Welcome back to the automation podcast. My name
is Sean Tierney from Insights and Automation, and
I wanna thank you for tuning back in
this week. In this episode, I meet up
with Dante Vaccaro
from Schneider Electric to talk about digitization
as well as to talk about trends in
industrial automation.
Dante, thank you for coming on the show.
It's great to have you here, and let
me let you introduce yourself to our audience,
and then we'll get into the topic we
wanna talk about. Yeah. No problem, Sean. Thanks
for having me on, and thanks for attending
our event here. It's It's, really nice to
have you here to check out the latest
and greatest. But my name is Dante Vaccaro.
So, I work as a digital transformation specialist
within Schneider Electric. So what I do is
help companies,
create digital roadmaps
aligned to, like, business strategic business outcomes and
then how to scale those across their
organizations.
Right? So we take into consideration
people, process, technology
to align your operating model with a data
driven approach.
Now my background, I've been with Schneider now
for nine years. But prior to that, I've
worked in automation my whole life. So I've
done everything from working for OEMs to motion
centric automation
to PLCs,
and then eventually gravitated more into the software
space, which is kinda where I sit now.
Yeah. And so our audience is made up
of those controls engineers. They're programming
PLCs that creating HMI systems, SCADA systems. You
know, some of them are using Schneider. Some
are using Aviva.
And so
for them, they've heard a lot of different
things about
digital transformations,
digitization,
digitalization,
you know, all these buzzwords they hear, but
you're actually working with
customers to help them find solutions.
Can you give us an example of,
like, what that would look like? Like, what
what challenges are you solving for your customers?
Yeah. So great question.
So digital transformation, like you said, is a
big buzzword. Right? So people have different interpretations
of what it is, what it means, and
how it is. So,
essentially, what digital transformation is, it's not a
product. It's not a software integration. It's really
a strategy where we go from
living in the dark and being reactive to
data driven in real time.
So it's really about maintaining a digital continuity
across both operations and IT. So operational technology
to IT
to gain actionable insights to what's going on
within your
supply chain or operations.
So it's not just focused to the floor,
but it's it's the collective strategy that we're
we're going to apply to become
more agile in our operations.
Yeah. And that's, I mean, that's important. Back
when this whole industry really get started late
sixties
with, you know, the Modicon o '84
and all the other products out there And
evolved over the years, you know, forty years
ago, we had companies like, you know, Wonderware
InTouch. We had OSI Pi and other great
companies being,
founded.
You know, we didn't have networks.
We didn't have, you know, Modbus or, you
know, Data Highway. They were not networks as
we know them today, as we as evolved
in the front office. But
today, on the plant floor, we almost,
in all applications,
find Ethernet. Right? Yeah. Whether it be modbus
TCP, Ethernet IP, you know, or other types
of Ethernet. But it's this common standard that
people can now use
to get their data. Right? And so my
audience would be very familiar with, like, data
collection through, like, an OSI pi Mhmm. Or,
you know, maybe a SCADA system
based on InTouch or something else like that,
another product like that. So the infrastructure there
is the connectivity is there, but turning that
data into something that's useful
more than just a trend on the screen.
Right?
I think that's kinda what you're doing. Right?
Can you talk a little bit about
what the what the give me an example.
Like, Tom, let's talk about a transformation itself.
Okay. How did you utilize their infrastructure? I'm
assuming most of your customers already have that
infrastructure.
How do you utilize that infrastructure to to
to
face it, to solve a challenge a customer's
having? Yeah. So the goal is obviously not
to rip and and replace. Right? It's really
just to assess kinda where you are,
your baseline situation
as far as technology,
as far as, like, your operating model. But
we have to align
the the goal to a strategic outcome. So
I can give you a couple different instances.
So I have a lot of customers right
now that
are having issues
with
maintaining plans from a centralized engineering team. So
it's harder to get controls engineers and different
resources,
that are out there, but they need to
understand what's going on into their their plants
at real time. This could be
whether it's,
centralized maintenance teams, it could be centralized energy
teams. But the goal is we need to
see what's going on in the plan. Now
if I'm a large organization, depending on how
many plants I have, I have to roll
up all that data and I have to
I can't control what I don't measure. Right?
So
the idea is to figure out, okay, what
is that strategic initiative? Is it are you
struggling with quality optimization? So I'll give you
one now where we have customers that have
very varying quality that they're trying to optimize
to reduce the standard deviation of that product.
