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Manufacturing Hub

Vlad Romanov & Dave Griffith

We bring you manufacturing news, insights, discuss opportunities, and cutting edge technologies. Our goal is to inform, educate, and inspire leaders and workers in manufacturing, automation, and related fields.

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  • 23 episodes
  • weekly
  • Avg 1 hr 8 min
  • English

Episodes 23

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  • #240
    March 12 · 1 hr 5 min

    Ep. 252 - Industrial AI in Manufacturing What Actually Works and What Does Not #industrialautomation

    Manufacturing Hub is back with Episode 252, where co hosts Vlad Romanov and Dave Griffith break down what an AI survival guide should actually look like for manufacturing and industrial automation professionals. This is not a hype conversation about replacing people with magic software. It is a grounded discussion about what AI tools can do today, where they fail, why context and data quality matter so much, and how industrial teams should think about experimentation without losing sight of real operating constraints. In this episode, Vlad and Dave unpack the evolution many engineers and technical leaders have already felt in real time, from early prompt engineering, to agent based workflows, to MCP servers, skills, context management, and the growing cost of tokens and infrastructure. The conversation moves beyond generic AI commentary and into the reality of plant floor environments, where success depends on process knowledge, data architecture, OT constraints, cybersecurity, governance, and clear business value. One of the strongest themes throughout the episode is that manufacturers cannot skip the hard work of structuring data, understanding workflows, and defining use cases simply because AI tools are moving quickly. Vlad brings a very practical industrial lens to the discussion. Drawing on years of hands on experience across controls, manufacturing systems, plant modernization, and digital transformation, he explains why industrial AI has to start with operational context. A maintenance team, an engineering team, and a quality team do not need the same data, do not ask the same questions, and should not be handed the same AI workflows. That distinction matters. This conversation also highlights why the best industrial AI implementations will likely come from teams that combine domain expertise with strong technical execution, rather than generic AI shops trying to force a solution into environments they do not fully understand. Dave adds an important systems and adoption perspective, especially around cost, scaling, management expectations, and the danger of trying to prompt your way past foundational architecture work. Together, Vlad and Dave explore why manufacturers are interested in AI, why many are afraid of being left behind, and why so many projects still stall once they hit the realities of obsolete equipment, weak data models, fragmented systems, and unclear ownership of information. They also discuss deterministic logic versus LLM behavior, reporting workflows, industrial dashboards, PLC code generation concerns, and the practical question every manufacturer should ask before investing: what problem are we solving, for whom, and what is the measurable return? For those new to Vlad, he is an electrical engineer and manufacturing leader with deep experience across industrial automation, controls, data systems, OT architecture, modernization strategy, and plant operations. Through Joltek, Vlad works with manufacturers on digital transformation, IT OT architecture and integration, modernization planning, operational improvement, and technical workforce enablement. Learn more here: Joltek: https://www.joltek.com IT OT Architecture and Integration: https://www.joltek.com/services/service-details-it-ot-architecture-integration If you are a plant leader, controls engineer, systems integrator, OT architect, SCADA or MES practitioner, or simply someone trying to separate useful AI workflows from noise, this episode will give you a much more realistic framework for thinking about industrial AI adoption. Timestamps 00:00 Welcome back and why this episode matters 01:00 Setting up the industrial AI theme for the coming weeks 03:10 From prompt engineering to structured AI workflows 05:30 AI agents, parallel workflows, tokens, and context windows 09:00 MCP tools, Playwright, and what new integrations unlock 16:20 How Vlad researches AI and where useful information actually lives 22:00 Real manufacturing problems versus AI in search of a problem 29:40 Why industrial data architecture is harder than most people think 37:00 OT expertise, workforce enablement, and who should build solutions 45:40 Practical advice for manufacturers starting the AI journey 50:30 Data governance, hallucinations, infrastructure, and cybersecurity 57:20 What looks promising today in reporting, dashboards, and industrial applications

