AI in Manufacturing

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  Time-Series Data Quality and Reliability for Manufacturing AI: Bert Baeck - Timeseer.AI 27.08.2025 52นาที

  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.
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  AI Agents for Industrial Sales and Application Engineers: Fay Goldstein - Co-Founder and CEO, Folio 18.06.2025 45นาที

  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 the portfolio that fit better? ⇨ Can it be installed without disrupting production? Now imagine trying to answer all of that... ⇨ Using PDFs. ⇨Email chains. ⇨ Gut instinct. ⇨ And hoping Bob from Engineering isn’t on vacation. With an AI Agents trained on your connected industrial knowledge: ✅ All technical documentation, manuals, spec sheets, CAD drawings, becomes queryable ✅ Reps and engineers can ask natural-language questions and get verified answers ✅ Compliance, compatibility, and environmental fit can be checked in seconds ✅ Human experts stay in the loop, but no longer stuck in the weeds I recently sat down with Fay Goldstein Co-Founder and CEO of Folio to discuss the application of AI Agents for Industrial Sales and Application Engineers. ABOUT FOLIO: Folio’s AI platform empowers industrial sales and application engineers by turning technical specs, configuration data, and application info into instant answers, recommendations, and agentic workflows, speeding work, cutting errors, and boosting revenue for industrial manufacturers and distributors. Learn more at www.folio.build ABOUT FAY: Fay Goldstein is the Co-Founder and CEO of Folio, an AI-powered platform that transforms how manufacturers and distributors sell and support complex and technical industrial product portfolios. Before founding Folio, she spent her summers managing direct and online sales at local automotive AC condenser and compressor shop, led strategic GTM and communications at an automotive telematics data company, and worked at an early-stage venture capital firm, where she supported dozens of early-stage startups on their initial GTM and communication strategies. Fay graduated magna cum laude from Florida International University and holds an MBA from Reichman University.   CONNECT WITH FAY 🌐 Website: https://www.folio.build/ 💼 LinkedIn: https://www.linkedin.com/in/faygoldstein/
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  Real-Time Industrial Process Optimization and Control with AI: Aldo Ferrante- Sorbotics LLC 04.12.2024 56นาที

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  Driving Operational Excellence in Manufacturing with Practical AI: Mickey Shaposhnik - Next Plus 22.01.2026 44นาที

  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 
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  Autonomous AI Agents for Industrial Process Optimization: Bryan DeBois - RoviSys 06.08.2025 59นาที

  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
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  Causal Models and Agentic AI in Manufacturing: Michael Carroll - LNS Research 11.03.2026 1ชม. 1นาที

  # AI in Manufacturing Podcast — Episode Show Notes ## Episode Details- **Podcast Name:** AI in Manufacturing Podcast (Industry40.tv)- **Episode Title:** Unlocking Productivity With Casual Models and Agentic AI in Manufacturing- **Host:** Kudzai Manditereza- **Guest:** Michael Carroll- **Guest Title/Role:** Strategic Advisor & Fellow COO Council at LNS Research; Chief Strategy Officer at Trek AI- **Target Audience:** Manufacturing data leaders, COOs, VP of Operations, IT/OT solution architects, and digital transformation professionals --- ## 1. EPISODE SUMMARY Agentic AI is not another digital tool to add to the manufacturing technology stack — it is a fundamentally different species of software that treats decisions, not transactions, as the atomic unit of work. In this episode, Michael Carroll, Strategic Advisor at LNS Research and Chief Strategy Officer at Trek AI, explains why US manufacturing productivity has been flat since 2010 despite massive investments in digital tools, and why agentic AI with causal reasoning represents the structural fix. Carroll draws on his 15 years leading digital transformation at Georgia Pacific to reveal how the real productivity killer is not a lack of data or technology, but a cognitive overload crisis combined with organizational permission bottlenecks that drain value from companies in real time. He introduces a practical diagnostic framework — mapping inferencing load and permission load — that any operations leader can apply today to identify where value is leaking from their organization and where agentic AI can deliver immediate impact. --- ## 2. KEY QUESTIONS ANSWERED IN THIS EPISODE - Why has US manufacturing productivity been flat since 2010 despite massive digital investments?- What is agentic AI, and how is it fundamentally different from traditional manufacturing software like MES and ERP?- What is causal reasoning, and why does it matter more than explainable AI for manufacturing decisions?- How does the permission architecture in manufacturing organizations destroy value and slow decision velocity?- Where should COOs and VPs of Operations start when preparing their organizations for agentic AI?- Why do alignment meetings signal that a company's numbers can't be trusted?- How should IT and OT organizations restructure their relationship to enable competitive advantage? --- 
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  Ep 28 Predictive Analytics in Manufacturing - Maciek Wasiak, CEO Xpanse AI 06.10.2022 54นาที