K. So we have a business imperative. We
must,
must fix our quality
to then
reduce the standard deviation to sell, like, a
higher moisture content product. Right. Because you sell
by weight. I want to sell tons. I
don't want to over dry or under dry
because
that goes against two or three different things
that I'm doing. If I use too much
energy to that, it hits my sustainability goal.
If I overdrive the product, I'm selling more
physical product to to get out and,
accomplish that. So the the idea is you
connect the strategy with whatever that business
imperative is, and then we work backwards from
there. Okay. In order to achieve this, right,
what
how do we develop use cases that align
to that strategic goal? And then what technologies,
infrastructure can we put in place to make
that happen? And then the next part of
that is once we have we're connected, we're
collected, we're analyzing, we're acting,
how are we actually going to respond to
that? Who is responsible for that data? How
does what we're seeing get tied back into
those processes?
So the digital strategy enables your operating model.
So So if I'm running continuous improvement, I'll
give you another example. I have a lot
of people that come to me and they're
like, our digital transformation is we need to
monitor OE in real time.
I'm like, okay, great. OE is a good
starting point, but what's your plan for that?
Like, what do you mean? Like, if we
if we just monitor OEE, we're gonna
increase 5% efficiencies. I was like, that's like
buying a scale and trying to lose weight.
Right? If I step on that scale every
five minutes, am I gonna lose weight?
It's like and they just look at each
other. It's about that moment. It's like you
have to focus on the drivers, right? The
drivers are diet and exercise and manufacturing.
It could be a people problem. It could
be a training problem. It could be a
reliability problem. But the idea is that you
build the strategy over time. So I start
with my baselining. Right? What am I doing?
What's causing
getting my Pareto
analysis of downtime?
And then I work cross functionally with teams
to determine how I'm going to do this.
And this is where that closed loop,
continuous improvement methodology comes in. Right? So I
look at my Pareto and let's say I
have infeed jams on a packaging line or
something like that. And I see that repetitively
and it's causing
50% of my downtime or something like that
that I have. I'm gonna then my my
continuous improvement people are gonna work with my
controls engineers to figure out or mechanical engineers
depending on if it is it a control
function issue or is it a mechanical issue
and come together with the solution to close
that. So that's
kind of like
a long winded response to yours, but I'm
just going over, like, some of the ways
that we approach it. Oh, that's excellent. I
think, you know, if we go back twenty
five years, maybe twenty years,
well, you know, people would come to us
and say, I need to collect data. I
need to have all this data. And then
you would go on a few years later,
and they had all this data. Maybe it
was in OSIpi or InTouch had data logged
it, but they weren't using it. And it
was actually causing them a lot of problems
because they were running out of, you know,
storage space Oh, yeah. Especially back then.
And so the the point was, hey. You
need some analytics. You need that you need
a reporting package to go on top of
that. You need to start using just don't
collect data and never use it. You need
to actually,
you know, use that data. When you're talking
about OEE, having the ability having the Pareto
chart saying, hey. This is your number one
this this is your number one, two, three,
four, and five causes for downtime. Just having
that is not enough. You have to take
action from that. So I definitely hear where
you're coming from that. And, you know, one
of the things
you know, it's so easy, especially, I'm from
the Northeast. Right? So I think you're from
PA? Pittsburgh. Pittsburgh. Right? Born and raised.
So a lot of what, a lot in
the Northeast, we get a I think it's
rightly so. A lot of us can be
cynical at times. Right? And so one of
the questions I like to ask, so in
the work you're doing, you're touching a lot
of different customers,
plus you're touching a lot of different product
groups within Schneider. Right? Mhmm. And so I
wanna ask you, like,
what are some of the positive trends you're
seeing? Like, we all know, you know,
maybe my my I have to use a
different notch on my belt or I have
to loosen my collar or I can't fit
my tie on, but those are those are
trends I think we all deal with. But
what about,
positive trends? What positive trends are you seeing
out there with your customers and within your
company?
I think,
it really comes down to how we're gonna
leverage data. And now what we're seeing is,
like, everybody wants an AI strategy right now.
So when we say trends, like, I I
hate to get buzzwordy, but it's there's practical
uses of AI. I think AI became mainstream
with generative AI, which is just one form
of that. There's been tried, tested, improved versions
of that within manufacturing,
such as like multivariable predictive control or when
we get into like regular machine learning algorithms,
regular machine learning algorithms. But I think
what I see Schneider doing is really separating
the portfolio up to producers,
aggregators,
and consumers of data.
