<![CDATA[Abelara Ascent]]>Abelara Ascent - Long-form conversations about what it actually takes to modernize manufacturing.

Abelara Ascent is where architecture meets execution. Interviews with the people building real systems. Panel discussions that go deeper than the keynote. And solo episodes that break down the frameworks, decisions, and trade-offs behind industrial digital transformation.

Hosted by Zack Scriven, Director of Sales and Marketing at Abelara, with regular appearances from cofounders Dylan DuFresne, Chief Architect, and Glenn Gardner, President.

If you are a manufacturing leader, plant manager, controls engineer, or solutions architect trying to figure out where to start, what to prioritize, and who to trust, this is the show.

Published by Abelara. Event coverage powered by Abelara Live.

]]>www.abelara.comRiverside.fm (https://riverside.com)Sun, 14 Jun 2026 05:57:14 GMTTue, 14 Apr 2026 17:23:17 GMT60Abelara<p><b>Abelara Ascent - </b>Long-form conversations about what it actually takes to modernize manufacturing.</p><p></p><p>Abelara Ascent is where architecture meets execution. Interviews with the people building real systems. Panel discussions that go deeper than the keynote. And solo episodes that break down the frameworks, decisions, and trade-offs behind industrial digital transformation.</p><p></p><p>Hosted by Zack Scriven, Director of Sales and Marketing at Abelara, with regular appearances from cofounders Dylan DuFresne, Chief Architect, and Glenn Gardner, President.</p><p></p><p>If you are a manufacturing leader, plant manager, controls engineer, or solutions architect trying to figure out where to start, what to prioritize, and who to trust, this is the show.</p><p></p><p>Published by Abelara. Event coverage powered by Abelara Live.</p>episodicAbelaraadmin@abelara.comno<![CDATA[Simplify your enterprise architecture with Fuuz + Litmus]]>Most manufacturers we talk to aren't asking for more software. They're asking for less.

Forty applications. A dozen integrations holding it all together.

We cover:

  • The supply chain ↔ factory floor disconnect, and why the language of customer POs almost never lines up with the work orders that actually get run
  • Why standard cost variances catch you at quarter close instead of when you can still do something about them
  • What a C-suite KPI dashboard really looks like under the hood — and how many hours of manual reconciliation hide behind every "give me the top three reasons we missed."
  • Craig Scott (CEO, Fuuz) on why this is an architecture problem, not a technology one — and what a shared operational model actually is
  • Dylan DuFresne on Fuuz as "IT software that understands OT," Litmus's 250+ device drivers, and modeling data as close to the edge as possible
  • Hussein (Litmus) on edge containerization, metadata at scale, and the UNS flexibility you need to actually move fast
  • The security question — role-based access control, encrypted data, the Fuuz Gateway through the DMZ, and what "MCP-ready" actually means for OT data
  • The brownfield reality — no rip-and-replace, just better connection into the SCADAs, historians, and OPC servers already in place
  • Glenn's mic drop — if you built top-down from the C-suite, you'd build Fuuz. If you built bottom-up from the factory floor, you'd build Litmus. They're the two most important anchor points in the whole industrial software ecosystem.

CHAPTERS
00:00 Intro — the simplification thesis
00:51 Glenn's pain point #1 — supply chain ↔ factory floor disconnect
03:02 Pain point #2 — late awareness of cost variances
04:45 Context — why enterprises are cutting logos
05:48 Pain point #3 — C-suite KPIs vs. how they're really built
07:40 Cappy Hour reference (see the Prove It session)
09:22 The wish — one platform to solve all three
10:16 Craig Scott — it's an architecture problem, not a technology one
11:31 Excel is still the dominant MES
12:27 Shared operational model — red, blue, and purple data
14:09 Walker Reynolds, UNS, and an oil-and-gas project with 50+ software vendors
15:09 Death by a thousand cuts
16:08 Cost visibility isn't a reporting problem — it's a latency problem
17:36 Burying A-players in spreadsheets
18:45 Bridging C-suite KPIs to the plant floor
20:46 The operational model as DNA
22:35 Dylan — getting data from the edge, the right way
25:22 Litmus's 250+ drivers and edge data modeling
27:48 Kepware vs. Litmus — containerization at the enterprise
29:25 Edge modeling as the differentiator
30:48 PLC-level hygiene and OEM involvement
33:40 Patrick's question — security, RBAC, and the Fuuz Gateway
35:46 Litmus + Fuuz visualization — overlap and complement
38:36 Brownfield — Litmus and Fuuz alongside existing OPC, SCADA, historians
43:02 Why point-to-point solutions break at scale
44:38 Glenn's mic drop — top-down vs. bottom-up
46:15 The Lighthouse Partner — how to work with Abelara
47:19 Closing — we always start from a fresh sheet of paper

\WORK WITH ABELARA — LIGHTHOUSE PARTNER PROGRAM

Companies come to Abelara to architect agnostic solutions and skip past the trial-and-error. We're accepting a small number of Lighthouse Partners — a digital transformation workshop, architected solution, and hitting the ground running.

]]>3231e4a2-e204-4ed1-93f2-902902771e4bFri, 29 May 2026 18:41:59 GMT<p>Most manufacturers we talk to aren't asking for more software. They're asking for less.</p><p>Forty applications. A dozen integrations holding it all together.<br /></p><p>We cover:</p><ul><li>The supply chain ↔ factory floor disconnect, and why the language of customer POs almost never lines up with the work orders that actually get run</li><li>Why standard cost variances catch you at quarter close instead of when you can still do something about them</li><li>What a C-suite KPI dashboard really looks like under the hood — and how many hours of manual reconciliation hide behind every "give me the top three reasons we missed."</li><li>Craig Scott (CEO, Fuuz) on why this is an architecture problem, not a technology one — and what a shared operational model actually is</li><li>Dylan DuFresne on Fuuz as "IT software that understands OT," Litmus's 250+ device drivers, and modeling data as close to the edge as possible</li><li>Hussein (Litmus) on edge containerization, metadata at scale, and the UNS flexibility you need to actually move fast</li><li>The security question — role-based access control, encrypted data, the Fuuz Gateway through the DMZ, and what "MCP-ready" actually means for OT data</li><li>The brownfield reality — no rip-and-replace, just better connection into the SCADAs, historians, and OPC servers already in place</li><li>Glenn's mic drop — if you built top-down from the C-suite, you'd build Fuuz. If you built bottom-up from the factory floor, you'd build Litmus. They're the two most important anchor points in the whole industrial software ecosystem.</li></ul><p></p><p>CHAPTERS <br />00:00 Intro — the simplification thesis <br />00:51 Glenn's pain point #1 — supply chain ↔ factory floor disconnect <br />03:02 Pain point #2 — late awareness of cost variances <br />04:45 Context — why enterprises are cutting logos <br />05:48 Pain point #3 — C-suite KPIs vs. how they're really built <br />07:40 Cappy Hour reference (see the Prove It session) <br />09:22 The wish — one platform to solve all three <br />10:16 Craig Scott — it's an architecture problem, not a technology one <br />11:31 Excel is still the dominant MES <br />12:27 Shared operational model — red, blue, and purple data <br />14:09 Walker Reynolds, UNS, and an oil-and-gas project with 50+ software vendors <br />15:09 Death by a thousand cuts <br />16:08 Cost visibility isn't a reporting problem — it's a latency problem <br />17:36 Burying A-players in spreadsheets <br />18:45 Bridging C-suite KPIs to the plant floor <br />20:46 The operational model as DNA <br />22:35 Dylan — getting data from the edge, the right way <br />25:22 Litmus's 250+ drivers and edge data modeling <br />27:48 Kepware vs. Litmus — containerization at the enterprise <br />29:25 Edge modeling as the differentiator <br />30:48 PLC-level hygiene and OEM involvement <br />33:40 Patrick's question — security, RBAC, and the Fuuz Gateway <br />35:46 Litmus + Fuuz visualization — overlap and complement <br />38:36 Brownfield — Litmus and Fuuz alongside existing OPC, SCADA, historians <br />43:02 Why point-to-point solutions break at scale <br />44:38 Glenn's mic drop — top-down vs. bottom-up <br />46:15 The Lighthouse Partner — how to work with Abelara <br />47:19 Closing — we always start from a fresh sheet of paper</p><p></p><p>\WORK WITH ABELARA — LIGHTHOUSE PARTNER PROGRAM</p><p>Companies come to Abelara to architect agnostic solutions and skip past the trial-and-error. We're accepting a small number of Lighthouse Partners — a digital transformation workshop, architected solution, and hitting the ground running.</p><p></p>no00:49:21Simplify your enterprise architecture with Fuuz + Litmusfull<![CDATA[Predictive Maintenance in Practice: From Prediction to Prevention]]>The conversation begins with an introduction to predictive maintenance and the expert panel. Glenn Gardner discusses the evolution of predictive maintenance, highlighting the transformation of the industry over the past 20 years. He then delves into the concept of vibration as a predictive technology, providing a detailed explanation of its relevance and power. The discussion shifts to the role of CMMS in predictive maintenance, emphasizing the importance of both predictive technology and CMMS for an effective maintenance program. Mark Kingkade shares insights on vibration analysis in real systems, showcasing the practical application of vibration analysis in identifying machinery flaws. The conversation covered challenges in equipment repair, integration of machine data with maintenance workflows, data integration and predictive maintenance, and integrating advanced maintenance technologies. The challenges included difficulty in finding replacement parts, extended lead times for replacements, and the impact of supply chain challenges. The integration of machine data with maintenance workflows highlighted the direct integration of diagnostic insights with work orders, automated generation and assignment of work orders, and the importance of getting information to the right people. Data integration and predictive maintenance discussed the integration of machine data from various sources, usage-based predictive maintenance, and a phased approach to implementing predictive maintenance. Integrating advanced maintenance technologies addressed challenges in integrating different maintenance technologies, the importance of keeping analysis in purpose-built tools, and sending scalar data to centralized systems.

Takeaways

  • Predictive maintenance has evolved significantly over the past 20 years, with a notable reduction in cost and time for implementing a world-class program.
  • Vibration analysis is a powerful technology for identifying machinery flaws, and when combined with CMMS, it forms an effective predictive maintenance program. Challenges in equipment repair due to supply chain issues
  • Importance of direct integration of diagnostic insights with work orders

Chapters

  • 00:00 Introduction to Predictive Maintenance
  • 11:46 CMMS and Predictive Maintenance
  • 32:29 Challenges in Equipment Repair
  • 47:24 Data Integration and Predictive Maintenance
  • 58:02 Integrating Advanced Maintenance Technologies
]]>5ff5657d-d903-435d-afbe-8148827cb4f4Fri, 29 May 2026 18:34:25 GMT<p>The conversation begins with an introduction to predictive maintenance and the expert panel. Glenn Gardner discusses the evolution of predictive maintenance, highlighting the transformation of the industry over the past 20 years. He then delves into the concept of vibration as a predictive technology, providing a detailed explanation of its relevance and power. The discussion shifts to the role of CMMS in predictive maintenance, emphasizing the importance of both predictive technology and CMMS for an effective maintenance program. Mark Kingkade shares insights on vibration analysis in real systems, showcasing the practical application of vibration analysis in identifying machinery flaws. The conversation covered challenges in equipment repair, integration of machine data with maintenance workflows, data integration and predictive maintenance, and integrating advanced maintenance technologies. The challenges included difficulty in finding replacement parts, extended lead times for replacements, and the impact of supply chain challenges. The integration of machine data with maintenance workflows highlighted the direct integration of diagnostic insights with work orders, automated generation and assignment of work orders, and the importance of getting information to the right people. Data integration and predictive maintenance discussed the integration of machine data from various sources, usage-based predictive maintenance, and a phased approach to implementing predictive maintenance. Integrating advanced maintenance technologies addressed challenges in integrating different maintenance technologies, the importance of keeping analysis in purpose-built tools, and sending scalar data to centralized systems.</p><p></p><p>Takeaways</p><ul><li>Predictive maintenance has evolved significantly over the past 20 years, with a notable reduction in cost and time for implementing a world-class program.</li><li>Vibration analysis is a powerful technology for identifying machinery flaws, and when combined with CMMS, it forms an effective predictive maintenance program. Challenges in equipment repair due to supply chain issues</li><li>Importance of direct integration of diagnostic insights with work orders</li></ul><p></p><p>Chapters</p><ul><li>00:00 Introduction to Predictive Maintenance</li><li>11:46 CMMS and Predictive Maintenance</li><li>32:29 Challenges in Equipment Repair</li><li>47:24 Data Integration and Predictive Maintenance</li><li>58:02 Integrating Advanced Maintenance Technologies</li></ul>no01:01:23Predictive Maintenance in Practice: From Prediction to Preventionfull<![CDATA[Why AI Fails in the Factory? - AI Panel at MX.0 Southeast 2026]]>Why does AI fail in the factory? Three reasons: unstructured data, fragmented data, and no governance.

At MX.0 Southeast in Greenville, SC, Zack Scriven sits down with Matt from HiveMQ and Remus from Concept Reply for a field panel on why most manufacturers aren't ready for AI — and what to build first. They cover what a Unified Namespace actually is (two definitions), where it's being misused, the Excel-to-MQTT trick, AI governance as a critical skill, and how to get started when you can't even get budget approval for a consultant.

