Industrial Engineering Workflow Automation

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.

This is the Boeing 737 wheel well. And it’s closer to a spacecraft than most people realize. Thousands of parts operating in a volume smaller than a walk-in closet. Hydraulic systems running at maximum possible psi. Thermal swings, vibration, contamination, human maintenance variables all at once. Failure tolerance? Essentially zero. What’s remarkable isn’t the complexity. It’s that this system works tens of millions of flight hours globally. Much of this engineering in the legacy aircraft still relies on static models, fragmented simulations, and experience locked in people’s heads. This is where digital twins + AI become mission-critical. Not dashboards. Not buzzwords. But living system models that: • Predict fatigue before it manifests • Correlate anomalies across entire fleets • Simulate maintenance actions before technicians touch hardware • Optimize mass, routing, and reliability before first article The leaders in this space already know this: Future advantage isn’t just better hardware it’s systems intelligence at scale. The next leap in aerospace , space & defense won’t look dramatic. It will look like fewer surprises. #AerospaceEngineering #SpaceSystems #MissionAssurance #DigitalEngineering #DigitalTwin #AIinAerospace #SystemsEngineering #Defense

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?  

Reflections and Insights: 2024 and Beyond In 2024, I learned that the most impactful transitions are not departures but transformations. As I stepped back from operational roles, I observed a pivotal shift I had long anticipated: mobile robotics have moved beyond being tactical tools to becoming strategic necessities, especially in public safety and defense. This year underscored four critical insights into our industry’s evolution: 1) The integration of mobile robotics within the Tactical Bubble is no longer optional—it’s essential for modern operations. 2) Private mesh networks (MANET) are solidifying their role as the backbone of reliable tactical communications. 3) Bridging the gap between technical capabilities and tactical operations remains our greatest challenge—and our greatest opportunity. 4) It's not just hardware; proper software (from AI to TAK, to autonomy) are the key to fully leveraging the benefits of uncrewed systems in the air, on the ground, on water and sub water. Key Developments Shaping Our Industry in 2024: Deployment and training of advanced mobile robotics across multiple agencies. Seamless integration of air, ground, and maritime robotics into unified tactical operations. Transformation of the Tech/Tac Bubble concept into actionable, real-world implementations. Significant industry shifts in military drone and mobile robotics capabilities amidst growing competition. Looking Ahead to 2025 While I didn’t initially expect to see this new year, I’ve made it here—and my focus remains steadfast. As I continue to scale back operational roles, my efforts will center on advancing mobile robotics innovation through strategic advisory and knowledge sharing. Key projects I’ve nurtured for years are being transitioned to capable individuals and entities, ensuring they remain aligned with the industry's pressing needs: standardization, immersive training, connectivity, and user-friendly solutions. To the global public safety community, defense sector, and mobile robotics innovators and manufacturers: The technology is proven. The infrastructure is advancing. We have validated countless claims and use cases. Now, the focus must shift to proper implementation, selecting the right hardware and software, ensuring comprehensive tactical training, and maintaining data-driven validation of claims. Together, we are shaping the future of mobile robotics, ensuring they serve as a force multiplier for safety, security, and innovation. Wishing you all a safe start into 2025 and a year of health, success, passion and the ability to stay grounded. #UAVsForGood #MobileRobotics #PublicSafety #TacBubble #Drones #UAVs #Training #2024Review #2025Forecast Image courtesy of FLYMOTION