So a lot of the innovation that you
see down in the product side is
tons of register values of data.
Like, how many times have you seen people
like our variable frequency drive, there's probably, I
don't know, a thousand different tags you can
pull out of that. And most people hook
it up with a four to 20 in
and out. Right? But there's, like, water loss
detection algorithms in there that tell you when
you're losing stuff. There's,
other, like, alarms, kilowatt hour consumptions if you
wanna trend any of that data. Like, there's
a ton of metadata that contains in the
products that manufacturers are making today. There's just
nothing leveraged well to understand that. So I
would say AI is the trend. Data enables
AI history, organization,
contextualization.
And from a control engineering perspective,
maintaining, like, a discipline on data ontology is
super critical in this part.
Yeah. Could you go into that a little
bit more? Yeah. So On ontology.
Ontology. So think about it as, like, our
data and tagging naming structures.
Structure or standard to follow.
And where we see some issues and whether
it's AI or whether it's just trying to
get someone like these operational KPIs
is that oftentimes
the detractor from that is, you know, the
sins of our fathers on the control side
where
it's been hurry up and get the system
up and running. You have two days, just
get it. So we don't have
discipline and structures and hierarchies and PLCs
are usually flat. Right? So how do we
normalize that and how do we understand where
that data is coming from? Because as I
start to compound the value of that data
and start to create, like, relational relationships of
what I'm pulling out across the the value
train, I need to know where it's coming
from. And without, like, that ontological
discipline, right, like, the the naming structures, it's
very hard to figure out and scale stuff
because I don't know where that is. So
if every tag is named pump, but I
don't know what machine it's coming from, it's
very hard to create like
the
repeatable template.
So that's like one of the the key
limiter limiting factors of that. Now that also
transpires onto the OEM side too because if
I buy a piece of skid equipment, right,
it's on address. It's just a ransom
wash program that gets in there. So
a lot of, you know, what control engineers
or what end users do is like you
have to be specific on on what you
want there. Right? So there has to be
things to follow. But my my word of
advice is
there's some
extra work that is that needs to be
done at the beginning
phases for
the proper structures of your tags.
It will pay dividends down the line. If
not, you're spending all half your time overcoming
technical debt that you've inherited or
decided to overlook.
Yeah. I think, the integrators who are watching
and the and and a lot of OEMs
too, they know the importance
of having some naming conventions and a lot
of very large,
you know, greenfield plants also too. They'll they'll
put these standards out to the, you know,
the the OEM supply and equipment and skids.
And it's so very important because even from
a training aspect of your personnel, your maintenance
technicians, your electricians,
but also if you start thinking like I
was just on the planet floor talking to
Kareem and Mahua and,
you know, you start looking at AI.
Right? But if you ask AI to create
a routine for you, right, that's gonna interface
with this new BFD you just got from
Schneider,
you want it to have the right naming
convention from your company. And if it's just
used as some random naming convention, nobody's trained
on that. So I I can see it's
important. But for brownfields,
I can see that can be a daunting
task as well because,
like you said, you have a legacy
plant full of equipment that was developed over
time by different individuals and different integrators, different
OEMs. So that could that can be I
can I I can that can be pretty
pretty,
a daunting task to get that right? But,
of course, you can always abstract that in
the software layer.
You know, I thought that I think that's
really interesting. I you know,
I guess what I'd like to ask you
now is,
you know, I was on the like I
said, I was on the show floor,
walking around, seeing all the great products you
guys have out there. I've actually had a
chance to do some hands on with some
of those products. Thanks to partnering with Schneider
to do that.
But from your from your thought process, you
know, in what you do, right,
when you look at those, products on the
show floor today, which ones
do you think of when you're thinking about
the digital transformation?
Which ones do you come up mind first?
I mean, there's a lot that comes into
mind. Right? Because I'm a PLC guy by
heart, so I always go back to that.
I also like,
high performance stuff. So one of the things
I like is, like, the, m six sixty,
like the new motion controller. I mean, you're
talking 300 axes of coordinated motion in a
single controller. Like, that one makes me happy
because think about simplifying the architecture within a
plant, multi lines, multi accesses of coordinated motion,
like, pretty pretty neat design. So I think
there's a lot of innovation and power that
comes out of there, and it's fast. So
I like that. But I also like,
what we're doing with some of the other
products, just some of the metadata that's contained
within them. Okay. So, like, even on motor
controls, right, it's always been, like, on off
overload. But the TSYS Island, I don't know
if you had a chance to play with
that. Yes. We've had them on the show.