00:00 — Introductions
00:37 — Why AI fails: three root causes
02:16 — HiveMQ as the data backbone
03:01 — No data equals no AI
03:21 — The Excel problem
04:24 — What is a Unified Namespace?
07:40 — Where UNS is misused
09:03 — Don't build a UNS without a problem statement
10:35 — AI as accelerant for building foundations
11:52 — AI governance as a future skill
12:35 — Overcoming internal resistance
14:57 — People, process, technology
16:34 — Start with what you control

Abelara: https://www.abelara.com

#Abelara #AbelaraAscent #AI #UNS #MQTT #HiveMQ #ManufacturingTransformation #MXO

➡️ Learn more: https://abelara.com/
➡️ Follow us: https://www.linkedin.com/company/abelara/

Subscribe for insights on digital manufacturing, leadership, and system architecture.

]]>e52c5ab0-3148-41aa-8a0a-059c98c835a1Fri, 29 May 2026 18:31:13 GMT<p>Why does AI fail in the factory? Three reasons: unstructured data, fragmented data, and no governance.<br /><br />At MX.0 Southeast in Greenville, SC, Zack Scriven sits down with Matt from HiveMQ and Remus from Concept Reply for a field panel on why most manufacturers aren't ready for AI — and what to build first. They cover what a Unified Namespace actually is (two definitions), where it's being misused, the Excel-to-MQTT trick, AI governance as a critical skill, and how to get started when you can't even get budget approval for a consultant.<br /><br />00:00 — Introductions<br />00:37 — Why AI fails: three root causes<br />02:16 — HiveMQ as the data backbone<br />03:01 — No data equals no AI<br />03:21 — The Excel problem<br />04:24 — What is a Unified Namespace?<br />07:40 — Where UNS is misused<br />09:03 — Don't build a UNS without a problem statement<br />10:35 — AI as accelerant for building foundations<br />11:52 — AI governance as a future skill<br />12:35 — Overcoming internal resistance<br />14:57 — People, process, technology<br />16:34 — Start with what you control<br /><br />Abelara: <a rel="noopener noreferrer nofollow" href="https://www.abelara.com" target="_blank">https://www.abelara.com</a><br /><br />#Abelara #AbelaraAscent #AI #UNS #MQTT #HiveMQ #ManufacturingTransformation #MXO<br /><br />➡️ Learn more: <a rel="noopener noreferrer nofollow" href="https://abelara.com/" target="_blank">https://abelara.com/</a><br />➡️ Follow us: <a rel="noopener noreferrer nofollow" href="https://www.linkedin.com/company/abelara/" target="_blank">https://www.linkedin.com/company/abelara/</a><br /><br />Subscribe for insights on digital manufacturing, leadership, and system architecture.</p>no00:17:55Why AI Fails in the Factory? - AI Panel at MX.0 Southeast 2026full<![CDATA[ProveIt! Live Q&A + Whiteboard Session with Walker Reynolds & Dylan DuFresne]]>🔴 LIVE from ProveIt! 2026 Whiteboard Q&A with Walker Reynolds & Dylan DuFresne

Wednesday morning on the ProveIt! Main Stage, Walker Reynolds returns to the whiteboard, and he is joined by Dylan DuFresne, Co-Founder & Solutions Architect at Abelara.

This educational style is what the industry 4.0 community loves:
Real questions.
Real Unified Namespace architecture.
No slides. No scripts. No fluff.

✔️ Live UNS mapping discussion
✔️ Deep dives into enterprise IT ↔ OT architecture
✔️ Real-world implementation tradeoffs
✔️ Direct audience Q&A

Sponsored by Fuuz — the Industrial Intelligence Platform helping Fuuz the Gap between enterprise systems and the factory floor.

If you care about visibility, ownership, and building systems you actually understand… this session is for you.

📍 ProveIt! Conference 2026
Hyatt Regency Dallas
February 18th, 2026

Subscribe for more conversations at the intersection of manufacturing, architecture, and human-first digital transformation.

#ProveIt2026 #UnifiedNamespace #Industry40 #SmartManufacturing #Abelara #FUUZ #DigitalTransformation

]]>dc0154c3-09b1-4e46-a38e-c93b86a182f9Fri, 29 May 2026 18:30:27 GMT<p>🔴 LIVE from ProveIt! 2026 Whiteboard Q&amp;A with Walker Reynolds &amp; Dylan DuFresne<br /><br />Wednesday morning on the ProveIt! Main Stage, Walker Reynolds returns to the whiteboard, and he is joined by Dylan DuFresne, Co-Founder &amp; Solutions Architect at Abelara.<br /><br />This educational style is what the industry 4.0 community loves:<br />Real questions.<br />Real Unified Namespace architecture.<br />No slides. No scripts. No fluff.<br /><br />✔️ Live UNS mapping discussion<br />✔️ Deep dives into enterprise IT ↔ OT architecture<br />✔️ Real-world implementation tradeoffs<br />✔️ Direct audience Q&amp;A<br /><br />Sponsored by Fuuz — the Industrial Intelligence Platform helping Fuuz the Gap between enterprise systems and the factory floor.<br /><br />If you care about visibility, ownership, and building systems you actually understand… this session is for you.<br /><br />📍 ProveIt! Conference 2026<br />Hyatt Regency Dallas<br />February 18th, 2026<br /><br />Subscribe for more conversations at the intersection of manufacturing, architecture, and human-first digital transformation.<br /><br />#ProveIt2026 #UnifiedNamespace #Industry40 #SmartManufacturing #Abelara #FUUZ #DigitalTransformation</p>no00:56:56ProveIt! Live Q&A + Whiteboard Session with Walker Reynolds & Dylan DuFresnefull<![CDATA[From Pilot Purgatory to ROI in 90 Days | Abelara at ProveIt! 2026]]>At ProveIt! Conference 2026, Abelara did something different. We built a fictional bottling company called Cappy Hour and re-enacted a problem manufacturers face every day: data is flowing, infrastructure is in place, but leadership still can't answer basic operational questions.

What ran yesterday? What's in the tanks? How much product is going down the drain?

Glenn Gardner plays the president of Cappy Hour — a multi-site beverage manufacturer under cost pressure, losing market share, and facing layoffs if waste isn't found fast. Dylan DuFresne runs the architecture workshop.

Ricardo Santos from Tupinix implements the plant-floor solution.

And Dan Clark from Axelon shows what happens when the internal engineering team takes ownership and builds on the foundation — using AI.

This session walks through the full path from problem to payback:

00:00 — Introduction 03:05 — Glenn as CEO of Cappy Hour: the five languages of manufacturing 11:10 — The cost problem: why Cappy Hour is in trouble
12:30 — Abelara's $25K architecture workshop
13:15 — Ricardo and Tupinix: boots on the ground with Litmus Edge
15:43 — Live waste tracking: $551/hour going down the drain
17:48 — Phase 1 results: $29K investment, 3-month payback, $292K annual savings
19:29 — Phase 2: building a composable MES with Ignition + Postgres
23:33 — Dan and Axelon: AI copilot builds a downtime screen during lunch
27:35 — Glenn retells the full story from the CEO's perspective
29:31 — Dylan on everything else we built for ProveIt
35:00 — Audience Q&A

The philosophy behind everything we do: you can't improve what you can't see. Ownership starts with visibility.

*About Abelara:* Abelara works with manufacturing leaders to bridge strategy and execution. We help teams deploy modern architectures, unify disconnected systems, and unlock real operational visibility. No jargon, no fluff—just practical solutions built to scale in the real world.

➡️ Learn more: https://abelara.com/
➡️ Follow us: https://www.linkedin.com/company/abelara/

Subscribe for insights on digital manufacturing, leadership, and system architecture.

]]>fec84b00-1614-4ca4-992e-d56402ba4833Fri, 29 May 2026 18:29:22 GMT<p>At ProveIt! Conference 2026, Abelara did something different. We built a fictional bottling company called Cappy Hour and re-enacted a problem manufacturers face every day: data is flowing, infrastructure is in place, but leadership still can't answer basic operational questions.<br /><br />What ran yesterday? What's in the tanks? How much product is going down the drain?<br /><br />Glenn Gardner plays the president of Cappy Hour — a multi-site beverage manufacturer under cost pressure, losing market share, and facing layoffs if waste isn't found fast. Dylan DuFresne runs the architecture workshop.<br /><br />Ricardo Santos from Tupinix implements the plant-floor solution. <br /><br />And Dan Clark from Axelon shows what happens when the internal engineering team takes ownership and builds on the foundation — using AI.<br /><br />This session walks through the full path from problem to payback:<br /><br />00:00 — Introduction 03:05 — Glenn as CEO of Cappy Hour: the five languages of manufacturing 11:10 — The cost problem: why Cappy Hour is in trouble<br />12:30 — Abelara's $25K architecture workshop<br />13:15 — Ricardo and Tupinix: boots on the ground with Litmus Edge<br />15:43 — Live waste tracking: $551/hour going down the drain<br />17:48 — Phase 1 results: $29K investment, 3-month payback, $292K annual savings<br />19:29 — Phase 2: building a composable MES with Ignition + Postgres<br />23:33 — Dan and Axelon: AI copilot builds a downtime screen during lunch<br />27:35 — Glenn retells the full story from the CEO's perspective<br />29:31 — Dylan on everything else we built for ProveIt<br />35:00 — Audience Q&amp;A<br /><br />The philosophy behind everything we do: you can't improve what you can't see. Ownership starts with visibility.<br /><br />*About Abelara:* Abelara works with manufacturing leaders to bridge strategy and execution. We help teams deploy modern architectures, unify disconnected systems, and unlock real operational visibility. No jargon, no fluff—just practical solutions built to scale in the real world.<br /><br />➡️ Learn more: <a rel="noopener noreferrer nofollow" href="https://abelara.com/" target="_blank">https://abelara.com/</a><br />➡️ Follow us: <a rel="noopener noreferrer nofollow" href="https://www.linkedin.com/company/abelara/" target="_blank">https://www.linkedin.com/company/abelara/</a><br /><br />Subscribe for insights on digital manufacturing, leadership, and system architecture.</p>no00:48:12From Pilot Purgatory to ROI in 90 Days | Abelara at ProveIt! 2026full<![CDATA[Fuuz x The Crysler Club: People, Process, Tech and Why “Shiny Objects” Fail]]>Recorded at Fuuz HQ in Detroit, this conversation brings together Craig Scott (Founder & CEO of Fuuz), Dave Crysler (Founder of The Crysler Club), and Zack Scriven (Abelara) for a real, operator-to-operator discussion on what actually drives manufacturing results.

This is not a vendor pitch. It’s a grounded conversation about how manufacturers really scale: what works, what breaks, and where technology actually belongs.

*You’ll hear:*
- Dave’s “planning, people, process, technology” framework and how he applies it across industries
- Craig’s story building Fuuz out of real system integrator pain: too many tools, too many skill sets, too much complexity
- Why good vendors say “no” when they’re not the right fit
- How to think about data, ownership, and adoption without losing the humans in the system
- Quick hot takes on AI: where it helps, where it distracts, and why authenticity is coming back

If you’re a plant leader, operations leader, or IT/OT leader trying to connect shop-floor reality to enterprise outcomes, this one will hit.

*About Abelara:* Abelara works with manufacturing leaders to bridge strategy and execution. We help teams deploy modern architectures, unify disconnected systems, and unlock real operational visibility. No jargon, no fluff—just practical solutions built to scale in the real world.

Thank you to Craig Scott and Dave Crysler for joining this Abelara Ascent podcast.

➡️ Learn More about Fuuz: https://www.fuuz.com

➡️ Learn More about Crysler Club: https://thecrysler.club

➡️ Learn more about Abelara: https://abelara.com/
https://www.linkedin.com/company/abelara/

Subscribe for insights on digital manufacturing, leadership, and system architecture.

