October 6, 2025 • 19 mins
Artificial intelligence is revolutionizing manufacturing, but not in the way most people think. While traditional AI responds to prompts, AI agents represent something far more powerful – autonomous programs that learn, adapt, and make decisions independently in real-time. These intelligent systems are transforming how factories operate in an era defined by supply chain volatility, labor shortages, and rapidly changing market demands.
The predictive capabilities of AI agents offer a fundamental shift from reactive to anticipatory operations. By simultaneously analyzing data from sensors, drives, PLCs, ERP systems, and quality control mechanisms, these agents create a comprehensive view of factory operations, uncovering patterns that signal potential issues days or weeks before they occur. Manufacturers implementing these systems report remarkable results – up to 78% reduction in unplanned downtime, 45% decrease in maintenance costs, and equipment lifespans extended by more than three years.
When paired with digital twin technology, AI agents can simulate thousands of production scenarios in hours rather than months, optimizing configurations for maximum efficiency. Quality control systems achieve near-perfect defect detection rates while supply chains become more resilient through AI-powered forecasting and dynamic adjustment. The factory of the future isn't just automated – it's autonomous, continuously learning and optimizing itself.
At EECO, we're committed to walking beside you through this technological transformation. Our team can help you explore how AI agents might integrate with your existing systems, providing the expertise and partnership mindset needed to turn these possibilities into practical realities. The future of manufacturing belongs to those who anticipate tomorrow's opportunities rather than react to yesterday's challenges.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:01):
Welcome to Eco Ask why, a podcast that dives into
industrial manufacturing topicsand spotlights the heroes that
keep America running.
I'm your host, chris Granger,and on this podcast, we do not
cover the latest features andbenefits on products that come
to market.
Instead, we focus on advice andinsight from the top minds of
industry, because people andideas will be how America
(00:24):
remains number one inmanufacturing in the world.
Welcome to EECO Asks why.
I'm your host, chris Granger,and I'm looking forward to
spending some time with youtoday.
We've been talking a lot latelyabout digital twins and these
evolving technologies that arehappening all throughout
industrial manufacturing.
(00:44):
Twins and these evolvingtechnologies that are happening
all throughout industrialmanufacturing.
And for today, I'm going topull on something that's another
thread I think you're going tohave a lot of fun with, and
that's the predictive power ofAI, particularly these AI agents
in manufacturing operationsdirectly.
Okay, so this is going to be alot of fun.
Hopefully you're going to enjoythis, because we have to
recognize that the manufacturingsector is in really a time of
(01:07):
transformation right now.
I mean, there's lots ofpressures to optimize, there's
lots of pressures to reducecosts and we got to get as much
productivity out the door right,because the supply chain out
there is volatile, we have laborshortages and market demands
are changing at just incrediblerates.
And we think about traditionalautomation that really follows a
(01:32):
lot of static, pre-programmedrules right, and sometimes the
need to adapt is moving so veryquick and it's very complex.
So for manufacturers out therethat are trying to remain
competitive, you have to thinkoutside of just the standard box
of reactive processes and startthinking about intelligent
systems that can, in real time,adapt, learn and make these
(01:53):
decisions for you as you moveforward.
So at ECO, we recognize thatthere's power in the
predictability, particularlywith AI that's out there, these
artificial intelligence agentsthat are out there, and they're
game changers.
Ok, and lots of solutions, lotsof things represent far more
(02:13):
than just like littleincremental improvements.
We're talking about afundamental shift here.
It could be really, really abig time in industrial
manufacturing, manufacturingwhere you can think about
optimizing, you can think aboutintelligent manufacturing
ecosystems.
And at EECO, we want to walkbeside you.
We want to walk beside you withthe manufacturers that we
(02:34):
represent so that it may helpyou evaluate, pivot, start
scaling this technology and helpuncover where AI agents may
have the greatest impact in yourparticular facility.
Ok, so we need to think aboutthis in some realm outside of
traditional AI, because AIagents are these programs
(02:55):
designed to perform tasks andmake decisions on their own, so
it's not like traditional AI oreven generative AI.
So it's not like traditional AIor even generative AI.
That AI that you think of, likeChatGVT, responds on what A
prompt that you give it.
