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Industry40.tv

Kudzai Manditerezaindustry40tv.podbean.com
Each episode of Industry40.tv Podcast will treat you to an in-depth interview with leading AI practitioners, exploring the Application of Artificial Intelligence in Manufacturing and offering practical guidance for successful implementation.
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Episodes

CDOT AI Code - A New Language for Parts: Serra Tuzcuoglu CEO and Co Founder, Cosmodot - CDOT AI Code

Part traceability in manufacturing has long relied on traditional barcodes that fail where it matters most: under heat, blasting, and coating, e.t.c. As a result, manufacturers normally place barcodes after key part transformations. That means, for 70%+ of the production process, you're flying blind. You're guessing which parts went through which treatments. And when something fails? You're looking at massive recalls, supplier penalties, and lost time. What if we could code physical parts in a w...

Jul 09, 202550 minEp. 49

Building and Scaling AI-Driven Transformation in Manufacturing: Jonathan Alexander - Global Manufacturing AI and Advanced Analytics Manager, Albemarle Corporation

Learn how Jonathan and his team at Albemarle Corp went from pilots to $150M in annual improvements through a business-first, scalable AI strategy. In the latest episode of the AI in Manufacturing podcast, I spoke with Jonathan Alexander , Global Manufacturing AI and Advanced Analytics Manager at Albemarle Corporation, about building, scaling and sustaining AI-driven Transformation in Manufacturing. Here’s the outline of our conversation: ⇨ Key Data Challenges in Implementing AI at Scale ⇨ Data C...

Jul 02, 20251 hr 1 minEp. 48

AI Agents for Industrial Sales and Application Engineers: Fay Goldstein - Co-Founder and CEO, Folio

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 using — Siemens or Rockwell? ⇨ Will the sensor integrate without conflict? ⇨ Are there newer models in...

Jun 18, 202545 minEp. 47

Building and Scaling Closed-Loop AI for Manufacturing Operations: Dr. Nikita Golovko - Software Architect for Industrial AI, Siemens

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 learn more about building and scaling closed-loop AI for industrial operations I recently sat down wit...

Jun 11, 202550 minEp. 46

Edge AI Architecture For Integrating Industrial AI Into Control Systems: Ander Garcia Gangoiti - Director of Data Intelligence, Vicomtech

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 AI models into industrial system. The architecture has been successfully validated controlling the vacuu...

Jun 04, 202556 minEp. 45

Software Defined Control , Unified Namespace and AI-Optimization in Process Industries : Huize (Mercy) Zhang, VP - SUPCON, Founder - FreezoneX

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, centralized intelligence, and flexible deployment across complex industrial environments. When integrated...

May 14, 202549 minEp. 44

AI Agents for Advanced Time Series Data Analytics : Jeff Tao - CEO and Founder, TDengine

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 feeds while simultaneously referencing historical records, enabling instant anomaly detection and c...

May 07, 202545 minEp. 25

Powering Industrial AI and Digital Twin Use Cases with Knowledge Graphs : João Dias-Ferreira - Head of AI, Knowledge Graphs and IoT, SCANIA

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 Semantic‑Modeling Best Practices for Scaling Value Creation Using Knowledge Graphs as Infrastructure for...

Apr 30, 202557 minEp. 24

Real-Time Quality Control Using AI-Powered Visual Inspection : Priyansha Bagaria, PhD -Founder and CEO, Loopr AI

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 advanced algorithms and machine‑learning models, they analyze images with high accuracy, identifying def...

Apr 23, 202546 minEp. 23

Vector Databases and Data Structure for Industrial AI Agents : Humza Akhtar, PhD - Senior Industry Principal - Manufacturing and Automotive, MongoDB

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 has real consequences. Critical warning signs of equipment problems or process inefficiencies can be m...

Apr 09, 202556 minSeason 2Ep. 22

Industrial Machine Downtime Reduction Using Generative AI : Jose Dos Santos - Co-founder & CEO, Industrial AI

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 of documentation while the clock is ticking. A long MTTR doesn’t just mean downtime; it means: - Lo...

Apr 02, 202552 minEp. 21

Industrial Intelligence Solutions with Causal AI : Daniele Gamba - CEO, AISent Srl

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 often coincide with defects. Traditional analytics might alert you when it happens—but not why. Is it t...

Mar 26, 202558 minEp. 20

Finding Opportunities for AI Application in Manufacturing : Patrick Byrne - Co-founder & CEO, Annora AI

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 for information that should be automated. 𝐒𝐢𝐥𝐨𝐞𝐝 𝐈𝐧𝐟𝐨𝐫𝐦...

Mar 19, 202549 minSeason 2Ep. 19

Maximize OEE & Production Line Safety with Video AI Agents : Karim Saleh - Co-founder & CEO, Cerrion

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-moving processes. Despite best efforts, traditional methods struggle to keep up with the complexity a...

