IoT Solutions for Industry

2026 is the year AI leaps off our screens and starts to impact our physical world at scale - unlocking data to inform, advise and empower us to make every operation more efficient, and enable every employee to work smarter. Industrial AI is overcoming friction to acquire data and creating a powerful set of analytics to improve customer economics.   Think of a handheld device on the factory floor that conversationally guides an inexperienced worker through a sophisticated repair using decades of data. Think of the ability to monitor every square foot of space across your network of stores – keeping each one comfortable for customers and employees and making minute-by-minute adjustments to manage power consumption and costs. Think of running a complex operation like a refinery where domain rich AI applications drive plants to perform at optimum level.   The future of AI is in how it connects everything from buildings to plants to refineries, but more importantly how it elevates the people who work with them. Physical AI isn’t just coming; it’s unlocking entirely new possibilities and outcomes across industries. That’s why this tops my #BigIdeas2026 list: I’m confident this is the year the future of physical AI will accelerate.  

From Blueprint to Battlefield: Reinventing Enterprise Architecture for Smart Manufacturing Agility
   Core Principle: Transition from a static, process-centric EA to a cognitive, data-driven, and ecosystem-integrated architecture that enables autonomous decision-making, hyper-agility, and self-optimizing production systems.   To support a future-ready manufacturing model, the EA must evolve across 10 foundational shifts — from static control to dynamic orchestration.   Step 1: Embed “AI-First” Design in Architecture Action: - Replace siloed automation with AI agents that orchestrate workflows across IT, OT, and supply chains. - Example: A semiconductor fab replaced PLC-based logic with AI agents that dynamically adjust wafer production parameters (temperature, pressure) in real time, reducing defects by 22%.   Shift: From rule-based automation → self-learning systems.   Step 2: Build a Federated Data Mesh Action: - Dismantle centralized data lakes: Deploy domain-specific data products (e.g., machine health, energy consumption) owned by cross-functional teams. - Example: An aerospace manufacturer created a “Quality Data Product” combining IoT sensor data (CNC machines) and supplier QC reports, cutting rework by 35%.   Shift: From centralized data ownership → decentralized, domain-driven data ecosystems.   Step 3: Adopt Composable Architecture Action: - Modularize legacy MES/ERP: Break monolithic systems into microservices (e.g., “inventory optimization” as a standalone service). - Example: A tire manufacturer decoupled its scheduling system into API-driven modules, enabling real-time rescheduling during rubber supply shortages.   Shift: From rigid, monolithic systems → plug-and-play “Lego blocks”.   Step 4: Enable Edge-to-Cloud Continuum Action: - Process latency-critical tasks (e.g., robotic vision) at the edge to optimize response times and reduce data gravity. - Example: A heavy machinery company used edge AI to inspect welds in 50ms (vs. 2s with cloud), avoiding $8M/year in recall costs.   Shift: From cloud-centric → edge intelligence with hybrid governance.   Step 5: Create a “Living” Digital Twin Ecosystem Action: - Integrate physics-based models with live IoT/ERP data to simulate, predict, and prescribe actions. - Example: A chemical plant’s digital twin autonomously adjusted reactor conditions using weather + demand forecasts, boosting yield by 18%.   Shift: From descriptive dashboards → prescriptive, closed-loop twins.   Step 6: Implement Autonomous Governance Action: - Embed compliance into architecture using blockchain and smart contracts for trustless, audit-ready execution. - Example: A EV battery supplier enforced ethical mining by embedding IoT/blockchain traceability into its EA, resolving 95% of audit queries instantly.   Shift: From manual audits → machine-executable policies.   Continue in 1st and 2nd comments.   Transform Partner – Your Strategic Champion for Digital Transformation   Image Source: Gartner

