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Episodes
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.
Episodes
Tuesday Feb 03, 2026
Tuesday Feb 03, 2026
1. EPISODE SUMMARY
This episode explores why most manufacturing AI initiatives fail and what companies must do to build a foundation for AI-native industrial intelligence. Craig Scott, Founder and CEO of Fuuz, an industrial intelligence platform, shares insights from nearly a decade of bridging the gap between shop floor data and enterprise systems. The conversation reveals why the missing "shim" between operational technology and enterprise systems is the root cause of unreliable data in manufacturing, and why model-driven approaches are essential for scaling AI across industrial operations. Craig explains how organizations can achieve a single source of truth by implementing a persistent contextualization layer that governs data before AI ever touches it. Whether you're struggling with fragmented point solutions, evaluating industrial data platforms, or preparing your data infrastructure for AI, this episode provides a practical framework for building scalable industrial intelligence.
2. KEY QUESTIONS ANSWERED IN THIS EPISODE
- What is fundamentally broken with current manufacturing data infrastructure and how does it impact AI initiatives?
- Why do most AI pilots fail to scale in manufacturing environments?
- What is a model-driven approach to industrial data, and why is it superior to in-line data transformation?
- How do you balance enterprise governance with plant-level flexibility in industrial data architectures?
- Should manufacturers adopt industry-standard data models like ISA-95 or build custom models?
- What is the difference between a data lake and an operational intelligence platform?
- How can manufacturers prepare their data foundation before investing in AI technologies?
3. EPISODE HIGHLIGHTS WITH TIMESTAMPS
[0:00] - Introduction — Craig Scott's background from hands-on manufacturing at age 15 to founding Fuuz, and why the company's purple branding represents the merger of "red" (OT) and "blue" (IT) data.
[6:56] - What's Fundamentally Broken — Discussion of how critical manufacturing knowledge is leaving the business as the workforce ages, and why data-driven approaches are essential to capture and retain institutional knowledge.
[8:09] - The Missing Shim Problem — Craig explains the gap between real-time shop floor data (SCADA/historians) and enterprise systems (ERP/PLM), and why neither system alone provides a single source of truth.
[16:20] - MCP and I3x Integration — How Fuuz is implementing Model Context Protocol and aligning with the I3x initiative for standardized GraphQL APIs to enable AI connectivity.
[18:52] - Model-Driven vs. In-Line Transformation — Why data transformation tools that reshape data in motion create scaling challenges, and how persistent data models solve enterprise-wide consistency.
[24:06] - AI Governance and Hallucination Prevention — Why deterministic data models are essential for trustworthy AI—Claude can't "make up" OEE numbers when the data graph dictates values.
[28:41] - Custom vs. Standard Data Models — Discussion of when to use ISA-95 accelerators versus custom models, using an automotive OEM wall-to-wall deployment as an example.
[33:46] - Red and Blue Namespace Architecture — How Fuuz balances enterprise governance with plant-level flexibility through extensible tenant-based data models.
[37:28] - What Category is Fuuz? — Craig explains how the platform spans MES, WMS, data ops, and application development—an operational intelligence layer, not a data lake.
[47:13] - Technical Architecture Deep Dive — Overview of Kubernetes backend, Node.js framework, RabbitMQ messaging, MongoDB with custom ORM, and the hybrid gateway for edge connectivity.
[51:16] - Real-World Deployments — Case studies including an automotive OEM running an entire car plant on Fuuz, Highbar Steel's solar-powered green steel mill, and PepsiCo co-packer integrations.
[53:52] - Advice for Getting Started — Craig's recommendation to define the problem first, assemble cross-functional IT/OT teams, and start small with the understanding that small problems often expose bigger ones.
4. KEY TAKEAWAYS
- The "shim" between shop floor and enterprise is the missing piece: ERP and PLM systems are only accurate for the first 15 minutes after data entry. Without a real-time contextualization layer synchronizing shop floor and enterprise data, there is no true single source of truth.
- Model-driven persistence beats in-line transformation for scale: While edge tools that transform data in motion work for one or two sites, they require re-implementation across every site and system. A persistent data model is defined once and becomes the consistent interface for all enterprise systems.
- AI governance requires deterministic data models: LLMs cannot reliably do math and will hallucinate if given unstructured data. By forcing AI to read from governed data graphs, organizations can move toward semi-autonomous and eventually autonomous operations with trustworthy outputs.
- Extensible models balance governance and flexibility: Enterprise IT can define governed core models while individual plants extend them with additional metadata—they can add context but cannot change underlying structures, preserving data integrity while enabling local adaptation.
