How Data Drives Smart Manufacturing

Here’s what most Manufacturing AI leaders get wrong: They start with the tech. “What model should we use?” “Can we try GenAI for this?” That’s the fastest way to burn your AI budget. Here’s what actually works: Start by asking this: 👉 Where are we losing time or money on manual decisions and do we have data on those steps? Let’s break that down: 🔍 Step 1: Spot the friction - Look for: Repetitive tasks (scheduling, inspection, calibration) Frequent decisions made by humans under pressure Any workflow where small mistakes cost big money 📊 Step 2: Check for data - Ask: Do we collect timestamps, sensor logs, machine status, operator input? Can we trace what decisions were made, by whom, and when? 💥 Step 3: Now, apply AI - Examples that actually move the needle: Predictive maintenance from vibration data AI-driven scheduling based on real-time bottlenecks Defect detection using existing camera feeds Most “AI projects” fail because they’re solving invisible problems with expensive tools. Here’s the truth: AI isn’t a magic wand. It’s a force multiplier. If your process is broken, it just breaks "faster." So forget buzzwords. Build better questions. That’s the real blueprint for impact. #manufacturing #AI #industrialAI #smartfactory #automation #aiops #productivity #digifabai #AIstrategy

While GenAI is capturing the headlines, Autonomous Mobile Robots are beginning to revolutionize internal logistics and material handling on factory floors. AMRs are intelligent, flexible systems leveraging advanced sensors, AI, and real-time data to navigate dynamic environments. Beyond task automation, AMRs are data sources, providing a wealth of information on material flow patterns, transport times, location histories, task completion rates, battery status, and environmental conditions. This is more than just robot telemetry; it's a dataset reflecting the pulse of your operations. For CIOs and manufacturing leaders, this data isn't just interesting; it's the potential backbone of a data-driven manufacturing environment. By strategically leveraging this data and integrating it with existing enterprise systems like Manufacturing Execution Systems (MES), Warehouse Management Systems (WMS), and Enterprise Resource Planning (ERP), we can unlock incredible value. This integration is often complex, particularly with legacy systems that may lack modern APIs or use proprietary data formats. It requires careful planning, potential custom development or middleware, and ensuring robust network infrastructure like industrial-grade Wi-Fi coverage. This reminds me of the challenges we faced in getting up to the minute supply chain data at Sportsman’s Warehouse during the pandemic enabling us to offer realistic delivery commitments to customers. The payoff is real-time visibility into material handling dynamics and operational bottlenecks, enabling data-driven decision-making that optimizes material flow, dynamically adjusts routes based on congestion, predicts maintenance needs, and enhances overall production efficiency. Think about the possibilities: Optimizing material delivery timing just-in-time for specific workstations based on real-time production needs detected via MES, automatically rerouting AMRs around unexpected obstacles, or using historical AMR data combined with WMS data to identify inefficiencies in facility layout or inventory placement. That’s not just moving boxes; it is optimizing the entire internal logistics ecosystem. The CIO has the opportunity to champion the holistic approach required for this tight systemic and data integration. It involves developing a clear AMR strategy aligned with business goals, preparing necessary IT infrastructure, championing robust cybersecurity for these connected systems, guiding vendor evaluation, driving change management, and establishing strong data governance frameworks. A "start small, learn fast, scale smart" approach through pilot projects is invaluable for de-risking and optimizing subsequent phases, especially for mid-sized manufacturers. What operational insights do you believe can be unlocked by integrating AMR data with existing systems? Share your thoughts below! 👇 #Manufacturing #Robotics #AI #DataAnalytics #Industry40