  • #239
    March 5 · 1 hr 3 min

    Ep. 251 - Ignition 8.3 ProveIt How Inductive Automation Scales Multi Site Factories w/ MQTT and UNS

    In this episode of Manufacturing Hub, Vlad and Dave sit down with Travis Cox and Kevin McCluskey from Inductive Automation to unpack what was actually proven at ProveIt and why it matters for teams trying to modernize plants without building a fragile mess of point to point integrations. If you have ever looked at a shiny demo and wondered what the real architecture looks like, how it scales beyond a single line, and what it takes to roll out across multiple sites without turning every change into a high risk event, this conversation is for you. Travis and Kevin walk through their ProveIt Enterprise B build and the thinking behind it. The core idea is simple but powerful: treat the factory like a system that needs a shared digital infrastructure, built on open standards, where data is contextualized and reusable. They break down how they used Ignition Edge close to PLCs for resiliency, local HMIs, and disciplined data modeling, then moved data through MQTT into a Unified Namespace so multiple applications can consume the same trusted signals and context. This is the difference between “we can connect to anything” and “we can scale without rewriting everything every time the business changes.” Open standards show up repeatedly in the conversation because ProveIt is specifically designed to force interoperability and practical implementation tradeoffs. Inductive Automation has also written about ProveIt as a place where MQTT, OPC UA, and SQL show up as real foundations rather than slogans. From there, the episode gets into the part that should make both OT and IT teams pay attention: modern deployment practices applied to industrial applications. Kevin outlines a clear maturity path from a single designer workflow to version control, then to containerized deployments, and finally to full GitOps style promotion across dev, staging, and production using tools like Argo CD, Helm, Kubernetes, and release promotion concepts that look like what the software world has used for years. Argo CD is explicitly built around Git repositories as the source of truth for desired state, which is exactly why it fits this style of deployment. The live portion of the conversation demonstrates how fast this can get when the infrastructure is treated as code: they spin up a brand new “site four” by submitting a form, generating a pull request, merging it, and letting the pipeline do the rest. Timestamps 00:00 Welcome back and why this ProveIt recap matters 01:35 Meet Travis Cox and Kevin McCluskey from Inductive Automation 03:10 What ProveIt is and the key vendor questions it forces 05:20 Enterprise B architecture overview from PLC to Edge to site to enterprise 07:30 HMI walkthrough across liquid processing, filling, packaging, palletizing 09:05 Why deploy Ignition Edge instead of only a centralized site gateway 12:05 Design once, reuse everywhere and what that means for scaling quickly 14:35 On prem realities versus cloud infrastructure in the ProveIt environment 17:10 MCP, n8n workflows, and bringing live operational context into AI 20:40 i3X style API access to models, history, and alarms for interoperability 23:15 GitHub, Docker Compose, Helm, Kubernetes, Argo CD, Cargo and GitOps promotion 36:55 Spinning up a new site live and what it changes for multi site rollouts About the hosts Vlad Romanov is an electrical engineer and MBA who has spent over a decade building and modernizing manufacturing systems across industrial automation, controls, and plant operations. Through Joltek, Vlad works with manufacturers to assess current state OT foundations, reduce modernization risk, improve reliability, and build internal capability through practical training and standards that stick. Dave Griffith co hosts Manufacturing Hub and brings a practitioner lens focused on what works on the plant floor, how architectures survive real constraints, and how industrial teams can modernize without breaking production. About the guests Travis Cox is Chief Technology Evangelist at Inductive Automation and has spent over two decades helping customers and partners design scalable architectures, apply best practices, and deliver real solutions with Ignition. Kevin McCluskey is Chief Technology Architect at Inductive Automation and works with organizations on architecture decisions, platform direction, and enabling the next generation of industrial applications. Learn more about Joltek https://www.joltek.com/services https://www.joltek.com/book-a-modernization-consultation

  • #238
    February 12 · 59 min

    Ep. 246 A - Factory of the Future Without the Hype: Siemens on Data Transparency, Orchestration, and Trust in AI

