From Control To Intelligence

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Welcome to Eco Ask Why, a podcast that dives into industrial manufacturing topics and spotlights the heroes that keep America running.
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I'm your host, Chris Granger, and on this podcast, we do not cover the latest features and benefits on products that come to market.
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Instead, we focus on advice and insight from the top minds of industry because people and ideas will be how America remains number one in manufacturing in the world.
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Welcome to Eco Ask Why.
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I'm your host, Chris Granger.
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I look forward to spending some time with you today.
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And we're going to be having some fun on this episode, diving into a topic that is it's at the doorstep of anyone in industrial manufacturing.
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Uh and it's data-driven manufacturing.
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And what does that look like?
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And how can we move beyond AI hype and basic control and really make sense of this?
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And we're super excited for this here at Electrical Equipment Company.
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Again, we're we're coming into our 100th year of being in the business, and it's just something we're celebrating big time.
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Looking forward to that big celebration.
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It's gonna be happening later this year, but you'll be hearing more and more about that, hopefully on EcoSY as we talk about it.
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And uh something that that that recently we recognize uh within industrial transformation conversations have evolved around two things, okay.
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And this is not gonna be earth shattering, but this is important automation and now AI.
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But here's the reality, here's some things that we recognize to be true.
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Most plants out there, industrial manufacturers, don't need more algorithms, right?
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What we need is better data discipline, okay, because the true frontier of modern industrial operations isn't just smarter machines, it's to shift towards a fully data-driven manufacturing ecosystem where that operational data becomes a strategic asset and not something like this as an overthought, okay?
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Because AI is powerful.
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I mean, we we all can admit that AI is powerful, but without clean, contextualized data, it's just noise at scale, and that's not going to serve us at all, no matter where we're at in manufacturing.
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And the real shift we need to think about is from reactive control to operational intelligence.
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Because traditional automation systems were reactive by design.
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Let's just think about that.
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Like PLC logic executed pre-programmed rules.
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When a signal changes state, this is gonna happen, right?
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This alarm's gonna be triggered, uh, and and um alarms will go off when limits were crossed.
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And that system worked very well, but it really didn't learn, and that's the differentiator because it's not whether or not your plant's automated, most likely it is.
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It's whether your plant understands itself.
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And when manufacturers begin capturing granular operational data, like machine speeds and tor profiles or any fluctuations in voltage or or operational operator interventions, they move from reactive to proactive, right?
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So we're not just reacting to control, we're proactively being intelligent, and that's a shift that's gonna change everything.
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Because the old model, think about that, they really rely heavily on clipboards, on pens, and just tribal knowledge, all that stuff, right?
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And the new model captures time-stamped, contextualized data streams that allow you and your teams to identify the bottlenecks invisible to just the human eye.
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Okay, and and you can't optimize.
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Think about this, you really can't optimize what you don't measure, and you definitely can't simulate it if you can't capture it.
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So you need to be able to simulate accurate data.
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Okay, so data quality is so important, it's the foundational of digital twins and predictive systems out there.
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And there's a there's a really a growing fascination with this idea of digital twins.
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We spent some time on EcoAs Wild talking about this because you're virtually representing physical systems, and that allows you to simulate performance on what that's gonna look like before you make a real world change.
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But here's what's missed so often when we think about this that the digital twin is only as good as the fidelity of the input data.
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That's it.
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So if you don't know exactly like when a machine is stopped, or why it's stopped, or under which low conditions forced it to stop, or what upstream or downstream events influenced it, then you don't have a digital twin, right?
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You have a digital guess, and that's not gonna serve you well.
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And AI agents require structured, high-resolution, real-time data to build usable relationships between these variables.
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And without that foundation, you really can't optimize, right?
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And things are gonna collapse under ambiguity.
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So data-driven manufacturing starts really at the component level.
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This we're gonna talk about just for a little bit.
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The components, smart components are no longer passive devices, and you have devices out there like the E300 or C445, these are smart motor overload relays, and that's a fundamental shift in data architecture and how this works.
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And these are not just productive devices anymore, right?
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These are data hubs.
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I mean, this is really unreal the type of data you can get.
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So instead of just tripping or during a fall condition, which they're gonna do, they're continuously sampling and reporting back on voltage profiles, temperature trends, vibration, average peak current, current imbalance, you got torque estimations, you got energy consumption.
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The list goes on and on.
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And that transformation is gonna make it possible to go from reactive to predictive maintenance.
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Okay, instead of waiting for a catastrophic failure, you're you're gonna be able to receive alerts when performance is sort of starting to drift from anything that's from the baseline.
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And motors can be serviced during scheduled downtime.
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I have a big lot of a big history in being able to do this.
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Uh, and let me tell you what, when you service motors on your downtime versus an emergency shutdown, it's so much cheaper, so much less stress for everyone.
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Damn, that downtime becomes planned, not chaotic.
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And that difference shows up really big time on your PL and your balance sheet at the end of the day.
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So let's talk about OEE for a second, okay?