AI agents and physical AI are shifting industrial automation from equipment supply to autonomous, self-optimizing systems. The most mature vendors are moving from pilots to production, with robots navigating complex environments and digital twins optimizing the value chain. This CB Insights brief gives a good view of where the top 20 industrial automation companies stand on AI maturity. Three key trends. 1. Leaders like Siemens Industry and ABB are linking AI systems across design, logistics, manufacturing, and maintenance creating compounding benefits. 2. Optimization dominates near-term priorities, while digital twins are emerging as the backbone for connecting hardware and software. 3. Partnerships with tech companies like Microsoft, Google, and Nvidia are essential, but they create new dependencies that must be managed. Siemens at the top of the ranking, combining copilots, edge platforms, and digital twins. Its work with Microsoft and Nvidia expands capabilities but increases reliance on external tech. Honeywell takes a more focused approach, embedding AI into devices and workflows. Its Qualcomm partnership highlights product-level integration over broad system building. ABB advances through its OmniCore platform and acquisitions such as Sevensense and SensorFact, blending robotics, software, and energy management. Schneider Electric pushes AI in energy management, using digital twins and partnerships with Nvidia, Microsoft, and Itron to extend from factory optimization into grid intelligence. The path forward in industrial AI is moving beyond pilots or isolated tools. It will depend on how well vendors embed AI into their platforms, link technologies across domains, and balance the benefits of external partners with the need for strategic independence. Those that will get it right will turn AI from experimentation into durable advantage. Just as critical is how their customers adopt these technologies. Industrial firms must shift from isolated use cases to embedding AI in design, production, energy, and logistics. Success requires not only advanced tools, but also the data, skills, and processes to make AI scale in complex operations.

The majority of Industrial AI isn’t going into some futuristic, fully autonomous factory. It’s going into: • Catching defects • Keeping lines running • Fixing machines before they break That’s it. Over half the use cases are sitting right there in quality, production, and maintenance. What I found more interesting wasn’t the top of the list… it was the movement. 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 & 𝐑&𝐃 𝐮𝐩 𝐚𝐥𝐦𝐨𝐬𝐭 𝟑𝐱. 😮 AI is starting to show up before anything hits the floor. Not just improving execution… influencing how things are designed, tested, and brought into production. This means different conversations and different people involved. And then there’s the part that made me laugh a bit…“Other” dropped by 70%. 🤣 Fewer side projects. More focus on the parts of the business that run every day. Also worth noting…You don’t see a category here that screams GenAI. Most of this is: • Vision • Time-series data • Operational models The kind of AI that doesn’t demo well… but does show up in results. My biggest takeaway from this chart: Companies are putting AI where: • The problem already hurts • The data already exists • The outcome actually matters to the business Not everywhere. Just where it counts. I wrote a deeper breakdown of what the latest Industrial AI data and trends reveal based on the huge amount of research conducted by IoT Analytics in their 399-page 2025 Industrial AI Report. 𝐅𝐮𝐥𝐥 𝐀𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/e2-GJZYJ ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

As we close out 2025, I’ve been reflecting on the seismic shifts that defined industry, and what they signal for the future. 2025 was a year of compressed transformation. Persistent volatility in energy prices, supply chains, and labor markets accelerated adoption of IoT, AI, edge computing, and 5G. These technologies are no longer optional, they’re the backbone of modern industrial ecosystems. Analysts confirm this trajectory: 🔹 Deloitte reports that 80% of manufacturing executives plan to allocate 20% or more of their improvement budgets to smart manufacturing initiatives, prioritizing real-time visibility and predictive maintenance.  🔹 McKinsey & Company finds that 88% of companies now use AI in at least one function, but scaling remains a challenge - high performers redesign workflows to unlock growth and innovation.  🔹 Market forecasts show industrial automation growing from $206B in 2024 to $378B by 2030 (10.8% CAGR), driven by Industry 4.0, and AI integration.  🔹 Edge computing is surging too, expected to reach $45B by 2033, enabling low-latency analytics and predictive quality control. What does this mean for our industry? Automation is becoming open, software-defined, and decoupled from proprietary hardware, creating a foundation for adaptability, sustainability, and resilience. AI is moving from pilot projects to embedded intelligence, powering predictive maintenance, autonomous operations, and sustainability gains. At Schneider Electric, we see this every day: open, software-defined automation unlocks innovation through openness, interoperability, and flexibility, enabling manufacturers to scale faster and respond dynamically to market shifts. Looking ahead: AI will not just augment operations, it will redefine competitive advantage. From generative design to autonomous workflows, the next wave of industrial transformation is already here. 👉 What are your reflections on 2025, and where do you see the biggest opportunities in 2026 and beyond?  