    This episode wraps up our Technology Modernization theme with a Siemens perspective that feels very grounded in what factories are actually dealing with right now. Brian Albrecht and Louis Hughes from the Siemens XD team walk through what they are seeing in the field across brownfield and greenfield conversations, why executives keep asking for industrial AI before the foundations are ready, and what it really takes to turn messy plant data into something you can trust for analytics, operations, and eventually AI enabled workflows. A big thread in this conversation is that modern manufacturing is not blocked by ambition, it is blocked by readiness. Everyone wants faster decisions, fewer surprises, and higher uptime, but the path there usually starts with boring work that is not optional. Data transparency across machine, plant, MES, and cloud layers. A clear definition of what real time actually needs to mean for a given use case. And a plan to contextualize and orchestrate data so that AI does not get fed junk inputs. Brian and Louis explain how they approach those early customer conversations, how workshops turn vision into prioritized use cases, and why trust, pilots, and repeatability matter more than flashy demos when you are working in regulated or high consequence environments. If you have been hearing nonstop AI buzz but you are still wrestling with legacy controls, inconsistent tags, documentation that no one can find, and seven layers of security constraints, this episode is for you. We get into practical use cases like AI vision and anomaly detection, LLMs for tribal knowledge and troubleshooting workflows, and the idea of fast versus slow AI, meaning AI that must act during production versus AI that can analyze after the fact. Timestamps 00:00 Welcome and why this episode closes the modernization theme 02:10 Meet Brian Albrecht and Louis Hughes from the Siemens XD team 05:25 Vertical differences across oil and gas, discrete, and process manufacturing 07:50 What executives ask for right now beyond AI, factory of the future and data transparency 10:50 Brownfield reality and why most modernization work starts with legacy systems 12:30 The AI conversation when foundations are missing, meeting customers where they are 15:10 Current AI use cases in manufacturing, downtime, throughput, LLMs, and vision 18:10 What it means to be AI ready, data silos, contextualization, and orchestration 23:50 Fast versus slow AI and why production time decisions are different from analytics 25:30 Edge versus cloud architecture, latency, and where the data should live 33:40 Cybersecurity, trust, and why perception can lag behind the technology 36:50 Hallucinations, guardrails, and why recommendations usually come before automation 51:10 Book recommendations, career advice, and future predictions for industrial AI About the hosts Vlad Romanov is an electrical engineer with an MBA from McGill University and over a decade of experience in manufacturing and industrial automation. He has worked across large scale environments including Procter and Gamble, Kraft Heinz, and Post Holdings, and he now leads Joltek, helping manufacturers modernize systems, improve reliability, strengthen IT and OT architecture, and upskill technical teams through practical training and on site enablement. Dave Griffith is the cohost of Manufacturing Hub and an industrial automation practitioner who focuses on how modern technologies translate into real factory outcomes, from controls and data foundations to scalable implementation strategies. About the guests Brian Albrecht started in electrical engineering and spent about a decade in systems integration in Oklahoma City focused on oil and gas, building SCADA, networking, and automation solutions and leading teams delivering real world projects. He now works with Siemens customers on building relationships and delivering solutions that create measurable value. Louis Hughes has roughly 20 years of manufacturing experience, starting in software development for manufacturing and engineering applications, then moving into solution architecture, services delivery, and experience center leadership. He now leads a smart manufacturing team, bringing a software and systems view into automation conversations focused on solving customer problems, not just deploying tools. Joltek Services - https://www.joltek.com/services Contact Joltek - https://www.joltek.com/contact Referenced in the episode ProveIt Conference - https://www.proveitconference.com/ Siemens - https://www.siemens.com/ Crossing the Chasm by Geoffrey A Moore https://en.wikipedia.org/wiki/Crossing_the_Chasm Extreme Ownership by Jocko Willink and Leif Babin https://en.wikipedia.org/wiki/Extreme_Ownership

Showing 21–23 of 23 episodes