Keywords: manufacturing operations, continuous improvement, lean manufacturing, digital transformation, IT/OT, ERP integration, MES, data visibility, Unified Namespace, Detroit manufacturing, system integrator, manufacturing consulting, Fuuz, The Crysler Club, Abelara, Craig Scott, Dave Crysler, Zack Scriven. #manufacturing #operations #digitaltransformation #continuousimprovement #IIoT #ITOT #MES #ERP

]]>d4c8a830-0df1-4efc-95dd-6511bdfc023dFri, 29 May 2026 18:28:02 GMT<p>Recorded at Fuuz HQ in Detroit, this conversation brings together Craig Scott (Founder &amp; CEO of Fuuz), Dave Crysler (Founder of The Crysler Club), and Zack Scriven (Abelara) for a real, operator-to-operator discussion on what actually drives manufacturing results.<br /><br />This is not a vendor pitch. It’s a grounded conversation about how manufacturers really scale: what works, what breaks, and where technology actually belongs.<br /><br />*You’ll hear:*<br />- Dave’s “planning, people, process, technology” framework and how he applies it across industries<br />- Craig’s story building Fuuz out of real system integrator pain: too many tools, too many skill sets, too much complexity<br />- Why good vendors say “no” when they’re not the right fit<br />- How to think about data, ownership, and adoption without losing the humans in the system<br />- Quick hot takes on AI: where it helps, where it distracts, and why authenticity is coming back<br /><br />If you’re a plant leader, operations leader, or IT/OT leader trying to connect shop-floor reality to enterprise outcomes, this one will hit.<br /><br />*About Abelara:* Abelara works with manufacturing leaders to bridge strategy and execution. We help teams deploy modern architectures, unify disconnected systems, and unlock real operational visibility. No jargon, no fluff—just practical solutions built to scale in the real world.<br /><br />Thank you to Craig Scott and Dave Crysler for joining this Abelara Ascent podcast.<br /><br />➡️ <i>Learn More about Fuuz:</i> <a rel="noopener noreferrer nofollow" href="https://www.fuuz.com" target="_blank">https://www.fuuz.com</a><br /><br />➡️ <i>Learn More about Crysler Club:</i> <a rel="noopener noreferrer nofollow" href="https://thecrysler.club" target="_blank">https://thecrysler.club</a><br /><br />➡️ <i>Learn more about Abelara:</i> <a rel="noopener noreferrer nofollow" href="https://abelara.com/" target="_blank">https://abelara.com/</a><br /><a rel="noopener noreferrer nofollow" href="https://www.linkedin.com/company/abelara/" target="_blank">https://www.linkedin.com/company/abelara/</a><br /><br />Subscribe for insights on digital manufacturing, leadership, and system architecture.<br /><br />Keywords: manufacturing operations, continuous improvement, lean manufacturing, digital transformation, IT/OT, ERP integration, MES, data visibility, Unified Namespace, Detroit manufacturing, system integrator, manufacturing consulting, Fuuz, The Crysler Club, Abelara, Craig Scott, Dave Crysler, Zack Scriven. #manufacturing #operations #digitaltransformation #continuousimprovement #IIoT #ITOT #MES #ERP</p>no00:53:49Fuuz x The Crysler Club: People, Process, Tech and Why “Shiny Objects” Failfull<![CDATA[Stop Commissioning Blind]]>The conversation covers the experience of building realistic simulators in Ignition, creating detailed tag structures, and the transition from accounting to industrial controls. It also delves into go-live events, post-implementation support, and the process of improving development processes and training interns. The discussion further explores training AI models, script development, and leveraging ChatGPT for project assistance. The conversation delves into the use of AI for script creation, fine-tuning, and debugging, as well as the process of simulator creation and manual execution. It also explores the evolution of automated code generation, simulated testing in the office, and physical testing on site. Additionally, it highlights the end user experience, including simulation in PLCs, testing and collaboration on site, and the importance of ownership and accountability. The conversation covers topics such as efficient panel conversion, simulating systems with Docker, exciting developments in simulation and automation, handling complex routing and indirection in PLCs, testing and simulation workflow, and DevOps principles and project management practices. The speakers discuss the benefits of rapid panel conversion, challenges with external integrators, automated OPC module creation, and the utilization of graph databases for complex systems. They also emphasize the importance of testing and simulating systems before commissioning and highlight the alignment of project management practices and complementary approaches to project management.

Takeaways

  • Building realistic simulators in Ignition
  • Training and managing teams
  • Leveraging AI models for project optimization AI-assisted script creation and fine-tuning
  • Automated code generation and simulated testing
  • End user experience and ownership Efficient panel conversion process
  • Utilizing Docker for system simulation
  • Exciting developments in simulation and automation

Chapters

  • 00:00 Building Realistic Simulators in Ignition
  • 06:04 Experience with Ignition and Python Scripting
  • 18:13 Improving Development Processes and Training Interns
  • 23:31 Training AI and Script Development
  • 29:16 Leveraging ChatGPT for Project Assistance
  • 45:34 Automated Code Generation
  • 54:47 End User Experience
  • 01:01:25 Closing the Gap with Axelon
  • 01:13:42 Testing and Simulation Workflow
  • 01:23:32 DevOps and Project Management
]]>82f1861e-ba84-4394-afa2-47f4126bf5bbThu, 28 May 2026 23:05:42 GMT<p>The conversation covers the experience of building realistic simulators in Ignition, creating detailed tag structures, and the transition from accounting to industrial controls. It also delves into go-live events, post-implementation support, and the process of improving development processes and training interns. The discussion further explores training AI models, script development, and leveraging ChatGPT for project assistance. The conversation delves into the use of AI for script creation, fine-tuning, and debugging, as well as the process of simulator creation and manual execution. It also explores the evolution of automated code generation, simulated testing in the office, and physical testing on site. Additionally, it highlights the end user experience, including simulation in PLCs, testing and collaboration on site, and the importance of ownership and accountability. The conversation covers topics such as efficient panel conversion, simulating systems with Docker, exciting developments in simulation and automation, handling complex routing and indirection in PLCs, testing and simulation workflow, and DevOps principles and project management practices. The speakers discuss the benefits of rapid panel conversion, challenges with external integrators, automated OPC module creation, and the utilization of graph databases for complex systems. They also emphasize the importance of testing and simulating systems before commissioning and highlight the alignment of project management practices and complementary approaches to project management.</p><p></p><p>Takeaways</p><ul><li>Building realistic simulators in Ignition</li><li>Training and managing teams</li><li>Leveraging AI models for project optimization AI-assisted script creation and fine-tuning</li><li>Automated code generation and simulated testing</li><li>End user experience and ownership Efficient panel conversion process</li><li>Utilizing Docker for system simulation</li><li>Exciting developments in simulation and automation</li></ul><p></p><p>Chapters</p><ul><li>00:00 Building Realistic Simulators in Ignition</li><li>06:04 Experience with Ignition and Python Scripting</li><li>18:13 Improving Development Processes and Training Interns</li><li>23:31 Training AI and Script Development</li><li>29:16 Leveraging ChatGPT for Project Assistance</li><li>45:34 Automated Code Generation</li><li>54:47 End User Experience</li><li>01:01:25 Closing the Gap with Axelon</li><li>01:13:42 Testing and Simulation Workflow</li><li>01:23:32 DevOps and Project Management</li></ul>no01:23:34Stop Commissioning Blindfull<![CDATA[The death of data without context]]>The conversation explores the importance of historical data with context, the challenges of data collection and storage, and the role of unified namespace in data management. It delves into the need for context in time series data, the impact of historical data on decision-making, and the challenges of implementing a unified namespace. The discussion also covers the significance of understanding data consumers and the cultural and technical aspects of implementing a unified namespace. The conversation delves into the practical application of UNS (Unified Namespace) and its role in data storage, historization, and AI enablement. It explores the use of MQTT brokers, OPC servers, and time-based storage solutions, emphasizing the importance of context, historical data, and statistical analysis in manufacturing operations.

Takeaways

  • Historical data with context is crucial for decision-making and process improvement.
  • The implementation of a unified namespace requires consideration of both technical and cultural aspects. UNS as a broader concept
  • Importance of context and historical data
  • Practical application of AI in manufacturing

Chapters

  • 00:00 The Role of Unified Namespace in Data Management
  • 36:26 Data Storage and Historization
  • 42:23 Requirements for UNS and AI Enablement
]]>81eaf801-7366-4afa-8df2-54a74325b3fdFri, 22 May 2026 15:07:03 GMT<p>The conversation explores the importance of historical data with context, the challenges of data collection and storage, and the role of unified namespace in data management. It delves into the need for context in time series data, the impact of historical data on decision-making, and the challenges of implementing a unified namespace. The discussion also covers the significance of understanding data consumers and the cultural and technical aspects of implementing a unified namespace. The conversation delves into the practical application of UNS (Unified Namespace) and its role in data storage, historization, and AI enablement. It explores the use of MQTT brokers, OPC servers, and time-based storage solutions, emphasizing the importance of context, historical data, and statistical analysis in manufacturing operations.</p><p></p><p>Takeaways</p><ul><li>Historical data with context is crucial for decision-making and process improvement.</li><li>The implementation of a unified namespace requires consideration of both technical and cultural aspects. UNS as a broader concept</li><li>Importance of context and historical data</li><li>Practical application of AI in manufacturing</li></ul><p></p><p>Chapters</p><ul><li>00:00 The Role of Unified Namespace in Data Management</li><li>36:26 Data Storage and Historization</li><li>42:23 Requirements for UNS and AI Enablement</li></ul>no00:54:5013The death of data without contextfull<![CDATA[Stop doing "Industry 4.0" Here's what to do instead]]>You spent half a million dollars on a unified namespace. Six months later, your plant manager can't answer a simple question: what ran on Line B yesterday?

Despite the sensors. Despite the dashboards. Despite the check you wrote.

In this episode, we walk through the Capybara Inc. case study — a fictional multi-site beverage bottling operation built entirely from real problems. This is the story every industrial transformation leader recognizes. The pilot that proved connectivity but delivered nothing. The integrator who charged a change order every time you asked a question about your own data. The database you paid for that you don't have the password to.

We trace exactly what went wrong — and how Abelara and implementation partner Tupinix fixed it.

What you'll learn:

- Why a unified namespace without operational context is just an expensive screensaver

- The four failure modes that kill UNS pilots before they deliver value

- Why the first phase of implementation wasn't technical — it was change management

- Three use cases that generated real ROI: plant floor visibility, in-process inventory, and warehouse accountability

- How contextualizing existing sensor data eliminated 15% of unexplained downtime with a $200 fix

- Why a 6ml overfill on a 500ml bottle was costing $4,000 per week in lost product

- The open-source philosophy that separates real implementation partners from vendors holding you hostage

The company is fictional. Every single problem in this story is real.

Presented at the Prove It conference.

---

Subscribe on YouTube: https://www.youtube.com/@AbelaraAscent

Watch the full series: https://www.youtube.com/playlist?list=PLc0mbSF_NFx_rQO4rVO8kWhirbjyZHx4r

Listen on Spotify: https://open.spotify.com/show/5yaazbKgmavQpiYMrrCQ0J

Listen on Apple Podcasts: https://podcasts.apple.com/us/podcast/abelara-ascent/id1893477567

]]>350ba5fe-d3f7-4c89-bc1b-3438fc287accWed, 13 May 2026 21:24:39 GMT<p>You spent half a million dollars on a unified namespace. Six months later, your plant manager can't answer a simple question: what ran on Line B yesterday?</p><p>Despite the sensors. Despite the dashboards. Despite the check you wrote.</p><p>In this episode, we walk through the Capybara Inc. case study — a fictional multi-site beverage bottling operation built entirely from real problems. This is the story every industrial transformation leader recognizes. The pilot that proved connectivity but delivered nothing. The integrator who charged a change order every time you asked a question about your own data. The database you paid for that you don't have the password to.</p><p>We trace exactly what went wrong — and how Abelara and implementation partner Tupinix fixed it.</p><p>What you'll learn:</p><p>- Why a unified namespace without operational context is just an expensive screensaver</p><p>- The four failure modes that kill UNS pilots before they deliver value</p><p>- Why the first phase of implementation wasn't technical — it was change management</p><p>- Three use cases that generated real ROI: plant floor visibility, in-process inventory, and warehouse accountability</p><p>- How contextualizing existing sensor data eliminated 15% of unexplained downtime with a $200 fix</p><p>- Why a 6ml overfill on a 500ml bottle was costing $4,000 per week in lost product</p><p>- The open-source philosophy that separates real implementation partners from vendors holding you hostage</p><p>The company is fictional. Every single problem in this story is real.</p><p>Presented at the Prove It conference.</p><p>---</p><p>Subscribe on YouTube: <a rel="noopener noreferrer nofollow" href="https://www.youtube.com/@AbelaraAscent" target="_blank">https://www.youtube.com/@AbelaraAscent</a><br /></p><p>Watch the full series: <a rel="noopener noreferrer nofollow" href="https://www.youtube.com/playlist?list=PLc0mbSF_NFx_rQO4rVO8kWhirbjyZHx4r" target="_blank">https://www.youtube.com/playlist?list=PLc0mbSF_NFx_rQO4rVO8kWhirbjyZHx4r</a><br /></p><p>Listen on Spotify: <a rel="noopener noreferrer nofollow" href="https://open.spotify.com/show/5yaazbKgmavQpiYMrrCQ0J" target="_blank">https://open.spotify.com/show/5yaazbKgmavQpiYMrrCQ0J</a><br /></p><p>Listen on Apple Podcasts: <a rel="noopener noreferrer nofollow" href="https://podcasts.apple.com/us/podcast/abelara-ascent/id1893477567" target="_blank">https://podcasts.apple.com/us/podcast/abelara-ascent/id1893477567</a></p>no00:18:1912Stop doing "Industry 4.0" Here's what to do insteadfull<![CDATA[CPG Manufacturing Masterclass: Fixing "Pilot Purgatory" with UNS & Data Foundation]]>You have an MQTT broker. You have dashboards. You have data flowing from PLCs. And you still can't make any decisions. That is pilot purgatory. And it is more common than anyone in the industry wants to admit.

In this episode, Ricardo Santos, Director at Tupinix, joins Zack Scriven and Dylan DuFresne to walk through what it actually takes to get value from a Unified Namespace in a CPG and food and beverage environment. Using Enterprise B, Capy Hour Inc., as the virtual factory for Abelara's ProveIt 2026 presentation, the team covers real use cases with real numbers.