💡 OT DATA: Manufacturers now realize the hard truth - collecting data is easy, but turning it into value at scale is a complex challenge requiring industrial-grade solutions. I've spent time with manufacturers who've been down the DIY path with their shop floor data: 🛠️cobbling together open-source tools, wrestling with security issues, and struggling to scale beyond pilot projects. All while their valuable data remains trapped in operational silos. 🏆What separates winners in this space? True industrial-grade edge computing doesn't just collect data - it transforms operations. Here's what makes Siemens Industrial Edge fundamentally different: 1️⃣ Deployment flexibility: Unlike competitors offering only cloud orchestration, we provide both on-premise AND cloud management, fitting your existing IT infrastructure 2️⃣ Software-defined automation: Our platform extends beyond basic data collection to actual application deployment - including the world's first failsafe virtual PLC 3️⃣ Seamless integration: Edge isn't an island - it connects with Mendix for low-code development, Senseye for predictive maintenance, and our complete portfolio from planning to optimization 4️⃣ Open ecosystem built on OT foundations: We've partnered with leaders like Amazon Web Services (AWS) to bridge IT/OT while maintaining industrial robustness that DIY solutions can't match 📈 The most forward-thinking manufacturers understand this isn't about collecting MORE data, but making data more VALUABLE. They're leveraging platforms built from the ground up for industrial needs. ❓What's your experience with edge computing in manufacturing? Are you getting true value from your operational data or just collecting it? More info at links in first comment below this post👇🏼 #ManufacturingInnovation #IndustrialEdge #OTdata #SiemensXcelerator #DigitalTransformation #ITOT

India’s manufacturing sector is undergoing a transformation, fueled by data analytics, AI, and IoT. As global 𝐬𝐮𝐩𝐩𝐥𝐲 𝐜𝐡𝐚𝐢𝐧𝐬 𝐟𝐚𝐜𝐞 𝐝𝐢𝐬𝐫𝐮𝐩𝐭𝐢𝐨𝐧𝐬 and increasing 𝐝𝐞𝐦𝐚𝐧𝐝𝐬 𝐟𝐨𝐫 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲, Indian industries are turning to data-driven solutions to stay competitive. 🔹 Predictive Analytics for Demand Forecasting Manufacturers are leveraging predictive analytics to analyze historical data, market trends, and external factors like weather and geopolitical risks. This helps them anticipate demand fluctuations, reduce overproduction, and optimize inventory—ensuring that goods are produced and distributed more efficiently. 🔹 AI-Powered Optimization AI-driven automation is streamlining production lines, detecting bottlenecks, and recommending process improvements in real-time. Machine learning models are reducing downtime by predicting equipment failures before they occur, saving costs on maintenance and minimizing disruptions. 🔹 IoT for Real-Time Supply Chain Visibility With IoT sensors integrated across supply chains, manufacturers can track shipments, monitor storage conditions, and ensure quality compliance. Real-time data from connected devices enhances transparency, allowing swift decision-making and reducing losses due to spoilage, theft, or delays. 🔹 Reducing Waste & Enhancing Sustainability Data analytics is helping manufacturers reduce material waste by optimizing production processes. AI-powered quality control ensures that defects are detected early, lowering rejection rates. Companies are also using data to implement sustainable practices, such as reducing energy consumption and improving recycling efficiency. 🔹 Empowering MSMEs with Data-Driven Insights Micro, Small, and Medium Enterprises (MSMEs), which form the backbone of India's manufacturing sector, are increasingly adopting cloud-based analytics solutions. These tools enable small businesses to optimize procurement, manage inventory efficiently, and compete with larger players through data-backed decision-making. India’s march toward becoming a global manufacturing powerhouse depends on how effectively industries harness data analytics. The future lies in an intelligent, connected, and efficient supply chain ecosystem. 𝑯𝒐𝒘 𝒅𝒐 𝒚𝒐𝒖 𝒔𝒆𝒆 𝒅𝒂𝒕𝒂 𝒂𝒏𝒂𝒍𝒚𝒕𝒊𝒄𝒔 𝒔𝒉𝒂𝒑𝒊𝒏𝒈 𝒕𝒉𝒆 𝒇𝒖𝒕𝒖𝒓𝒆 𝒐𝒇 𝒎𝒂𝒏𝒖𝒇𝒂𝒄𝒕𝒖𝒓𝒊𝒏𝒈? #SCM #DataDrivenDecisionMaking #DataAnalytics #DataAnalyticsinManufacturing #dataanalyticsinsupplychain