How to make a super automated module product line？ First, high-quality and highly automated machines are the basics. Take the cell tabbing and stringing machine as an example, Canadian Solar Inc. is the first one in the industry to bring half-cell and multiple busbar tech into the mass production. After seven years ‘development, Canadian Solar increased the soldering speed by about 3 times and lowered the defect rate by 50% when wafers are thinned by more than 70%. Second, we leverage #AI to do what they do best - image and video analysis, defect identification and root cause analysis. We started to use neural networks to find defects in EL inspection images as early in 2018. Now, all EL and appearance defect identification and analysis are done by AI at our automated lines, greatly improve the efficiency and quality of this highly repetitive work. Third, we use conveyor lines to transport products at work and automated guided vehicles (AGVs) to transport materials. There is no need for people to do the lifting and transportation work any longer, which reduces the labor intensity significantly. Fourth, an information system enabling the info flow from customers and material suppliers to the production lines is essential. Our info system connects customer relationship management (CRM), supplier relationship management (SRM), enterprise resource planning (ERP) and manufacturing execution system (MES). Highly personalized requests from customers can be implemented on automated production lines flawlessly.  Last by not least, we have a dedicated and experienced team to run the lines. In the era of artificial intelligence, people are still the core, which is Canadian Solar’s irreplaceable asset. This team has increased production efficiency fourfold since we first introduced half-cell and muti-busbar automated module line seven years ago. I am proud of them and believe they will bring more progress to the industry in the future.  #automation #automanufacture #solar #autoproduction

This year, India’s defense sector unveiled advancements in AI that are reshaping military strategies & boosting national security. Here’s what the data tells us: --> AI is now central to defense modernization. --> Collaboration across sectors is driving innovation. Let’s explore these in detail. 1️⃣ AI-Powered Technologies Transforming Defense India’s armed forces are deploying AI across critical areas: ➤ Autonomy in operations: AI-enabled systems like swarm drones & autonomous intercept boats enhance mission precision, reduce human risk, & improve tactical outcomes. ➤ Intelligence, Surveillance, & Reconnaissance (ISR): AI-based motion detection & target identification systems provide real-time alerts for better situational awareness along borders. ➤ Advanced robotics: Silent Sentry, a 3D-printed AI rail-mounted robot, supports automated perimeter security & intrusion detection. Example: Swarm drones use distributed AI algorithms for dynamic collision avoidance, target identification, & coordinated aerial maneuvers, providing versatility in both offensive & defensive tasks. 2️⃣ Collaboration as the Catalyst for Innovation India’s AI advancements are the result of partnerships between the government, private industries, & research institutions. ➤ Indigenous solutions: 100% indigenously developed systems like the Sapper Scout UGV for mine detection. ➤ Startups and SMEs: Innovative contributions from tech firms and startups have fueled projects like AI-enabled predictive maintenance for naval ships and drones. ➤ Global export potential: Systems like Project Drone Feed Analysis and maritime anomaly detection tools are export-ready, positioning India as a major global defense tech player. 3️⃣ The Data-Driven Case for AI ➤ Efficiency: AI-driven systems exponentially improve surveillance coverage and reduce operational time. For example, the Drone Feed Analysis system decreases mission costs while expanding surveillance areas. ➤ Safety: Predictive AI systems in vehicles and maritime platforms enhance safety by identifying potential risks before failures occur. ➤ Economic impact: AI-powered predictive maintenance for critical assets like naval ships and aircraft maximizes uptime while minimizing costs. Real Impact ➤ Swarm drones: Affordable, scalable, and capable of BVLOS operations, offering precision in combat. ➤ AI-enabled maritime systems: Detect anomalies in vessel traffic, securing trade routes and protecting economic interests. ➤ AI-driven mine detection: Enhances soldier safety while automating high-risk tasks. What does this mean for defense organizations? AI isn’t just modernizing defense; it’s placing it firmly in the global defense innovation market. With bold policies, dedicated budgets, and a growing ecosystem of public and private sector players, this will help lead the next wave of AI-driven defense technologies. But the question remains: How do we ensure these technologies are deployed ethically and responsibly? Agree?