Chapters

00:00 — Welcome and Context

02:34 — Pilot Purgatory and Vendor Lock

04:58 — Ricardo's CPG Background

09:12 — When Digital Is Not the Answer Yet

19:57 — The Capy Hour Use Cases: Waste, OEE, Quality 26:14 — CIP Optimization with Data Visibility

31:47 — True OEE vs. Management OEE

41:36 — Why Raw Event Data Is the Foundation

43:57 — Utilities and Energy Management

51:00 — Tribal Knowledge and the Retiring Expert

58:16 — ProveIt 2026 and What to Expect

Connect with Ricardo Santos: https://www.linkedin.com/in/ricardomarquessantos/


Connect with Dylan DuFresne: https://www.linkedin.com/in/dylan-dufresne-solutions/


Connect with Zack Scriven:
https://www.linkedin.com/in/zackscriven/

Tupinix: https://tupinix.com/

Abelara: https://abelara.com

Subscribe to Abelara Ascent: https://www.youtube.com/@AbelaraAscent

#Abelara #AbelaraAscent #Industry40 #ManufacturingTransformation #DigitalTransformation #CPG #FoodAndBeverage #UnifiedNamespace #OEE #Bottling

]]>f0cca4db-f921-453e-9de6-aab25d019239Tue, 05 May 2026 20:34:04 GMT<p>You have an MQTT broker. You have dashboards. You have data flowing from PLCs. And you still can't make any decisions. That is pilot purgatory. And it is more common than anyone in the industry wants to admit.</p><p></p><p>In this episode, Ricardo Santos, Director at Tupinix, joins Zack Scriven and Dylan DuFresne to walk through what it actually takes to get value from a Unified Namespace in a CPG and food and beverage environment. Using Enterprise B, Capy Hour Inc., as the virtual factory for Abelara's ProveIt 2026 presentation, the team covers real use cases with real numbers.</p><p></p><p><b>Chapters</b></p><p>00:00 — Welcome and Context</p><p>02:34 — Pilot Purgatory and Vendor Lock</p><p>04:58 — Ricardo's CPG Background</p><p>09:12 — When Digital Is Not the Answer Yet</p><p>19:57 — The Capy Hour Use Cases: Waste, OEE, Quality 26:14 — CIP Optimization with Data Visibility</p><p>31:47 — True OEE vs. Management OEE</p><p>41:36 — Why Raw Event Data Is the Foundation</p><p>43:57 — Utilities and Energy Management</p><p>51:00 — Tribal Knowledge and the Retiring Expert</p><p>58:16 — ProveIt 2026 and What to Expect</p><p></p><p>Connect with Ricardo Santos: <a rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/ricardomarquessantos/" target="_blank">https://www.linkedin.com/in/ricardomarquessantos/</a></p><p><br />Connect with Dylan DuFresne: <a rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/dylan-dufresne-solutions/" target="_blank">https://www.linkedin.com/in/dylan-dufresne-solutions/</a></p><p><br />Connect with Zack Scriven:<br /><a rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/zackscriven/" target="_blank">https://www.linkedin.com/in/zackscriven/</a></p><p></p><p>Tupinix: <a rel="noopener noreferrer nofollow" href="https://tupinix.com/" target="_blank">https://tupinix.com/</a></p><p>Abelara: <a rel="noopener noreferrer nofollow" href="https://abelara.com" target="_blank">https://abelara.com</a></p><p></p><p><b>Subscribe to Abelara </b>Ascent: <a rel="noopener noreferrer nofollow" href="https://www.youtube.com/@AbelaraAscent" target="_blank">https://www.youtube.com/@AbelaraAscent</a></p><p></p><p>#Abelara #AbelaraAscent #Industry40 #ManufacturingTransformation #DigitalTransformation #CPG #FoodAndBeverage #UnifiedNamespace #OEE #Bottling</p>no01:10:06CPG Manufacturing Masterclass: Fixing "Pilot Purgatory" with UNS & Data Foundationfull<![CDATA[ Modern Manufacturing: The Full Stack with Dylan DuFresne, Travis Cox, Remus Pop & Craig Scott]]>The podcast features a discussion among industry experts in manufacturing, focusing on the evolution of technology, the impact of Ignition, and the collaborative nature of the industry. The conversation delves into the challenges faced by end users, the importance of community, and the need for education and guidance in adopting new technologies. The conversation delves into the challenges and opportunities of enterprise-scale digital transformation, focusing on the concept of Unified Namespace (UNS) and its impact on manufacturing and industrial operations. The discussion emphasizes the importance of intentional deployment, data management, and the role of technology in driving value and transformation. The conversation covers the strategic relationship between Fuuz and Inductive, the role of data model-driven solutions, the importance of standards, the value of AI in factory connectivity, and the responsible use of AI and UNS. It also explores the impact of MQTT and Sparkplug in transforming architectures and the significance of leveraging AI and UNS for customers.

Takeaways

  • Industry 4.0 and digital transformation
  • Community collaboration and open standards Enterprise-scale digital transformation requires intentional deployment and a focus on driving value through technology.
  • The concept of Unified Namespace (UNS) plays a crucial role in creating interoperability and data management in industrial operations. Strategic relationship between Fuuz and Inductive
  • Role of data model-driven solutions
  • Importance of standards and AI in factory connectivity
  • Responsible use of AI and UNS
  • Impact of MQTT and Sparkplug in transforming architectures

Chapters

  • 00:00 Introduction to the Full Stack of Manufacturing
  • 05:48 Legacy Pain Points and Modern Tools
  • 13:15 The New Paradigm in Manufacturing
  • 20:18 The Role of Fuuz in Enterprise Layer
  • 28:18 Challenges of Enterprise-Scale Deployment
  • 33:18 Unified Namespace (UNS) and Interoperability
  • 43:29 Success Stories and Adoption of UNS
  • 52:08 Mindset and Approach to Technology
  • 57:59 Value of AI and UNS for Customers
  • 01:15:14 Impact of MQTT and Sparkplug in Transforming Architectures
]]>a2f9ad19-bcb0-407b-9422-92db96b6399eMon, 27 Apr 2026 19:46:40 GMT<p>The podcast features a discussion among industry experts in manufacturing, focusing on the evolution of technology, the impact of Ignition, and the collaborative nature of the industry. The conversation delves into the challenges faced by end users, the importance of community, and the need for education and guidance in adopting new technologies. The conversation delves into the challenges and opportunities of enterprise-scale digital transformation, focusing on the concept of Unified Namespace (UNS) and its impact on manufacturing and industrial operations. The discussion emphasizes the importance of intentional deployment, data management, and the role of technology in driving value and transformation. The conversation covers the strategic relationship between Fuuz and Inductive, the role of data model-driven solutions, the importance of standards, the value of AI in factory connectivity, and the responsible use of AI and UNS. It also explores the impact of MQTT and Sparkplug in transforming architectures and the significance of leveraging AI and UNS for customers.</p><p></p><p>Takeaways</p><ul><li>Industry 4.0 and digital transformation</li><li>Community collaboration and open standards Enterprise-scale digital transformation requires intentional deployment and a focus on driving value through technology.</li><li>The concept of Unified Namespace (UNS) plays a crucial role in creating interoperability and data management in industrial operations. Strategic relationship between Fuuz and Inductive</li><li>Role of data model-driven solutions</li><li>Importance of standards and AI in factory connectivity</li><li>Responsible use of AI and UNS</li><li>Impact of MQTT and Sparkplug in transforming architectures</li></ul><p></p><p>Chapters</p><ul><li>00:00 Introduction to the Full Stack of Manufacturing</li><li>05:48 Legacy Pain Points and Modern Tools</li><li>13:15 The New Paradigm in Manufacturing</li><li>20:18 The Role of Fuuz in Enterprise Layer</li><li>28:18 Challenges of Enterprise-Scale Deployment</li><li>33:18 Unified Namespace (UNS) and Interoperability</li><li>43:29 Success Stories and Adoption of UNS</li><li>52:08 Mindset and Approach to Technology</li><li>57:59 Value of AI and UNS for Customers</li><li>01:15:14 Impact of MQTT and Sparkplug in Transforming Architectures</li></ul>no01:18:56110 Modern Manufacturing: The Full Stack with Dylan DuFresne, Travis Cox, Remus Pop & Craig Scottfull<![CDATA[Data Modeling Across 5 Platforms: Ignition UDTs vs HighByte vs Litmus Edge vs Flow vs Fuuz]]>Summary Dylan DuFresne and Zack Scriven go tool-by-tool through the five platforms that run most serious industrial stacks — Ignition, Flow Software, Litmus Edge, HighByte, and Fuuz — and answer the question every IT/OT team is asking: when do I use which, and why. This is Episode 2 of the Prove It series on Abelara Ascent, building toward the live reference architecture at the Prove It conference.

About the Speakers Dylan DuFresne leads architecture and digital transformation engagements at Abelara. Zack Scriven hosts Abelara's podcast and livestream content. Both are preparing Abelara's Prove It booth — a full Enterprise B reference architecture running locally on-site at the conference.

Key Topics

  • The "motor overheating" problem that explains what data ops actually is
  • Each platform's native home turf — composable SCADA, analytics, edge, integration, cloud-native MES
  • HighByte vs Flow: data in motion vs data at rest
  • The Prove It Enterprise B architecture — 3 sites, 5 platforms, different combinations per site
  • Fuuz security: single outbound tunnel, no third-party surface
  • Rapid-fire: when NOT to use each platform
  • Pricing reality across all five vendors
  • SESME / SMIPS: the interoperability initiative for shared data models

Key Takeaways

  1. Start with a problem, not a technology. Every digital transformation that skips this tends to fail.
  2. Data in motion → HighByte. Data at rest → Flow. Device connectivity at scale → Litmus. Plant-floor visibility → Ignition. Cloud-native enterprise backbone → Fuuz.
  3. For small companies without process maturity, a clipboard and Excel beat any of these tools. People, process, technology — in that order.

Notable Quotes

"HighByte is for data in motion and Flow is best for data at rest." — Dylan DuFresne

"For the really small companies, a good process is way more important than the tools you're using." — Dylan DuFresne

"It's people, process, and technology in that order for a reason." — Dylan DuFresne

