- The Path to Machine Learning and AI, take zero.
(claps)
(upbeat music)
Notice I'm wearing a different shirt,
although generally when we shoot
our content for a specific month, I look the same.
So interestingly, we only shot two videos yesterday.
Today's Wednesday, yesterday was Tuesday.
We shot the mindset video.
We actually decided yesterday
that the core of our content for the month
is going to be this path to machine learning and AI,
and that's actually a direct result
of a post that Zack put on LinkedIn
under my account a couple of days ago
where Zack wrote for me, on my behalf,
"How do you think you can compete
"with a manufacturer that is doing
"machine learning and you aren't?"
Oh, wait, that was the post.
In today, in a couple of days,
it's gotten over 3100 views and 54 comments.
I intentionally waited 24 hours
to get a lotta comments built up
to see what people's responses were
before I chimed in on that message.
The core of the communication from the community
was essentially, machine learning and AI is a pipe dream.
Machine learning and AI, on top of bad data on the edge,
is a waste of money, it's a waste of capital.
Focus on getting good data from the edge first,
and then do machine learning and AI.
Or, I've seen too many ML and AI initiatives
that were a complete waste of time
and therefore it's a wasted investment.
So as a result of that, as a result of that post,
we're gonna go ahead and talk about
the path to machine learning and AI.
So that is, what is it that manufacturers need to do?
What are the steps that they have to take
so that they can successfully deploy
a machine learning and AI initiative?
Now we haven't prepped for this
in any way, shape, or form, so it's
gonna be a complete stream of consciousness.
I'm just gonna basically whiteboard it.
I haven't prepped it in any way.
There are no bullet points I'm operating off of,
which we generally don't do.
But normally for something this complex,
I would try and at least wire frame
what I was gonna talk about, and we haven't done that yet.
So, I'm gonna actually just kinda
walk through some of the comments
and sketch out my response to some of those comments.
So, Jim Gavigan actually wrote,
he had a bunch of great comments on that post,
but the core of Jim's message was,
Jim Gavigan, he's a time-series specialist,
he specializes in OSI PI, he's got a consulting firm,
I think based in Florida.
His firm really specializes in doing
OSI PI time-series data integrations.
But essentially what he said is,
is that he would take an Industry 3.0 organization
that has top-quality time-series data from the edge
over an Industry 4.0 company that's
doing machine learning and AI but has
poor quality data coming from the edge, any day.
And I don't disagree with him.
I mean, what I was saying,
and what we're saying in our messaging is
we're operating under the assumption
that any organization that's doing machine learning and AI
has an IIoT strategy, so that is
a strategy to get their data, all data from the edge,
into a unified namespace, report by exception,
that is, we take the edge device
and we point it into a namespace
so that we're not just getting the best data,
we're getting all data, we're getting all events,
we're getting all rising edge
and falling edge of every Boolean.
We're getting every unchanged data point
for any analog or real value.
We are operating under that assumption
that that edge data's good.
And here's why.
The companies who do not have an IIoT strategy
are not going to be in business.
We've already said this over, and over, and over again.
If you do not have an IIoT strategy
today or within the next 12, to 18, to 36 months,
if you haven't deployed an IIoT strategy,
that is a plan to get all your data from the edge
into a unified namespace so that all of your applications
can publish and subscribe to that data,
subscribe to the data and then
publish back into that namespace.
If you don't have a plan to do that,
you're not gonna compete.
I mean, again, I'm not trying to scare you,
I'm just telling you the reality.
I don't even have a product to sell to you
that most people would try to scare you
and then try to sell you something by scaring you.
I don't have a product to sell you.
Other people have products to sell you.
I don't have the product.
I'm telling you this because it's the simple reality
that if you don't have an IIoT strategy,
if you don't have a mechanism for getting all of
your data throughout your entire enterprise
into a single unified namespace,
if you don't have that strategy in place,
you're not gonna be in business, okay?
