The IT/OT Insider Podcast - Pioneers & Pathfinders

This episode was one of our most engaging yet on the topic of AI. David sat down with Dr. Wilhelm Klein, an expert in Automated Quality Control and holder of a PhD in Ethics. As the co-founder and CEO of Zetamotion, Wilhelm brings a mix of hands-on experience and deep understanding of the ethical questions surrounding technology. Over the hour, we covered a lot of ground—how AI has evolved, its role in manufacturing, and the challenges of scaling systems from Proof of Concept (PoC) to full production. Wilhelm explained how computer vision is changing quality control. We also explored the ethical questions raised by AI, touching on its impact on industries, jobs, and decision-making. If you’re interested in AI’s practical applications and the questions it raises about the way we work and live, this episode has plenty to offer. The Starship Enterprise Wilhelm’s journey began with a childhood fascination with science fiction, tinkering, and a deep curiosity about the inner workings of technology and society. His academic path in technology ethics and sustainability, combined with his entrepreneurial work at Zetamotion, provides a unique perspective on AI's role in reshaping manufacturing processes, particularly in quality control. Wilhelm focuses on integrating AI and machine vision to optimize manufacturing quality control. But as he emphasized during the conversation, the story of AI in this domain is more than just technology; it’s about aligning innovations with human values, operational realities, and societal needs. The Last Mile Problem: Scaling AI Beyond Proof of Concept One of the discussions revolved around the "last mile problem" in AI implementations. While many organizations can successfully deploy AI in Proof of Concept (PoC) stages, transitioning these systems into scalable, production-ready solutions is an entirely different challenge. This gap arises from unforeseen complexities, including technical integration, stakeholder alignment, and the adaptation of processes to new workflows. “Scaling isn't just about having a functional prototype. It's about systemically embedding AI into the fabric of operations, which often reveals blind spots that were invisible during the PoC phase.” Thanks for reading The IT/OT Insider! Subscribe for free to receive new posts and support our work. Ethics and the Future of AI We also delved into the ethics of AI—a field Wilhelm has explored extensively. In the current debate, people often find themselves polarized between AI optimism and AI doom. Wilhelm offered a refreshingly balanced view, recognizing both the transformative potential of AI and the risks inherent in its misuse or unregulated growth. "What I find interesting," he noted, "is that both optimists and pessimists bring valid arguments. The critical task is to address these challenges proactively while ensuring that AI development remains aligned with societal well-being." From bias in algorithms to potential job displacement, Wilhelm argued for a more nuanced understanding of AI's broader impacts, advocating for policies and practices that emphasize transparency, accountability, and inclusivity. Practical AI in Action At Zetamotion, Wilhelm and his team are leveraging AI to transform quality control processes. By automating inspection workflows, AI not only reduces human error but also enables faster decision-making and significant cost savings. These advancements have profound implications for sustainability as well, minimizing waste and enhancing resource efficiency across industries. Yet, as Wilhelm pointed out, technology alone isn’t enough. The success of such initiatives depends on an organization’s ability to integrate AI into human-centric processes. This means involving frontline workers, addressing their concerns, and creating systems that are intuitive and supportive rather than alienating. Looking Ahead: AI’s Place in Industry and Society "The next five to ten years are going to be revolutionary. AI has already transformed many aspects of business and personal life, but the scale and speed of change we’re about to witness will challenge us in ways we can barely imagine." Whether it’s navigating the ethics of AI, bridging the gap between innovation and operational utility, or understanding the cultural shifts AI demands, this episode underscored the importance of thoughtful engagement with technology. Wilhelm’s insights remind us that the future of AI isn’t just about algorithms or automation—it’s about shaping a world where technology serves humanity, not the other way around. If you’re interested in the practical and philosophical dimensions of AI—or simply want a deeper understanding of its implications for industry and society—this podcast is a must-listen. It’s a conversation that challenges, inspires, and equips us to navigate the extraordinary opportunities and challenges that lie ahead. Want to learn more? Connect with Wilhelm on LinkedIn [https://www.linkedin.com/in/wilhelm-e-j-klein-a717a7166/].More about AI & Quality Control: https://zetamotion.com/ [https://zetamotion.com/] Subscribe on Youtube, Apple or Spotify Youtube: https://www.youtube.com/@TheITOTInsider [https://www.youtube.com/@TheITOTInsider] Apple Podcasts: Spotify Podcasts: This is a public episode. 