Why recreate humans when you can redesign the process? Tesla's Robot Strategy: A Manufacturing Reality Check Here's a number that caught my attention: $200K for a humanoid robot vs. $20K for specialized automation that does the job better. I've been working with AI in production environments for years, and Tesla's Optimus approach makes me think... there might be a more efficient way to solve this. Everyone gets excited about humanoid robots replacing workers. But here's the question I keep asking: Why recreate humans when you can redesign the process? What I Learned About Manufacturing Automation In production AI, I discovered something important: the best automation doesn't copy humans: it eliminates the need for human-like movements entirely. During my time at Intel Corporation, the most successful improvements came from: → Redesigning workflows around machine capabilities (not making machines work like humans) → Using specialized tools for specific jobs (not general-purpose solutions) → Working with existing systems (not replacing everything) Tesla's humanoid approach seems like the expensive path. What Manufacturing Really Needs Think about this: Why build a robot with hands when you can change the assembly line to not need hands at all? What actually works in manufacturing: • Pick-and-place systems → 99.9% accuracy, $50K investment • Vision inspection → 24/7 quality control, finds defects immediately • Collaborative robot arms → Work with humans, deploy in weeks not years These solutions aren't as exciting, but they change production lines in months. The Numbers Tell a Different Story This is what I find interesting: A $20K specialized robot often outperforms a $200K humanoid robot for specific manufacturing tasks. Looking at the data: • Specialized automation: 6-month return on investment • General humanoid robots: 5+ years (maybe never) • Process redesign + targeted automation: 3-month return Tesla's Real Opportunity Instead of expensive human-like robots, what if Tesla focused on: Manufacturing AI that: - Predicts when machines will break before it happens - Optimizes assembly steps in real-time - Prevents quality problems through smart process control This approach could transform manufacturing faster. My Take While everyone builds humanoid robots, I see a big opportunity in smart automation that makes existing manufacturing much more efficient. The future of manufacturing might not be robots that look like us. It might be systems so intelligent they make human-like robots unnecessary. Through DigiFab, I work on bridging AI and manufacturing. Sometimes the best solutions don't look like science fiction, they just work much better.

THE TECHNOLOGY BEHIND VEHICLE MANUFACTURING PRODUCTION LINES ENTIRELY OPERATED BY ROBOTS. Robotic vehicle manufacturing lines are fully automated production environments where robotic arms, AI systems, autonomous carts, and smart inspection tools perform every major function in assembling a vehicle—from welding, painting, bolting, and component installation to real-time quality control—without direct human intervention. These production lines use industrial 6-axis robotic arms, vision-guided robots, and AI-powered PLC controllers that allow machines to detect parts, adapt to tolerances, correct errors, and even learn improvements over time. Cobots (collaborative robots) also interact safely with humans in inspection zones or final detailing. AGVs (automated guided vehicles) and AMRs (autonomous mobile robots) transport parts, while high-precision robots handle laser welding, adhesive application, part alignment, and painting using electrostatic technology. Entire lines are often monitored via centralized IIoT dashboards, providing predictive maintenance and real-time analytics. Applications and Benefits Include: Complete vehicle body assembly with zero human contact Laser-guided chassis and engine installations 3D vision systems for defect detection and alignment Enhanced speed, precision, and consistency Reduced human error and injury risk Scalability with minimal downtime Top 12 Fully Robotic Vehicle Manufacturing Lines (With Manufacturer & Location): Tesla Gigafactory (Model Y Line) – USA/Germany/China – ~$5B setup BMW iFACTORY Robotic Plant – Germany – ~$2.3B setup Toyota Smart Factory (Tsutsumi Plant) – Japan – ~$2.8B setup Volkswagen Transparent Factory – Germany – ~$1.7B setup Hyundai Ulsan Robotic Assembly – South Korea – ~$3.1B setup NIO NeoPark Fully Automated Facility – China – ~$2.5B setup BYD Xi’an Intelligent EV Plant – China – ~$2B setup Ford BlueOval City Plant – USA – ~$5.6B setup Mercedes-Benz Factory 56 – Germany – ~$1.6B setup Volvo Torslanda Smart Plant – Sweden – ~$1.9B setup Geely Robotic Smart Plant – China – ~$2.1B setup Lucid AMP-1 Robotic Facility – USA – ~$1.3B setup These fully robotic production lines represent the future of automotive manufacturing, where precision never sleeps, productivity never halts, and innovation flows through every robotic joint and conveyor belt.