𝐁𝐫𝐢𝐝𝐠𝐢𝐧𝐠 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠: 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐚𝐥 𝐈𝐨𝐓 𝐆𝐚𝐭𝐞𝐰𝐚𝐲𝐬 🌐 The boundary between Information Technology (IT) and Operational Technology (OT) has long hindered holistic industry operations. Industrial IoT gateways are the champions heralding change. ✨ 𝐒𝐧𝐚𝐩𝐬𝐡𝐨𝐭 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: - The IIoT gateway market surged ~14.7% within a year, nearing the $860 million mark, and this trajectory is predicted to continue through 2027. - Major players in this shift are Cisco, Siemens, Advantech, and MOXA. 🏭 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 𝐄𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧: IIoT gateways are pivotal in reshaping the manufacturing landscape. By retrofitting even older systems, they facilitate real-time data exchange between operations and IT/cloud realms. This harmonization yields key outcomes: reduced downtimes (as illustrated by Vitesco's preemptive malfunction detection), significant labor cost reductions, and optimized energy use. The result? Streamlined operations, significant savings, and enhanced productivity. 🚀 🛠️ 𝐃𝐞𝐞𝐩 𝐃𝐢𝐯𝐞: 1) 𝑰𝑻/𝑶𝑻 𝑺𝒚𝒏𝒄𝒉𝒓𝒐𝒏𝒊𝒛𝒂𝒕𝒊𝒐𝒏: Legacy equipment, often disconnected, is now plugged into the digital grid. IIoT gateways serve as conduits, ensuring swift, seamless data transitions to IT platforms. 2) 𝑮𝒂𝒕𝒆𝒘𝒂𝒚 𝑭𝒓𝒂𝒎𝒆𝒘𝒐𝒓𝒌𝒔: They're not one-size-fits-all. Four distinct architectures accommodate diverse enterprise needs, ensuring smooth data flows and heightened efficiency. 3) 𝑽𝒆𝒓𝒔𝒂𝒕𝒊𝒍𝒊𝒕𝒚: Modern IIoT gateways juggle multiple roles - from protocol translation to security management, making them indispensable in a robust IIoT ecosystem. 💼 𝐅𝐮𝐫𝐭𝐡𝐞𝐫 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: 1) 𝑺𝒐𝒇𝒕𝒘𝒂𝒓𝒆 𝑴𝒊𝒈𝒓𝒂𝒕𝒊𝒐𝒏: Companies are transitioning key applications to the cloud, elevating IIoT gateways as primary data traffic controllers. 2) 𝑯𝒂𝒓𝒅𝒘𝒂𝒓𝒆 𝑬𝒗𝒐𝒍𝒖𝒕𝒊𝒐𝒏: Gateways now sport multi-core processors, AI chipsets, and enhanced security elements, ensuring swifter and safer data processing. 3) 𝑩𝒆𝒏𝒆𝒇𝒊𝒕: IIoT gateways have led to profound IT/OT integrations. Examples include Vitesco Technologies Italy's advanced malfunction prediction and Corpacero's reduced repair costs thanks to predictive maintenance. The once aspirational fusion of IT and OT is now tangible, courtesy of IIoT gateways. The forthcoming industrial epoch? Seamlessly integrated, vastly efficient, and pioneering. 🔍 Source: IoT Analytics (https://lnkd.in/euj3wiUD)

