How ImageGrafix turned AI in maintenance into a boardroom tool

🚀 𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 𝐄𝐱𝐜𝐞𝐥𝐥𝐞𝐧𝐜𝐞: 𝐋𝐞𝐚𝐧 𝐒𝐢𝐱 𝐒𝐢𝐠𝐦𝐚 𝟒.𝟎 𝐢𝐬 𝐇𝐞𝐫𝐞 The manufacturing landscape is experiencing a revolutionary transformation. 𝐋𝐞𝐚𝐧 𝐒𝐢𝐱 𝐒𝐢𝐠𝐦𝐚 𝟒.𝟎 (𝐋𝐒𝐒 𝟒.𝟎) represents the next evolution of continuous improvement, seamlessly integrating traditional DMAIC methodology with cutting-edge Industry 4.0 technologies. What makes LSS 4.0 a game-changer? 🔹 𝐈𝐨𝐓-𝐃𝐫𝐢𝐯𝐞𝐧 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠: Real-time sensor data replaces manual measurements, providing continuous visibility into every aspect of production 🔹 𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: Machine learning algorithms predict quality issues and equipment failures before they occur, shifting from reactive to proactive optimization 🔹 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧 𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧𝐬: Virtual replicas of production processes enable risk-free testing of improvements, reducing implementation cycles from weeks to days 🔹 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Systems self-adjust parameters based on real-time data, continuously improving efficiency without human intervention 𝐓𝐡𝐞 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐢𝐬 𝐩𝐫𝐨𝐟𝐨𝐮𝐧𝐝: ✅ Traditional DMAIC → Data-driven DMAIC 4.0 ✅ Reactive control → Predictive intelligence ✅ Manual analysis → AI-augmented decision-making ✅ Periodic improvements → Continuous autonomous optimization Research shows that organizations implementing LSS 4.0 achieve up to 27% improvement in Overall Equipment Effectiveness while reducing quality variations by 64%. The framework doesn't just eliminate waste—it prevents it from occurring in the first place. 𝐓𝐡𝐞 𝐛𝐨𝐭𝐭𝐨𝐦 𝐥𝐢𝐧𝐞: LSS 4.0 is transforming manufacturing from reactive problem-solving to intelligent, self-optimizing systems that maximize productivity, quality, and sustainability simultaneously. Are you seeing these technologies reshape quality management in your industry? What's your experience with integrating AI and IoT into traditional Lean practices? #LeanSixSigma #Industry40 #SmartManufacturing #DigitalTransformation #ContinuousImprovement #IoT #ArtificialIntelligence #DigitalTwins #ManufacturingExcellence #DMAIC #PredictiveAnalytics #QualityManagement #OperationalExcellence #Manufacturing #Automation

Machine vision vs AI vision ! AI Vision is still a hard sell despite its advantages and guaranteed performance benefits. why ? Here are top 3 reasons! - Customers usually stick to known territories. What's known to them is a easier sell despite its shortcomings - The maintenence teams are tuned to handle Traditional Vision systems better and they often discourage investing in new ideas which may contribute to downtime and affecting their direct KPI - Budgeting for vision systems still happen mainly along with Capex making its existence purely as a " good to have " than " must have". This increases the dependence on system integrators instead of vision companies to design new systems. Do you dare to think beyond machine vision?

🚀 𝐒𝐄𝐂𝐎 𝐫𝐞𝐥𝐞𝐚𝐬𝐞𝐬 𝐧𝐞𝐰 𝐀𝐈 𝐀𝐩𝐩𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐀𝐩𝐩 𝐇𝐮𝐛, 𝐧𝐨𝐰 𝐰𝐢𝐭𝐡 𝐟𝐮𝐥𝐥 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐚 𝐭𝐫𝐢𝐚𝐥 𝐨𝐟 𝐂𝐥𝐞𝐚 🧩🧠 Read the full PR here: 👉 https://lnkd.in/dDFqqYAX SECO has launched seven new #AI solutions in the past month, each one rigorously validated within SECO’s #hardware ecosystem to ensure seamless #integration and #deployment. This release also introduces: 🔹 A new “#Deploy” button for instant access to a 90-day Clea trial 🔹 Full #documentation available in the #SECODeveloperCenter, with step-by-step #guides and #specifications 🧩🧠 The new #applications span diverse use cases – from industrial #QualityControl and predictive #analytics to on-device #ConversationalAI – enabling #clients to focus on what truly differentiates their #products while SECO handles the complexities of #AI. ✨ 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀 𝗼𝗳 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗮𝗽𝗽𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: 🔹 #Llama – Local Chatbot with Intel Corporation® #OpenVINO™ 🔹 #SensorForecast – Smart Building (NeuralProphet) 🔹 #FaceID + Edge Impulse 🔹 #SceneTextDetection – High Accuracy & Light Model 🔹 #PCB Defect Detection 🔹 #YAMNet – Training Experience 💬 “We are enabling our clients to build powerful, custom #AI/ML #pipelines without getting bogged down by the underlying technological complexities,” says Fausto Di Segni, Head of IoT and AI at SECO. Ready to transform your #product with #AI? Start here with SECO Application Hub 👉https://apphub.seco.com/ #SECOBusiness #SECOAppHub #Clea #AI #EdgeComputing #QualityControl #PredictiveAnalytics #ConversationalAI

