(upbeat music)
- [Walker] I'm gonna walk you guys through
the process of getting everything set up
and then you guys are going to publish your data
into this Unified Namespace.
Now the only thing that we probably won't do is
we won't organize all the data.
We'll have everything in the namespace
but we won't get it organized in a ISE 95 structure
because we won't have enough time probably to get to that.
But the whole reason that we're doing this is
we want to help organizations achieve the Holy grail, okay.
So what is the Holy grail?
It's a fully integrated business
made up of digital factories right?
So that means everything
and everyone is plugged into the network.
The layers of the business are integrated
and they operate based on data and information
from all the other layers in real time.
Stakeholders know the state of the business in real time
and analyze data and information.
I'm actually gonna talk about this real quick
before we move on.
Machine learning will predict future outcomes
based on past patterns and current states.
AI recommends operational adjustments
to improve future outcomes and stakeholders execute
or not execute recommended operational adjustments.
So we're gonna talk about machine learning and AI again
and augmented reality.
How do those all play into this, right?
I mean, when we walk into a plant, a manufacturing facility
right now, pretty much anywhere in the world
there, we know that they're gonna have PLCs in a facility.
We can absolutely 100% guarantee
that there's at least one PLC somewhere, okay.
Either the OEM put it in the machine,
or they've done that our own installations
that where they have PLCs
that are essentially gateway sitting
on top of all the automation.
And we can pretty much guarantee that they have HMIs, right?
We can guarantee that they have some type of ERP system.
So whether that's even just QuickBooks
we can guarantee they have some type of ERP.
The other thing we can guarantee is
that they've got people executing stuff, right?
And that's pretty much where it ends, every manufacturer.
Here's another thing that we can guarantee.
If we walk into the plant, knowing what we know
we are never going to come to the conclusion
that the business we're viewing is running optimally.
In fact has anyone here ever seen a manufacturer
that just blew you out of the water.
That they ran so optimally, they were so bleeding edge,
they just blew you out of the water.
I'd love to hear that example.
All right, so I have one example.
Actually I got two, one, one's a manufacturer though.
Number one is Tesla, okay.
If you want the gold standard or Industry 4.0
in manufacturing, it is Tesla.
Primarily, they got lucky, right?
They started building the facility right at the time
Industry 4.0 started happening and IIoT technologies existed.
But if you walk inside of a Tesla facility
you'll shit yourself, I promise you.
It will blow your mind and you'll be like
how, why is it everyone's not doing this?
Now that doesn't mean Tesla didn't have some growing pains.
They absolutely did.
It was painful.
I mean, Elon Musk was living inside his facility
but Tesla will blow your mind.
The other thing that'll blow your mind is if you walk inside
of an Amazon Distribution Center.
If you go inside of an Amazon DC, it'll blow your mind.
Now, Amazon across the board.
I mean so Tesla and Amazon,
they have one thing really in common.
Do you have recommendations otherwise understand
how Tesla does what it does.
Yeah, there's a couple of use cases,
but they're not published.
Rafi should have seen them.
Rafi should have seen the Tesla use case.
Well, so Tesla is very, I can get you the Tesla use cases,
the Amazon one, we can put the Tesla use cases.
In a nut shell what Tesla does is they approach each process
as a node.
And it contains all the infrastructure that, you know
they treat each production line as its own plant,
as its own facility.
And it has its own infrastructure that it then publishes
into the plant level infrastructure that they then publish
into the enterprise infrastructure that they then plug
into the Tesla ecosystem.
For those of you who have no experience
with machine learning in any way, shape or form,
and you're not artificial intelligence people
let's do a quick definition of machine learning and AI.
I know you've heard of it,
but understanding what it actually means.
So machine learning, okay, is nothing more than writing
an algorithm, okay?
So you essentially write a program
or you use existing programs to look at data points
over time to identify patterns.
So what machine learning can do is
when you call it modeling, modeling is the structure.
I've got all the time series data.
I've got my failures and now I'm gonna ask the model
to go ahead and learn.
So it's gonna learn from that data
and the information I gave to it,
to look for a pattern in the time series data to say
okay, I've now learned what it looks like in the days,
weeks and months leading up to a failure of the bearing.
Machine learning is not a pipe dream, okay?
Machine learning is real.
It's all around us all day long.
Artificial intelligence are neural networks
for the most part, they are,
they're essentially neural networks
that replaced the decision making process
that we go through.
So artificial intelligence for industry is going
to consume the predictions of machine learning,
many predictions, so not just one bearing failure
but artificial intelligence will consume many predictions
and listen to some and not listen to others
an optimal recommended decision.
That's what artificial intelligence is gonna do in industry.
The reason that we are sharing this information
with the rest of the industry is because
we're mission driven.
Most people in my position would not be giving
this knowledge away.
And most people think I'm crazy for giving it away.
That's not my philosophy.
My philosophy is my mom got murdered
when I was a little kid.
When I was seven, I grew up dirt ass poor in the Rust Belt
where every, all my friend's parents lost all their jobs
because in manufacturing jobs went away.
And I have decided that my life is going to be about
recreating the middle class.
So that's why I give this stuff away.
That's why I talk about where I come from.
And then just remember, solution driven is this idea
that we're gonna put in this monolith.
And it's really the concept
that we're gonna approach every project as standalone.
When we have a round peg, we're gonna try
and put it in the square hole.
So I may have a big bin full of problems
and not all of them are the same shape as, as my solution
but I'm gonna jam it in there and make it fit.
Technology driven is that I'm gonna use a round hole
for a round problem and a square hole for a square problem.
I'm going to have minimum requirements to say
that anything I use, I mean, think about it right now.
We already do this before, before a company
will purchase a piece of software, it goes through vetting
with their IT group, for minimum security requirements.
It goes through vetting, right?
What we don't vet right now is architecture.
We do not vet the technology, why?
Because no one understands it.
What title of person should we be looking for?
And I said, there isn't a title.
There are a couple of different types of people
you wanna be talking to, right?
We wanna, we wanna get in front of.
Number one, we wanna get in front of the floor level
champion or the plant level champion, who's the visionary,
you know, what we're really looking for
is the person who's gonna quit.
The engineer, the electrical technician, the SCADA engineer,
the person who's gonna quit, if the company doesn't leverage
new technology, right?
I mean, if you look at manufacturers and my apologies
to anybody here who work in manufacturing,
but the best engineers in the world don't work
at manufacturers.
You know, basically they may have started there,
but what happens in manufacturing,
because manufacturing so slow to move.
They, you know, the bottom 30% are incompetent
and they get fired, the top 30% move on,
and either start their own company,
go to work for integrators.
And they get left with the middle 40%.
That's, who's working at the plant,
but they have the people and I'm not disparaging
those people, this is, I mean I worked in the,
this is where I started and then I worked in many plants.
So I don't mean to say that if you still work at a plant,
you're a middle 40%. (upbeat music)
But if you retire from a plant
I can pretty much guarantee you're a middle 40%.
If you've been there for 30 years,
you're the person who can go along to get along.
You're the one who tows the line.
You don't rock the boat too much.
That's just how you succeed in that environment.