Right, you give it a prompt, itgives you an output.
Okay, and these systems don'treally like if a prompt given to
(03:23):
AI system doesn't analyze data.
They're acting on insights fromwhat you give it.
Okay, and the cool part aboutthese AI agents that we're
talking about today is they havethe ability to learn, to adapt
and collaborate with not just us, but also other agent sets and
really make a big impact in yourmanufacturing.
(03:44):
Okay, so we're talking about thenext step of AI evolution, and
this is where you haveindependent action, continuous
improvement and probably problemsolving in a highly dynamic
environment.
Ok, so this is really cool.
This is really fun stuff, and Iknow there's lots of questions
out there on this stuff and wehave questions too, but we're
here to walk with you, to helpdemystify this new generation of
(04:07):
AI that's out there, and wehave lots of manufacturers we
work with that are leaning intoAI and the results are clear out
there for sure.
So, again, as you're listeningto this, if you think, man, I
think I'd like to have aconversation about that.
Please reach out to us.
We're here to serve, we're hereto serve, we're here to help
and, with our network anddifferent resources that we have
, we can have a greatconversation with you and maybe
(04:30):
move the ball down the field alittle bit.
Okay, so you have to think abouthow you're going to harness
data to predict and optimizeperformance, because the core
strength of an AI agent lies inthe ability to process and
analyze massive amounts of data.
Okay, so I mean, just thinkabout that.
So much data out there and thething about these agents they're
(04:50):
doing it simultaneously.
Okay, so think abouttraditional manufacturing
facilities.
These inputs can come from manydifferent places.
I think about when I go backwhen I used to call on plants on
a regular basis and just walkthrough.
You had sensors, you havedrives, you have, obviously,
plcs.
You have all these differentdevices, the IoT devices, that
(05:12):
are just bringing in data.
Then you have your ERP system,then you have your scheduling
system, you have your qualitycontrol system and you have your
environmental system.
All these things together right.
All these things together right.
So when you start integratingthis in this type of model, the
data starts providing a holistic, real view, time of okay,
(05:33):
real-time view, rather of thefactory, and this using advanced
machining learning algorithms.
These agents can really uncoverpatterns and corrections that
may be buried in the noise.
And so think about it Like youcould connect a conveyor
throughput machine, vibrationsignatures and like ambient,
(05:57):
just standard old temperaturegauges, right.
And an agent could detecttrends that signal a performance
dip, and that could be hours ordays in advance.
So the predictability here isreally interesting when you
start thinking about it, becauseit's shifting the way that you
could plan.
Instead of focusing juststrictly on yesterday's numbers,
(06:20):
you could start anticipatingtomorrow's challenges, and
that's pretty useful right there.
And think about how you measuretraditional OEE overall
equipment effectiveness right.
But if you start modeling howinterconnected factors affect
the throughput man, you couldreally have a big impact on that
(06:41):
before problems happen.
And there's research out there,there's studies out there and
there's companies that havestarted adopting this level
analysis and some have reportingup to like a 31% increase in
OEE, or a 67% faster responsetime.
That's crazy when you startthinking about it, right?
So we're starting to see a fewuse cases where this is proving
(07:03):
itself out.
And it's not just, you know,just magic in a box, right?
No, these things are happeningand they're aligning with
existing automationinfrastructure and the
priorities that the plant has.
So it's not like you have tomake this massive, massive
investment.
Sometimes you have thetechnology there, you have the
data there.
It's just bringing in thisagent to walk alongside you,
(07:25):
okay.
Now, speaking specifically aboutpredictive maintenance, this is
a clear area that AI agents canhelp.
Okay, and think about yourtraditional strategy with
maintenance and there'sdifferent ones and some stuff
you just have because that'sjust the way it is right.
(07:46):
You're going to have reactivemaintenance.
That's basically you fix itwhen it breaks or you have that
scheduled maintenance, right?
So you're going to do thatmaintenance on a defined period
of time, regardless of condition.
We're going to change thisbearing, we're going to do this
grease schedule, whatever it maybe, and sometimes you may be
doing things that aren't quitenecessary, but both approaches
(08:09):
have a cost.