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In this episode of the IT OT Insider podcast, host David interviews Davy Demeyer, an expert in industrial automation. Davy shares his extensive background in automation engineering, discussing the challenges faced in programming PLCs and the divide between IT and OT. He emphasizes the need for modern software development practices, such as DevOps and DesignOps, to improve automation workflows. Davy also explores the potential of generative AI in automation engineering and introduces the Society of Automation and Software Engineers, a community focused on combining automation and software principles. The conversation highlights the importance of evolving engineering practices to meet the demands of Industry 4.0. Chapters 00:00 Introduction to Davy de Meijer and His Journey 04:50 Understanding the Control Layer in Automation 09:55 Programming PLCs: Standards and Challenges 14:59 Bridging the Gap: Learning from Software Development 19:49 The Future of Automation: DesignOps and Generative AI 27:54 The Society of Automation and Software Engineers 32:58 The Importance of Design in Automation Engineering Want to know more? Find Davy on LinkedIn: https://www.linkedin.com/in/demeyerdavy/ [https://www.linkedin.com/in/demeyerdavy/] More about SASE: https://sase.space/ [https://sase.space/] Davy Demeyer has spent his career bridging the gap between traditional automation and the rapidly advancing world of digital technology. With decades of experience working on automation projects, he’s a passionate advocate for rethinking how we approach automation in the age of Industry 4.0. Understanding the Basics: What Are PLCs and DCS? Davy broke down two cornerstone technologies in automation: * PLCs: Often referred to as the backbone of automation, PLCs are specialized computers designed to control machinery and industrial processes. They are programmed using proprietary languages like Ladder Logic or Structured Text, a method that hasn’t evolved significantly during the last decades. * DCS (Distributed Control Systems): These are more complex systems, typically used for large-scale, continuous processes such as in chemical plants or refineries. They offer a centralized view and control of entire plants, integrating with various PLCs and other devices. Despite their importance, Davy highlighted how their programming methodologies remain rooted in the past, limiting their adaptability to modern software development practices. Thanks for reading The IT/OT Insider! Subscribe for free to receive new posts and support my work. The Programming Gap We talked about the differences between traditional automation programming and modern software development. While the software industry has embraced Agile, DevOps, and cloud-native design, automation engineering often remains tied to rigid, manual workflows. This divergence creates a bottleneck for scalability and innovation in automation, which is essential for Industry 4.0. Even Excel plays a critical role in ‘modern’ software development… 😣 Davy emphasized how automation programming’s reliance on vendor-specific tools and proprietary languages makes collaboration difficult and slows down the pace of digital transformation. Digital Transformation and Industry 4.0: The Bottleneck Why does this gap matter for Industry 4.0? Digital transformation initiatives rely on seamless data flow, agile responses to changing conditions, and scalable solutions. However, the slow evolution of automation practices hinders: * Scalability: New solutions remain siloed, with pilot projects often stuck in proof-of-concept stages. * Integration: Connecting PLCs to IT systems, cloud platforms, or advanced analytics often requires costly custom solutions. * Innovation: Without adopting modern practices, the automation industry risks falling behind in leveraging emerging technologies like AI or machine learning. The Future: DesignOps for Automation Davy proposed a vision for the future of automation: DesignOps for Automation Engineers. Borrowing from the software industry, DesignOps would focus on creating collaborative, integrated environments where engineers and developers work in harmony. He wants to Automate the Automation Engineer. This vision isn’t just theoretical—it’s already being championed in forward-thinking organizations. SASE: Society of Automation Software Engineers In line with this future, Davy introduced the Society of Automation Software Engineers (SASE), a community-driven initiative aimed at fostering collaboration and innovation in automation. SASE provides a platform for professionals to share best practices, develop new standards, and advocate for modernizing the industry. Make sure to listen to this very interesting episode! (And subscribe to get our weekly new content 🙂) Want to know more? Find Davy on LinkedIn: https://www.linkedin.com/in/demeyerdavy/ [https://www.linkedin.com/in/demeyerdavy/] More about SASE: https://sase.space/ [https://sase.space/] This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com [https://itotinsider.