Industry40.tv

Industrial teams still rely on fragmented and manual processes to match complex product specs with use-case-specific needs. Take this example: You're selling a vision sensor to a factory. To get it right, you need to know: ⇨ What’s the size and speed of the conveyor line? ⇨ Is the plant located in Munich or Arizona? ⇨ Will this sensor withstand that temperature range? ⇨ What PLC is the customer us...

In theory, AI should learn, adapt, and improve continuously. But in reality, most deployments are static and disconnected from the evolving complexity of shop floor operations. Most businesses lack tools to close the loop between: ⇨ Data collection ⇨ AI training ⇨ Deployment ⇨ Continuous retraining ⇨ Business impact validation And they struggle to connect domain experts with data scientists. To le...

Many small and mid-sized manufacturers want to explore AI to improve efficiency, reduce waste, or make their processes smarter.   However, this process requires OT and IT knowledge not present in many industrial companies, mainly SMEs.   Ander Garcia Gangoiti and his team built a micro-service edge architecture based on MQTT, TimescaleDB, Node-Red and Grafana stack to ease the integration of soft...

Imagine a control system that learns, optimizes in real-time, and integrates seamlessly with both field assets and cloud-native AI platforms.  This is the next chapter of industrial process automation. Already implemented at the largest Oil refinery in the world, Software-defined control systems break the traditional link between hardware and logic. This separation allows for dynamic control, cent...

In manufacturing, time-series data is everywhere, but most plants are still relying on static dashboards, lagging insights, and manual root-cause analysis. The result? - Downtime that’s explained, not prevented - Insights that arrive, after the line slows down - Human effort wasted on repeat investigations AI agents transform the way manufacturers harness time-series data. They process live sensor...

Learn how Joao and and team are using Knowledge Graphs and IIoT to power Industrial AI and Digital Twin use cases at Scania.   Here’s the outline of our conversation: Core Challenges in Managing Industrial Data for Data‑Driven Manufacturing The Role of Ontologies and Knowledge Graphs in Advancing Industrial Data Interoperability and Analytics IIoT Data Integration and Standardization Approaches  S...

As manufacturing demands increase, integrating AI-powered visual systems into quality inspection processes becomes increasingly beneficial. While traditional inspection methods have been the cornerstone of quality control in manufacturing, they come with limitations such as subjectivity, fatigue, and scalability challenges. AI-powered visual inspection systems address these issues. Leveraging adva...

Modern manufacturing environments generate a staggering amount of data from machines, processes, quality checks, logistics, and inventory. And yet, most of it goes unseen, unused, and unanalyzed. Why? Because the data is too vast, too fast, and too fragmented for any human to handle in real-time. Even the best engineers can’t monitor thousands of variables 24/7. And failing to harness this data ha...

Every minute a machine is offline costs money. That’s why Mean Time to Repair (MTTR) is one of the most vital metrics in manufacturing. It tells you how fast your team can identify an issue, find the solution, and get the line moving again. Unfortunately, in many facilities, this process is slow and cumbersome: when a technician sees an error code, they often have to sift through hundreds of pages...

For decades, manufacturers have relied on traditional analytics—correlations, trendlines, dashboards—to make operational decisions. But there's a limit: Correlation ≠ Causation Just because two variables move together doesn’t mean one causes the other.  This blind spot can lead to poor decisions and surface-level fixes that don’t solve the real issue. For example, a machine’s temperature spikes of...

Manufacturing leaders are familiar with physical waste; scrap, rework, and inefficiencies in production. But digital waste is the hidden inefficiency that’s just as costly. It includes: 𝐔𝐧𝐮𝐬𝐞𝐝 𝐃𝐚𝐭𝐚: Factories generate massive amounts of data, but much of it is never analyzed or leveraged for decision-making. 𝐈𝐧𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐃𝐚𝐭𝐚 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠: Engineers waste time manually entering, cleaning, or searching...

Manufacturers are constantly battling two critical challenges: Inefficiencies in Equipment Usage: Downtime, slow cycle times, and unidentified bottlenecks reduce Overall Equipment Effectiveness (OEE), leading to wasted resources and missed production targets. Safety Risks: Ensuring worker safety while maintaining productivity is difficult, especially in environments with heavy machinery and fast-m...