- Operational intelligence is not the same as a data lake: Data lakes are good for reporting and analytics but don't help run real-time operations. An operational intelligence platform provides both persistent contextualized state and real-time event streaming for actual operational execution.
- Start with the problem, not the technology: Many companies approach vendors saying they "need an MES" without understanding why. Defining value drivers first allows solutions to start small and expand as bigger problems reveal themselves.
- Build tools that enable AI, don't rely on AI as the platform: LLMs are evolving rapidly and may be replaced by new model architectures. Building platforms around deterministic data foundations protects against technical debt from betting on novel technologies.
5. NOTABLE QUOTES
"There's a reason why our color is purple, because if you mix red and blue together, it makes purple. We are the part that's in between—the highly structured enterprise data like ERP and PLM and the really unstructured data that's happening on the plant floor." — Craig Scott, CEO at Fuuz
"The ERP is a good source of truth for like the first 15 minutes that the data goes into the system, and then immediately, when you start generating real time data from the shop floor, it's out of date. Nothing is in sync anymore." — Craig Scott, CEO at Fuuz
"When I connect Claude to Fuuz, Claude can't make anything up. It can't imagine an OEE for my line or my machine because it's being dictated by our data graph." — Craig Scott, CEO at Fuuz
"I still look at AI as a tool, and I don't know that we're ready to acknowledge AI as the platform yet. We want to build tools and platforms that enable the technology, not rely on the new technology to be our platform." — Craig Scott, CEO at Fuuz
"Data is money, and if we can turn that data into actionable insights, now we can make more money for your business." — Craig Scott, CEO at Fuuz
6. KEY CONCEPTS EXPLAINED
Industrial Intelligence Platform
Definition: A software layer that sits between operational technology (SCADA, historians, PLCs) and enterprise systems (ERP, PLM, CRM) to provide real-time data contextualization, persistence, and governance.
Why it matters: Traditional architectures leave a gap between shop floor data and business systems, causing data inconsistency and preventing AI from accessing trustworthy operational information.
Episode context: Craig describes Fuuz as the "shim" or "purple" layer that bridges red (OT) and blue (IT) data, enabling real-time synchronization and a true single source of truth.
Model-Driven Architecture
Definition: An approach where data models are defined first as persistent, governed structures, and all systems read from and write to this single canonical model rather than transforming data in-line during transit.
Why it matters: In-line transformation tools work for small deployments but require re-implementation at every site. Model-driven persistence enables "once and done" enterprise-wide data consistency.
Episode context: Craig contrasted this with edge tools that reshape data in motion, explaining that persistent models scale across global enterprises with multiple ERPs and systems.
Unified Namespace (UNS)
Definition: An architectural pattern that provides a single, hierarchical structure for all operational data, making it accessible to any system that needs it.
Why it matters: UNS is gaining adoption as a way to democratize data access, but without persistent contextualized state, it only provides current values—not the historical context needed for operations and AI.
Episode context: Craig acknowledged UNS as a great concept but emphasized that operational intelligence requires persistent state of contextualized data, not just real-time streaming.
Model Context Protocol (MCP)
Definition: A protocol that enables AI systems to connect to and understand data from external platforms through standardized interfaces.
Why it matters: MCP allows AI tools like Claude to access governed industrial data without requiring custom integrations or exposing companies to AI hallucination risks.
Episode context: Fuuz added MCP capability to expose their data graph to AI systems, ensuring AI outputs are governed by deterministic data rather than generating unreliable information.
I3x Initiative
Definition: An industry initiative working on standardized GraphQL APIs for industrial data exchange, enabling interoperability between industrial systems.
Why it matters: Standardized APIs reduce integration complexity and allow best-of-breed systems to share data through common interfaces.
Episode context: Craig mentioned Fuuz has been GraphQL-based since inception and is building an I3x connector to expose all enterprise system data through this emerging standard.