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Welcome back to the IT/OT Insider Podcast, where we dig into the nuances of digital transformation and Industry 4.0. Today we welcome Jon “The Factory Guy” Weiss on the podcast! With a career that spans global leadership roles at GE Digital, Software AG, and Amazon, Jon now operates as an Industry 4.0 expert. His diverse experience gives him a unique perspective on how technology impacts the manufacturing landscape. The State of Manufacturing Today Kicking off the conversation, Jon reflects on a familiar theme: the manufacturing world is under strain, juggling aging infrastructure with a dwindling workforce. Labor shortages and skill gaps dominate the conversation, with manufacturers scrambling to retain institutional knowledge as veteran operators retire without adequate replacements. Jon contextualizes today’s challenges by tracing the evolution of industrial revolutions, especially post-WWII. Following a manufacturing boom, especially in the U.S., companies expanded rapidly but without modernizing infrastructure, leading to a reliance on aging systems.  Data’s Pivotal Role and DataOps As the conversation shifts to data, Jon emphasizes the critical role of DataOps in optimizing manufacturing. Building a robust data foundation is a vital first step in deploying AI solutions effectively. Without it, any data-driven project risks failure. Manufacturing data isn’t just about numbers; it’s real-time insights on machine health, output efficiency, and product quality. A strong DataOps practice ensures that manufacturers can collect, clean, and utilize this data across various systems and departments. What Can AI Bring to Manufacturing? Jon offers a balanced view on AI, highlighting both its promise and its limits. While AI can automate specific tasks, optimize equipment, and predict maintenance needs, it’s not a silver bullet. In manufacturing, AI excels at identifying patterns and improving efficiency in structured, predictable environments. But its impact diminishes without high-quality, well-curated data. Manufacturers must recognize that implementing AI is a journey, requiring continuous improvement and the right expertise to maximize its benefits. The Three Pitfalls in AI Implementation Jon also shares insights into common pitfalls in manufacturing AI projects: Rushing into Production: Companies often move too quickly from pilot projects to full production without thorough testing, resulting in issues that can halt or complicate operations. A slower, more phased approach ensures AI is reliable and integrated seamlessly. Building a Solid Data Foundation: Quality data is essential, yet many companies still overlook it. Investing in data infrastructure—collection, storage, and processing—is crucial to making AI effective. Justifying a True Business Case: AI can be flashy, but without a clear, measurable business case, it’s easy for projects to fall short of expectations. Manufacturers must evaluate the ROI of AI, focusing on realistic goals that align with broader business objectives. ROI Discussion: Is AI Worth It? Jon wraps up with thoughts on ROI, stressing that AI projects need to demonstrate value beyond the hype. This includes both direct financial gains, such as cost savings through predictive maintenance, and indirect benefits, like improved safety and reduced downtime. Achieving ROI in AI requires patience, strategic planning, and a commitment to building a strong data infrastructure from the start. Listen to the full episode to hear Jon’s insights on navigating the AI landscape in manufacturing. Subscribe to the IT/OT Insider Podcast for more discussions on the latest in digital transformation and smart manufacturing. You can find Jon on https://www.thefactoryguy.ai/ [https://www.thefactoryguy.ai/] or on LinkedIn [https://www.linkedin.com/in/jweiss/] .  This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com [https://itotinsider.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Welcome to the 10th episode ( 🎉) of The IT/OT Insider Podcast! David talks with Gregory Grauwels [https://www.linkedin.com/in/grauwelsgregory/], who is the current Group OT Manager at Cloetta, a confectionery company in Europe. With years of experience in both industrial automation and digital transformation, Gregory has an impressive track record in OT management. In this podcast, he shares his insights and advice for those aspiring to start a career as an OT manager. Amongst other things, we talked about Doing things step by step, about Land and Expand and about Open architectures. Gregory's Journey to OT Management Gregory’s path to becoming an OT manager started with a deep technical background in industrial automation. After gaining experience in the petrochemical industry, where he worked on programming and integrating