Jeff Winter's Linkedin Analytics

Your data isn’t broken. Your pipeline is leaking. Manufacturers today aren’t short on data—they’re swimming in it. But if... • Your dashboards aren’t telling you what’s really going on… • Your reports are outdated before they land in your inbox… • And your best data is stuck in some forgotten server or spreadsheet… Then congrats! You’re not “data-driven”… you’re “data-dripping.” The problem isn’t more data. It’s fixing the leaks: • 𝐃𝐚𝐫𝐤 𝐃𝐚𝐭𝐚 – hidden and unused • 𝐒𝐢𝐥𝐨𝐞𝐝 𝐃𝐚𝐭𝐚 – stuck and disconnected • 𝐁𝐚𝐝 𝐃𝐚𝐭𝐚 – messy and misleading • 𝐒𝐥𝐨𝐰 𝐃𝐚𝐭𝐚 – delayed and useless The goal? Turn that chaotic flood of raw data into a smooth stream of insight that powers better, faster decisions. Because in the data game, it’s not what you collect—it’s what you connect. So grab that wrench, inspect your pipeline, and patch the gaps. 𝐅𝐨𝐫 𝐚 𝐝𝐞𝐞𝐩 𝐝𝐢𝐯𝐞 𝐨𝐧 𝐭𝐡𝐢𝐬 𝐭𝐨𝐩𝐢𝐜: https://lnkd.in/eRkRiYyB 💬 Curious which leak is costing you the most? Let’s chat. ******************************************* • 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!

Somewhere along the way, maintenance became a checkbox. A calendar event. A cost to control. But the factory floor is evolving. And so must the mindset. We don’t just repair anymore... We predict. We prescribe. We optimize. And when you optimize consistently, you stop reacting to problems…and start unlocking performance. That’s the real promise of Maintenance 4.0. Not just fewer breakdowns, but smarter resource planning, tighter production schedules, and data-driven capital decisions. It’s maintenance, yes. But not as you know it. To appreciate the significance of Maintenance 4.0, it's essential to understand its evolution of maintenance strategies: • 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝟏.𝟎 focused on reactive strategies, where actions were taken only after a failure occurred. This approach often led to significant downtime and high repair costs. • 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝟐.𝟎 introduced preventative maintenance, scheduling regular check-ups based on time or usage to prevent failures. However, this method sometimes resulted in unnecessary maintenance activities, wasting resources. • 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝟑.𝟎 saw the advent of condition-based maintenance, utilizing sensors to monitor equipment and perform maintenance based on actual conditions. This strategy marked a shift towards more data-driven decisions but still lacked predictive capabilities. • 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝟒.𝟎 builds upon the foundations laid by its predecessors by leveraging advanced predictive and prescriptive maintenance techniques. Utilizing AI and machine learning algorithms, Maintenance 4.0 can anticipate equipment failures before they occur and prescribe optimal maintenance actions. In addition, the data-driven insights provided by Maintenance 4.0 can facilitate strategic decision-making regarding equipment investments, production planning, and innovation initiatives through better integration with other programs and systems, such as Enterprise Asset Management (EAM) and Asset Performance Management (APM). 𝐅𝐨𝐫 𝐚 𝐝𝐞𝐞𝐩𝐞𝐫 𝐝𝐢𝐯𝐞: https://lnkd.in/djjfivw8 ******************************************* • 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!

Everyone's talking about what GenAI could do. But IoT Analytics just dropped a reality check: what GenAI is actually being used for. They analyzed 𝟓𝟑𝟎 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐩𝐮𝐛𝐥𝐢𝐜 𝐜𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐢𝐞𝐬 — not surveys, not guesses — and broke them down into 𝟔𝟗 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞𝐬 𝐚𝐜𝐫𝐨𝐬𝐬 𝟏𝟒 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬, inspired by Porter’s Value Chain. Here’s what the data shows: • 𝟒𝟗% of projects were aimed at customer support — solving issues, answering inquiries, and managing post-sale care. • Tech companies and North America each accounted for 𝟓𝟔% of total projects — leading by a wide margin. • 14 use cases had 𝐳𝐞𝐫𝐨 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧, showing the gap between what’s possible and where the value is really at. And here’s the deeper insight most people will miss: Generative AI can be applied across every department. But so far, it’s being used where 𝐩𝐚𝐢𝐧 𝐢𝐬 𝐨𝐛𝐯𝐢𝐨𝐮𝐬, 𝐯𝐚𝐥𝐮𝐞 𝐢𝐬 𝐢𝐦𝐦𝐞𝐝𝐢𝐚𝐭𝐞, and 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐚𝐫𝐞 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞. Not every function is ready. Not every use case makes sense. Not every department can immediately benefit. But if you’re in manufacturing, the opportunity is even bigger — and more complicated. Unlike many industries, manufacturers operate across all 14 functions. You don't just have marketing and customer support. You have production, maintenance, supply chain, field service, logistics, and R&D — all with potential GenAI applications. (And when you filter this data by manufacturing, stuff like process optimization nearly doubles) The early movers aren't the ones building fancy demos. They’re the ones quietly re-architecting how work gets done — end to end. 𝐅𝐨𝐫 𝐦𝐨𝐫𝐞 𝐢𝐧𝐟𝐨 𝐨𝐧 𝐭𝐡𝐞𝐬𝐞 𝐜𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐢𝐞𝐬: https://lnkd.in/ecrpFawm ******************************************* • 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!