⚙️ PLC vs DCS vs SCADA — Same World, Very Different Jobs In industrial automation, these three systems often get mentioned together. But they don’t compete they operate at different layers of control. Think of them as hands, brain, and eyes of an industrial operation. 🔹 PLC – The Fast Decision Maker (Machine Level) A Programmable Logic Controller (PLC) is built to make instant decisions on the shop floor. What it really does: • Executes logic, interlocks, and sequences • Reads sensors, drives actuators • Runs deterministic scan cycles (milliseconds) Typical control logic: IF (Sensor = TRUE) AND (Interlock = OK) THEN Output = ON Where PLCs shine: • Assembly & packaging lines • Discrete manufacturing • High-speed, repeatable operations 💡 Key strength: Speed + reliability + cost efficiency 🔹 DCS – The Process Brain (Plant Level) A Distributed Control System (DCS) is designed for continuous, tightly coupled processes where stability matters more than speed. What it really does: • Maintains steady-state operation • Coordinates multiple unit operations • Uses advanced control (PID, cascade, ratio) Classic control equation (PID): u(t) = Kp·e(t) + Ki∫e(t)dt + Kd·de(t)/dt Where: • e(t) = setpoint − process variable • u(t) = control output (valve, drive, etc.) Where DCS dominates: • Refineries & chemical plants • Power generation • Large continuous processes 💡 Key strength: Integrated control, redundancy, plant-wide optimization 🔹 SCADA – The System Overseer (Supervisory Level) SCADA doesn’t usually control fast processes directly it supervises them. What it really does: • Collects data from PLCs/DCS • Displays trends, alarms, dashboards • Sends high-level commands over large distances Typical data flow: Field Devices → PLC/DCS → SCADA → Operator Where SCADA excels: • Pipelines & power distribution • Water and wastewater networks • Remote assets spread over kilometers 💡 Key strength: Visibility, data logging, and remote control 🧩 The Big Picture (How They Fit Together) • PLC → Controls the machine • DCS → Controls the process • SCADA → Supervises the system Or simply: PLC acts, DCS stabilizes, SCADA visualizes. Understanding this hierarchy is critical when designing automation systems that are safe, scalable, and maintainable. #Automation #IndustrialControl #PLC #DCS #SCADA #ProcessControl #ControlSystems #Engineering #Industry

I believe AI creates real value when it tackles hard, physical problems — the kind that live in factories, warehouses, and service tasks. Recently, I learned the attached from a plastics machine manufacturer and logistics provider struggling with unpredictable production schedules, warehouse congestion, and reactive maintenance routines. When a structured AI implementation approach was brought into the equation the following outcome was achieved 👇 🔹 Smart Production Planning – Machine learning models forecasted demand and optimized resin batch production, cutting material waste by 18%. 🔹 AI-Driven Warehouse Logistics – Intelligent slotting and routing algorithms boosted order fulfillment rates by 25%, reducing forklift travel time and idle inventory. 🔹 Predictive Maintenance for Service Teams – Sensor data and pattern recognition flagged early signs of machine wear, reducing unplanned downtime by 30%. The result wasn’t automation replacing people — it was augmentation empowering people. Operators, warehouse managers, and service engineers gained real-time insights to make faster, better decisions. 💡 Takeaway: AI success in industrial environments isn’t about technology first — it’s about aligning data, people, and process to create measurable operational impact. #AI #IndustrialServices #SmartManufacturing #WarehouseOptimization #PredictiveMaintenance #DigitalTransformation #OperationalExcellence