They do, they have a cost.
And if you're reactive oneverything, if it's a
catastrophic failure of a pieceof equipment that could take
your entire line down right.
So these AI agents change thegame because really they're
leaning into the wholepredictive maintenance element a
lot harder and they'recontinually analyzing this IoT
(08:33):
sensor data.
So think about the IoT sensordata that's out there.
We represent a lot ofmanufacturers and we have these
alignments.
So you have sensors likevibration sensors, you got the
temperature sensors, you haveacoustic sensors and if you
couple these with the right AIagent, what they can do is they
(08:55):
can give you early indicators ofwear and tear.
I mean, we're talking aboutweeks or even months before
something occurs.
But prediction is only acomponent of where AI can help
here, because the way that theywork, they may be able to help
you automatically schedule thesemaintenance windows, even order
replacement parts or adjustproduction schedules to minimize
(09:18):
the impact.
So it really starts to takesome of the guesswork out of
what adjustments need to be made.
And again, manufacturers outthere some of them are leaning
into this.
Some of them have seen up to a78% reduction in unplanned
downtime.
That's crazy right.
Or a 45% decrease inmaintenance costs crazy right,
(09:39):
or a 45% decrease in maintenancecosts.
And there are even reports outthere that there are
manufacturers that are seeing3.2 years extension of equipment
lifespan.
Now, when you start thinkingabout those type of numbers and
those type of impacts, you haveto start leaning in and
considering okay, what does thislook like for me?
And at EECO again, we've beenbig proponents of predictive
maintenance.
We recognize this is a big areafor manufacturing.
(10:00):
It's just kind of cool rightnow to get to explore how can we
bring AI into this right and ifyou can start really getting a
rapid return on investment whileyou build this, that gets
exciting there.
That gets really exciting.
So another area worthconsidering is simulation.
So, beyond prediction, which isreally cool, ai agents can
(10:25):
really have a lot of advantagebecause they can experiment
Right.
If you, if you kind of pair anAI agent with what we've been
talking about for our last twoepisodes of Eco.
That's why the digital twinsyou know, you can really play a
lot of games and test thousandsof what-if scenarios, right.
So we're not just talking aboutvisualization, we're talking
(10:46):
about simulation, where you canmaybe even adjust the machine
speed or an output, where youshift the material flows or
alter production sequences totry to figure out what is the
best configuration right, and sothis could take, you know, just
trial and error, with monthsand weeks to model this stuff
out.
Where this could be done withAI agent, potentially within
(11:07):
hours you could find this, andthere are studies out there that
pharmaceutical companies haveused this digital twin coupling
to redesign complex productionlines and some of them have seen
a 28% increase in throughput.
Think about that and you knowall the type of regulatory
compliance that goes with thepharmaceutical industry.
(11:29):
So they're keeping that whileincreasing throughput.
So I mean, that's kind of crazyto think about.
So when you have risk, when youhave the need for capacity
moving faster and faster andfaster, these AI agents can give
you opportunities to innovatewith confidence.
And again, we're here to helpyou, we're here to support you,
(11:51):
we're here to help you explorethese tools and maybe think
about simulations, okay.
And when you start thinkingabout the power of AI, a lot of
it comes down to data analysis,right, and it's not just
analyzing data for analyzingdata's sake, but if we're trying
to enhance our quality andreally improve our supply chain
management, so start thinkingabout the quality controls, like
(12:13):
AI agents.
If you start integrating itmaybe with, like a computer
vision system, okay, and you'retrying to really detect defects
that are happening.
Man, these AI agents can do itat an extremely high accuracy
rate.
I mean we're talking aboutthere's reports out there like
99.97% accuracy rate.
(12:34):
That's crazy, I mean it'sincredible.
We're talking about spottingmicroscopic cracks or little
color variations right, it cango even beyond detection.
They can shift qualityassurance toward prevention by
predicting when the processesare really starting to get out
of spec, and then you can startcorrecting it.
Right, and then, from a supplychain standpoint, you can
(12:56):
analyze supplier performance,inventory levels, trends from
your customers and just startthinking about the overall
networks that you're workingwith.
Then you may be able to startaccurately demanding forecasts
and dynamically adjusting yourproduction plans based off that.