7. RESOURCES & REFERENCES
Technologies & Platforms
- Fuuz Industrial Intelligence Platform
- Ignition (Inductive Automation) - SCADA platform
- Kepware - Industrial connectivity
- Plex - Cloud ERP for manufacturing
- MongoDB - Document database
- RabbitMQ - Message broker
- GraphQL - API query language
- Kubernetes - Container orchestration
- React - Frontend framework
Standards & Frameworks
- ISA-95 - Manufacturing operations standard
- I3x Initiative - Standardized industrial APIs
- Model Context Protocol (MCP)
- OPC UA - Industrial interoperability standard
- MQTT - Industrial messaging protocol
Concepts
- Unified Namespace (UNS)
- Knowledge Graphs
- Red and Blue Data (Walker Reynolds concept)
- Data Lakes vs. Operational Intelligence
Events
- PROVE IT Conference
8. GUEST BIO & LINKS
Craig Scott is the Founder and CEO of Fuuz, an industrial intelligence platform that bridges the gap between shop floor operations and enterprise systems. With a career spanning manufacturing engineering, owning tool-and-die and precision machining companies, consulting for Plex ERP, and founding a system integration business, Craig brings a unique perspective that combines hands-on manufacturing experience with deep technology expertise. He holds a degree in manufacturing engineering and a master's in manufacturing administration.
- Company Website: fuuz.com
- Knowledge Base: support.fuuz.com
- Free Training: Fuuz Academy (self-paced, available via website)
- LinkedIn: Search "Craig Scott Fuuz"
9. FAQ SECTION
Q: What is the difference between a data lake and an operational intelligence platform?
A: A data lake stores denormalized data optimized for reporting and analytics but doesn't help run real-time operations. An operational intelligence platform provides persistent contextualized state and real-time event streaming, enabling both operational execution and analytics from a single governed source.
Q: Why do most manufacturing AI pilots fail to scale?
A: Most AI initiatives skip the foundational work of preparing and contextualizing data. Companies pipe raw data into data lakes or directly into AI tools without governance, leading to unreliable outputs. AI is only as good as the data it receives—without a persistent, governed data model, AI cannot produce trustworthy results at scale.
Q: Should manufacturers use standard data models like ISA-95 or build custom models?
A: Both approaches are valid depending on the use case. ISA-95-based accelerators work well for discrete manufacturing processes, but complex operations like automotive assembly with multiple lines require extensible custom models. The key is having a platform that allows starting with standards and extending as needed.
Q: How do you balance enterprise data governance with plant-level flexibility?
A: Implement governed core models at the enterprise level, then allow individual plants (as separate tenants) to extend those models with additional metadata. Plants can add context but cannot change underlying structures, preserving data integrity while enabling local adaptation.
Q: What should manufacturers do first to prepare for AI adoption?
A: Define the specific problem you're trying to solve and identify the value drivers behind it. Assemble a cross-functional team between IT and OT. Take inventory of your data sources and understand which hold the most valuable information. Start small—solving small problems often exposes bigger opportunities, and having the right platform in place makes solving those bigger problems easier.
Q: Can I use an industrial intelligence platform without replacing my existing ERP or MES?
A: Yes. Platforms like Fuuz are designed to integrate with existing systems rather than replace them. You can expose data from multiple ERPs, PLMs, and operational systems through a single governed layer, adding AI and modern API capabilities without wholesale system replacement.
10. RELATED TOPICS & EPISODES
- Unified Namespace Architecture — Understanding how UNS patterns complement operational intelligence platforms
- Industrial AI Agents — Building autonomous systems that can trust their data foundation
- MES Modernization — Strategies for extending legacy manufacturing execution systems
- IT/OT Convergence — Bridging the gap between information technology and operational technology teams
- I3x Initiative Deep Dive — Upcoming episode exploring standardized industrial APIs
Thursday Jan 22, 2026
Thursday Jan 22, 2026
Traditional MES platforms were built for a manufacturing world that no longer exists.
They assume stable product lines.
They assume you have time for lengthy implementations, tolerance for complexity, and operators who can navigate digital forms while running production.
But here's the challenge. Today's manufacturing reality is different:
⇨ Markets demand the flexibility to shift from 1.5-liter bottles to 1-liter bottles overnight
⇨ Low volume, high mix production is now the norm
⇨ Tribal knowledge is retiring faster than it's being captured
⇨ Workers stay 2-3 years, not 20, making traditional training models obsolete
The cost of this disconnect?
❌ Frontline workforce unable to contribute operational intelligence at scale
❌ ROI delayed by complexity, not capability
❌ Two-year deployment cycles for basic systems
❌ Digital initiatives stuck in pilot purgatory
That's why leading manufacturers are rethinking execution from the ground up, shifting from monolithic systems to AI-native, human-centric platforms built for today's workforce reality.
This new approach is effective because it’s built with an AI-native mindset, not a digitized version of paper-based processes
✅ AI-generated SOPs from video, cutting engineering time by 80%
✅ Learning systems that surface troubleshooting guidance from historical fault data
✅ Human-centric design that captures operational intelligence without disrupting workflows
✅ AI-powered interfaces that enable natural interaction; think voice, not dropdowns
✅ Rapid deployment measured in weeks
✅ Scalable without complexity; connect thousands of machines without lengthy integrations
The companies winning today aren’t planning more; they’re executing faster and adapting continuously.