🚀 𝐒𝐄𝐂𝐎 𝐫𝐞𝐥𝐞𝐚𝐬𝐞𝐬 𝐧𝐞𝐰 𝐀𝐈 𝐀𝐩𝐩𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐀𝐩𝐩 𝐇𝐮𝐛 – 𝐧𝐨𝐰 𝐰𝐢𝐭𝐡 𝐟𝐮𝐥𝐥 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐚 𝟗𝟎-𝐝𝐚𝐲 𝐂𝐥𝐞𝐚 𝐭𝐫𝐢𝐚𝐥 🧩🧠 With this release, #Clea takes center stage as the enabler of on-device #AI. Through the new “#Deploy” button, you can instantly activate a 90-day free trial of #Clea and start experimenting with validated #AI solutions across SECO’s #hardware ecosystem. 🔹 Full #documentation, guides, and specs are now available in the #SECODeveloperCenter – making it easier than ever to deploy, customize, and scale your AI pipelines with Clea. 🔹 From #QualityControl and #PredictiveAnalytics to #ConversationalAI, Clea empowers you to innovate while seamlessly managing deployment and integration. ✨ Discover all the new apps now available! 👉 Start your Clea journey today on the SECO Application Hub: https://apphub.seco.com/ #Clea #SECOAppHub #AI #EdgeComputing #IoT #Innovation #MachineLearning

The manual management of AI models is rapidly becoming obsolete. The rise of sophisticated AI systems, incorporating multi-modal capabilities and IoT integration, has created significant AI orchestration challenges. Intelligent automation is now crucial, transforming complex manual processes into streamlined, self-optimizing ecosystems. #AgenticAI empowers autonomous agents to manage infrastructure and processes, dynamically allocating resources, orchestrating data pipelines, and even switching between models (#LLMSwitching) for optimal performance. The primary advantage is an adaptive and resilient AI landscape. Systems become proactive, featuring predictive maintenance, automated retraining, and real-time performance monitoring. This frees up valuable human capital, allowing teams to focus on innovation and strategic initiatives. Businesses experience significant benefits, including reduced operational costs, accelerated deployment cycles, and enhanced system reliability. Scalability is dramatically improved, and #IoTAutomation enables AI agents to control and optimize connected devices, creating a truly integrated and responsive environment. At AgentNexus, we are at the forefront of advanced intelligent automation, recognizing its critical role in the future of #AIOchestration. We are developing a comprehensive framework where multi-modal, IoT-integrated agents act as architects of efficient and self-managing systems. This approach unlocks the full potential of AI, enabling organizations to achieve unprecedented levels of automation and optimization. Explore how these principles can redefine your #DevOpsAI strategy and accelerate your transition towards autonomous, high-performing AI. We invite you to consider your current orchestration challenges and explore how intelligent automation can provide effective solutions.

The Power of AI & ML in Industry Artificial Intelligence (AI) and Machine Learning (ML) are transforming the way we work—especially in industrial automation and IoT. These technologies aren’t just tools; they act as collaborators, allowing machines to learn, adapt, and make decisions autonomously, helping businesses operate smarter and faster. Industrial firms are already leveraging AI to: ✅ Optimize design processes ✅ Detect asset patterns and anomalies for better production ✅ Predict supply chain disruptions ✅ Enable advanced predictive maintenance ✅ Enhance quality control and inspection ✅ Reduce downtime and accelerate time-to-market AI-driven robotics and drones are also improving precision in tasks like inspection, maintenance, and material handling, reducing human intervention, minimizing accidents, and extending equipment life. On the human side, AI supports employees in research, writing, collaboration, and ideation, empowering teams to work more efficiently and confidently. The potential of AI & ML in industrial settings is limitless, but the challenge remains: how can businesses harness it effectively? Exploring the possibilities now can set the stage for a smarter, more productive future. #ArtificialIntelligence #MachineLearning #IIoT #IndustrialAutomation #PredictiveMaintenance #Innovation #SmartManufacturing #AIinBusiness