If you have that strategy in place and you deploy it,
then we can rest assured that you have
the quality data coming from the edge.
So we're operating under the assumption
that everyone who's doing machine learning and AI
has that good data.
It was a good point by Jim,
but I just wanted to drive home that point
that we're just ignoring those people,
the ones who don't have quality data coming from the edge.
They're inconsequential to us, okay?
There was a bunch of other comments
Amy Lummus made a comment about
investing in machine learning,
and actually, let me read her, what she said.
She said, "Echoing a lot of what was said below,
"machine learning on top of bad processes
"is wasted effort, wasted money,
"and if you're the one implementing it, unhappy clients.
"Machine learning, or any technology,
"is not a business strategy, it's a tool."
I do disagree with the last sentence.
It is a business strategy.
Machine learning and AI is a business strategy.
I'm gonna sketch that here in a second.
Essentially what Amy's saying here
is the same thing that Bill Gates said years ago,
which was, "Automating bad processes
"just magnifies bad outcomes," right?
And that's essentially what she's saying.
Again, we are operating under the assumption
that you don't have bad processes
because if you have bad processes,
mother nature's gonna weed you out, right?
That's the survival of the fittest piece.
So, let's talk about AI and machine learning as a strategy.
You guys'll remember from the videos last month,
we have our data on the edge,
so these are our PLCs and our HMIs,
our sensors, and our instruments, okay?
So, this is gonna include all of our PLCs,
this is gonna include anything that's talking heart,
anything that's got any intelligence on it.
This is the stuff that's running our business,
running all of our equipment, okay?
And then what we do is we bring
this information up to the operator level.
So this is generally where we have the MES,
the manufacturing execution data,
we have SCATA here in the middle
so that we can control and collect,
and then we have our MES data.
We use this MES data, think of this MES data
as an object in a workflow,
where the operator is interacting
with metrics like availability,
quality, performance, OEE, sometimes TEEP,
although the TEEP number isn't really or the operator,
is interacting with these numbers
to determine for them to consume that data
and them make a decision about
how they should modify the process in real time.
Generally, it's the performance number.
If the machine is running at
lower performance than it should be,
then the operator generally is gonna do one of two things.
They're either gonna speed up the machine,
or they're gonna adjust the rate
at which they should've been running.
That is what we call the standard or scheduled rate.
But the operators, they're gonna
consume this information, they're gonna ask a question,
and then they're gonna make a decision,
and they're going to adjust a set point
that's gonna make their equipment run better.
What machine learning does is,
machine learning consumes this information as well.
So we also have our ERP layer, okay?
This is gonna have cost information.
It's also gonna have schedule information.
It's gonna have raw material information.
And then we have our machine learning here.
Here's what machine learning does.
Machine learning learns your processes
by recognizing patterns.
That's basically what it does.
AI adjusts your processes based on
information it gleans from machine learning,
that is indicators coming from the patterns,
and then it adapts your processes
to these changing variables, okay?
AI basically does what the person is supposed to be doing.
So what machine learning does is
it consumes information from all these places.
It asks a question and then it makes a decision.
And it changes a set point or whatever,
or it gives the operator information,
and the operator decides, yes,
I'm gonna go ahead and go with the optimal decision,
or the optimal recommendation that machine learning made.
This is a business strategy.
This is an actual business strategy.
That is, our strategy, our strategy
is to use closed-loop processes
rather than open-loop processes.
When you're using operators to make these decisions here,
that's open loop.
This is a closed-loop process,
and that is a business strategy.
And again, the real answer here
is much, much more detailed that what I just laid out,
but I wanna drive home the point
that deploying machine learning and AI
is a business strategy, it is not a tool.
Well, that's not true.
It is a tool, but it's more than a tool.
It's a business strategy, okay?
So, in the next video I wanna
actually start talking about the path.
You know, where businesses are today
and how they can get to deploying machine learning and AI.
(upbeat music)