A guy who spent a decade running Amazon India now runs Nestlé India. He's pushing Amazon-style digitization... targeting full rollout by 2026.   What makes this interesting isn’t the digitization itself. Every FMCG company talks about digitization. What’s different is the approach Tiwary brings from Amazon.   At Amazon, you digitize the last mile first. You build real-time visibility at the point closest to the customer, then work backwards. Most FMCG digitization does the opposite. They start with SAP at headquarters and hope it trickles down to the distributor in a tier-3 town. It rarely does.   Nestle India reaches 5.2 million retail outlets through 10,000+ distributors across 209,050 villages. That’s not a network you digitize from the top down. You need tracking, verification, and data capture at the edges. Where the actual delivery happens.   Meanwhile, Nestle globally just completed phase one of the world’s largest SAP S/4HANA Cloud deployment. 50,000 users. 112 countries. 19.5 hours of downtime. The infrastructure backbone is being built.   But the real test for Tiwary will be the last 10 kilometres. The stretch between a distributor’s warehouse and a kirana store in rural India. That’s where digitization either works or becomes an expensive ERP implementation that nobody at the ground level uses.   If Nestle India is betting its future on supply chain digitization, every FMCG company needs to ask: are you digitizing from the top down, or the last mile up? #supplychain #operations #logistics #digitizing #ai #nestle

Precision in Separation: Distillation Column Control Demystified Distillation columns are the workhorses of separation in oil refineries, petrochemical plants, and chemical industries. Whether it’s crude fractionation, ethylene recovery, or solvent purification, effective control of these columns is essential for achieving stable operations, product quality, and energy efficiency. What Does a Distillation Column Do? At its core, a distillation column separates a multi-component mixture based on differences in boiling points. It creates a temperature gradient: hotter at the bottom and cooler at the top. Vapors rise, liquids descend, and internal trays or packing facilitate mass and heat transfer. Key Components: Feed tray: Where the mixture is introduced. Trays or packing: Provides contact area. Reboiler: Heats the bottom to produce vapor. Condenser: Cools the top vapor to create reflux. Reflux Drum & Pump: Controls the reflux ratio. Control Strategies: What’s Being Controlled and How? 1. Top and Bottom Product Composition: Controlled using temperature as an indirect measure of composition (when analyzers aren’t installed). Controlled by manipulating reflux flow (top) and reboiler steam (bottom). 2. Column Pressure Control: Affects boiling points and separation efficiency. Maintained via vent valves, backpressure regulators, or condenser cooling. 3. Feed Flow and Preheat: Fluctuations in feed rate or composition can destabilize the column. Compensation using feedforward or ratio control is essential. 4. Reflux Ratio and Reboiler Duty: Reflux improves overhead purity; reboiler supports vapor generation. Balance is key: too much reflux = energy loss; too little = poor separation. Advanced Control Tactics: Cascade Control: Secondary loop stabilizes steam or coolant flow to quickly correct disturbances. Model Predictive Control (MPC): Optimizes column performance by predicting future responses and adjusting multiple variables simultaneously. Inferential Control: Uses soft sensors to estimate composition from temperature profiles. Challenges You Might Encounter: Column flooding or weeping Sluggish temperature response Composition control delay Interaction between pressure and temperature Foaming or entrainment Why This Matters: A well-tuned distillation control system: Maximizes product yield Reduces energy usage Extends equipment life Improves operator confidence Distillation is more than just heating and condensing it’s a fine-tuned balance of thermodynamics, fluid dynamics, and control engineering. Have you dealt with startup tuning, swing in feed quality, or retrofitting analyzers in your plant? I'd love to hear your experiences or challenges in distillation control. #Distillation #ProcessControl #ChemicalEngineering #ControlSystems #OilAndGas #Petrochemicals #PlantOptimization #RefineryOperations #Instrumentation #Automation