Feb 26, 202535 minEp. 18

Connectivity for Enabling AI In Manufacturing Use Cases : Prof Dr Bernd Hafenrichter - CTO of soffico GmbH,

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—powering everything from predictive maintenance to automated quality control and beyond. To learn more a...

Feb 19, 202552 minEp. 17

Industrial AI Co-Pilot for Frontline Operations: Mason Glidden - Chief Product Officer, Tulip

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 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 AI surfaces real-time insights, helping teams reduce ...

Feb 12, 202532 minEp. 16

Data-Driven Manufacturing Optimization with AI: Zhitao Gao - CEO and Co-Founder of eXlens.ai

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 human teams might miss. For instance: - By analyzing machine sensor data, AI can trace yield drops to su...

Jan 22, 202559 minSeason 2Ep. 15

Using AI and Digital Twins to Enhance Manufacturing Workflow Efficiency: Andrew Scheuermann - CEO and Co-Founder , Arch Systems

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 Descriptive Analytics – Understanding what happened. 2️⃣ Diagnostic Analytics – Pinpointing root causes. 3️...

Jan 15, 20251 hr 1 minSeason 2Ep. 13

AI Copilots for Manufacturing Assembly Optimization: Zeeshan Zia - Co-Founder & CEO, Retrocausal

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 are slashing error rates and scrap costs by up to 90% while empowering workers with real-time guidance...

Dec 11, 20241 hr 4 minSeason 2Ep. 13

AI Assistants for Advanced Manufacturing Data Analytics: Stefan Suwelack- Co-Founder & CEO, Renumics

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 too complex and static for domain experts to use effectively, leaving them without the critical ins...

Nov 27, 202459 minSeason 2Ep. 11

Practical Applications of AI in Manufacturing: Markus Guerster - Founder and CEO, MontblancAI

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. With predictive maintenance, companies can transition from reactive to proactive maintenance, leadin...

Nov 13, 202459 minSeason 2Ep. 9

Generative AI Use Cases in Engineering and Manufacturing: Vlad Larichev - Generative AI Lead, Accenture Industry X

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: - Tailoring AI to understand specialized language and scenarios enhances its relevance and effectiveness. - In...

Nov 06, 20241 hr 7 minSeason 2Ep. 8

Scaling Industrial AI Across Factories with Federated Learning: Michael Kuehne-Schlinkert - CEO, Katulu

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 maintaining data privacy and compliance. Collaboration on Model Training Without Compromising Privacy...

Oct 30, 20241 hr 3 minSeason 2Ep. 7

Automating Material Handling with AI-Powered Robots: Arshan Poursohi - CEO, Third Wave Automation

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 robotics and research, having worked with Sun Microsystems, Google Research, and Toyota Research. Hi...

Oct 23, 202431 minSeason 2Ep. 6

Transforming Manufacturing Data Into Actions with Agentic AI - Yousef Mohassab, CEO of Facilis.AI

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 science teams that create bottlenecks. Agentic AI empowers subject matter experts (SMEs) to directly ...

Oct 16, 20241 hr 5 minSeason 2Ep. 5

Modernizing Your Industrial Data Architecture for AI Readiness: Jonathan Wise - Chief Technology Architect, CESMII

In this episode, I had the pleasure of interviewing Jonathan Wise, Chief Technology Architect at CESMII (Smart Manufacturing Institute). We discussed how you can modernize your industrial data architecture to harness the full potential of AI, enhancing both production efficiency and innovation. Jonathan highlighted three key pillars essential for AI readiness: Data Accessibility - You can’t train AI without accessible data. Jonathan explains why ensuring your data flows seamlessly across systems...

Oct 09, 20241 hr 6 minSeason 1Ep. 4

AI Powered Smart-Guidance for Smart Manufacturing: Nikunj Mehta - Founder & CEO, Falkonry

In this episode, we dive deep into the world of smart manufacturing with industry expert Nikunj Mehta from Falkonry. If you're curious about how data is transforming industrial operations and the future of maintenance and reliability, this episode is for you! Here are some key takeaways: 82% of Failures Are Random Nikunj explains that a staggering 82% of failures in industrial systems appear random. Without understanding their causes, manufacturers struggle to prevent them. This is where smart, ...

Oct 02, 202455 minSeason 1Ep. 3

Visual Intelligence Applications in Manufacturing: Cyrus Shaoul - CEO, Leela AI

In our latest podcast episode, I had the pleasure of speaking with Cyrus Shaoul, CEO of Leela AI, about visual intelligence and its transformative impact on manufacturing operations. Here are some Key Takeaways: 1️⃣ Beyond Traditional Machine Vision Unlike traditional machine vision systems that focus on product inspection, visual intelligence looks at the entire manufacturing process. It helps identify value-adding activities in real-time, ensuring operational excellence is met consistently. 2️...

Sep 25, 202456 minSeason 2Ep. 2
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