That's a pretty cool little product as well
too. Right? So there's predictive
algorithms that are in there. You can see,
cycle time. So all that, like, if I'm
driving
a reliability
play or I want, asset performance management,
it's like a insignificant cost in the value
that it delivers from that little platform. So
I'm starting to see, like, a lot of
these little things that we're doing to solve
these problems that are data rich
that can be leveraged
to drive other initiatives as well that I
don't think a lot of people see that
right now because it's just this is the
way we've we've always done it.
Yeah. That makes a lot of sense. You
know, I'm wondering if somebody is listening. Right?
And they're like, you know, I wanna I
wanna implement this. This I love what they're
talking about. I wanna implement this in my
plan. I mean, I can just tell them
to call you. But, I mean, what what
would be some of their first steps that
they would wanna do before they maybe set
an appointment with you or your one of
your colleagues at Schneider? Yeah. So I think
the key to one of the reasons that
digital transformations fail is there's no leadership commitment.
So it's not a it's not done in
a vacuum. Right? And it's not just playing
with technology. The key is to getting it
to scale to solve repeatable problems across the
organization.
And that so I always conflate the 80%
should be consistent and standard. And if you
look at Schneider Electric's digital transformation
story, they've scaled, they started their smart factory
program 2018,
started with
11 factories and scaled to 150
today. So a short period of time, seven
years taking COVID into consideration.
We now have what we would evaluate as
a 150
smart factories. Seven of those are world economic
lighthouse facilities.
But the key to that scale is common
consistent core across that. So our VP that
went through that likes to say it was
a boring transformation. If you go to any
plant around the country, they look exactly the
same. The same technology, the same use cases.
So 80% is,
consistent across how we're gonna operate our plants,
how we're gonna calculate OEE, how we're gonna
run
our manufacturing
system, which is our, you know, manufacturing bible.
And then 20% is more customer nichey to
that location.
So a lot of people wanna do the
the nichey one offs, but it's about understanding
like where the business has to go. So
if I'm a control engineer and I'm being
tasked with this,
I want to get aligned with where the
business is going. I want to be tied
in with operational excellence to figure out how
they're running continuous improvement.
I want to simplify my designs before I
start to transform,
because a lot of times, right, we got
clunky systems that aren't as efficient. So I
don't want to automate inefficiencies.
So simplify, standardize, digitize. Right? But it's it's
really getting a clear definition of where the
company is and where it wants to go,
and then aligning to that strategy.
And I didn't ask you beforehand if we
could talk about this, but would it be
okay for you to mention your podcast?
Oh, yeah. So I run a podcast on
digital transformation. It's called Practitioners Unplugged.
You can find me on LinkedIn, Dante Vaquero,
or on Industrial Sage. But,
really, the the basis of that podcast is
just to talk digital transformation
stories. Right? What works? What doesn't work? Learn
about different industries. We don't focus as much
on the technology, but, hey, how do we
sell this to leadership?
What what are what works well? What doesn't?
What importance does it have to have a
sound manufacturing philosophy?
So we just try to, you know, get
stories from the front lines and don't really
push product or software, but it's it's just
a community to connect on with people going
through struggles to to talk and figure out
how do we learn from the past or
not.
Well, I think too, if you're gonna make
a proposal up a management, it's good to
have that kind of to be able to
hear those kinds of stories.
Because, the the day of just putting AI
in something and getting it approved, those days
are coming to an end. And, you know,
budgets are getting tightened. In some places, they
get an open, and other places, they get
in tightened. So, you know, being able
to spend you know, have more of that
information from your pockets, I think, would be
extremely helpful, which is why I appreciate you
sharing that. You know, I,
come to the end of my list here,
and I'd like to end this, you know,
these podcasts by kinda looking forward and saying,
you know, what do you see coming down
the pike? And it doesn't have to be
limited to
digital transformations
per se, but just, you know, if we
had a crystal ball and we were looking
out,
you know, next year, two, three, what are
some of the trends you're seeing?
I would say the biggest focus now is
gonna be,
like, increased automation
and more agentic AI.