automation systems such as PLCs, HMIs, and SCADA systems, Gregory transitioned into more senior roles focused on digital transformation. Before joining Cloetta, Gregory held a role at Bayer, focusing on digital manufacturing initiatives like cybersecurity, digital maturity assessments, and overseeing Manufacturing Execution Systems (MES) and Manufacturing Operations Management (MOM). His move to Cloetta marked a shift from the petrochemical sector to the confectionery industry, which brought unique challenges. As he describes it, "I went from petrochemical to confectionery, which was completely different but exciting." At Cloetta, he was tasked with modernizing their operational technology infrastructure while maintaining a balance between long-standing, traditional machinery and cutting-edge digital systems. Key Challenges One of the key challenges Gregory faces at Cloetta is managing a complex mix of old and new technologies. Cloetta, with its long history, still operates some older production machines alongside the latest modern equipment. “We have lines that have been in operation for decades, and some of these machines are still vital to our production processes,” Gregory explains. As an OT manager, one must ensure that these systems run smoothly and integrate with new digital initiatives. Variation is also a unique challenge. Producing different types of confectionery—whether it’s chocolate, wine gums, or jelly beans—requires different technologies and processes. "Each product comes with its own set of machines and technology. The way we produce jelly beans is entirely different from how we make wine gums, and each of these technologies requires specialized knowledge and equipment,” he says. Managing these varied technologies means an OT manager must be adept at navigating both older machinery and modern automation tools. Gregory emphasizes the importance of ensuring that every machine, whether old or new, works harmoniously to maintain production efficiency and quality. Unified Namespace as the Data-Glue between all lines A key concept in modern industrial digital transformation is the Unified Namespace (UNS) [https://itotinsider.substack.com/p/the-unified-namespace-uns-demystified], which also came up during our conversation. The UNS serves as a central repository or hub for real-time data exchange across all systems in an organization. In the context of IT/OT convergence, this approach allows for seamless communication between different systems—whether it's legacy equipment, modern IoT devices, or enterprise-level applications like ERP systems. Gregory explained that the Unified Namespace provides a structured, standardized framework that ensures data from various sources is consistently accessible and usable by both OT and IT teams. "The idea behind the UNS is to create a single source of truth for all operational data," Gregory noted. By doing so, organizations can eliminate data silos, improve interoperability, and enable more effective decision-making based on real-time insights. This is particularly useful for industries with diverse systems, where aligning data formats and communication protocols has traditionally been a significant challenge. Advice for Aspiring OT Managers * Develop a Strong Foundation in Industrial Technology: Gregory’s background in automation and digital manufacturing laid the groundwork for his success as an OT manager. Aspiring OT managers should focus on building a deep technical understanding of automation systems like PLCs, SCADA, and MES, as well as new digital technologies that are reshaping the industry. “A strong technical foundation is key because OT is all about managing the technology that keeps production running,” he advises. * Learn to Manage Both Legacy and Modern Systems: In many industries, production lines often include a combination of legacy systems and the latest technologies. Gregory stresses the importance of balancing these two worlds. “You don’t replace a functioning machine just for the sake of digitalization. The challenge is to integrate new technologies in a way that complements the existing systems without disrupting production,” he explains. * Focus on Practical Problem-Solving: Problem-solving is at the heart of OT management. Gregory emphasizes that a good OT manager needs to focus on practical, efficient solutions to keep production moving smoothly. “It’s not just about implementing the latest tools or systems; it’s about ensuring that everything works together seamlessly,” he says. This often involves finding creative solutions to integrate digital tools into established processes. * Collaborate Across Teams and Departments: One of the most critical skills for an OT manager is the ability to collaborate effectively with different teams, from operators on the shop floor to upper management. Gregory highlights the importance of understanding the needs and challenges of each stakeholder involved in production. “As an OT manager, you’re the link between the technology and the people who use it. Strong communication and collaboration skills are essential,” he advises. * Keep a Long-Term Vision for Digital Transformation: Gregory views digital transformation as an ongoing process that requires careful planning and a forward-looking mindset. “Digital transformation isn’t just about adopting new technologies; it’s about making continuous improvements to streamline production, reduce costs, and improve product quality,” he explains. For aspiring OT managers, having a strategic vision for how to integrate digital solutions into manufacturing is crucial for long-term success. Thank you, Gregory, for joining us!  