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Because OEE in the past, it was like a lagging indicator, right?
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But it could be a predictive lever, and and it's usually thought about overall equipment effectiveness is like a performance metric.
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But when you're doing a data-driven ecosystem, this it changes, it shifts from historical scoreboard to a predictive lever.
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Because when you have hundreds of variables being analyzed and thousands of variables simultaneously, like conductor speed or filler speed or torque or operator type of interventions, optimization of opportunities start to come to the surface, right?
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And this is what so think about shifting the the this question.
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What was our OEE yesterday?
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The question could be what variables can we adjust virtually to increase OEE tomorrow, right?
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Without increasing any risk.
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And that's the power of simulation when you layer it on clean data, and it's how manufacturers find those really sweet spots without breaking any physical equipment.
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So you really want to move from guessing to root cause precision and data that doesn't just optimize output, it clarifies accountability.
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Uh we we've had instances at an electrical equipment company where we saw a railroad manufacturing facility, they had repeat motor failures, and that led to assumptions of electrical defects, and the costs were this extravagant costs.
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But we were able to put in some of these smart motor relays and told a different story.
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It showed how these little snapshots and load history revealed how the how the equipment was being operated and how it was being overfed, right?
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And that really that uh overfeeding of the equipment caused the issues, okay.
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The issue wasn't hardware, the issue was our behavior.
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But without the data, we wouldn't have known it, right?
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We just kept replacing equipment.
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But with the data, we could address the root cause, and that's the difference between guessing and knowing.
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And the human advantage is is is data, right?
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Uh in the data-driven culture we live in.
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And the greatest, like if you start thinking about a misconception out there about AI and automation, the big misconception is that it's going to replace people.
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Like, no, bro, it's not going to replace people.
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They elevate people, right?
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Because when you have automated systems that are that are handling repetitive monitoring and data storing, engineers and technicians are freed up at that point to focus on strategic optimization or cross-functional system thinking or creative problem solving for things that are just popping up, you know, that that's newly risen to the surface or continually improving, right?
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That's what it's all about.
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Talented people, here it is.
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Talented people don't want to fight daily fires, they want to solve meaningful problems.
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And data-driven environments create space to do that, and they help build, uh, bridge these generational transitions because you have younger technicians and you have uh these guys are you know they grew up on smartphones and laptops and all that, right?
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Uh and dashboards, and now they're able to come together and learn so much about the systems that they're integrating with and that they're working with.
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And this is not about replacing experience.
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We're about we need to amplify it.
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Amplify it.
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That's really what it's all about.
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And becoming data-driven is not like just something you just go buy some software.
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Like it's it starts with culture, it's a cultural commitment.
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It requires discipline, really, it's a discipline and data collection.
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Because if we're if we're gonna get good out, we've got to put good in.
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But it also means we need to make investment because you need to have sensors that can can handle and provide you the data that you need to make the right decisions.
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You need clean architecture between components and analytics, right?
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You got to have that clean architecture to be able to actually uh work with the data.
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You gotta have a leadership commitment, right?
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And you have to be willing to confront uncomfortable truths when the metrics show you that, right?
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You can't just hide from it and then run from it.
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And the plants that thrive in the next decade are not the ones that just gonna have AI, they'll have data integrity coupled with it, they'll have systems that learn, and they'll have teams that empower to act on insight instead of instinct.
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Now that instinct is bad, but instinct, when you couple it with insight, that's a powerful combination.
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And the future of manufacturing isn't just about smarter machines alone, it's about the overall smarter ecosystem.
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When raw operational data becomes strategic intelligence, right?
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That's a big deal because they because you'll, as the manufacturer, become more predictive, more optimized, you'll be reducing any type of waste, you'll have more engaged teams, and you'll create, and everybody's gonna love this, a substantial competitive advantage.
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So think about that.
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AI, when used correctly as a tool, changes everything.
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But data, clean, accurate data is the fuel.
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Culture's the engine.
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All right, so now manufacturers who line and all three of these are going to define the next era of industrial performance.
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And we're here to help you, we're here to serve it to serve you to walk along with you.
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If you need help with this, if you'd like to talk about it, if you'd like to get one of our experts to come to your manufacturing facility and talk directly, or maybe you want to come to one of our labs at Electrical Equipment Company, which we would love to have you come do that with us, reach out.
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We will have links in the show notes for you to connect with us.
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Uh, if this conversation, this just insights is if you found helpful and you want to share with others, we would be extremely grateful for that.
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Again, electrical equipment company were 100 years.
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We were just big, super excited for this night uh uh 2026, just think about 1926.
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I don't even know if this was on the radar uh for those guys that put that founded the company that we would still be here 100 years later, but here we are, stronger than ever, out here serving and growing, and we want to walk with you.
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So reach out to us at ecoonline.com.
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Uh again, share this podcast out and give us a direct review.
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Uh, look forward to seeing you next time.
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And remember, just keep asking why.
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Thank you for listening to Eco Ask Why.
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This show is supported ad-free by Electrical Equipment Company.
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