𝗛𝗼𝘄 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘀 𝟭𝟬𝘅 𝗥𝗢𝗜 Walk through a modern factory floor and everything looks calm. Machines hum, production flows, operators stay focused. But beneath that surface, a quiet revolution is underway. 🔴 The old way → wait for machines to break, then scramble to fix them. 🟢 The smart way → know exactly when maintenance is needed before anything goes wrong. Here’s how predictive maintenance (PdM) transforms operations in 6 steps 1️⃣ 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗲𝗱 𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝘀 – smart sensors track vibration, temperature & pressure in real time. 2️⃣ 𝗦𝗲𝗮𝗺𝗹𝗲𝘀𝘀 𝗗𝗮𝘁𝗮 𝗙𝗹𝗼𝘄 – IoT networks stream data from every asset to centralized platforms. 3️⃣ 𝗥𝗲𝗺𝗼𝘁𝗲 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 – dashboards provide instant visibility into health & performance. 4️⃣ 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 – AI/ML forecast failures weeks or months in advance. 5️⃣ 𝗣𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 𝗔𝗹𝗲𝗿𝘁𝘀 – teams get early warnings with enough lead time to plan repairs. 6️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗪𝗼𝗿𝗸 𝗢𝗿𝗱𝗲𝗿𝘀 – systems schedule technicians, order parts & optimize workflows. The impact is measurable: • 35–50% less unplanned downtime( US Department of Energy) • 5–10% lower maintenance costs(Deloitte Research) • 20–40% longer equipment life (Nucleus Research) • 70–75% fewer breakdowns(Nucleus Research) • Potential for 10x ROI ( US Department of Energy) This isn’t future tech. It’s happening today , in factories, energy plants, and logistics hubs worldwide. Companies embracing PdM aren’t just cutting costs; they’re building resilience and competitive advantage. The real shift isn’t just technological. It’s cultural. From firefighting to foresight. From breakdowns to precision. From cost to strategy. #PredictiveMaintenance #Industry40 #SmartFactories #Sustainability #AI Ref: Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0 - Zeki Murat Çınar et all.

🚨💼 When top experts leave, their knowledge often goes with them ...  How can companies keep this expertise while stabilizing complex production processes? Skills shortage, knowledge loss, and process complexity – 3️⃣ major challenges in mechanical and plant engineering. 👉🏼 Example: In plastics processing, a temperature error can cause hundreds of meters of scrap. Traditional methods fail to detect and analyze such process deviations early enough. AI assistance systems combine machine learning with expert know-how – creating robust hybrid solutions. ✅ Secure and share expert knowledge across the team ✅ Increase process stability and detect errors early ✅ Enable data-driven optimization and new digital services 💡 For machine and plant builders, this means fewer unplanned downtimes, reduced scrap, and a stronger service business. IoT-driven AI assistance systems also open the door to new digital offerings – from predictive maintenance to “as-a-Service” models. 👉 Explore how XITASO develops tailored systems like these – from use case identification to system architecture and IoT platform integration: 🇺🇸/🇬🇧: https://lnkd.in/em7teKNG 🇩🇪:  https://lnkd.in/ejschcrR #AIassistancesystem #AI #Industry40 Michael Buchenberg Kathrin Mönch Ulrich Huggenberger

𝗜𝗼𝗧 𝗱𝗲𝘃𝗶𝗰𝗲𝘀 𝗰𝗮𝗽𝘁𝘂𝗿𝗲 𝗱𝗮𝘁𝗮. 𝗔𝗜/𝗠𝗟 𝗺𝗮𝗸𝗲𝘀 𝗶𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁. Huge thanks to Dr. Katerina Stamou, PhD (PhD in Blood flow modelling), for an insightful workshop on “𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗻𝗴 𝗠𝗤𝗧𝗧 𝘄𝗶𝘁𝗵 𝗔𝗜/𝗠𝗟”! It gave me fresh perspectives on turning raw sensor streams into actionable intelligence, especially for 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗺𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲. 𝗠𝘆 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀:  • IoT without AI = “data exhaust.”  • AI without IoT = models sitting idle.  • Together = powerful insights and automation. 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲𝘀: 🔹 Predictive Maintenance: proactively schedule maintenance using sensor insights 🔹 Anomaly Detection: identify unexpected patterns for safety and efficiency 🔹 Smart Automation: trigger immediate actions from data One question that really got me thinking: should ML models for IoT run on the edge for speed, or in the cloud for computational power? In practice, it’s not just “edge vs cloud”—it’s about smartly partitioning the ML pipeline: ⚡ 𝗘𝗱𝗴𝗲: preprocessing, feature extraction, latency-critical inference ☁️ 𝗖𝗹𝗼𝘂𝗱: deep learning, fleet-level insights, model retraining 𝗠𝗤𝗧𝗧’𝘀 𝗿𝗼𝗹𝗲: the efficient highway carrying raw data where needed, condensed insights where possible to save bandwidth. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: In aviation predictive maintenance, should edge devices detect anomalies onboard, or should raw sensor data go to the cloud first for validation and inference? 𝘈𝘵𝘵𝘢𝘤𝘩𝘦𝘥 𝘢𝘳𝘦 𝘮𝘺 𝘵𝘢𝘬𝘦𝘢𝘸𝘢𝘺𝘴 𝘧𝘳𝘰𝘮 𝘵𝘩𝘦 𝘸𝘰𝘳𝘬𝘴𝘩𝘰𝘱. 💡 I’d love to hear how others in AI/IoT tackle pipeline partitioning, MQTT design, and edge/cloud tradeoffs — especially in real-world predictive maintenance scenarios. #MQTT #IoT #AI #PredictiveMaintenance #MachineLearning #EdgeComputing #CloudComputing #DataScience