If you have an interesting story to share, feel free to reach out to David [https://itotinsider.substack.com/about] ! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com [https://itotinsider.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

In this new podcast David had an insightful conversation with Klaas Dobbelaere, IIoT Connectivity Director at Electrolux (also known in some markets under their brands AEG or Frigidaire). Klaas shared valuable insights into the world of Industrial Internet of Things (IIoT) and how Electrolux is embracing IT/OT convergence to drive digital transformation in its manufacturing operations.  Thanks for reading The IT/OT Insider! Subscribe for free and get all new posts The Electrolux Transformation Journey Klaas started by highlighting Electrolux's digital transformation efforts across its global footprint. As a leading household appliance manufacturer, Electrolux has to innovate continuously while ensuring its operations remain efficient and sustainable. The company's focus on leveraging IIoT for seamless connectivity across its operations helps optimize everything from energy consumption to predictive maintenance. One of the key takeaways from our conversation was the importance of actionable data. For Klaas, collecting data from machines, sensors, and production lines is not enough—what matters is translating that data into insights that can inform better decisions and improve operational efficiency. He stressed the significance of finding the right balance between cutting-edge technologies and the practical, everyday needs of the plant floor. Building Bridges Between IT and OT Historically, IT and OT have operated in silos—IT managing information systems, while OT focuses on controlling physical operations. This divide has often caused friction in industrial environments, but as Klaas explained, the boundaries are blurring rapidly. At Electrolux, IT/OT integration is a critical driver for innovation. By bridging the gap, teams can create a more collaborative environment where both the data insights from IT systems and the operational know-how from OT experts can come together to drive better outcomes. One concrete example Klaas gave was their efforts to deploy real-time monitoring systems that allow engineers to analyze machine performance instantly, identifying issues before they lead to costly downtimes. Navigating the Challenges Of course, IT/OT convergence isn’t without its challenges. Klaas was candid about the growing pains Electrolux faced, including technical hurdles like legacy equipment integration and organizational barriers that often slow down progress. However, he emphasized the need for patience and strong leadership to guide teams through these transitions. One challenge particularly close to Klaas' heart is the cultural shift that needs to occur. At Electrolux, as in many manufacturing companies, there's a deeply ingrained culture of precision, safety, and reliability. While these are strengths in traditional operations, they can slow down the adoption of new, agile technologies. For Klaas, the solution lies in fostering a mindset of continuous learning among employees and providing the right training to bridge the knowledge gap between IT and OT. Future-Proofing Manufacturing with IIoT Looking ahead, Klaas believes that the future of manufacturing lies in smart, connected ecosystems. He painted a vision of a factory where every machine, sensor, and operator is linked in a vast network, feeding real-time data into AI-driven systems. These systems will not only make predictions but will autonomously make decisions to optimize production processes. However, he also issued a word of caution: “Technology can only take you so far. Without the right people and processes in place, even the most advanced systems will fall short.” His message was clear—people remain the most important asset in any digital transformation. Conclusion Our conversation with Klaas Dobbelaere underscores the critical role IT/OT convergence plays in the modern manufacturing landscape. For companies like Electrolux, harnessing the power of IIoT and data-driven insights is key to staying competitive and driving innovation. But success doesn’t come easy—it requires breaking down silos, fostering a culture of collaboration, and being willing to embrace change. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit itotinsider.substack.com [https://itotinsider.substack.com?utm_medium=podcast&utm_campaign=CTA_1]