So I mean, disruptions happen,right.
(13:16):
I mean you maybe have a delayedshipment out there, right?
Well, if the AI agent can pullthat data in, it may be able to
automatically identifyalternative suppliers or reroute
logistics or rebalance thatinventories, so you can cut
disruption down.
That's where the big thing is.
We're trying to cut thatdisruption down to make better
decisions.
(13:36):
And I'm telling you, I thinkthis is something that more and
more manufacturers are going tobe leaning into, because the
markets are volatile, themarkets are competitive, so you
have to be thinking what can youdo to gain a competitive edge,
and bringing in tools andresources like this could be
that.
And I mean we have to considerfrom industrial manufacturing,
(13:56):
the future is predictive andautonomous, right, so the
integration of these agents isnot just an enhancement of what
you already have.
Okay, this is kind of like afundamental re-imaging right, or
kind of rethinking of howmanufacturing could be, because
it's these types of intelligentsystems that create factories
(14:20):
that are not only automated butare increasingly autonomous.
Ok, so they're learning it allthe time, they're adapting
dynamically and they'reoptimizing proactively.
I mean, it's like it's likefuture scifi movies, except it's
here now.
And so it shifts fromforecasting to the OEE models
(14:41):
and enabling predictivemaintenance to really optimizing
processes through using digitaltwins and safeguarding quality.
And I'm telling you, theopportunities are just endless,
right, but there's measurableimpact.
We're talking about greateruptime, lower costs, more
product that you're getting outthe door and that resilience
right, the equipment is beingmore resilient.
(15:03):
So, again, we have several casesand resources that we pull to
kind of help us understand thisat ECO and at the end of the day
, we're here to walk beside you.
We recognize this is a newfrontier.
We recognize this could beviewed as scary, it could be
viewed as unknown, but we havelots of experience and we have
(15:25):
lots of resources within ourecosystem to support you, from
electrical systems to automationmodernization, and we can bring
some technical expertise.
More importantly than that, apartnership mindset.
So you're not going into thisalone, right?
You're going to have somebodywithin an electrical equipment
company who's willing to walkalongside you, because our goal
(15:47):
is just not to introduce you tonew technology by.
Good luck, wish you the best.
No, we want to help deliver youlong-term value.
We want to help you unlock thefuture potential If these AI
agents work for you and yourmanufacturer facility and your
model, and we want to help youexplore that.
And at the same time, we knowwe can't just live in the clouds
(16:07):
.
We need to stay grounded inoperational realities and that's
what we're here to do, becausewe recognize the future.
Manufacturing really is notgoing to be defined by those who
react to yesterday's challenges, but the ones who recognize and
anticipate tomorrow'sopportunities.
Have predictive power of theseAI agents and you align it with
(16:28):
partnerships like electricalequipment company, you can take
a big step forward in buildingtruly intelligent, resilient and
future ready operations, andthat is worth getting excited
about.
So we're here for you.
Again, you can reach out.
(16:49):
There's going to be links forour podcast.
There's going to be links, ifyou're checking this out on
YouTube, to connect with us.
Probably one of the best waysto connect is just set up a time
to come to one of our labs.
We have labs throughout ourservice area, so come connect.
We have, again, subject matterexperts.
That would be love, love, love.
To sit down to hear what yourapplications are, to hear where
(17:10):
you're at with your industrialfacility, and also just to hear
what your goals are.
Just to do some somebrainstorming, some fact finding
, some goal setting, somedreaming even, and just to see,
ok, based off of that, how wouldwe approach it, what would we
recommend and get you connectedwith the right resources out
there to turn this from from adream into reality.
(17:31):
All right, so if you enjoyedthis, if you thought there was
some value, maybe sharing thiswith others, that would be
wonderful.
Obviously, if you give us arating and review, that would be
awesome.
But, most importantly, comecheck us out, go to ecoonlinecom
.
Again, connect with us on theresources.
We'll have links underneath thepodcast for you to connect with
us directly, but schedule sometime.
If you don't want to come intothe lab, reach out to us, we'll
(17:57):
come to you.