In this episode of the AI in Manufacturing podcast, I speak with Mickey Shaposhnik, Founder and CEO of Next Plus, about how practical, AI-powered frontline execution is redefining operational excellence.
Watch/Listen now
Wednesday Oct 22, 2025
Wednesday Oct 22, 2025
Agentic AI Framework for Manufacturing Operations
AI in Manufacturing Podcast Show Notes
Episode Guest: Gilad Langer, Head of Digital Transformation Practice at Tulip Interfaces
Host: Kudzai Manditereza
Publication Date: [Insert Date]
Episode Summary
Manufacturing systems are complex adaptive systems that require a fundamentally different approach to AI implementation than traditional monolithic architectures. In this episode, Gilad Langer draws on 30 years of manufacturing experience—including PhD research that laid the groundwork for Industry 4.0—to introduce a composable agentic framework specifically designed for frontline operations. He explains why adaptability has become a competitive necessity in today's disrupted markets and how multi-agent systems can transform innate factory equipment into intelligent, communicating entities. The conversation covers practical implementation strategies, the artifact model for structuring manufacturing data, and why cultural change remains the biggest obstacle to agentic AI adoption.
Key Questions Answered in This Episode
- What is an agentic AI framework for manufacturing and why do factories need one?
- How do complex adaptive systems apply to manufacturing operations?
- What are the five pillars of composability in manufacturing?
- How should manufacturers structure their data for AI agents using the artifact model?
- What is the difference between staff agents, builder agents, and artifact agents?
- How do you implement agentic AI in a brownfield manufacturing facility?
- Why do traditional MES systems fail to deliver the adaptability modern manufacturing requires?
Episode Highlights with Timestamps
[0:00] — Introduction — Kudzai introduces Gilad Langer and previews the discussion on composable agentic frameworks for frontline operations.
[1:19] — Gilad's Background — Gilad shares his 30-year manufacturing journey, including PhD research in the 1990s that anticipated Industry 4.0 concepts like IIoT and multi-agent systems.
[6:58] — The Vision Realized — Discussion of how today's technology finally enables the adaptive manufacturing concepts envisioned decades ago.
[7:49] — Why Adaptability is Now Essential — Gilad explains how tariffs, supply chain disruptions, and COVID have made manufacturing adaptability a competitive necessity, not just an aspiration.
[14:10] — Complex Adaptive Systems Explained — Deep dive into how manufacturing systems share characteristics with traffic and weather patterns, including the concept of attractors and emergence.
[15:38] — The Toyota System Connection — Gilad explains how Toyota understood complex adaptive systems and used lean methods to keep manufacturing in the "orderly space."
[26:21] — The Danger of Uncontrolled Agents — Discussion of how agents without proper frameworks can cause catastrophic "butterfly effects" in manufacturing operations.
[28:19] — Agent Taxonomy Introduction — Gilad walks through the four types of agents: staff helpers, builder agents, operational agents, and artifact agents.
[35:06] — Agents as Digital Twins — Why each discrete item should have its own agent rather than one agent controlling multiple machines.
[40:20] — The Artifact Model Explained — Comprehensive breakdown of how to structure manufacturing data using physical and operational artifacts.
[51:07] — Implementation Strategy — Practical guidance on starting small with agentic AI, beginning with a single machine and growing from there.
[57:15] — Tulip Platform Overview — How Tulip's no-code frontline operations platform enables composable agentic manufacturing.
[1:00:46] — The Composability Test — How to determine if your implementation is truly composable: can you solve a problem within an hour?
Key Takeaways
Manufacturing systems are complex adaptive systems that require emergent, bottoms-up approaches. Traditional blueprint-based implementations lock organizations into rigid structures. Truly adaptive manufacturing systems mimic natural phenomena—like plants growing toward sunlight—by solving problems iteratively and adapting to obstacles without predetermined plans.
The five pillars of composability provide a framework for evaluating any manufacturing technology. Ask whether a platform supports bottoms-up development, lean improvement, democratized access, human-centric design, and compliance requirements. If a technology fails any of these tests, it cannot deliver true composability.
Agents can either help humans or bring innate objects to life. Staff agents assist workers with tasks like monitoring and scheduling. Artifact agents wrap physical equipment with intelligence, enabling machines, materials, and systems to communicate with each other and with humans.