So if you look at that, like, MCP
protocol is starting to pick up a little
bit, model chain protocol.
And what the agentic aspect of that allows
you to do is run lower,
let's call it lower bandwidth models, right? So
I can create smaller agents that are trained
on subsets of data instead of like this
big monolithic chat gbt thing. So, you know,
when we query chat gbt, it scans the
whole entire Internet, but here you create agent
to agent interactions.
So I have a quarterback agent that goes
out to individual expert agents within my plan.
So I could have a quality agent, I
could have a asset,
predictive analytics
agent.
And I ask the system what's going on,
and I get the information I want back.
So I think the landscape is gonna change
over the next couple of years as this
technology evolves. But the the MCP protocol and
the agentic AI on the industrial floor,
I think it in the infancy stages, we'll
come back in an advisory capacity, but three,
four years down the road, it might take
control of, like, operations.
Yeah. And I think, and and I I
think too, another trend will be maybe people
will understand AI better because I think,
as you kinda alluded to earlier, AI today,
a lot of people just think of chat
GPT. Right? So that that's what they think
AI is. I I was on vacation a
couple years ago, and a gentleman,
recently retired from business, was telling me that
AIs were programming themselves.
And so, you know, the difference between machine
learning and AI and the different types like
generic general, general AI versus other AI,
I think that's important. There's a learning curve
I think we're all on. Yeah. And I
also think too and have you found this
and I know I'm kinda going off the
topic here a little bit, but have you
found that people you're educating people on AI?
Because a lot of times people are applying
AI, even the buzzword AI. Look back like
smartphones, smart TVs. Everything was smart. I always
told my wife, none of this is smart.
Yeah. They just have apps that doesn't make
them smart. But, are you finding you're spending
a lot of time educating people on AI
and what is and what isn't in the
different types of AI? Yeah. We spend a
little bit of time on that. I think
everybody says our CEO wants an AI strategy,
so we have to pull it out. And
it goes back to that onto the logic
onto the logical discussion. Right? If you don't
have the data, you don't have organization,
history, context,
you're not gonna get what you want out.
Right? So if I don't know where the
data is coming from, I don't have a
reference
of history. How am I supposed to model
something out of that? So it's chicken or
the egg. Yeah. But we need AI. It's
not that simple. Right? It doesn't work without
data. So you have to have a plan
for your data. There are steps to follow
to that,
you know, historization, organization, contextualization,
and then analytics can go on top of
that. But if I don't know where it's
coming from, what it means, then
you have, you know, crap in crap out.
Right? So that's just what it is. Right?
We've seen people prompt
chat GBT and like, I don't like what
I got out. You said,
what's the weather like today? So it doesn't
know what city you're in,
where you're at. So, again, it's like you
have to prepare for it. So I I
tried to take a step back. It's like,
okay. You haven't done steps. You you're not
even in industry four point o yet. Right?
So you don't even have any of this
information. So how are you just gonna jump
ahead? It's not magic. Right?
Yep.
Well, that's it for my questions. Was there
anything else you wanted to mention before we
wrap it up? Any other anything, maybe, we
talked about your podcast.
Maybe, talk about, any any events coming up.
Are you doing any sessions here at the
show that people can look up afterwards?
I'm not doing any sessions. I'm recording a
couple podcasts that are gonna be out here.
So, we had a CEO that we just
interviewed this morning on interoperability.
So how to sell the concept of interoperability,
another buzz word, guys.
So we we unpack what that means and
how to sell that to leadership in in
that episode, and we have a couple other
things that we're doing. But,
yeah, I'm involved in the community.
I love manufacturing. My mission is to help,
like, US manufacturing excel and
and get back to that resurgence because I
I really believe that, you know, technology can
help
all of these issues that we have. So
this is really what I'm passionate with, automation,
digital transformation.
And thank you so much for coming out,
spending the time, and inviting me on your
show. Oh, thank you for coming on the
show and letting me interview you. Well, I
hope you enjoyed that episode. I wanna thank
Dante for coming on the show to talk
to us about digitization,
trends in automation,
and about his podcast as well. Check that
out if you're interested.
And, I also wanna thank Schneider who not
only sponsored this episode to us so we
could release it to you ad free, but
also flew me out to their event so
I could actually record
all of these episodes that will be coming
out in the coming weeks.
With that said, I do wanna thank you
again for tuning in this week. I wanna
wish you all good health and happiness. And
until next time, my friends,
peace.