We love coming to industrialmanufacturers, to seeing the
facilities, to understanding thecomplexities and just having a
fresh set of eyes and sometimesa fresh set of eyes is a big
deal, okay, so we love toconnect with you again.
All those links will be there.
Follow us on LinkedIn as welland you know what's coming right
.
Just remember to keep askingwhy.
Thank you for listening to.
(18:26):
Eeco Asks why this show issupported ad-free by Electrical
Equipment Company.
Eeco is redefining theexpectations of an electrical
distributor by placing peopleand ideas before products.
Please subscribe and share withyour colleagues and friends.
Also leave comments, feedbackand any new topics that you
would like to hear.
To learn more or to share yourinsights, visit EECOASYcom.
(18:51):
That's E-E-C-O-A-S-K-S-W-H-Ycom.
Welcome to Eco Ask why, a podcast that dives into
industrial manufacturing topicsand spotlights the heroes that
keep America running.
I'm your host, chris Granger,and on this podcast, we do not
cover the latest features andbenefits on products that come
to market.
Instead, we focus on advice andinsight from the top minds of
industry, because people andideas will be how America
(00:24):
remains number one inmanufacturing in the world.
Welcome to EECO Asks why.
I'm your host, chris Granger,and I'm looking forward to
spending some time with youtoday.
We've been talking a lot latelyabout digital twins and these
evolving technologies that arehappening all throughout
industrial manufacturing.
(00:44):
Twins and these evolvingtechnologies that are happening
all throughout industrialmanufacturing.
And for today, I'm going topull on something that's another
thread I think you're going tohave a lot of fun with, and
that's the predictive power ofAI, particularly these AI agents
in manufacturing operationsdirectly.
Okay, so this is going to be alot of fun.
Hopefully you're going to enjoythis, because we have to
recognize that the manufacturingsector is in really a time of
(01:07):
transformation right now.
I mean, there's lots ofpressures to optimize, there's
lots of pressures to reducecosts and we got to get as much
productivity out the door right,because the supply chain out
there is volatile, we have laborshortages and market demands
are changing at just incrediblerates.
And we think about traditionalautomation that really follows a
(01:32):
lot of static, pre-programmedrules right, and sometimes the
need to adapt is moving so veryquick and it's very complex.
So for manufacturers out therethat are trying to remain
competitive, you have to thinkoutside of just the standard box
of reactive processes and startthinking about intelligent
systems that can, in real time,adapt, learn and make these
(01:53):
decisions for you as you moveforward.
So at ECO, we recognize thatthere's power in the
predictability, particularlywith AI that's out there, these
artificial intelligence agentsthat are out there, and they're
game changers.
Ok, and lots of solutions, lotsof things represent far more
(02:13):
than just like littleincremental improvements.
We're talking about afundamental shift here.
It could be really, really abig time in industrial
manufacturing, manufacturingwhere you can think about
optimizing, you can think aboutintelligent manufacturing
ecosystems.
And at EECO, we want to walkbeside you.
We want to walk beside you withthe manufacturers that we
(02:34):
represent so that it may helpyou evaluate, pivot, start
scaling this technology and helpuncover where AI agents may
have the greatest impact in yourparticular facility.
Ok, so we need to think aboutthis in some realm outside of
traditional AI, because AIagents are these programs
(02:55):
designed to perform tasks andmake decisions on their own, so
it's not like traditional AI oreven generative AI.
So it's not like traditional AIor even generative AI.
That AI that you think of, likeChatGVT, responds on what A
prompt that you give it.
Right, you give it a prompt, itgives you an output.
Okay, and these systems don'treally like if a prompt given to
(03:23):
AI system doesn't analyze data.
They're acting on insights fromwhat you give it.
Okay, and the cool part aboutthese AI agents that we're
talking about today is they havethe ability to learn, to adapt
and collaborate with not just us, but also other agent sets and
really make a big impact in yourmanufacturing.
(03:44):
Okay, so we're talking about thenext step of AI evolution, and
this is where you haveindependent action, continuous
improvement and probably problemsolving in a highly dynamic
environment.
Ok, so this is really cool.