Timestamps

  • [00:00] — Welcome + Prove It series recap
  • [04:44] — The five platforms and their native homes
  • [15:01] — Why more platforms doesn't mean more data models
  • [18:35] — HighByte vs Flow: data in motion vs at rest
  • [23:19] — The Prove It Enterprise B architecture walkthrough
  • [28:26] — Fuuz security: the single outbound tunnel
  • [37:24] — Rapid fire: when NOT to use each platform
  • [44:23] — Pricing reality check
  • [48:54] — SESME / SMIPS and the interoperability vision
]]>74637696-95f7-491d-be8c-c5fa3c2ab21bTue, 21 Apr 2026 16:34:57 GMT<p><b>Summary</b> Dylan DuFresne and Zack Scriven go tool-by-tool through the five platforms that run most serious industrial stacks — Ignition, Flow Software, Litmus Edge, HighByte, and Fuuz — and answer the question every IT/OT team is asking: when do I use which, and why. This is Episode 2 of the Prove It series on Abelara Ascent, building toward the live reference architecture at the Prove It conference.</p><p></p><p><b>About the Speakers</b> Dylan DuFresne leads architecture and digital transformation engagements at Abelara. Zack Scriven hosts Abelara's podcast and livestream content. Both are preparing Abelara's Prove It booth — a full Enterprise B reference architecture running locally on-site at the conference.</p><p></p><p><b>Key Topics</b></p><ul><li>The "motor overheating" problem that explains what data ops actually is</li><li>Each platform's native home turf — composable SCADA, analytics, edge, integration, cloud-native MES</li><li>HighByte vs Flow: data in motion vs data at rest</li><li>The Prove It Enterprise B architecture — 3 sites, 5 platforms, different combinations per site</li><li>Fuuz security: single outbound tunnel, no third-party surface</li><li>Rapid-fire: when NOT to use each platform</li><li>Pricing reality across all five vendors</li><li>SESME / SMIPS: the interoperability initiative for shared data models</li></ul><p></p><p><b>Key Takeaways</b></p><ol><li>Start with a problem, not a technology. Every digital transformation that skips this tends to fail.</li><li>Data in motion → HighByte. Data at rest → Flow. Device connectivity at scale → Litmus. Plant-floor visibility → Ignition. Cloud-native enterprise backbone → Fuuz.</li><li>For small companies without process maturity, a clipboard and Excel beat any of these tools. People, process, technology — in that order.</li></ol><p></p><p><b>Notable Quotes</b></p><blockquote><p>"HighByte is for data in motion and Flow is best for data at rest." — Dylan DuFresne</p></blockquote><blockquote><p>"For the really small companies, a good process is way more important than the tools you're using." — Dylan DuFresne</p></blockquote><blockquote><p>"It's people, process, and technology in that order for a reason." — Dylan DuFresne</p></blockquote><p><b>Timestamps</b></p><ul><li>[00:00] — Welcome + Prove It series recap</li><li>[04:44] — The five platforms and their native homes</li><li>[15:01] — Why more platforms doesn't mean more data models</li><li>[18:35] — HighByte vs Flow: data in motion vs at rest</li><li>[23:19] — The Prove It Enterprise B architecture walkthrough</li><li>[28:26] — Fuuz security: the single outbound tunnel</li><li>[37:24] — Rapid fire: when NOT to use each platform</li><li>[44:23] — Pricing reality check</li><li>[48:54] — SESME / SMIPS and the interoperability vision</li></ul>no00:52:3719Data Modeling Across 5 Platforms: Ignition UDTs vs HighByte vs Litmus Edge vs Flow vs Fuuzfull<![CDATA[Live Architecture Review with Dylan DuFresne]]>Abelara Lead Architect Dylan DuFresne walks Zack Scriven through the multi-site industrial automation stack Abelara planned and deployed for its Prove It 2026 sponsorship — naming specific vendors at every layer of the stack and explaining the architectural reasoning behind each choice. Starting at the edge (Opto 22 controllers), moving through site-level infrastructure (Ignition, TimescaleDB, Flow Software, TimeBase), into data operations (HighByte, Litmus Edge), across the enterprise UNS (HiveMQ broker), and up through multi-site enterprise applications (Fuuz, MaintainX, Google Cloud), Dylan builds the architecture layer by layer and takes live audience questions throughout. The through line isn't the specific tool list — it's the *why* behind each decision. Why a separate tag server in Ignition 8.3 for DevOps isolation. Why HighByte for data in motion but Flow Software for data at rest. Why vision for SCADA but perspective for MES. Why the blue-vs-red namespace distinction matters. Why Fuuz sits where it sits. The result is one of the clearest end-to-end walkthroughs of a real, deployable Industry 4.0 stack you'll find. Three things to take away 1. "Edge" means whatever the vendor wants it to mean — know what they mean.** Dylan's working definition: *"Edge is shorthand for as far into the stack as I care to look."* To a cloud vendor, the entire plant is the edge. To an OT engineer, it's the PLC. When a product labels itself "edge," you have to ask which edge. 2. HighByte for data in motion, Flow Software for data at rest.** The two tools live at the same layer of the stack, but do different jobs. HighByte is the ETL / data ops engine — pulling data from disparate sources, modeling it, moving it. Flow Software is the analytics engine — pulling data at rest from multiple stores and calculating KPIs against it. Not an "or" decision; an "and" decision. 3. Fuuz is to the enterprise what Ignition is to the site.** If Ignition is the Swiss Army knife platform for the plant floor — SCADA, MES, IoT connectivity — Fuuz is the same kind of platform for L3/L4 enterprise apps: iPaaS foundation, MES, WMS, supply chain. Different jobs, different data models, both Swiss Army knives at their respective layers. The stack, in one pass - Device layer (L0/L1): Opto 22 controllers - Edge connectivity: Ignition Edge (IOT licenses), running on bare-metal Linux or Docker - Site SCADA: Ignition Vision (for local multi-monitor operator screens) - Site MES: Ignition Perspective (for browser-based MES frontends) - Site tag server: Ignition 8.3 dedicated tag server — single source of truth for site current state - Historian: TimeBase (Flow Software) — telemetry, scalar data, in-flight time series - MES database: TimescaleDB on Postgres — contextual event data, state changes, tabular MES functions - Analytics: Flow Software — connects all the above, calculates KPIs, contextualizes - Data ops (Site A): HighByte — ETL from tag server to UNS broker - Data ops (Site B): Litmus Edge — standalone data pipeline, no local SCADA/MES needed - Enterprise UNS broker: HiveMQ (the Prove It shared broker) - CMMS: MaintainX - Enterprise apps / L3: Fuuz — MES, WMS, supply chain, ERP integration, multi-site consistency - Data warehouse: Google BigQuery / Google Cloud - Deployment: Portainer Timestamps:

00:00 — Intro + the planned stack

05:11 — The credibility moment: "All 8 title sponsors, by coincidence"

07:45 — Edge devices, Opto 22, and why IPCs run Linux

13:00 — "Edge is shorthand for as far into the stack as I care to look"

15:16 — The Ignition 8.3 tag server

18:02 — TimeBase vs. TimescaleDB, MES core vs. MES custom

24:51 — SCADA in Vision, MES in Perspective — why

27:31 — Medallion architecture, HighByte, and the enterprise UNS

34:34 — Fuuz as the enterprise Swiss Army knife

45:09 — Q&A

]]>a9f01abd-9eae-44be-a295-badcec6188a9Mon, 20 Apr 2026 16:30:34 GMT<pre><code>Abelara Lead Architect Dylan DuFresne walks Zack Scriven through the multi-site industrial automation stack Abelara planned and deployed for its Prove It 2026 sponsorship — naming specific vendors at every layer of the stack and explaining the architectural reasoning behind each choice. Starting at the edge (Opto 22 controllers), moving through site-level infrastructure (Ignition, TimescaleDB, Flow Software, TimeBase), into data operations (HighByte, Litmus Edge), across the enterprise UNS (HiveMQ broker), and up through multi-site enterprise applications (Fuuz, MaintainX, Google Cloud), Dylan builds the architecture layer by layer and takes live audience questions throughout. The through line isn't the specific tool list — it's the *why* behind each decision. Why a separate tag server in Ignition 8.3 for DevOps isolation. Why HighByte for data in motion but Flow Software for data at rest. Why vision for SCADA but perspective for MES. Why the blue-vs-red namespace distinction matters. Why Fuuz sits where it sits. The result is one of the clearest end-to-end walkthroughs of a real, deployable Industry 4.0 stack you'll find. Three things to take away 1. "Edge" means whatever the vendor wants it to mean — know what they mean.** Dylan's working definition: *"Edge is shorthand for as far into the stack as I care to look."* To a cloud vendor, the entire plant is the edge. To an OT engineer, it's the PLC. When a product labels itself "edge," you have to ask which edge. 2. HighByte for data in motion, Flow Software for data at rest.** The two tools live at the same layer of the stack, but do different jobs. HighByte is the ETL / data ops engine — pulling data from disparate sources, modeling it, moving it. Flow Software is the analytics engine — pulling data at rest from multiple stores and calculating KPIs against it. Not an "or" decision; an "and" decision. 3. Fuuz is to the enterprise what Ignition is to the site.** If Ignition is the Swiss Army knife platform for the plant floor — SCADA, MES, IoT connectivity — Fuuz is the same kind of platform for L3/L4 enterprise apps: iPaaS foundation, MES, WMS, supply chain. Different jobs, different data models, both Swiss Army knives at their respective layers. The stack, in one pass - Device layer (L0/L1): Opto 22 controllers - Edge connectivity: Ignition Edge (IOT licenses), running on bare-metal Linux or Docker - Site SCADA: Ignition Vision (for local multi-monitor operator screens) - Site MES: Ignition Perspective (for browser-based MES frontends) - Site tag server: Ignition 8.3 dedicated tag server — single source of truth for site current state - Historian: TimeBase (Flow Software) — telemetry, scalar data, in-flight time series - MES database: TimescaleDB on Postgres — contextual event data, state changes, tabular MES functions - Analytics: Flow Software — connects all the above, calculates KPIs, contextualizes - Data ops (Site A): HighByte — ETL from tag server to UNS broker - Data ops (Site B): Litmus Edge — standalone data pipeline, no local SCADA/MES needed - Enterprise UNS broker: HiveMQ (the Prove It shared broker) - CMMS: MaintainX - Enterprise apps / L3: Fuuz — MES, WMS, supply chain, ERP integration, multi-site consistency - Data warehouse: Google BigQuery / Google Cloud - Deployment: Portainer Timestamps:</code></pre><p>00:00 — Intro + the planned stack</p><p>05:11 — The credibility moment: "All 8 title sponsors, by coincidence"</p><p>07:45 — Edge devices, Opto 22, and why IPCs run Linux</p><p>13:00 — "Edge is shorthand for as far into the stack as I care to look"</p><p>15:16 — The Ignition 8.3 tag server</p><p>18:02 — TimeBase vs. TimescaleDB, MES core vs. MES custom</p><p>24:51 — SCADA in Vision, MES in Perspective — why</p><p>27:31 — Medallion architecture, HighByte, and the enterprise UNS</p><p>34:34 — Fuuz as the enterprise Swiss Army knife</p><p>45:09 — Q&amp;A</p>no00:49:0118Live Architecture Review with Dylan DuFresnefull<![CDATA[Digital Transformation isn't a product]]>Summary

Zack Scriven sits down for an impromptu conversation with Dylan DuFresne (Lead Architect & Co-founder, Abelara) on three of the industry's most-abused terms: "digital transformation," "unified namespace," and "Industry 4.0." Dylan's core argument is that the terms themselves are buzzwords — vessels that vendors fill with whatever they're trying to sell. The only useful question is what problem are you actually solving, and the only useful follow-up is what is this vendor actually selling me.

The conversation moves from meta-critique into practical territory in the second half: Dylan walks through what a UNS has actually meant for the industry over the last decade (short answer: education), whether greenfield and brownfield sites need different answers (yes), and which tools he'd pick today if he were starting from scratch. It closes with one of the cleanest vendor-selection frameworks you'll hear on a podcast — and a discovery-call CTA for anyone who wants to talk through their own architecture.

Three takeaways

  1. "Digital transformation" is whatever the vendor needs it to be to close the sale. The term has meant ERP-to-cloud migration, workflow digitization, knowledge-base digitization, and now unified namespace. The throughline isn't technical — it's commercial. If you're buying "digital transformation," demand to know what product or service is actually being delivered, and whether you have a problem it solves.
  2. UNS is a tool, not an answer. The real value of the UNS movement was education. Over ~10 years, the UNS conversation did something no one argued against: it educated the market on the need to connect ERP to plant floor, and share data across layers that never shared it. But "I want a UNS" is still the wrong starting question. Greenfield deserves a UNS-shaped architecture. Brownfield with working historians usually does not.
  3. Pick tools by problem, pick vendors by partnership. Dylan's ideal greenfield stack: Ignition, HiveMQ, Flow Software, Solace — some combination gets you most of what you need. Broker market has commoditized; the differentiation is adjacent features and culture. When two vendors solve the same problem, pick the one who wants to grow with you — not the one with the nicer salesperson.

Chapters
00:00 — Cold open + intro 

00:32 — What "digital transformation" actually means (and who benefits from the ambiguity)

03:56 — Why digital transformation is change management, not a product 

05:08 — Unified namespace: what it is, what it became, what it's worth 

08:30 — Greenfield vs. brownfield — and the real value of UNS 

14:56 — Who actually benefited from the UNS movement 

17:27 — The stack Dylan would build today (+ CoreFlux deep dive) 

19:24 — How to pick vendors when products overlap 

24:41 — Discovery calls + weekly architecture Q&A

]]>e065cb68-68db-4218-a2b1-b8f6a7d26f2eSat, 18 Apr 2026 17:59:11 GMT<p><b>Summary </b></p><p>Zack Scriven sits down for an impromptu conversation with Dylan DuFresne (Lead Architect &amp; Co-founder, Abelara) on three of the industry's most-abused terms: "digital transformation," "unified namespace," and "Industry 4.0." Dylan's core argument is that the terms themselves are buzzwords — vessels that vendors fill with whatever they're trying to sell. The only useful question is <i>what problem are you actually solving</i>, and the only useful follow-up is <i>what is this vendor actually selling me</i>.</p><p>The conversation moves from meta-critique into practical territory in the second half: Dylan walks through what a UNS has actually meant for the industry over the last decade (short answer: education), whether greenfield and brownfield sites need different answers (yes), and which tools he'd pick today if he were starting from scratch. It closes with one of the cleanest vendor-selection frameworks you'll hear on a podcast — and a discovery-call CTA for anyone who wants to talk through their own architecture.</p><p></p><p><b>Three takeaways </b></p><ol><li><b>"Digital transformation" is whatever the vendor needs it to be to close the sale.</b> The term has meant ERP-to-cloud migration, workflow digitization, knowledge-base digitization, and now unified namespace. The throughline isn't technical — it's commercial. If you're buying "digital transformation," demand to know what product or service is actually being delivered, and whether you have a problem it solves.</li><li><b>UNS is a tool, not an answer. The real value of the UNS movement was education.</b> Over ~10 years, the UNS conversation did something no one argued against: it educated the market on the need to connect ERP to plant floor, and share data across layers that never shared it. But "I want a UNS" is still the wrong starting question. Greenfield deserves a UNS-shaped architecture. Brownfield with working historians usually does not.</li><li><b>Pick tools by problem, pick vendors by partnership.</b> Dylan's ideal greenfield stack: Ignition, HiveMQ, Flow Software, Solace — some combination gets you most of what you need. Broker market has commoditized; the differentiation is adjacent features and culture. When two vendors solve the same problem, pick the one who wants to grow with you — not the one with the nicer salesperson.</li></ol><p></p><p><b>Chapters</b><br />00:00 — Cold open + intro </p><p>00:32 — What "digital transformation" actually means (and who benefits from the ambiguity) </p><p>03:56 — Why digital transformation is change management, not a product </p><p>05:08 — Unified namespace: what it is, what it became, what it's worth </p><p>08:30 — Greenfield vs. brownfield — and the real value of UNS </p><p>14:56 — Who actually benefited from the UNS movement </p><p>17:27 — The stack Dylan would build today (+ CoreFlux deep dive) </p><p>19:24 — How to pick vendors when products overlap </p><p>24:41 — Discovery calls + weekly architecture Q&amp;A</p>no00:25:5917Digital Transformation isn't a productfull<![CDATA[Bridging the ERP / Plant-Floor Divide — How Fuuz Created a True MES and a Bumblebee Culture]]>Summary

Glenn Gardner (Abelara co-founder) sat down with Craig Scott (Fuuz CEO) at Fuuz HQ after spending a week embedded in bootcamp with the Fuuz team. The conversation threads through three topics: the unusual culture Fuuz has built around genuinely caring about manufacturing, the architectural decision to be the MES "shim" between ERP and the plant floor, and how democratized plant-floor data changes work for the personas nobody usually builds for — finance, accounting, and maintenance.