  I invited Maciek Wasiak for a podcast conversation on Predictive Analytics in Manufacturing and he delivered a masterclass. ​ Maciek is the CEO and Founder of Xpanse AI, a company that develops technology that rapidly accelerates Data Science delivery by replacing manual data science with AI-driven processing ​ Here's the outline of our conversation: ​ ✅ Xpanse AI ✅ Introduction to Predictive Analytics ✅ Real-World Data Science Use Cases in Manufacturing ✅ Semiconductor Fabrication Predictive Analytics Solution Demo ✅ Traditional vs Automated Predictive Analytics ✅ Predictive Analytics Workflow Based on AI and ML ✅ Identifying and qualifying plant-floor data sources for Predictive Analytics ✅ Managing plant-floor data variety for Predictive Modelling ✅ Predictive Modelling Techniques ✅ Meeting plant-floor real-time requirements with ML Processes ✅ Role played by domain-level expertise in Predictive Analytics ✅ Role Played by Industrial System Integrators in Predictive Analytics implementation ✅ Working with AI and ML platforms for non data scientists
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  Ep 42 Data Driven Optimization in Process Industries - Jim Gavigan, President, Industrial Insight 28.09.2023 1ชม. 8นาที

  Had the pleasure of hosting Jim Gavigan on my latest podcast episode, where we deep-dived into "Data-Driven Optimization in Process Industries."We discussed leveraging data for efficiency, the challenges of data quality, and choosing between foundational principles and cutting-edge ML algorithms.Jim also highlighted the significance of tools and strategies in this sphere, emphasizing the urgency of digitizing domain knowledge in the face of an impending knowledge drain.Jim, is the President and Founder of Industrial Insight, Inc. where he helps industrial companies turn data into actionable information to deliver tangible results for their organization.Here is the outline of our conversation:✅ Principles of Data-Driven Process Optimization ✅ Opportunities in data-driven optimization and use case ✅ Challenges faced by industries when implementing data-driven optimization strategies? ✅ Overcoming the hurdles of data quality and fidelity? ✅ First principles vs. Multivariate data analysis vs. ML algorithms? ✅ Evaluating readiness to effectively integrate AI/ML in process optimization ✅ Tech stack for data-driven optimization ✅ Impending knowledge drain, and capturing domain knowledge into digital tools.
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  Scaling Industrial Intelligence with I3X Common API: Matthew Parris - GE Appliances 30.04.2026 1ชม. 4นาที

  # AI in Manufacturing Podcast — Show Notes## Episode: Scaling Industrial Intelligence with the I3X Common API **Podcast Name:** AI in Manufacturing Podcast (Industry40.tv)**Episode Title:** Scaling Industrial Intelligence with the I3X Common API**Guest Name:** Matthew Parris**Guest Title/Role:** Director of Quality Test Systems, GE Appliances; Leading Contributor to the I3X Specification**Host:** Kudzai Manditereza --- ## 1. Episode Summary This episode explores how the Industrial Information Interoperability Exchange (I3X) common API is poised to become the universal interface for accessing manufacturing data across software platforms. Matthew Paris, Director of Quality Test Systems at GE Appliances and a leading contributor to the I3X specification, explains why the manufacturing industry has lacked a standardized way to retrieve information from Level 3 and Level 4 software systems — and how I3X solves this by leveraging simple, proven IT technologies: HTTP and JSON. Paris draws a compelling analogy between I3X and the early web browser revolution, comparing the I3X Explorer tool to Netscape's role in breaking down walled-garden internet portals. The conversation covers how I3X differs from OPC UA and MQTT, why a vanilla MQTT broker is insufficient for a true Unified Namespace, and how standardized interfaces accelerate AI deployment in manufacturing. Listeners will gain a clear understanding of where I3X fits in modern industrial architectures and why now is the time to get involved with the specification while it's in beta. --- ## 2. Key Questions Answered in This Episode - What is I3X and what problem does it solve for manufacturers?- How is I3X different from OPC UA and MQTT?- Why is an MQTT broker alone not sufficient for a Unified Namespace (UNS)?- How does I3X enable manufacturers to scale from data visibility to operational AI?- Where does I3X fit in a modern industrial architecture alongside UNS and MQTT brokers?- Why does I3X support OPC UA Part 5 information models, and how should manufacturers think about data typing?- How will I3X achieve vendor adoption without a chicken-and-egg problem? 
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  Context Engineering for Building Reliable Industrial AI Agents: Zach Etier - Flow Software 05.03.2026 1ชม. 12นาที