The artifact model simplifies manufacturing data into physical and operational categories. Physical artifacts include machines, tools, areas, and materials. Operational artifacts include orders, tasks, defects, and events. Most manufacturing plants have no more than ten distinct physical artifact types, making the data model inherently human-comprehensible.
Per-artifact agents deliver true adaptability that hierarchical approaches cannot match. When one agent fails in a distributed system, the others adapt and continue operating. A single controlling agent creates a single point of failure that can bring down entire operations.
Start small with agentic AI implementation. Pick one critical piece of equipment, add sensors, create a simple agent, and let operators interact with it. Scale gradually while building governance frameworks alongside the technology.
Cultural change is the biggest obstacle to agentic manufacturing adoption. Engineers trained in monolithic thinking will naturally gravitate toward building rigid systems even when given composable tools. Organizations need change agents who maintain discipline around composable principles.
Notable Quotes
"It's not the most intelligent of the species that survives. It's the species that is most adaptable to change that survives—that thrives." — Gilad Langer, referencing Darwin's principle as applied to manufacturing
"If you push the system, if you take out the slack, highly likely there's going to be a traffic jam. And it's the same thing in manufacturing." — Gilad Langer, on why pull-based systems outperform push-based systems
"If it takes months or more to connect a machine and create an agent, you don't have a composable system. The answer should be hours." — Gilad Langer, on the composability test
"We run manufacturing like that. As soon as an event we didn't expect happens, we just sit there and burn alive. Essentially, that's what we do." — Gilad Langer, on the limitations of blueprint-based manufacturing systems
Key Concepts Explained
Complex Adaptive Systems
Definition: A class of systems composed of discrete entities that exhibit emergent behavior and patterns without centralized control—including traffic, weather, and manufacturing operations.
Why it matters: Understanding manufacturing as a complex adaptive system reveals why traditional rigid architectures fail and why multi-agent approaches succeed.
Episode context: Gilad uses examples of traffic patterns and weather prediction to illustrate how patterns emerge in chaotic systems and how Toyota's lean methods leverage these dynamics.
Composability
Definition: An architectural approach built on five pillars—bottoms-up development, lean thinking, democratization, human-centricity, and compliance—that enables systems to adapt continuously rather than requiring upfront blueprints.
Why it matters: Composable systems can respond to market disruptions, supply chain changes, and unexpected events without costly re-engineering.
Episode context: Gilad contrasts composable platforms with traditional MES implementations that "lock you in a prison" through rigid blueprints and design reviews.
Artifact Model
Definition: A simplified data structure that categorizes manufacturing elements into physical artifacts (machines, tools, materials, areas) and operational artifacts (orders, tasks, defects, events), typically resulting in no more than ten distinct artifact types per facility.
Why it matters: The artifact model makes manufacturing data human-comprehensible and AI-ready by reflecting the actual reality of the shop floor rather than abstract database schemas.
Episode context: Gilad explains how this model emerged from 1990s research and enables both knowledge graphs and agent-based systems to operate effectively.
Emergence
Definition: A phenomenon where complex system behaviors arise from simple rules followed by individual entities, without centralized planning or blueprints.
Why it matters: Emergent systems achieve adaptability that hierarchical control structures cannot match.
Episode context: Gilad uses the example of a plant growing toward sunlight—adapting around obstacles without any blueprint—to illustrate how manufacturing systems should evolve.
Attractor (Chaos Theory)
Definition: A state toward which a system naturally tends to evolve; attractors can lead to either stable operations or catastrophic failures.
Why it matters: Agentic frameworks must include mechanisms to keep systems away from bad attractors that could cause plant shutdowns or quality disasters.
Episode context: Gilad emphasizes that without proper governance, multi-agent systems can rapidly cascade toward destructive states—the "butterfly effect" in manufacturing.
Resources & References
Technologies & Platforms:
- Tulip Interfaces — No-code frontline operations platform
- Unified Namespace (UNS) — Real-time data communication architecture
- Multi-Agent Systems (MAS) — Distributed AI architecture
Concepts & Frameworks:
- ISA-95 — Manufacturing operations standard
- Complex Adaptive Systems theory
- Chaos theory and attractors
- Toyota Production System / Lean Manufacturing
- Kaizen and Gemba walks
- Knowledge graphs
Historical References:
- Agility Forum (Lehigh University, 1990s)
- RWTH Aachen Institute (Industry 4.0 foundations)
- "Future Perfect" by Stanley Davis (1987) — Mass customization concepts
Companies Mentioned:
- Toyota — Lean manufacturing pioneer
- Amazon / Amazon Robotics — Adaptability exemplar
- DMG Mori, Mazak — CNC machine manufacturers
Guest Bio & Links
Gilad Langer is the Head of Digital Transformation Practice at Tulip Interfaces, bringing 30 years of manufacturing experience spanning integrators, software suppliers, and startups. His PhD research in the 1990s—conducted in collaboration with Germany's RWTH Institute—helped establish foundational concepts for what became Industry 4.0, including multi-agent systems and adaptive manufacturing architectures. He is a prolific writer on manufacturing technology and composable operations.