About the guest

Craig Scott — CEO and founder of Fuuz, a manufacturing operations platform purpose-built to unify ERP, plant floor, and operational workflows in a single data layer. Background spans CNC and robot programming, owning a manufacturing shop, and running a systems integration practice before Fuuz.

Three takeaways

  1. Culture is a hiring choice, not a slogan. Fuuz didn't hire developers who already knew manufacturing. They hired strong developers and put them on real implementation projects, forcing them to learn manufacturing by solving actual customer problems. Their CTO Lance now runs a maker space on the side.
  2. ERP and plant floor are different data models — stop trying to merge them. ERP is relational, transactional, 300 transactions/day. Plant floor is scalar time series, thousands of data points per second. Predictive maintenance is vector matrix tensor. You can't force all three through one pipe. What's needed is a unified data layer (the "shim") that speaks all three natively.
  3. The forgotten personas are the highest-leverage users. Finance and accounting teams burn cognitive cycles trying to reconstruct variances with spreadsheets. Maintenance teams can't get their predictive data to the enterprise. A platform that democratizes plant-floor data to those personas is worth more than another BI dashboard.

Notable quotes

"I found the park of people like me." — Glenn Gardner, on the Fuuz team's culture

"ERP doesn't have to be those things, but there has to be that shim, that MES layer in there." — Craig Scott

"I still have ulcers from those periods." — Glenn Gardner, on quarter-close variance hunting

Chapters

  • 00:00 — Cold open: MES theory vs. reality
  • 00:38 — The Bumblebee culture metaphor
  • 03:37 — Why Fuuz hired devs and taught them manufacturing
  • 09:26 — What kind of SI Fuuz was, and how Fuuz emerged
  • 13:00 — The MES shim explained (ISA-95 / Purdue layers)
  • 17:11 — Why iPaaS approaches fail
  • 19:10 — Scalar vs. time-series vs. tensor data
  • 21:57 — 300 ERP transactions vs. millions of telemetry events
  • 24:08 — The AWS outage and Fuuz's architectural resilience
  • 25:11 — Predictive maintenance and the vector-tensor database problem
  • 28:25 — Democratizing data to finance and accounting
  • 31:19 — The "ulcers" story — quarter-close variance chasing

Links & resources

]]>c9d96785-2a1a-4b11-86ff-fe2cdf3a8f3aFri, 17 Apr 2026 00:33:50 GMT<h2><b>Summary</b></h2><p>Glenn Gardner (Abelara co-founder) sat down with Craig Scott (Fuuz CEO) at Fuuz HQ after spending a week embedded in bootcamp with the Fuuz team. The conversation threads through three topics: the unusual culture Fuuz has built around genuinely caring about manufacturing, the architectural decision to be the MES "shim" between ERP and the plant floor, and how democratized plant-floor data changes work for the personas nobody usually builds for — finance, accounting, and maintenance.</p><h2><b>About the guest</b></h2><p><b>Craig Scott</b> — CEO and founder of Fuuz, a manufacturing operations platform purpose-built to unify ERP, plant floor, and operational workflows in a single data layer. Background spans CNC and robot programming, owning a manufacturing shop, and running a systems integration practice before Fuuz.</p><h2><b>Three takeaways</b></h2><ol><li><b>Culture is a hiring choice, not a slogan.</b> Fuuz didn't hire developers who already knew manufacturing. They hired strong developers and put them on real implementation projects, forcing them to learn manufacturing by solving actual customer problems. Their CTO Lance now runs a maker space on the side.</li><li><b>ERP and plant floor are different data models — stop trying to merge them.</b> ERP is relational, transactional, 300 transactions/day. Plant floor is scalar time series, thousands of data points per second. Predictive maintenance is vector matrix tensor. You can't force all three through one pipe. What's needed is a unified data layer (the "shim") that speaks all three natively.</li><li><b>The forgotten personas are the highest-leverage users.</b> Finance and accounting teams burn cognitive cycles trying to reconstruct variances with spreadsheets. Maintenance teams can't get their predictive data to the enterprise. A platform that democratizes plant-floor data to those personas is worth more than another BI dashboard.</li></ol><h2><b>Notable quotes</b></h2><blockquote><p>"I found the park of people like me." — Glenn Gardner, on the Fuuz team's culture</p></blockquote><blockquote><p>"ERP doesn't have to be those things, but there has to be that shim, that MES layer in there." — Craig Scott</p></blockquote><blockquote><p>"I still have ulcers from those periods." — Glenn Gardner, on quarter-close variance hunting</p></blockquote><h2><b>Chapters</b></h2><ul><li><code>00:00</code> — Cold open: MES theory vs. reality</li><li><code>00:38</code> — The Bumblebee culture metaphor</li><li><code>03:37</code> — Why Fuuz hired devs and taught them manufacturing</li><li><code>09:26</code> — What kind of SI Fuuz was, and how Fuuz emerged</li><li><code>13:00</code> — The MES shim explained (ISA-95 / Purdue layers)</li><li><code>17:11</code> — Why iPaaS approaches fail</li><li><code>19:10</code> — Scalar vs. time-series vs. tensor data</li><li><code>21:57</code> — 300 ERP transactions vs. millions of telemetry events</li><li><code>24:08</code> — The AWS outage and Fuuz's architectural resilience</li><li><code>25:11</code> — Predictive maintenance and the vector-tensor database problem</li><li><code>28:25</code> — Democratizing data to finance and accounting</li><li><code>31:19</code> — The "ulcers" story — quarter-close variance chasing</li></ul><h2><b>Links &amp; resources</b></h2><ul><li>Fuuz: <a rel="noopener noreferrer nofollow" href="https://www.fuuz.com" target="_blank">https://www.fuuz.com</a></li><li>Abelara: <a rel="noopener noreferrer nofollow" href="https://www.abelara.com" target="_blank">https://www.abelara.com</a></li><li>Craig Scott on LinkedIn: <a rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/craigascott1/" target="_blank">https://www.linkedin.com/in/craigascott1/</a></li></ul>no00:32:5416Bridging the ERP / Plant-Floor Divide — How Fuuz Created a True MES and a Bumblebee Culturefull<![CDATA[How Fuuz Changes Everything — A Deep Dive Conversation]]>Summary

The Abelara team — Glenn Gardner, Zack Scriven, and Dylan DuFresne — just spent a week at Fuuz corporate headquarters for bootcamp. In this roundtable debrief, they give their unfiltered take: what they loved, what needs work, how Fuuz compares to Ignition and Plex, where it fits in the stack, and why a greenfield site should probably run both Ignition and Fuuz together. The conversation covers real pricing comparisons, the MCP tooling announcement, and why "it does everything" is both Fuuz's greatest strength and its biggest go-to-market challenge.

Key Topics Covered

  • What each team member is most excited about after bootcamp
  • What Fuuz needs to improve: UX, user journeys, discoverability of features
  • Fuuz vs. Ignition: not competitors — different layers of the stack
  • Fuuz vs. Plex: why enterprises evaluating Plex should look at Fuuz
  • Pricing reality: single-site MES is comparable to Ignition; multi-site is where Fuuz pulls ahead
  • Edge gateway architecture: cloud platform with an on-prem edge component
  • MCP tooling: Fuuz is building it now while others have only announced it
  • The three pillars: schema designer, flow designer, screen designer
  • Why "it does everything" is both the value prop and the sales problem
  • Build vs. buy spectrum: Fuuz as the middle ground between full custom and rigid off-the-shelf

Key Takeaways

  1. Ignition owns the lower stack (SCADA, plant floor). Fuuz owns the upper stack (enterprise MES, iPaaS, ERP integration). A greenfield site should run both — they're best friends, not competitors.
  2. Fuuz's hardest problem isn't the product — it's explaining the product. It's six or seven commercializable products in one platform, and nobody can pitch that in a sentence without sounding like BS.
  3. MCP tooling on the Fuuz flow designer means a lot

Notable Quotes

  • "Ignition and Fuuz should probably be the best of friends." — Dylan DuFresne
  • "It's like seven different applications. And if someone said pitch me on Fuuz, I would sound like an idiot." — Glenn Gardner
  • "Other leading platforms have announced the ability to do this in the future. Fuuz is doing it now." — Dylan DuFresne (on MCP)

Timestamps / Chapters

[00:00] — What stood out most from Fuuz bootcamp
[01:56] — Zack: enterprise orchestration and best-of-breed integration
[03:08] — Dylan: the people, the platform, and where Fuuz fits in the stack
[04:48] — Honest critique: what needs to improve
[06:48] — The discoverability problem and the change logging trap
[09:31] — Edge gateway explained: cloud platform with edge component
[13:02] — "Fuuz is like six or seven different products"
[15:12] — Pricing reality: Fuuz vs. Ignition vs. enterprise iPaaS
[17:47] — Single-site vs. multi-site: where Fuuz takes the lead [20:06] — Ignition and Fuuz: complementary, not competitive
[21:44] — Fuuz vs. Plex: entirely different platforms
[23:29] — What module would you add next? SCP.
[24:40] — The most common problem: integrating ERP with the plant floor
[32:55] — MCP tooling: the AI unlock
[33:41] — Glenn's final take: card-carrying Fuuz fan

Links & Resources

]]>b8ab5f11-ea46-4356-9453-ef8affb4fde9Wed, 15 Apr 2026 00:33:49 GMT<p><b>Summary</b></p><p>The Abelara team — Glenn Gardner, Zack Scriven, and Dylan DuFresne — just spent a week at Fuuz corporate headquarters for bootcamp. In this roundtable debrief, they give their unfiltered take: what they loved, what needs work, how Fuuz compares to Ignition and Plex, where it fits in the stack, and why a greenfield site should probably run both Ignition and Fuuz together. The conversation covers real pricing comparisons, the MCP tooling announcement, and why "it does everything" is both Fuuz's greatest strength and its biggest go-to-market challenge.</p><p></p><p><b>Key Topics Covered</b></p><ul><li>What each team member is most excited about after bootcamp</li><li>What Fuuz needs to improve: UX, user journeys, discoverability of features</li><li>Fuuz vs. Ignition: not competitors — different layers of the stack</li><li>Fuuz vs. Plex: why enterprises evaluating Plex should look at Fuuz</li><li>Pricing reality: single-site MES is comparable to Ignition; multi-site is where Fuuz pulls ahead</li><li>Edge gateway architecture: cloud platform with an on-prem edge component</li><li>MCP tooling: Fuuz is building it now while others have only announced it</li><li>The three pillars: schema designer, flow designer, screen designer</li><li>Why "it does everything" is both the value prop and the sales problem</li><li>Build vs. buy spectrum: Fuuz as the middle ground between full custom and rigid off-the-shelf</li></ul><p></p><p><b>Key Takeaways</b></p><ol><li>Ignition owns the lower stack (SCADA, plant floor). Fuuz owns the upper stack (enterprise MES, iPaaS, ERP integration). A greenfield site should run both — they're best friends, not competitors.</li><li>Fuuz's hardest problem isn't the product — it's explaining the product. It's six or seven commercializable products in one platform, and nobody can pitch that in a sentence without sounding like BS.</li><li>MCP tooling on the Fuuz flow designer means a lot</li></ol><p></p><p><b>Notable Quotes</b></p><ul><li>"Ignition and Fuuz should probably be the best of friends." — Dylan DuFresne</li><li>"It's like seven different applications. And if someone said pitch me on Fuuz, I would sound like an idiot." — Glenn Gardner</li><li>"Other leading platforms have announced the ability to do this in the future. Fuuz is doing it now." — Dylan DuFresne (on MCP)</li></ul><p></p><p><b>Timestamps / Chapters</b></p><p>[00:00] — What stood out most from Fuuz bootcamp <br />[01:56] — Zack: enterprise orchestration and best-of-breed integration <br />[03:08] — Dylan: the people, the platform, and where Fuuz fits in the stack <br />[04:48] — Honest critique: what needs to improve <br />[06:48] — The discoverability problem and the change logging trap <br />[09:31] — Edge gateway explained: cloud platform with edge component <br />[13:02] — "Fuuz is like six or seven different products" <br />[15:12] — Pricing reality: Fuuz vs. Ignition vs. enterprise iPaaS <br />[17:47] — Single-site vs. multi-site: where Fuuz takes the lead [20:06] — Ignition and Fuuz: complementary, not competitive <br />[21:44] — Fuuz vs. Plex: entirely different platforms <br />[23:29] — What module would you add next? SCP. <br />[24:40] — The most common problem: integrating ERP with the plant floor <br />[32:55] — MCP tooling: the AI unlock <br />[33:41] — Glenn's final take: card-carrying Fuuz fan<br /></p><p><b>Links &amp; Resources</b></p><ul><li>Fuuz: <a rel="noopener noreferrer nofollow" href="http://fuuz.com" target="_blank">fuuz.com</a></li><li>Abelara: <a rel="noopener noreferrer nofollow" href="http://abelara.com" target="_blank">abelara.com</a></li><li>Ignition: <a rel="noopener noreferrer nofollow" href="http://inductiveautomation.com" target="_blank">inductiveautomation.com</a></li></ul>no00:34:0515How Fuuz Changes Everything — A Deep Dive Conversationfull<![CDATA[From ERP to Factory Floor — How CIOs Are Powering Modern Manufacturing with Fuuz]]>Summary

Glenn Gardner frames the quality problem every manufacturer faces: DPPM spikes and nobody can figure out whether it's a catastrophe or a blip — because the data lives in a dozen different systems. Steve Modrall walks through how Fuuz solved this for HighBar, the world's first solar-powered steel mill, deploying MES, WMS, yard management, scale house, and vendor portal in nine months. Craig Scott demos Fuuz's schema designer and shows how embedded quality checks and digital work instructions eliminate the "more training" corrective action that plagues every 8D process.