This is a great little case study by Paulo Jorge Ribeiro that demonstrates how Business Intelligence and Analytics can be used successfully to make significant and beneficial changes. Summary: GlobalCo, a large manufacturing company, changed the way it handled management accounting by switching from spreadsheets to modern Business Intelligence and Analytics (BI&A) tools. Before, employees like Maria had to manually gather and organize data from different systems, which was slow and prone to mistakes. Budgets were based on past numbers and guesswork, making it hard to respond to sudden market changes. After adopting BI&A, GlobalCo created a single source for all their data, helping Maria and her team get accurate, real-time information. They could now quickly find out why costs were rising and use scenario planning to make smarter budgeting decisions. With less time spent on manual tasks, Maria and David were able to work more closely with other departments. They used data to solve problems, improve production, and cut costs. For example, they spotted energy waste during hot weather and fixed it before it got expensive. Maria helped reduce supply delays by changing how inventory was managed. David helped design better products by testing different materials through simulations. BI&A also helped track performance using KPIs and identify risks early, like supply shortages or price changes. Thanks to this new approach, GlobalCo built a strong, data-focused culture that helped them stay ahead in a fast-changing industry. Key Takeaways: 💡 Old System Problems: Manual data work was slow, error-prone, and didn’t support quick decisions. 💡 Better Tools, Better Insights: BI&A gave real-time data, made budgets smarter, and helped find the real reasons behind cost changes. 💡 Smarter Decisions: Employees used data to spot hidden problems and make improvements in operations and product design. 💡 Teamwork Improved: Accountants worked more closely with other teams to find solutions and improve performance. 💡 Tracking Success & Reducing Risks: BI&A made it easier to measure results and avoid future problems. 💡 A New Way of Working: GlobalCo became more flexible, smarter, and ready for the future by using data in every part of the business. Read the full article below, or download it by clicking the square of brackets in the lower right hand corner, then clicking the download arrow. To share this case study with your followers, please hit like/repost and leave a comment. --------------- ▪ Follow me🚶♂️🚶♀️for more insights ▪ Join 🤝 the PACE forum for discussion - (Linktree in my profile) ▪ Click the 🔔 to get notified of new posts (top right of my profile) ▪ Subscribe 🖊 to my monthly newsletter #accountingandaccountants #fpanda #casestudy Lukas Sundahl, CMA, CSCA, MBA Kevin Appleby Abdul Khaliq   

Today we flew to Calgary to find out how Optima are “Turning Data Into a Manufacturing Superpower”! Born in 1990, forged in the Canadian Rockies, and raised on the principle that “good enough” is never enough, OPTIMA is the kind of company that makes other shops wish they’d paid more attention in geometry class. Before Datanomix, tracking production at Optima was like using a sundial to time a drag race. But now Datanomix hands them a crystal-clear dashboard, showing exactly what’s happening on the shop floor, in real-time, with no manual input. That means no sticky notes, no tribal knowledge, and no wandering around asking if the job’s done yet. ERP systems, bless their digital hearts, are often only as good as the data they’re given. Most shops feed them leftovers and wonder why the forecast’s fuzzy. With Datanomix, Optima’s ERP is finally on a five-star diet of high-quality, real-time production intel. Optima is now tracking energy consumption like a financial advisor monitors spending during Black Friday. Every spindle turn, every idle second, every watt, it’s all logged and leveraged. With Datanomix, Optima can now plan ahead like a chess grandmaster hopped up on espresso. Lead times are commitments, powered by data-driven truth. And in an era where being late can cost you the job, that kind of foresight is a competitive superpower. If you’re still relying on whiteboards, sticky notes, and your uncle Dave’s gut feelings, just know that the future isn’t waiting around. It’s already being machined in Calgary. Videos coming soon to the MTDCNC channels. Greg Lisa Ember Cesar

Your manufacturing plant is already talking. The question is—are you listening? Every second, your production line sends invisible signals: Where it's slowing down. Where energy is being wasted. Where a future bottleneck is quietly forming. When something breaks, you fix it. When output dips, you analyze it. When quality drops, you investigate it. But what if… You could see it coming before it ever happened? That’s exactly what the world’s smartest factories are doing. And no—it’s not luck. It’s Digital Twins. Here’s how they’re quietly winning: ✅ They simulate everything—before touching the floor. Using Discrete Event Simulation, they model thousands of “what-if” scenarios ahead of time. ✅ They test scalability virtually. No downtime. No wasted effort. Just pure clarity on what works at 10 units—or 10,000. ✅ They build feedback loops that self-correct. Production issues don’t surprise them—they notify them. ✅ They optimize resource flow in advance. Material, machine, and manpower aligned like clockwork—before the day begins. ✅ They plan for “what if” scenarios—before they happen. What if a supplier delays shipment? What if demand spikes overnight? What if a station fails? Digital Twins let you test it all—before it hits the floor. ✅ They validate line changes without stopping production. Need to rearrange stations or introduce a new variant? It’s simulated, validated, and tweaked—all before operators touch it. ✅ They make daily operations visual and data-driven. From shift supervisors to plant managers—everyone sees the same digital reality. No guesswork. No misalignment. Just clarity. This isn’t a pipe dream. This isn’t reserved for billion-dollar tech companies. This is now. This is Digital Twin Technology. It’s like giving your factory a second brain: • One that never sleeps • One that learns faster than humans • One that speaks in data, not guesses And the outcome? - Less waste - More throughput - Smarter decisions at every level I broke this approach down in a visual you can show your CEO, ops team, or even your board. One page. Clear. Actionable. - Digital Twins are your factory’s second brain ♻️ Repost if you're scaling smart.

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!