This is really fun stuff, and Iknow there's lots of questions
out there on this stuff and wehave questions too, but we're
here to walk with you, to helpdemystify this new generation of
(04:07):
AI that's out there, and wehave lots of manufacturers we
work with that are leaning intoAI and the results are clear out
there for sure.
So, again, as you're listeningto this, if you think, man, I
think I'd like to have aconversation about that.
Please reach out to us.
We're here to serve, we're hereto serve, we're here to help
and, with our network anddifferent resources that we have
, we can have a greatconversation with you and maybe
(04:30):
move the ball down the field alittle bit.
Okay, so you have to think abouthow you're going to harness
data to predict and optimizeperformance, because the core
strength of an AI agent lies inthe ability to process and
analyze massive amounts of data.
Okay, so I mean, just thinkabout that.
So much data out there and thething about these agents they're
(04:50):
doing it simultaneously.
Okay, so think abouttraditional manufacturing
facilities.
These inputs can come from manydifferent places.
I think about when I go backwhen I used to call on plants on
a regular basis and just walkthrough.
You had sensors, you havedrives, you have, obviously,
plcs.
You have all these differentdevices, the IoT devices, that
(05:12):
are just bringing in data.
Then you have your ERP system,then you have your scheduling
system, you have your qualitycontrol system and you have your
environmental system.
All these things together right.
All these things together right.
So when you start integratingthis in this type of model, the
data starts providing a holistic, real view, time of okay,
(05:33):
real-time view, rather of thefactory, and this using advanced
machining learning algorithms.
These agents can really uncoverpatterns and corrections that
may be buried in the noise.
And so think about it Like youcould connect a conveyor
throughput machine, vibrationsignatures and like ambient,
(05:57):
just standard old temperaturegauges, right.
And an agent could detecttrends that signal a performance
dip, and that could be hours ordays in advance.
So the predictability here isreally interesting when you
start thinking about it, becauseit's shifting the way that you
could plan.
Instead of focusing juststrictly on yesterday's numbers,
(06:20):
you could start anticipatingtomorrow's challenges, and
that's pretty useful right there.
And think about how you measuretraditional OEE overall
equipment effectiveness right.
But if you start modeling howinterconnected factors affect
the throughput man, you couldreally have a big impact on that
(06:41):
before problems happen.
And there's research out there,there's studies out there and
there's companies that havestarted adopting this level
analysis and some have reportingup to like a 31% increase in
OEE, or a 67% faster responsetime.
That's crazy when you startthinking about it, right?
So we're starting to see a fewuse cases where this is proving
(07:03):
itself out.
And it's not just, you know,just magic in a box, right?
No, these things are happeningand they're aligning with
existing automationinfrastructure and the
priorities that the plant has.
So it's not like you have tomake this massive, massive
investment.
Sometimes you have thetechnology there, you have the
data there.
It's just bringing in thisagent to walk alongside you,
(07:25):
okay.
Now, speaking specifically aboutpredictive maintenance, this is
a clear area that AI agents canhelp.
Okay, and think about yourtraditional strategy with
maintenance and there'sdifferent ones and some stuff
you just have because that'sjust the way it is right.
(07:46):
You're going to have reactivemaintenance.
That's basically you fix itwhen it breaks or you have that
scheduled maintenance, right?
So you're going to do thatmaintenance on a defined period
of time, regardless of condition.
We're going to change thisbearing, we're going to do this
grease schedule, whatever it maybe, and sometimes you may be
doing things that aren't quitenecessary, but both approaches
(08:09):
have a cost.
They do, they have a cost.
And if you're reactive oneverything, if it's a
catastrophic failure of a pieceof equipment that could take
your entire line down right.
So these AI agents change thegame because really they're
leaning into the wholepredictive maintenance element a
lot harder and they'recontinually analyzing this IoT
(08:33):
sensor data.
So think about the IoT sensordata that's out there.
We represent a lot ofmanufacturers and we have these
alignments.
So you have sensors likevibration sensors, you got the
temperature sensors, you haveacoustic sensors and if you
couple these with the right AIagent, what they can do is they
(08:55):
can give you early indicators ofwear and tear.
I mean, we're talking aboutweeks or even months before
something occurs.