Key Topics Covered

  • Quality as the hardest problem in manufacturing: tracking DPPM across downstream, midstream, and upstream
  • Why ERP falls down for midstream and upstream quality data
  • HighBar Steel: world's first solar-powered steel mill, 2x output per FTE vs. industry
  • Red data vs. blue data: enterprise governance vs. plant-level detail
  • iPaaS as the bridge between ERP and plant floor (not point-to-point)
  • WIP visibility without SKU proliferation
  • Schema designer: modeling manufacturing data as normalized relationships
  • Poke-yoke through MES: embedding quality into process, not as an afterthought
  • 8D root cause failures when you run out of data
  • MCP integration for AI/LLM analysis on manufacturing data

Key Takeaways

  1. The ERP handles downstream data (RMAs, customer returns) well, but for midstream (factory floor) and upstream (supplier quality), you need MES, SCADA, and quality systems — the ERP was never designed for that level of detail.
  2. Don't track WIP in the ERP — it explodes your SKU count by an order of magnitude. Keep raw materials and finished goods in ERP; let the MES own the WIP detail.
  3. If your 8D corrective action says "more training," you've run out of data. Embed quality checks into the process so the failure literally cannot propagate forward.

Notable Quotes

  • "Your business doesn't happen in the ERP. It happens on the plant floor." — Zack Scriven (quoting Walker Reynolds)
  • "You start making stuff up because you're out of data." — Glenn Gardner (on 8D failures)
  • "They're running this mill with half the staff and generating double the output per FTE." — Craig Scott (on HighBar)

Timestamps / Chapters

[00:16] — Welcome and introductions
[01:34] — Glenn: the manufacturing reality check
[03:02] — Quality as the most direct impact to revenue, margin, and customer sat
[05:14] — Downstream data: ERP handles RMAs well
[06:30] — Midstream: where ERP falls down
[08:20] — Upstream: supplier quality
[12:08] — "Your business happens on the plant floor" [13:22] — Steve: HighBar Steel case study
[15:35] — Fuuz iPaaS: agnostic integration vs. point-to-point [24:08] — Craig: MCP and AI/LLM on manufacturing data [27:47] — Why serial-level detail doesn't belong in the ERP [30:28] — Fuuz as the unified namespace for this architecture
[34:34] — WIP in ERP = SKU hell
[36:21] — Red data vs. blue data explained
[40:45] — Craig: live demo of the schema designer
[54:00] — Poke-yoke through MES
[57:09] — ISO 9001: embedded procedures vs. printed SOPs
[59:29] — The drunk guy looking for his keys: why 8Ds fail

Links & Resources

]]>ec3ba50a-2752-4222-9363-60f7fe0d6638Wed, 15 Apr 2026 00:21:28 GMT<p><b>Summary</b></p><p>Glenn Gardner frames the quality problem every manufacturer faces: DPPM spikes and nobody can figure out whether it's a catastrophe or a blip — because the data lives in a dozen different systems. Steve Modrall walks through how Fuuz solved this for HighBar, the world's first solar-powered steel mill, deploying MES, WMS, yard management, scale house, and vendor portal in nine months. Craig Scott demos Fuuz's schema designer and shows how embedded quality checks and digital work instructions eliminate the "more training" corrective action that plagues every 8D process.</p><p></p><p><b>Key Topics Covered</b></p><ul><li>Quality as the hardest problem in manufacturing: tracking DPPM across downstream, midstream, and upstream</li><li>Why ERP falls down for midstream and upstream quality data</li><li>HighBar Steel: world's first solar-powered steel mill, 2x output per FTE vs. industry</li><li>Red data vs. blue data: enterprise governance vs. plant-level detail</li><li>iPaaS as the bridge between ERP and plant floor (not point-to-point)</li><li>WIP visibility without SKU proliferation</li><li>Schema designer: modeling manufacturing data as normalized relationships</li><li>Poke-yoke through MES: embedding quality into process, not as an afterthought</li><li>8D root cause failures when you run out of data</li><li>MCP integration for AI/LLM analysis on manufacturing data</li></ul><p></p><p><b>Key Takeaways</b></p><ol><li>The ERP handles downstream data (RMAs, customer returns) well, but for midstream (factory floor) and upstream (supplier quality), you need MES, SCADA, and quality systems — the ERP was never designed for that level of detail.</li><li>Don't track WIP in the ERP — it explodes your SKU count by an order of magnitude. Keep raw materials and finished goods in ERP; let the MES own the WIP detail.</li><li>If your 8D corrective action says "more training," you've run out of data. Embed quality checks into the process so the failure literally cannot propagate forward.</li></ol><p></p><p><b>Notable Quotes</b></p><ul><li>"Your business doesn't happen in the ERP. It happens on the plant floor." — Zack Scriven (quoting Walker Reynolds)</li><li>"You start making stuff up because you're out of data." — Glenn Gardner (on 8D failures)</li><li>"They're running this mill with half the staff and generating double the output per FTE." — Craig Scott (on HighBar)</li></ul><p></p><p><b>Timestamps / Chapters</b></p><p>[00:16] — Welcome and introductions <br />[01:34] — Glenn: the manufacturing reality check <br />[03:02] — Quality as the most direct impact to revenue, margin, and customer sat <br />[05:14] — Downstream data: ERP handles RMAs well <br />[06:30] — Midstream: where ERP falls down <br />[08:20] — Upstream: supplier quality<br />[12:08] — "Your business happens on the plant floor" [13:22] — Steve: HighBar Steel case study <br />[15:35] — Fuuz iPaaS: agnostic integration vs. point-to-point [24:08] — Craig: MCP and AI/LLM on manufacturing data [27:47] — Why serial-level detail doesn't belong in the ERP [30:28] — Fuuz as the unified namespace for this architecture <br />[34:34] — WIP in ERP = SKU hell <br />[36:21] — Red data vs. blue data explained <br />[40:45] — Craig: live demo of the schema designer <br />[54:00] — Poke-yoke through MES <br />[57:09] — ISO 9001: embedded procedures vs. printed SOPs <br />[59:29] — The drunk guy looking for his keys: why 8Ds fail</p><p></p><p><b>Links &amp; Resources</b></p><ul><li>Fuuz: <a rel="noopener noreferrer nofollow" href="http://fuuz.com" target="_blank">fuuz.com</a></li><li>Abelara: <a rel="noopener noreferrer nofollow" href="http://abelara.com" target="_blank">abelara.com</a></li><li>Oracle NetSuite: <a rel="noopener noreferrer nofollow" href="http://netsuite.com" target="_blank">netsuite.com</a></li></ul>no01:02:4614From ERP to Factory Floor — How CIOs Are Powering Modern Manufacturing with Fuuzfull<![CDATA[The Golden Triangle: PLM, MES and ERP with Steve from Fuuz]]>Summary

Glenn from Abelara visits Fuuz corporate headquarters during boot camp week and sits down with Steve to walk through the real manufacturing pain points that Fuse solves — from the supply chain and factory floor divide, to quality traceability, bill of materials wars, real-time variance tracking, and the multi-plant standardization trap. Steve frames Fuse as the system that lives "where the carpet meets the concrete."

About the Guest

Steve is a senior leader at Fuuz with deep manufacturing operations experience. He positions Fuse as a composable platform covering MES, WMS, quality, scheduling, EDI, and iPaaS integration — designed to complement ERP rather than replace it.

Key Topics Covered

  • The supply chain vs. factory floor divide: pegging sales orders to work orders
  • ERP's visibility gap below the shop floor level
  • Quality traceability: tracking components from dock to shipment
  • Recording PLC parameters per serial number for root cause
  • The bill of materials war: supply chain, manufacturing, and R&D views
  • The golden triangle: PLM ↔ MES ↔ ERP integration
  • Real-time variance tracking by work center and operator
  • Multi-plant standardization without forcing ERP onto the floor
  • Reducing tech stack from 40 vendors with a composable platform
  • Balancing corporate control vs. local plant innovation

Key Takeaways

  1. Pegging sales orders to work orders lets planners make partial shipment decisions in real time — most systems produce blanket work orders blind to customer demand.
  2. The million-unit recall problem exists because companies test everywhere but trace nowhere — traceability is the missing layer, not more testing.
  3. Use ERP for financials and front office; use MES for the shop floor. Forcing ERP down to the plant floor is why no enterprise has ever achieved one-ERP standardization.

Notable Quotes

  • "Where the carpet meets the concrete — that's where Fuse takes over." — Steve
  • "The money's made and lost on the shop floor." — Steve
  • "No two machines are alike. Even from the same manufacturer, they're all different." — Steve

Timestamps / Chapters

[00:00] — Inside Fuse HQ: a software company that knows manufacturing
[01:34] — The supply chain and factory floor divide
[03:14] — Pegging sales orders to work orders
[04:30] — ERP's visibility gap on the shop floor
[06:55] — Quality: the hardest challenge in manufacturing [08:09] — Tracking components from dock to shipment [09:48] — The million-unit recall problem
[11:05] — Recording 120 PLC parameters per serial number [12:48] — What R&D leaders really need from production data [14:18] — The bill of materials war
[15:29] — The golden triangle: PLM, MES, and ERP
[18:53] — Real-time gross margin and variance tracking [21:42] — IT's challenge: future-proofing manufacturing [24:04] — The multi-plant standardization trap
[27:49] — Employee development across standardized plants
[29:48] — Closing thoughts

Links & Resources

]]>9741220e-f2e9-4e3e-b107-d3939e071155Tue, 14 Apr 2026 23:25:44 GMT<p><b>Summary</b></p><p>Glenn from Abelara visits Fuuz corporate headquarters during boot camp week and sits down with Steve to walk through the real manufacturing pain points that Fuse solves — from the supply chain and factory floor divide, to quality traceability, bill of materials wars, real-time variance tracking, and the multi-plant standardization trap. Steve frames Fuse as the system that lives "where the carpet meets the concrete."</p><p></p><p><b>About the Guest</b></p><p>Steve is a senior leader at Fuuz with deep manufacturing operations experience. He positions Fuse as a composable platform covering MES, WMS, quality, scheduling, EDI, and iPaaS integration — designed to complement ERP rather than replace it.</p><p></p><p><b>Key Topics Covered</b></p><ul><li>The supply chain vs. factory floor divide: pegging sales orders to work orders</li><li>ERP's visibility gap below the shop floor level</li><li>Quality traceability: tracking components from dock to shipment</li><li>Recording PLC parameters per serial number for root cause</li><li>The bill of materials war: supply chain, manufacturing, and R&amp;D views</li><li>The golden triangle: PLM ↔ MES ↔ ERP integration</li><li>Real-time variance tracking by work center and operator</li><li>Multi-plant standardization without forcing ERP onto the floor</li><li>Reducing tech stack from 40 vendors with a composable platform</li><li>Balancing corporate control vs. local plant innovation</li></ul><p></p><p><b>Key Takeaways</b></p><ol><li>Pegging sales orders to work orders lets planners make partial shipment decisions in real time — most systems produce blanket work orders blind to customer demand.</li><li>The million-unit recall problem exists because companies test everywhere but trace nowhere — traceability is the missing layer, not more testing.</li><li>Use ERP for financials and front office; use MES for the shop floor. Forcing ERP down to the plant floor is why no enterprise has ever achieved one-ERP standardization.</li></ol><p></p><p><b>Notable Quotes</b></p><ul><li>"Where the carpet meets the concrete — that's where Fuse takes over." — Steve</li><li>"The money's made and lost on the shop floor." — Steve</li><li>"No two machines are alike. Even from the same manufacturer, they're all different." — Steve</li></ul><p></p><p><b>Timestamps / Chapters</b></p><p>[00:00] — Inside Fuse HQ: a software company that knows manufacturing<br />[01:34] — The supply chain and factory floor divide<br />[03:14] — Pegging sales orders to work orders<br />[04:30] — ERP's visibility gap on the shop floor<br />[06:55] — Quality: the hardest challenge in manufacturing [08:09] — Tracking components from dock to shipment [09:48] — The million-unit recall problem<br />[11:05] — Recording 120 PLC parameters per serial number [12:48] — What R&amp;D leaders really need from production data [14:18] — The bill of materials war<br />[15:29] — The golden triangle: PLM, MES, and ERP<br />[18:53] — Real-time gross margin and variance tracking [21:42] — IT's challenge: future-proofing manufacturing [24:04] — The multi-plant standardization trap<br />[27:49] — Employee development across standardized plants<br />[29:48] — Closing thoughts</p><p></p><p><b>Links &amp; Resources</b></p><ul><li>Fuse: <a rel="noopener noreferrer nofollow" href="http://fuse.com" target="_blank">fuse.com</a></li><li>Steve Modrall: <a rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/steve-modrall-60a5395/" target="_blank">LinkedIn</a></li><li>Glenn Gardner: <a rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/glenngardner1/" target="_blank">LinkedIn</a></li><li>Abelara: <a rel="noopener noreferrer nofollow" href="http://abelara.com" target="_blank">abelara.com</a></li></ul>no00:30:2513The Golden Triangle: PLM, MES and ERP with Steve from Fuuzfull<![CDATA[The Ignition Reference Architecture for Modern Manufacturing with Dylan DuFresne]]>Dylan DuFresne walks through Abelara’s reference architecture for modern industrial systems — from edge controllers through Ignition’s tag gateway pattern, UNS publishing, historian integration with Timebase, Flow Software for contextualization, and enterprise-scale deployment with HiveMQ cloud bridging and Fuuz as an MES layer. The episode covers how to phase these components in with zero technical debt and how Abelara helps enterprises cut through analysis paralysis.