  Podcast Name: AI in Manufacturing Podcast (Industry40.tv)Episode Title: Context Engineering Techniques for Building Reliable Industrial AI AgentsGuest: Zach Etier, VP of Architecture at Flow SoftwareHost: Kudzai Manditereza Episode SummaryThis episode explores context engineering — the discipline of curating and managing the information supplied to AI agents — and why it is the key to building reliable industrial AI systems. Zach Etier, VP of Architecture at Flow Software, joins host Kudzai Manditereza to break down why simply pumping more data into an AI agent's context window actually degrades performance through dilution, hallucination, and lost instructions. Zach walks through three core context engineering techniques — persisting context, summarization/compaction, and isolation via sub-agents — and explains how each one maps to real manufacturing use cases like automated shift-handover reports. The conversation also covers the practical differences between skills, MCP servers, and sub-agents, and why deterministic code should handle calculations while agents handle orchestration. Finally, Zach makes the case that knowledge graphs with formal ontologies will become essential data architecture for scaling industrial AI across the enterprise. Whether you are evaluating your first agent pilot or planning multi-site deployment, this episode provides a concrete framework for engineering context that agents can reliably act on. Key Questions Answered in This EpisodeWhat is an industrial AI agent, and how does it differ from a chatbot or general-purpose LLM?Why does giving an AI agent more context actually reduce its performance?What is context engineering, and why is it replacing prompt engineering for agentic AI?What are the three core techniques for managing an AI agent's context window in manufacturing?How should you decide when to use skills vs. MCP servers vs. sub-agents?Why should deterministic code handle calculations instead of letting the AI agent compute them?How do knowledge graphs and ontologies enable enterprise-scale industrial AI?
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  Software Defined Control , UNS and AI-Optimization in Process Industries : Huize Zhang - FreezoneX 14.05.2025 49นาที

  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 with time-series foundation models, these systems harness AI for intelligent loop control, advanced process optimization, and even reinforcement learning, driving unprecedented levels of performance in control environments.In the latest episode of the AI in Manufacturing podcast, I sat down with Huize Zhang to explore this transformation. Huize is the Vice President at SUPCON, China’s leading DCS provider, and the founder of FREEZONEX, an open-source IIoT platform. Here’s the outline of our conversation:-The Control Platform of The Future-Open Standards and Platforms-AI-Driven Optimization in Process Industries-Time-Series Pre-Trained Transformers-Reinforcement Learning in Process Industries-UNS Integration with AI Agents
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  Real-Time Quality Control Using AI-Powered Visual Inspection : Priyansha Bagaria, PhD - Loopr AI 23.04.2025 45นาที

  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 defects that may be invisible to the human eye. This not only enhances the reliability of quality assessments but also increases operational efficiency, allowing manufacturers to streamline their processes and reduce costs. The capability to detect anomalies in real-time empowers companies to address issues before they escalate, ensuring that only the highest-quality components progress through production.To find out more about the application of Visual AI Inspection in manufacturing, I recently sat down with Priyansha Bagaria who is the Founder and CEO of Loopr AI. 
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  Vector Databases and Data Structure for Industrial AI Agents : Humza Akhtar, PhD - MongoDB 09.04.2025 55นาที

  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 missed, leading to unplanned downtime and quality issues.The biggest challenge AI Agents solve in industrial enterprises is transforming this overwhelming amount of complex data into actionable intelligence.However, AI Agents are only powerful for manufacturing data analytics when paired with the right context. That means feeding them, sensor data, maintenance logs, ERP & MES records, operator notes, engineering drawings, and SOP documents e.t.c. And quickly surfacing the most relevant information to power rapid AI-driven decision-making.This is where Vector Storage and Search comes into play.To learn more about Vector Databases and Data Structure for Industrial AI Agents I had a chat with Humza Akhtar, PhD who is the Senior Industry Principal for Manufacturing and Automotive at MongoDB.
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  Using AI and Digital Twins For Manufacturing Workflow Efficiency: Andrew Scheuermann - Arch Systems 15.01.2025 1ชม.