But prediction is only acomponent of where AI can help
here, because the way that theywork, they may be able to help
you automatically schedule thesemaintenance windows, even order
replacement parts or adjustproduction schedules to minimize
(09:18):
the impact.
So it really starts to takesome of the guesswork out of
what adjustments need to be made.
And again, manufacturers outthere some of them are leaning
into this.
Some of them have seen up to a78% reduction in unplanned
downtime.
That's crazy right.
Or a 45% decrease inmaintenance costs crazy right,
(09:39):
or a 45% decrease in maintenancecosts.
And there are even reports outthere that there are
manufacturers that are seeing3.2 years extension of equipment
lifespan.
Now, when you start thinkingabout those type of numbers and
those type of impacts, you haveto start leaning in and
considering okay, what does thislook like for me?
And at EECO again, we've beenbig proponents of predictive
maintenance.
We recognize this is a big areafor manufacturing.
(10:00):
It's just kind of cool rightnow to get to explore how can we
bring AI into this right and ifyou can start really getting a
rapid return on investment whileyou build this, that gets
exciting there.
That gets really exciting.
So another area worthconsidering is simulation.
So, beyond prediction, which isreally cool, ai agents can
(10:25):
really have a lot of advantagebecause they can experiment
Right.
If you, if you kind of pair anAI agent with what we've been
talking about for our last twoepisodes of Eco.
That's why the digital twinsyou know, you can really play a
lot of games and test thousandsof what-if scenarios, right.
So we're not just talking aboutvisualization, we're talking
(10:46):
about simulation, where you canmaybe even adjust the machine
speed or an output, where youshift the material flows or
alter production sequences totry to figure out what is the
best configuration right, and sothis could take, you know, just
trial and error, with monthsand weeks to model this stuff
out.
Where this could be done withAI agent, potentially within
(11:07):
hours you could find this, andthere are studies out there that
pharmaceutical companies haveused this digital twin coupling
to redesign complex productionlines and some of them have seen
a 28% increase in throughput.
Think about that and you knowall the type of regulatory
compliance that goes with thepharmaceutical industry.
(11:29):
So they're keeping that whileincreasing throughput.
So I mean, that's kind of crazyto think about.
So when you have risk, when youhave the need for capacity
moving faster and faster andfaster, these AI agents can give
you opportunities to innovatewith confidence.
And again, we're here to helpyou, we're here to support you,
(11:51):
we're here to help you explorethese tools and maybe think
about simulations, okay.
And when you start thinkingabout the power of AI, a lot of
it comes down to data analysis,right, and it's not just
analyzing data for analyzingdata's sake, but if we're trying
to enhance our quality andreally improve our supply chain
management, so start thinkingabout the quality controls, like
(12:13):
AI agents.
If you start integrating itmaybe with, like a computer
vision system, okay, and you'retrying to really detect defects
that are happening.
Man, these AI agents can do itat an extremely high accuracy
rate.
I mean we're talking aboutthere's reports out there like
99.97% accuracy rate.
(12:34):
That's crazy, I mean it'sincredible.
We're talking about spottingmicroscopic cracks or little
color variations right, it cango even beyond detection.
They can shift qualityassurance toward prevention by
predicting when the processesare really starting to get out
of spec, and then you can startcorrecting it.
Right, and then, from a supplychain standpoint, you can
(12:56):
analyze supplier performance,inventory levels, trends from
your customers and just startthinking about the overall
networks that you're workingwith.
Then you may be able to startaccurately demanding forecasts
and dynamically adjusting yourproduction plans based off that.
So I mean, disruptions happen,right.
(13:16):
I mean you maybe have a delayedshipment out there, right?
Well, if the AI agent can pullthat data in, it may be able to
automatically identifyalternative suppliers or reroute
logistics or rebalance thatinventories, so you can cut
disruption down.
That's where the big thing is.
We're trying to cut thatdisruption down to make better
decisions.
(13:36):
And I'm telling you, I thinkthis is something that more and
more manufacturers are going tobe leaning into, because the
markets are volatile, themarkets are competitive, so you
have to be thinking what can youdo to gain a competitive edge,
and bringing in tools andresources like this could be
that.
