How Manufacturers can Improve Operations With AI

𝗗𝗼𝗻’𝘁 𝗝𝘂𝘀𝘁 𝗥𝗲𝗮𝗱 𝗔𝗯𝗼𝘂𝘁 𝗔𝗜 𝗶𝗻 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴. 𝗔𝗽𝗽𝗹𝘆 𝗜𝘁. The AI headlines are exciting. But if you're a founder, engineer, or educator in manufacturing, here's the question that actually matters: 𝗪𝗵𝗮𝘁 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗱𝗼 𝘵𝘰𝘥𝘢𝘺 𝘁𝗼 𝘁𝘂𝗿𝗻 𝘁𝗵𝗲𝘀𝗲 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻𝘁𝗼 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻? Let’s get tactical. 𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗱𝗲𝗺𝗮𝗻𝗱 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 Tool to try: Lenovo’s LeForecast A foundation model for time-series forecasting. Trained on manufacturing-specific datasets. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re battling supply chain volatility and need better inventory planning. 👉 Tip: Start by connecting your ERP data. Don’t wait for perfect integration: small wins snowball. 𝟮. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝘄𝗶𝗻 𝗯𝗲𝗳𝗼𝗿𝗲 𝗯𝘂𝘆𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗻𝗲𝘅𝘁 𝗿𝗼𝗯𝗼𝘁 Tools behind the scenes: NVIDIA Omniverse, Microsoft Azure Digital Twins Schaeffler + Accenture used these to simulate humanoid robots (like Agility’s Digit) inside full-scale virtual factories. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re considering automation but can’t afford to mess up your live floor. 👉 Tip: Simulate your current workflows first. Even without a robot, you’ll find inefficiencies you didn’t know existed. 𝟯. 𝗕𝗿𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗤𝗔 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝟮𝟬𝟮𝟬𝘀 Example: GM uses AI to scan weld quality, detect microcracks, and spot battery defects: before they become recalls. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re relying on spot checks or human-only inspections. 👉 Tip: Start with one defect type. Use computer vision (CV) models trained with edge devices like NVIDIA Jetson or AWS Panorama. 𝟰. 𝗘𝗱𝗴𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗻𝘆𝗺𝗼𝗿𝗲 Why it matters: If your AI system reacts in seconds instead of milliseconds, it's too late for safety-critical tasks. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You're in high-speed assembly lines, robotics, or anything safety-regulated. 👉 Tip: Evaluate edge-ready AI platforms like Lenovo ThinkEdge or Honeywell’s new containerized UOC systems. 𝟱. 𝗕𝗲 𝗲𝗮𝗿𝗹𝘆 𝗼𝗻 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 The EU AI Act is live. China is doubling down on "self-reliant AI." The U.S.? Deregulating. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You're deploying GenAI, predictive models, or automation tools across borders. 👉 Tip: Start tagging your AI systems by risk level. This will save you time (and fines) later. Here are 5 actionable moves manufacturers can make today to level up with AI: pulled straight from the trenches of Hannover Messe, GM's plant floor, and what we’re building at DigiFab.ai. ✅ Forecast with tools like LeForecast ✅ Simulate before automating with digital twins ✅ Bring AI into your QA pipeline ✅ Push intelligence to the edge ✅ Get ahead of compliance rules (especially if you operate globally) 🧠 Each of these is something you can pilot now: not next quarter. Happy to share what’s worked (and what hasn’t). 👇 Save and repost. #AI #Manufacturing #DigitalTwins #EdgeAI #IndustrialAI #DigiFabAI