- Blog: GilardLConsulting.com
- Company: Tulip.co
- LinkedIn: Gilad Langer
FAQ Section
Q: What is an agentic AI framework for manufacturing?
A: An agentic AI framework for manufacturing is a structured approach for deploying multiple AI agents that work together to manage factory operations. Unlike traditional monolithic systems, this framework treats manufacturing as a complex adaptive system where discrete agents—representing machines, materials, and processes—communicate and coordinate autonomously while following governance rules that prevent chaotic behavior.
Q: How does the artifact model work in manufacturing AI?
A: The artifact model structures manufacturing data into two categories: physical artifacts (machines, tools, materials, areas) and operational artifacts (orders, tasks, defects, events). Most facilities have fewer than ten distinct artifact types, creating a simplified data model that humans can understand intuitively and AI agents can navigate effectively. Each artifact becomes the foundation for an agent that owns and manages its associated data.
Q: What are the five pillars of composability in manufacturing?
A: The five pillars are: (1) bottoms-up emergent development rather than top-down blueprints, (2) lean thinking that removes obstacles and maintains flow, (3) democratized access so frontline workers can create and modify solutions, (4) human-centric design that augments rather than replaces workers, and (5) compliance-ready architecture that satisfies regulatory requirements while remaining adaptable.
Q: Why can't traditional MES systems support agentic AI?
A: Traditional MES systems were architected in the 1990s around monolithic, blueprint-based designs that require locking in requirements before implementation. These rigid structures cannot support the emergent, adaptive behavior that multi-agent systems require. Attempting to add agentic capabilities to monolithic architectures is comparable to running electric vehicles on infrastructure designed for horse-drawn carriages.
Q: How should manufacturers start implementing agentic AI?
A: Start with a single critical piece of equipment, even legacy machinery that lacks modern connectivity. Add sensors, create a simple agent for that machine, and enable operators to interact with it. Validate the approach, then gradually expand to additional machines while developing governance frameworks alongside the technology. This bottoms-up approach aligns with composability principles and avoids the risks of big-bang implementations.
Q: What is the biggest challenge in adopting agentic AI in manufacturing?
A: Cultural change presents the greatest obstacle. Engineers trained in traditional methodologies will naturally gravitate toward building monolithic systems even when given composable tools. Organizations need dedicated change agents who maintain discipline around composable principles—similar to lean senseis who guide continuous improvement transformations.
Related Topics
- Unified Namespace Architecture — How UNS enables agent-to-agent communication
- Digital Twin Implementation — Building intelligent representations of physical assets
- Lean Manufacturing Meets AI — Connecting Toyota Production System principles with agentic frameworks
- Industrial AI Governance — Establishing guardrails for autonomous manufacturing systems
- Composable Enterprise Architecture — Extending composability beyond the shop floor
Wednesday Sep 17, 2025
Wednesday Sep 17, 2025
Context isn't static.
It's a living layer of knowledge built through problem-solving, conversation, and understanding the complex relationships on the factory floor.
This simple truth is often overlooked in industrial data strategies.
We’ve been conditioned to believe that context can be predefined; baked into standards, taxonomies, and hierarchies.
But in real-world manufacturing, things change, people think differently, and use cases evolve.
So how can we build this dynamic layer of understanding for industrial AI?
In our latest AI in Manufacturing episode, I spoke with Bob van de Kuilen, Director at Thred, about a more human-centric approach to industrial data contextualisation using Knowledge Graphs.
Thred is a tool that plugs into Ignition Platform, enabling users to visualize their factory assets in a knowledge graph, link related data points, embed domain expertise, and deliver structured, contextualized data to AI and analytics tools.
We discuss:
✅ Why traditional approaches to data context often fail
✅ Knowledge Graphs act as a mind map for data
✅ The practical steps to building context
✅ How this new context layer serves as the perfect foundation for AI agents.
Wednesday Sep 10, 2025
Wednesday Sep 10, 2025
SA-95 is a standard that’s often misunderstood, but incredibly powerful.