  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.3️⃣ Predictive Analytics – Forecasting potential equipment failures or quality issues.4️⃣ Prescriptive Analytics – Recommending the best actions to address challenges.Despite its promise, many manufacturers struggle with significant obstacles, which include data fragmentation.I recently had a sit down with Andrew Scheuermann the CEO and Co-Founder of Arch Systems to discuss why building a comprehensive Digital Twin is the key to overcoming these barriers and how manufacturers can use AI to enhance manufacturing workflow efficiency. 
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  Generative AI Use Cases in Engineering and Manufacturing: Vlad Larichev - Accenture Industry X 06.11.2024 1ชม. 7นาที

  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. - Integrating additional data sources, such as knowledge graphs, enriches the AI's understanding of relationships and processes unique to manufacturing.- Implementing safety checks and operational boundaries ensures that AI recommendations are viable, safe, and compliant with industry standards. When these measures are in place, Generative AI becomes a powerful tool applicable to a wide range of use cases. Tune in to the full episode with Vlad Larichev, the Industrial AI Lead at Accenture Industry X to learn more about Generative AI Use Cases in Engineering and Manufacturing. 
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  Modernizing Your Industrial Data Architecture for AI Readiness: Jonathan Wise - CESMII 09.10.2024 1ชม. 6นาที

  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 is the first critical step.Data Contextualization - Simply having data isn’t enough. Meaningful, contextualized data is crucial for any AI project to deliver accurate and actionable insights.Data Relationships - It’s not just about isolated data points. AI thrives on the connections between data points, much like how your operations depend on the synergy between suppliers and internal systems.Listen to the episode to learn more.
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  Data Modelling and Manufacturing Ontologies for Digital Twins: Erich Barnstedt - Microsoft 22.06.2023 52นาที

  Digital transformation in manufacturing fundamentally involves transforming unprocessed data into valuable insights to guide business decisions through automated systems or human intervention.Consequently, implementing a well-thought-out data modelling strategy is key to successful digital transformation as it helps to express the meaning of the data to digital systems.To learn more about Data modelling for Industrial IoT in general and for Digital Twin use cases in particular, I had a podcast conversation with Erich Barnstedt.Erich is the Chief Architect for Standards, Consortia and Industrial IoT in the Azure Edge and Platform team at MicrosoftHere's the outline of our conversation✅ Importance of data modelling for Industrial IoT. ✅ Key Elements of an Effective IIoT Data Model ✅ Standardising Configuration Interface for OPC UA Connectivity Mapping ✅ Manufacturing Ontologies Reference Solution for Digital Twins ✅ UA Cloud Publisher and UA Cloud Twin for Mappping Industrial Assets to Azure Digital Twins using ISA95 ✅ Significance of UA Cloud Commander at the Industrial Edge ✅ OPC UA Information Model Integration using UA Cloud Library ✅ Data Modelling Standards ✅ Web of Things for Endpoint and Interface Description of Industrial Assets. ✅ ChatGPT for fully automating onboarding of non-discoverable industrial assets ✅ Converting proprietary interfaces into OPC UA Information Model using UA Edge Translator. ✅ The role of the IEC/ISO in standardizing data models for IIoT ✅ The Scope OPC UA PubSub Over MQTT in Industrial IoT ✅ Metadata and Type Information in OPC UA PubSub ✅ Industrial Metaverse Reference Architecture with Open Interoperability Standards
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  Open Platform Strategy & Industrial Data Spaces for Industry4.0 - Sandeep Sreekumar - IndustryApps 30.05.2023 54นาที