AI’s success in manufacturing depends on the ability to seamlessly integrate data from machines and systems across the factory floor and supply chain. Without strong connectivity, AI remains underutilized, limited by data silos, and inconsistent integration. Connectivity isn’t just about linking devices; it’s about creating a unified data environment where AI can operate at its full potential—powe...

Frontline workers are the backbone of manufacturing, but they’re often held back by manual data entry, process inefficiencies, and knowledge gaps. AI-powered Industrial Copilots offer a solution that elevates their capabilities: 𝐍𝐨 𝐌𝐨𝐫𝐞 𝐌𝐚𝐧𝐮𝐚𝐥 𝐃𝐚𝐭𝐚 𝐄𝐧𝐭𝐫𝐲 AI Copilots automate data capture and seamlessly integrate with existing systems—eliminating wasted time and inaccuracies. 𝐒𝐦𝐚𝐫𝐭𝐞𝐫, 𝐅𝐚𝐬𝐭𝐞𝐫 𝐖𝐨𝐫𝐤𝐟𝐥...

Many factories today grapple with recurring production issues and inefficiencies; whether it’s inconsistent quality, unpredictable downtime, or process bottlenecks. The cost of inefficiencies keeps mounting, and while human intuition and manual checks have been valuable tools, they’re no longer enough to drive significant breakthroughs. AI offers an opportunity to uncover hidden patterns that huma...

While the promise of AI is immense, many manufacturers find themselves stuck in pilot projects, unable to unlock its full potential. The key lies in addressing foundational challenges and adopting a clear, phased strategy to transform operations. Fundamentally, AI offers manufacturers a pathway to achieving operational excellence by moving through the four stages of analytics maturity:   1️⃣ Descr...

In our latest episode of the AI in Manufacturing Podcast, I sat down with Zeeshan Zia, co-founder and CEO of Retrocausal, to dive deep into how AI co-pilots are transforming the manufacturing sector. Here are three key takeaways: 1️⃣ Labor Challenges Meet Smart Solutions Manufacturers face critical labor shortages, resulting in significant costs. Zeeshan shared how AI-powered Assembly Co-Pilots ar...

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Today's manufacturing industry faces significant challenges in managing its data environment. Vast amounts of unorganized data collected from various sources often become "data swamps," making it difficult to extract meaningful insights and generate value. This overwhelming complexity hinders decision-making and slows down innovation. Additionally, the analytics tools currently available are often...

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In this episode, we explore how artificial intelligence is transforming manufacturing from the ground up. We dive into cutting-edge applications and discuss the benefits and challenges AI introduces to the industry. Here’s a sneak peek at what we cover: 1. Predictive Maintenance for Machinery AI helps manufacturers predict equipment failures before they happen, reducing downtime and saving costs....

While large language models hold immense potential, there's a significant gap between what these tools offer out of the box and what the manufacturing industry needs. Manufacturing presents unique challenges that generic AI solutions often can't effectively address.  However, by customizing Generative AI systems to meet industry-specific requirements, this gap can be effectively bridged:  - Tailor...

In this episode, I sat down with Michael Kuehne-Schlinkert, CEO of Katulu to discuss how Federated Machine Learning is transforming industrial AI. Here are some key takeaways: Federated Learning Enables Cross-Factory Collaboration Federated learning allows multiple factories to improve AI models without sharing sensitive data. By exchanging learnings, factories can build more robust models while m...

In the latest episode of the AI in Manufacturing podcast on Industry 4.0 TV, host Kudzai Manditereza sits down with Ashan Posohi, CEO and co-founder of Third Wave Automation, to explore how AI-powered robots are transforming material handling. The focus is on autonomous forklifts and their impact on productivity, safety, and the future of manufacturing. Arshan Poursohi brings a rich background in...

In this episode, I sat down with Yousef Mohassab, CEO of Facilis.ai, to explore how Agentic AI is transforming the manufacturing industry. If you're looking for practical insights on scaling AI and boosting operational efficiency, this is the episode you can't miss! Here are the key takeaways: The Shift from Centralized to Agentic AI Manufacturers can no longer afford to rely on centralized data s...