While many think ISA-95 is rigid or overly complex, it actually enables flexibility by:
⇨ 𝐃𝐞𝐟𝐢𝐧𝐢𝐧𝐠 𝐚 𝐬𝐡𝐚𝐫𝐞𝐝 𝐯𝐨𝐜𝐚𝐛𝐮𝐥𝐚𝐫𝐲 for manufacturing concepts, creating a true ontology for your data.
⇨ 𝐂𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐩𝐥𝐚𝐜𝐞𝐡𝐨𝐥𝐝𝐞𝐫𝐬 for every type of data, so you can start small and add new use cases later without rebuilding everything.
⇨ 𝐏𝐫𝐨𝐯𝐢𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 "𝐰𝐡𝐲" 𝐛𝐞𝐡𝐢𝐧𝐝 𝐞𝐯𝐞𝐧𝐭𝐬, not just the "what," giving crucial context to your analytics and AI models.
But how do you move from theory to a practical, modern implementation?
In our latest AI in Manufacturing podcast episode, we explore exactly that with ISA-95 expert Jeroen Janssen, who is an MES/MOM Consultant at Rhize Manufacturing Data Hub.
In the episode, you’ll learn:
✅ How to overcome a data culture that creates so many silos.
✅ The "use case stacking" method for a phased, value-driven implementation.
✅ What a native ISA-95 data hub looks like and how a graph database can bring it to life.
✅ Why this standardized approach is the key to unlo
Wednesday Sep 03, 2025
Wednesday Sep 03, 2025
Is the Timebase free historian getting an AI-Native DataOps component with Knowledge Graphs capability? You’ll hear it here first.
In the latest episode of the AI in Manufacturing podcast, I sit down with Jeff Knepper, President at Flow Software Inc., to discuss the intersection of Information Management and AI in modern manufacturing, plus the exciting announcement of Timebase Atlas launch.
Here’s some of what we cover in this episode:
✅ Why manufacturers struggle to make use of their data
✅ Building reliable pipelines for AI-driven use cases
✅ AI Agents in Manufacturing – Where they fit and what they need
✅ Unified Analytics Framework vs. Unified Namespace
✅ Historization Strategies – Best practices from edge to cloud
✅ Timebase Atlas Launch Announcement: Data Modeling, Pipelines, Knowledge Graphs, and AI interfaces
✅ MCP and Flow AI Gateway: Beyond APIs to Context-Aware Agent Interfaces
Wednesday Aug 27, 2025
Wednesday Aug 27, 2025
Most data-quality initiatives focus on things like freshness or schema. That works for IT data, but not for sensor data.
Sensor data is different. It reflects physics. To trust it, you need contextual, physics-aware checks.
That means spotting:
→ Impossible jumps
→ Flatlines (long quiet periods)
→ Oscillations
→ Broken causal patterns (e.g., valve opens → flow should increase)
It’s no surprise that poor data quality is one of the biggest reasons manufacturers struggle to scale AI initiatives.
This isn’t just data science, it’s operations science.
Think of data quality as infrastructure: a trust layer between your OT data sources and your AI tools.
Making that real requires four building blocks:
1. 𝐒𝐜𝐨𝐫𝐢𝐧𝐠 – Physics-aware anomaly rules, baselines
2. 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 – Continuous validation at the right cadence (real-time or daily)
3. 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 & 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 – Auto-fix what you can; escalate what you can’t
4. 𝐔𝐧𝐢𝐟𝐨𝐫𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 & 𝐒𝐋𝐀𝐬 – Define “good enough” and enforce it before data is consumed
Why it matters:
✅ Data teams – Less cleansing, faster delivery
✅ AI models – Reliable inputs = repeatable results
✅ Ops teams – Catch failing sensors before downtime
✅ Business – Avoid safety incidents, billing errors, bad decisions
In the latest episode of the AI in Manufacturing podcast, I sat down with Bert Baeck, Co-Founder of Timeseer.AI, to discuss time-series data quality and reliability strategies for AI in manufacturing applications.
Wednesday Aug 20, 2025
Wednesday Aug 20, 2025
What really makes data and AI innovation teams succeed in manufacturing?
In this episode of the AI in Manufacturing Podcast, I speak with Van Tucker, VP of Harbor Lockers by Luxer One, a company that develops and manufactures smart public lockers.
We discuss the challenges and strategies for building effective innovation teams in manufacturing.
