing visibility, consistency, or control, this is a conversation worth paying attention to. Also, check out the Industry 4.0 podcast released today featuring John Krajewski, as he and Walker talk the Operations Control, and what this means for the community. #Industry40 #SCADA #DigitalTransformation #Manufacturing #ProveItConference #AVEVA #WalkerReynolds #40Solutions

This isn’t a hot take. It’s just reality. Everyone in this space already knows @HiveMQ has a damn good broker. Actually, let’s say it right: it’s the most performant broker in the world. For the ProveIt! Conference alone: ~100 million messages per minute. Lossless. At scale. Running for months without failure. That conversation is over. But here’s the part most people miss — that’s not where HiveMQ is headed. Because in an AI-driven industrial world, moving data is table stakes. If you can’t do that by now, you’re already irrelevant. The real problem is understanding the data. Here’s a real story. A high-tank alarm goes off at 2:00 a.m. on a Sunday. A pumper truck gets dispatched on triple time. Three hours later the driver calls back and says: “You didn’t need to send me. Inflow and outflow were basically the same. I could’ve come out on Wednesday.” Nothing was broken. The alarm wasn’t wrong. The information was useless. The operator didn’t need to know that the tank was high. They needed to know how much time they had. That’s the difference between data and information. And that’s where most “AI strategies” fall flat on their face. AI doesn’t fail because MQTT can’t scale. It fails because the data is unstructured, context-free, and untrustworthy. HiveMQ already solved scale. That’s done. With HiveMQ Pulse, the focus shifts to the part that actually matters next: • Structure • Context • Governance • Trust Because AI doesn’t fix bad architecture. It exposes it. And that’s exactly why HiveMQ is positioned to win what’s coming next. #Industry40 #HiveMQ #UnifiedNamespace #IndustrialAI #SmartManufacturing #DigitalTransformation #WalkerReynolds #40Solutions