Data without intelligence is potential; intelligence without action is waste. Databricks' 𝟐𝟎𝟐𝟒 𝐒𝐭𝐚𝐭𝐞 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐚𝐧𝐝 𝐀𝐈 𝐑𝐞𝐩𝐨𝐫𝐭 showcases a decisive shift as industries transition from AI experimentation to widespread production, with manufacturing emerging as a standout sector. Companies are leveraging AI to optimize production, enhance quality control, and integrate operational data into decision-making processes. Key takeaways from the report include: • 𝟏𝟏𝐱 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞 in machine learning models reaching production, indicating industries are prioritizing real-world AI applications. • 𝟏𝟒𝟖% 𝐲𝐞𝐚𝐫-𝐨𝐯𝐞𝐫-𝐲𝐞𝐚𝐫 𝐠𝐫𝐨𝐰𝐭𝐡 in natural language processing (NLP) use in manufacturing, driving improvements in quality control and customer feedback analysis. • 𝟑𝟕𝟕% 𝐠𝐫𝐨𝐰𝐭𝐡 in vector database adoption, supporting retrieval augmented generation (RAG) to integrate proprietary data for tailored AI applications. • Manufacturing and Automotive lead the charge with a staggering 𝟏𝟒𝟖% 𝐲𝐞𝐚𝐫-𝐨𝐯𝐞𝐫-𝐲𝐞𝐚𝐫 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞 in adopting Natural Language Processing (NLP).  Would anyone have picked Manufacturing growing the fastest in NLP?!?! 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐃𝐨 𝐰𝐢𝐭𝐡 𝐓𝐡𝐢𝐬 𝐈𝐧𝐟𝐨? If you’re still debating AI’s value, you’re already late to the game. Manufacturers are moving from “what if” to “what’s next” by putting more AI models into production than ever before — 𝟏𝟏 𝐭𝐢𝐦𝐞𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐥𝐚𝐬𝐭 𝐲𝐞𝐚𝐫!  The most successful organizations are cutting inefficiencies, standardizing processes with tools like data intelligence platforms, and deploying solutions faster. This isn’t just about keeping up with the Joneses; it’s about outpacing them entirely. 𝟏) 𝐈𝐧𝐯𝐞𝐬𝐭 𝐢𝐧 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Use tools like Retrieval Augmented Generation (RAG) and vector databases to turn AI into a competitive advantage by integrating your proprietary data. Don’t rely on off-the-shelf solutions that lack your industry’s nuance. 𝟐) 𝐀𝐝𝐨𝐩𝐭 𝐚 𝐂𝐮𝐥𝐭𝐮𝐫𝐞 𝐨𝐟 𝐒𝐩𝐞𝐞𝐝: The report highlights a 3x efficiency boost in getting models to production. Speed matters — not just for innovation, but for staying ahead of market demands. 𝟑) 𝐄𝐦𝐛𝐫𝐚𝐜𝐞 𝐎𝐩𝐞𝐧 𝐒𝐨𝐮𝐫𝐜𝐞 𝐚𝐧𝐝 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧:  The rise of open-source tools means you can innovate faster without vendor lock-in. Build smarter, more cost-effective systems that fit your needs. 𝟒) 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞 𝐀𝐈 𝐟𝐨𝐫 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐆𝐚𝐢𝐧𝐬: AI isn’t just for customer-facing solutions. Use it to supercharge processes like real-time equipment monitoring, predictive maintenance, and supply chain resilience. 𝐅𝐮𝐥𝐥 𝐑𝐞𝐩𝐨𝐫𝐭: https://lnkd.in/eZCrq_nF ******************************************* • 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!

The AI hype cycle is over. Now it’s time for real business value. Organizations spent the last year experimenting with AI tools, often with mixed results. Those who succeeded found that strategic integration is what drives ROI. Here's 11 ways top performers are achieving measurable ROI on their AI investment: 1. Process Automation Integration → Embed AI in existing workflows → 40-60% reduction in manual tasks → Focus on high-volume, repetitive processes Pro tip: Start with processes that have clear metrics and high error rates. 2. Customer Service Enhancement → AI-powered ticket routing and resolution → 30% reduction in response time → Improved customer satisfaction scores Pro tip: Train AI on your top performers' responses to maintain brand voice and solution quality. 3. Data Analytics Acceleration → Automated insight generation → Predictive modeling at scale → 50% faster decision-making cycles Pro tip: Build dashboards that translate AI insights into actionable recommendations for non-technical teams. 4. Revenue Generation → AI-enhanced lead scoring → Personalized customer journeys → 25% increase in conversion rates Pro tip: Use A/B testing to continuously refine AI models against actual sales outcomes. 5. Cost Optimization → Smart resource allocation → Predictive maintenance → 20-30% reduction in operational costs Pro tip: Create an AI savings tracker to document and communicate wins to stakeholders. 6. Product Development → AI-driven feature prioritization → Automated testing and QA → 40% faster time-to-market Pro tip: Implement AI feedback loops between customer support and product teams for continuous improvement. 7. Risk Management → Real-time fraud detection → Compliance monitoring → 65% reduction in false positives Pro tip: Regular model retraining with new fraud patterns keeps detection rates high. 8. Employee Productivity → AI-powered knowledge management → Automated routine tasks → 3-4 hours saved per employee weekly Pro tip: Create AI champions in each department to drive adoption and share best practices. 9. Supply Chain Optimization → Demand forecasting → Inventory management → 30% reduction in stockouts Pro tip: Combine internal data with external factors (weather, events, trends) for better predictions. 10. Content Creation → Automated first drafts → Multichannel optimization → 60% faster content production Pro tip: Build a prompt library of your best-performing content formats and styles. 11. Quality Control → Computer vision inspection → Defect prediction → 45% reduction in quality issues Pro tip: Start with human-in-the-loop systems before moving to full automation. The key? Integration. Success comes from embedding AI into core business processes, not treating it as a standalone solution. What's your organization's biggest AI ROI win? Share below 👇 ♻️ Repost if your network needs this AI implementation blueprint. Follow Carolyn Healey for more content like this.