  In the face of a rapidly evolving industrial landscape, agility and innovation have emerged as core drivers of growth. It is essential for manufacturers to adapt swiftly to changes, harnessing new technologies and embracing new processes that fuel their development. But achieving this level of agility and innovation is not without its challenges. So how do organizations successfully navigate these hurdles to lay the groundwork for a meaningful digital transformation? To understand the complexities of implementing Industry 4.0 digitalization programs and a robust pathway to digital transformation I had a podcast conversation with Sandeep Sreekumar. Sandeep is the Co-founder and COO of IndustryApps, a company focused on the Advanced Industrial data space and an Open Appstore for Industry 4.0. Below is the outline of our conversation: ✅ Importance of agility in implementing Industry 4.0 digitalization programs. ✅ Key challenges organizations face when trying to implement agile digitalization processes. ✅ An Open Platform Strategy for open innovation in the context of Industry 4.0 ✅ Scaling Governance and Compliance checks for Industry Apps ✅ Example use case of an Open Platform Strategy ✅ Scaling an open platform strategy across varying plants ✅ Industrial Data Space in Industry 4.0, and why is it critical for future business sustainability. ✅ Data modeling technologies for Industrial data space ✅ Ensuring security and privacy while leveraging the benefits of an Industrial Data Space ✅ Best practices to fully embrace the potential of Industry 4.0
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  Time-Series Databases for IIoT [ InfluxDB ] - Brian Gilmore, InfluxData 21.09.2021 44นาที

  By nature, industrial facilities consist of physical assets and processes that evolve through time. Therefore, each data point generated by such systems is essentially a snapshot of events at that particular point in time. By extension, this data wants to be stored in a way that reflects the sequential order of events, so that it can be rapidly queried and analysed, among many other reasons. But yet, this isn't a capability that is inherently baked into the more common Relational and NoSQL databases. Hence the rise in popularity of Time-Series Databases for industrial Telemetry Data storage over the past few years. At the forefront of this revolution is InfluxDB, an Open-Source Time-Series Database platform developed by InfluxData. To understand how Time-Series Databases work, and InfluxDB in particular, I had a chat with Brian Gilmore who is the Product Manager for IoT at InfluxData. Check out our full conversation in the video linked below. Outline: ✔️ Characteristics of IIoT Data ✔️ Why Time-Series Databases Matter for IIoT ✔️ Common IIoT Use Cases for Time Series Database ✔️ How to Plan an IIoT Data Architecture ✔️ InfluxDB Time-Series DB Platform ✔️ InfluxDB - Open Source vs Cloud vs Enterprise ✔️ InfluxDB Time-Series DB Migration ✔️ InfluxDB Deployment Options ✔️ Acquiring Industrial Telemetry Data into InfluxDB ✔️ Industrial Telemetry Data Enrichment in InfluxDB ✔️ InfluxDB Integration with Analytics & Visualisation Platforms ✔️ Factory-Floor to InfluxDB Data Pipeline
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  The Seven Core Capabilities of an Industrial Data Platform: David Ariens - The IT/OT Insider. 30.07.2025 59นาที

  The industrial data stack was never built for enterprise-wide intelligence. It was built in silos, optimized for local decisions.As a result, it is not designed to support unified, contextualized, and scalable data management across an organization.And that’s why Industrial Data Platforms are essential for scaling digital transformation. To help organizations understand what makes such a platform effective, David Ariens and The IT/OT Insider team created the Industrial Data Platform Capability Map, outlining the seven key capabilities every platform should have:1. 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐯𝐢𝐭𝐲 – a secure and scalable connectivity layer to integrate different data sources into the Industrial Data Platform.2. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 & 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 – delivering data enriched with the right context, so users don’t have to gather and piece together information from multiple sources manually.3. 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 – Detecting and fixing data issues in your pipeline, from sensor to final report.4. 𝐃𝐚𝐭𝐚 𝐁𝐫𝐨𝐤𝐞𝐫 𝐚𝐧𝐝 𝐒𝐭𝐨𝐫𝐞 – The ability to ingest, store, and manage contextualized data at scale, enabling efficient data subscription and large-scale querying. 5. 𝐄𝐝𝐠𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 – The capability to perform analytics and machine learning within the data platform, or at the edge, close to where the data is generated.6. 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 – The capability to deliver high-quality, contextualized data to users through intuitive and accessible interfaces for fast, informed decision-making.7. 𝐃𝐚𝐭𝐚 𝐒𝐡𝐚𝐫𝐢𝐧𝐠 – The capability to openly expose platform data to external users and applications through standard interfaces and integrations. In the latest episode of the AI in Manufacturing podcast, I sat down with David, Founder of IT/OT Insider, to dive deeper into these capabilities and how organizations can implement them.We also discussed the IT/OT Academy, an online training program designed to help IT and OT professionals build a shared vocabulary, framework, and collaboration strategy to move digital initiatives beyond pilot projects and into full-scale plant deployment.