Here are some of the insights that Van shared:
𝐂𝐮𝐥𝐭𝐮𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧
Innovation thrives when people, from the boardroom to the factory floor, believe in the mission. Core values must be lived daily, not just written on posters.
𝐀𝐠𝐢𝐥𝐢𝐭𝐲 𝐨𝐯𝐞𝐫 𝐩𝐞𝐫𝐟𝐞𝐜𝐭𝐢𝐨𝐧
Instead of waiting months for a polished rollout, start simple. Test small ideas quickly, gather feedback, and iterate. Even in hardware manufacturing, lightweight R&D “sandboxes” allow experimentation without disrupting core production.
𝐌𝐚𝐧𝐚𝐠𝐢𝐧𝐠 𝐩𝐫𝐞𝐬𝐬𝐮𝐫𝐞 𝐚𝐧𝐝 𝐛𝐮𝐫𝐧𝐨𝐮𝐭
Burnout shows up in declining quality and disengagement. The best leaders don’t wait, they stay close to their teams, recognize early warning signs, and act before problems escalate.
𝐁𝐫𝐢𝐝𝐠𝐢𝐧𝐠 𝐈𝐓 𝐚𝐧𝐝 𝐎𝐓
The old silos are gone. Effective leaders create environments where engineers from IT and OT collaborate, not compete. Quick collaborative wins build trust and momentum across functions.
Wednesday Aug 13, 2025
Wednesday Aug 13, 2025
Most industrial processes still run on the same foundation:
- Hard-coded logic in PLCs that follows predefined rules.
- The intuition of process and plant engineers, built from years of experience.
This combination has powered industry for decades, but it has limits.
When the challenge involves many interacting variables, unknown relationships, and non-linear effects, traditional control starts to strain.
Why?
Because fixed rules can’t adapt fast enough to changing conditions, and even the best human intuition can only process so much complexity at once.
Instead of relying on fixed instructions, RL agents learn directly from real-time feedback.
They can:
✅ Adapt continuously to new conditions.
✅ Handle high-dimensional problems with countless variables.
✅ Uncover novel, more efficient strategies that humans might overlook.
The result?
An optimization layer that works alongside your existing control system, making it smarter, more adaptive, and capable of delivering gains where complexity used to be a roadblock
In the latest episode of the AI in Manufacturing podcast, I sat down with Dr. Kyrill Schmid, the Lead AI Engineer at MaibornWolff GmbH, to discuss the application of reinforcement learning agents for optimizing industrial plants.
Wednesday Aug 06, 2025
Wednesday Aug 06, 2025
Can AI agents really make decisions in high-stakes industrial environments?
Generative AI agents, on their own, do not have a robust understanding of cause-and-effect for real-world decision-making.
However, when combined with Deep Reinforcement Learning, AI agents gain the ability to reason, learn from interaction, and make decisions that solve operational problems in complex, real-world environments, like the plant floor.
Case in point.
Bryan DeBois and his team at RoviSys developed an Autonomous AI agent to manage a notoriously difficult glass bottle production process, where small disruptions like temperature fluctuations can quickly push the process out of specification.
Here’s how they approached it:
✅ 𝐒𝐭𝐞𝐩 1 - 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐓𝐞𝐚𝐜𝐡𝐢𝐧𝐠
They captured the knowledge and decision-making strategies of expert human operators and used this to train the AI agent, essentially teaching it how to respond to different operating conditions.
✅ 𝐒𝐭𝐞𝐩 2 - 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐌𝐨𝐝𝐞
Initially, the agent didn’t control the process directly. It simply made recommendations.
Operators reviewed the suggestions and gave feedback using a simple green/red button system. This built trust and allowed the team to validate the AI’s decisions without risk.
✅ 𝐒𝐭𝐞𝐩 3 - 𝐂𝐥𝐨𝐬𝐞𝐝 𝐋𝐨𝐨𝐩 𝐂𝐨𝐧𝐭𝐫𝐨𝐥
Only after months of successful operation in support mode did they enable full automation.
Even then, strict safety measures were in place:
⇨ Limited control authority
⇨ Clearly defined operating boundaries
⇨ Automatic handover to human operators if conditions exceeded the agent’s training
The Results:
⇨ Human operators typically needed 7–20 minutes to bring the process back into spec
⇨ The AI agent consistently did it in under 5 minutes
⇨ And it maintained safety by operating strictly within validated limits
In the latest episode of the AI in Manufacturing podcast, I sat down with Bryan, Director of Industrial AI at RoviSys, to dive deeper into how manufacturers can leverage AI and autonomous agents to optimize manufacturing operations and improve efficiency