𝗜𝗺𝗮𝗴𝗶𝗻𝗲 𝗬𝗢𝗨𝗥 𝗳𝗮𝗰𝘁𝗼𝗿𝘆 𝘄𝗶𝘁𝗵: ✅ No more bulky fixtures ✅ No more reliance on mechanical guides ✅ Just AI-driven with real-time control My 𝗧𝗵𝘂𝗿𝘀𝗱𝗮𝘆 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 explains how we use AI to ensure the correct bolting sequences on some critical operations. 🔩🤖 In most factories, tightening bolts in the correct sequence is critical to ensuring a secure assembly. Think about how you tighten the bolts on a wheel— you don’t go in a circle; you follow a zigzag pattern. Today, ensuring the bolting tool is in the correct position before activation requires 𝗹𝗮𝗿𝗴𝗲 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝗰𝗮𝗹 𝗳𝗶𝘅𝘁𝘂𝗿𝗲𝘀 𝘄𝗶𝘁𝗵 𝘀𝗲𝗻𝘀𝗼𝗿𝘀. These structures detect the tool’s X, Y, and Z coordinates, preventing it from turning on unless it’s precisely positioned. 𝗕𝘂𝘁 𝘄𝗵𝗮𝘁 𝗶𝗳 𝘄𝗲 𝗰𝗼𝘂𝗹𝗱 𝗲𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗲 𝘁𝗵𝗮𝘁 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝗰𝗮𝗹 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝗹𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿? That’s precisely what we’ve done using computer vision AI. Like self-driving cars that detect objects in 3D space, we use AI to track the bolting tool in real-time, identifying its exact location without any physical positioning sensors. 💡 The AI knows where the socket is, whether your hand is in the way, and when the tool is in the correct position—allowing the system to activate the bolting tool only at the right moment. But that’s not all. 𝗗𝗮𝘁𝗮 𝗯𝗶𝗮𝘀 plays a crucial role in AI training. If we train the model on one set of hands, it may struggle to recognise others. However, we can also use bias to our advantage — for instance, deliberately training AI to recognise only hands with gloves to enforce safety protocols. 🔎 This our future of precision manufacturing—replacing physical constraints with AI-driven intelligence. Explore more of our manufacturing innovations by checking out our previous videos here: https://lnkd.in/dU6aJ9s2 📢 Stay ahead of the latest in AI and automation—like and follow our page for more insights! #ThursdayThought #AIinManufacturing #ComputerVision #IndustrialAutomation #SmartFactories #DigitalTransformation #BiasInAI #BoltingSolutions #FactoryAutomation #Jendamark #Odin

𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐖𝐞𝐟𝐚𝐛 𝐀𝐈 I've spent years building Vendosmart (𝐦𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠) and ProQsmart (𝐀𝐈 𝐩𝐫𝐨𝐜𝐮𝐫𝐞𝐦𝐞𝐧𝐭). Combined, we've processed thousands of manufacturing orders and optimized supply chain decisions. 𝐍𝐨𝐰 𝐰𝐞 𝐚𝐫𝐞 𝐥𝐚𝐮𝐧𝐜𝐡𝐢𝐧𝐠 𝐖𝐞𝐟𝐚𝐛 𝐀𝐈: the platform that unifies them.  A glimpse of what AI can do is shown here! 𝐓𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦: Hardware innovators lose 6-12 months to manufacturing delays. Poor supplier visibility. Manual coordination. Quality surprises. 𝐓𝐡𝐞 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧: AI that eliminates manufacturing friction. 𝐖𝐡𝐚𝐭 𝐰𝐞 𝐝𝐞𝐥𝐢𝐯𝐞𝐫: 🎯 34% faster design-to-production cycles → AI analyzes CAD files, suggests optimal materials, matches suppliers instantly 📊 Real-time manufacturing intelligence → AI predicts delays, automatically coordinates fixes across supply chain ⚙️ Zero manufacturing surprises → AI monitors quality at every step, flags issues before parts ship 🚀 Single source of truth → One dashboard. One contact. AI managing prototype to production. 👨🔬 Theory of Constraints consulting → Identify bottlenecks in project management to improve efficiency Who wins: Climate tech scaling carbon capture hardware. EV startups racing to market. Consumer tech & Robotics teams building the future. 𝐖𝐞 𝐀𝐑𝐄 𝐲𝐨𝐮𝐫 𝐦𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 𝐭𝐞𝐚𝐦, 𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐛𝐲 𝐀𝐈 𝐭𝐡𝐚𝐭 𝐧𝐞𝐯𝐞𝐫 𝐬𝐥𝐞𝐞𝐩𝐬. If you are looking to solve manufacturing hassle, 𝐬𝐢𝐠𝐧 𝐮𝐩 𝐟𝐨𝐫 𝐛𝐞𝐭𝐚: 𝐋𝐢𝐧𝐤 𝐢𝐧 𝐜𝐨𝐦𝐦𝐞𝐧𝐭𝐬.