About the Guest

Dylan DuFresne is an architect at Abelara specializing in Ignition, MQTT / Unified Namespace, and enterprise data systems.

Key Topics Covered

  • Tag gateway pattern: separating tag providers from front-end / back-end DevOps
  • Ignition 8.3 improvements and Gateway Area Network updates
  • Historian integration: Timebase direct from tag gateway vs collectors
  • Flow Software for contextualization, KPIs, and bidirectional flow
  • Blue namespace vs red namespace: site flexibility vs enterprise standards
  • HiveMQ cloud bridging to Snowflake, BigQuery, Azure
  • Fuuz as an MES layer replacing ETL between ERP and site systems
  • Phased implementation: SCADA → UNS → historian → analytics → AI

Key Takeaways

  • Use a dedicated tag gateway so front-end and back-end systems can run DevOps cycles without risking production tag state.
  • Standards should get stricter as you move up the stack — fluid near controllers, rigid near ERP.
  • Build additively with zero technical debt: SCADA, then UNS, historian, analytics, AI.

Timestamps

[00:00] Tag gateway pattern
[02:42] Timebase historian
[04:01] Flow Software
[07:45] HiveMQ + cloud data lakes
[13:04] Fuuz as enterprise MES
[15:06] Zero technical debt roadmap
[17:50] Abelara workshops

Links

Abelara: https://abelara.com
Ignition: https://inductiveautomation.com
Flow Software: https://flowsoftware.com
Timebase: https://timebased.io
HiveMQ: https://hivemq.com
Fuuz: https://fuuz.com
Litmus: https://litmus.io
Axilon: https://axilon.com

]]>77a24c47-460c-46b0-96c4-e952bd5c1382Tue, 14 Apr 2026 23:15:30 GMT<p>Dylan DuFresne walks through Abelara’s reference architecture for modern industrial systems — from edge controllers through Ignition’s tag gateway pattern, UNS publishing, historian integration with Timebase, Flow Software for contextualization, and enterprise-scale deployment with HiveMQ cloud bridging and Fuuz as an MES layer. The episode covers how to phase these components in with zero technical debt and how Abelara helps enterprises cut through analysis paralysis.</p><h2>About the Guest</h2><p>Dylan DuFresne is an architect at Abelara specializing in Ignition, MQTT / Unified Namespace, and enterprise data systems.</p><h2>Key Topics Covered</h2><ul><li>Tag gateway pattern: separating tag providers from front-end / back-end DevOps</li><li>Ignition 8.3 improvements and Gateway Area Network updates</li><li>Historian integration: Timebase direct from tag gateway vs collectors</li><li>Flow Software for contextualization, KPIs, and bidirectional flow</li><li>Blue namespace vs red namespace: site flexibility vs enterprise standards</li><li>HiveMQ cloud bridging to Snowflake, BigQuery, Azure</li><li>Fuuz as an MES layer replacing ETL between ERP and site systems</li><li>Phased implementation: SCADA → UNS → historian → analytics → AI</li></ul><h2>Key Takeaways</h2><ul><li>Use a dedicated tag gateway so front-end and back-end systems can run DevOps cycles without risking production tag state.</li><li>Standards should get stricter as you move up the stack — fluid near controllers, rigid near ERP.</li><li>Build additively with zero technical debt: SCADA, then UNS, historian, analytics, AI.</li></ul><h2>Timestamps</h2><p>[00:00] Tag gateway pattern<br />[02:42] Timebase historian<br />[04:01] Flow Software<br />[07:45] HiveMQ + cloud data lakes<br />[13:04] Fuuz as enterprise MES<br />[15:06] Zero technical debt roadmap<br />[17:50] Abelara workshops</p><h2>Links</h2><p>Abelara: <a rel="noopener noreferrer nofollow" href="https://abelara.com" target="_blank">https://abelara.com</a><br />Ignition: <a rel="noopener noreferrer nofollow" href="https://inductiveautomation.com" target="_blank">https://inductiveautomation.com</a><br />Flow Software: <a rel="noopener noreferrer nofollow" href="https://flowsoftware.com" target="_blank">https://flowsoftware.com</a><br />Timebase: <a rel="noopener noreferrer nofollow" href="https://timebased.io" target="_blank">https://timebased.io</a><br />HiveMQ: <a rel="noopener noreferrer nofollow" href="https://hivemq.com" target="_blank">https://hivemq.com</a><br />Fuuz: <a rel="noopener noreferrer nofollow" href="https://fuuz.com" target="_blank">https://fuuz.com</a><br />Litmus: <a rel="noopener noreferrer nofollow" href="https://litmus.io" target="_blank">https://litmus.io</a><br />Axilon: <a rel="noopener noreferrer nofollow" href="https://axilon.com" target="_blank">https://axilon.com</a></p>no00:21:1812The Ignition Reference Architecture for Modern Manufacturing with Dylan DuFresnefull<![CDATA[Dan Prudhoe — Why MQTT Wasn't Enough for Enterprise Manufacturing]]>Dan Prudhoe spent 15 years building industrial systems at a large chemical manufacturer — scaling from beta Ignition in 2009 to 70+ gateways across 30+ sites. In this ICC 2025 conversation, he walks through replacing a legacy Unix MES with Ignition, adopting Spark Plug B and MQTT, hitting MQTT's limitations, and building an enterprise event mesh with Solace that streams millions of tags from edge to cloud.

About the Guest

Dan Prudhoe is a Senior Solutions Engineer at Solace. Previously he spent 15 years at a large chemical manufacturer leading Ignition adoption from beta, building a standardized platform across 30+ sites, and architecting an event mesh for millions of historized tags.

Key Topics Covered

  • Ignition from beta (2009) to enterprise-scale across 30+ plants
  • Building modular, upgradeable standards across diverse sites
  • Spark Plug B and MQTT: decoupling systems, lightweight data collection
  • MQTT limitations for transactional and guaranteed delivery
  • Event mesh with Solace: edge to cloud, multi-protocol, guaranteed messaging
  • Self-service analytics with Seeq
  • UNS debate: centralized semantic layer vs. composable hierarchies
  • Graph databases and industrial data modeling
  • The path: self-service → automated → autonomous (agentic AI)

Key Takeaways

  1. Start with analytics, not SCADA — lower risk, proves value fast, builds political capital for mission-critical systems later.
  2. Build standards designed to flex — the first few deployments will break your standard, and that's expected.
  3. The path to autonomous manufacturing: self-service (SMEs solve their own problems), automated (event-driven workflows), then autonomous (agentic AI investigates and prescribes).


Notable Quotes

  • "We wanted to build a standard that's meant to have flexible standards." — Dan Prudhoe
  • "Instead of we don't have enough data, they're almost like oh my god there's so much data." — Dan Prudhoe
  • "We were able to allow them to start solving their own problems." — Dan Prudhoe

Timestamps / Chapters
[00:00] — Who is Dan Prudhoe?
[01:06] — Ignition beta testing in 2009
[02:20] — MES 101
[05:10] — Scaling to 70+ gateways across 30 sites
[08:53] — In-house vs. system integrator
[11:01] — Ignition becoming enterprise-grade
[12:00] — From Spark Plug B to event mesh
[15:14] — MQTT's limitations
[16:51] — Solace: edge to cloud architecture
[20:55] — Self-service analytics for SMEs
[26:31] — Predictive maintenance data provisioning
[28:30] — The UNS debate
[32:08] — Graph databases and ontology
[33:42] — Wrap-up

Links & Resources

]]>a2e30265-d40d-4923-bf9c-bfd6941346b5Tue, 14 Apr 2026 22:50:58 GMT<p>Dan Prudhoe spent 15 years building industrial systems at a large chemical manufacturer — scaling from beta Ignition in 2009 to 70+ gateways across 30+ sites. In this ICC 2025 conversation, he walks through replacing a legacy Unix MES with Ignition, adopting Spark Plug B and MQTT, hitting MQTT's limitations, and building an enterprise event mesh with Solace that streams millions of tags from edge to cloud.</p><p></p><p><b>About the Guest</b></p><p>Dan Prudhoe is a Senior Solutions Engineer at Solace. Previously he spent 15 years at a large chemical manufacturer leading Ignition adoption from beta, building a standardized platform across 30+ sites, and architecting an event mesh for millions of historized tags.</p><p></p><p><b>Key Topics Covered</b></p><ul><li>Ignition from beta (2009) to enterprise-scale across 30+ plants</li><li>Building modular, upgradeable standards across diverse sites</li><li>Spark Plug B and MQTT: decoupling systems, lightweight data collection</li><li>MQTT limitations for transactional and guaranteed delivery</li><li>Event mesh with Solace: edge to cloud, multi-protocol, guaranteed messaging</li><li>Self-service analytics with Seeq</li><li>UNS debate: centralized semantic layer vs. composable hierarchies</li><li>Graph databases and industrial data modeling</li><li>The path: self-service → automated → autonomous (agentic AI)</li></ul><p></p><p><b>Key Takeaways</b></p><ol><li>Start with analytics, not SCADA — lower risk, proves value fast, builds political capital for mission-critical systems later.</li><li>Build standards designed to flex — the first few deployments will break your standard, and that's expected.</li><li>The path to autonomous manufacturing: self-service (SMEs solve their own problems), automated (event-driven workflows), then autonomous (agentic AI investigates and prescribes).</li></ol><p><br /><b>Notable Quotes</b></p><ul><li>"We wanted to build a standard that's meant to have flexible standards." — Dan Prudhoe</li><li>"Instead of we don't have enough data, they're almost like oh my god there's so much data." — Dan Prudhoe</li><li>"We were able to allow them to start solving their own problems." — Dan Prudhoe</li></ul><p></p><p><b>Timestamps / Chapters</b><br />[00:00] — Who is Dan Prudhoe? <br />[01:06] — Ignition beta testing in 2009 <br />[02:20] — MES 101 <br />[05:10] — Scaling to 70+ gateways across 30 sites <br />[08:53] — In-house vs. system integrator <br />[11:01] — Ignition becoming enterprise-grade <br />[12:00] — From Spark Plug B to event mesh <br />[15:14] — MQTT's limitations <br />[16:51] — Solace: edge to cloud architecture <br />[20:55] — Self-service analytics for SMEs <br />[26:31] — Predictive maintenance data provisioning <br />[28:30] — The UNS debate <br />[32:08] — Graph databases and ontology <br />[33:42] — Wrap-up<br /></p><p><b>Links &amp; Resources</b></p><ul><li>Solace: <a rel="noopener noreferrer nofollow" href="http://solace.com" target="_blank">solace.com</a></li><li>Dan Prudhoe <a rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/dan-prudhoe-a7962788/" target="_blank">LinkedIn</a></li><li>Ignition: <a rel="noopener noreferrer nofollow" href="http://inductiveautomation.com" target="_blank">inductiveautomation.com</a></li><li>Flow Software: <a rel="noopener noreferrer nofollow" href="https://www.flow-software.com" target="_blank">www.flow-software.com</a></li><li>Spark Plug B: <a rel="noopener noreferrer nofollow" href="http://sparkplug.eclipse.org" target="_blank">sparkplug.eclipse.org</a></li><li>Azure Data Explorer: <a rel="noopener noreferrer nofollow" href="http://azure.microsoft.com/en-us/products/data-explorer" target="_blank">azure.microsoft.com/en-us/products/data-explorer</a></li></ul>no00:33:5311Dan Prudhoe — Why MQTT Wasn't Enough for Enterprise Manufacturingfull