And I mean we have to considerfrom industrial manufacturing,
(13:56):
the future is predictive andautonomous, right, so the
integration of these agents isnot just an enhancement of what
you already have.
Okay, this is kind of like afundamental re-imaging right, or
kind of rethinking of howmanufacturing could be, because
it's these types of intelligentsystems that create factories
(14:20):
that are not only automated butare increasingly autonomous.
Ok, so they're learning it allthe time, they're adapting
dynamically and they'reoptimizing proactively.
I mean, it's like it's likefuture scifi movies, except it's
here now.
And so it shifts fromforecasting to the OEE models
(14:41):
and enabling predictivemaintenance to really optimizing
processes through using digitaltwins and safeguarding quality.
And I'm telling you, theopportunities are just endless,
right, but there's measurableimpact.
We're talking about greateruptime, lower costs, more
product that you're getting outthe door and that resilience
right, the equipment is beingmore resilient.
(15:03):
So, again, we have several casesand resources that we pull to
kind of help us understand thisat ECO and at the end of the day
, we're here to walk beside you.
We recognize this is a newfrontier.
We recognize this could beviewed as scary, it could be
viewed as unknown, but we havelots of experience and we have
(15:25):
lots of resources within ourecosystem to support you, from
electrical systems to automationmodernization, and we can bring
some technical expertise.
More importantly than that, apartnership mindset.
So you're not going into thisalone, right?
You're going to have somebodywithin an electrical equipment
company who's willing to walkalongside you, because our goal
(15:47):
is just not to introduce you tonew technology by.
Good luck, wish you the best.
No, we want to help deliver youlong-term value.
We want to help you unlock thefuture potential If these AI
agents work for you and yourmanufacturer facility and your
model, and we want to help youexplore that.
And at the same time, we knowwe can't just live in the clouds
(16:07):
.
We need to stay grounded inoperational realities and that's
what we're here to do, becausewe recognize the future.
Manufacturing really is notgoing to be defined by those who
react to yesterday's challenges, but the ones who recognize and
anticipate tomorrow'sopportunities.
Have predictive power of theseAI agents and you align it with
(16:28):
partnerships like electricalequipment company, you can take
a big step forward in buildingtruly intelligent, resilient and
future ready operations, andthat is worth getting excited
about.
So we're here for you.
Again, you can reach out.
(16:49):
There's going to be links forour podcast.
There's going to be links, ifyou're checking this out on
YouTube, to connect with us.
Probably one of the best waysto connect is just set up a time
to come to one of our labs.
We have labs throughout ourservice area, so come connect.
We have, again, subject matterexperts.
That would be love, love, love.
To sit down to hear what yourapplications are, to hear where
(17:10):
you're at with your industrialfacility, and also just to hear
what your goals are.
Just to do some somebrainstorming, some fact finding
, some goal setting, somedreaming even, and just to see,
ok, based off of that, how wouldwe approach it, what would we
recommend and get you connectedwith the right resources out
there to turn this from from adream into reality.
(17:31):
All right, so if you enjoyedthis, if you thought there was
some value, maybe sharing thiswith others, that would be
wonderful.
Obviously, if you give us arating and review, that would be
awesome.
But, most importantly, comecheck us out, go to ecoonlinecom
.
Again, connect with us on theresources.
We'll have links underneath thepodcast for you to connect with
us directly, but schedule sometime.
If you don't want to come intothe lab, reach out to us, we'll
(17:57):
come to you.
We love coming to industrialmanufacturers, to seeing the
facilities, to understanding thecomplexities and just having a
fresh set of eyes and sometimesa fresh set of eyes is a big
deal, okay, so we love toconnect with you again.
All those links will be there.
Follow us on LinkedIn as welland you know what's coming right
.
Just remember to keep askingwhy.
Thank you for listening to.
(18:26):
Eeco Asks why this show issupported ad-free by Electrical
Equipment Company.
Eeco is redefining theexpectations of an electrical
distributor by placing peopleand ideas before products.
Please subscribe and share withyour colleagues and friends.
Also leave comments, feedbackand any new topics that you
would like to hear.
To learn more or to share yourinsights, visit EECOASYcom.
(18:51):
That's E-E-C-O-A-S-K-S-W-H-Ycom.
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