With all the hype around gen AI, it's easy to forget how broad a field AI is and the impact other methods currently have on the bottom line. Machine Learning (ML) has been transforming manufacturing (and more) for decades. Here's how Nils o. Janus increases his 'golden batch ratio' 3-5% to save millions of Euros a year at Covestro 👇 1. Gather knowledge from first principles AI models and combine it with sensor readings from plant machinery. 2. Train a machine learning model to learn how production processes should be run optimally. 3. Combine 1 and 2 to give real-time predictions to plant operators about 5 set points that they have an influence over, then recommend how to improve them. The result? The golden batch ratio increases 3-5%. That means: - More finished goods from the same raw materials. - Less waste. - Millions saved every year on the balance sheet. This same approach can be applied to improve efficiency in use cases ranging from finance to people operations to network infrastructure. It's all about using the right AI technique for the right job.

🙋♂️ Raise your hand if you’ve been personally victimized by AI. (Bonus points if you’re in manufacturing.) Too many orgs are still treating AI like a science fair project—just something to wave in front of the board to say, “Look! We’re innovative!” when really, it’s just a robot awkwardly moving pallets to the wrong corner of the plant. And I get it. I really do. We’re not exactly swimming in free time out here. Nobody’s asking for another overhyped tool to babysit. But if your AI isn’t reducing downtime, increasing throughput, or improving quality in real-time, you’re not innovating. You’re lighting money on fire and calling it “strategy.” So instead, let’s talk tactics—because this one’s actually worth your time: Case Study: John Deere’s AI-Driven Welding Quality Control Problem: Porosity defects in robotic welding = costly mess. ✅ First Green Flag: They identified real pain points, not hypothetical “opportunities.” ✅ Second Green Flag: Partnered with Intel Corporation, not some rando AI startup that promises a digital twin of your soul and then ghosts you. ✅ Third Green Flag—they measured outcomes: 80% faster weld inspections 10% more efficient welding 40% quicker material restocks 18,000 parts inspected in under 6 seconds 5% cycle time reduction with real-time defect stops The smart manufacturing market is set to explode from $392B in 2025 to $900B+ by 2034. The companies that win aren’t the ones with the flashiest AI demo. They’re the ones who make AI serve operations, not optics. #SmartManufacturing #Manufacturing #AI #Industry40

If you're in manufacturing, you know that accurate demand forecasting is critical. It's the difference between smooth operations, happy customers, and a healthy bottom line – versus scrambling to meet unexpected demand, dealing with excess inventory and having liquidity issues, or losing out on potential sales and not meeting your Sales / EBITDA targets. But with constantly shifting customer preferences, disruptive market trends, and global events throwing curveballs, it's also one of the toughest nuts to crack. While often reliable in stable environments (especially in settings with lots of high-frequency transactions and no data sparsity), traditional stats-based forecasting methods aren't built for the complexity and volatility of today's market. They rely on historical data and often miss those subtle signals, indicating a major shift is on the horizon. Traditional stats-based approaches are also not that effective for businesses with high data sparsity (e.g., larger tickets, choppier transaction volume) That's where AI/ML-enabled forecasting comes in. Unlike foundational stats forecasting, it can include various structured and unstructured data, such as social media sentiment, competitor activity, and various economic indicators. One of the most significant advancements in recent years is the rise of powerful open-source AI/ML packages for forecasting. These tools, once the domain of large enterprises with extensive resources or turnkey solution providers (with hefty price tags), are now readily accessible to companies of all sizes, offering a significant opportunity to level the playing field and drive smarter decision-making. The power of AI and ML in demand forecasting is more than just theoretical. Companies across various industries are already reaping the benefits: • Marshalls: This UK manufacturer used AI to optimize inventory management during the pandemic. It made thousands of model-driven decisions daily and managed orders worth hundreds of thousands of pounds. • P&G: Their PredictIQ platform, powered by AI and ML, significantly reduced forecast errors, improving inventory management and cost savings. • Other Industries: Retailers, e-commerce companies, and even the energy sector are using AI to predict everything from consumer behavior to energy demand, with impressive results. If you're in manufacturing or distribution and haven't explored upgrading your demand forecasting (and S&OP) capabilities, I highly encourage you to invest. These capabilities are table stakes nowadays, and forecasting on random spreadsheets and basic methods (year-over-year performance, moving average, etc.) is not cutting it anymore.