- The path to machine learning and AI,
step by step, take zero.
(upbeat music)
- So, what I'm gonna do is I'm gonna explain here,
I'm gonna try to put into context
the value of machine learning and artificial intelligence.
This is really an more of a value discussion
by using an illustration, okay.
So, let's say I've decided that I'm gonna go ahead and
start a machine learning initiative.
And the whole concept of machine learning, again,
is to acquire knowledge, okay.
And in this case, what we're going to do,
is we're only gonna try to acquire knowledge
about the efficiency of our business.
That is, the efficiency of our manufacturing operations
by machine, by area, and by plan.
Okay, now we haven't attributed any metric,
that is, how are we gonna measure efficiency,
well, all we want to do is acquire knowledge
about how efficient we are, okay.
Machine learning, what machine learning does,
is machine learning acquires knowledge over time.
So, its accuracy of knowledge early on
in the machine learning pilot
is gonna be much lower than it will be
much later in the pilot,
because machine learning is more accurate
the more data it's seen over time, okay.
You can eliminate anomalies, you can ignore anomalies,
that kind of stuff, okay.
But we're gonna use this example to discuss
these steps here.
So, the first step in the path to machine learning
and AI is we have to understand what is machine learning
and what is AI.
We've already established that machine learning is
the acquisition of knowledge,
and artificial intelligence is
the acquisition of wisdom.
That is, machine learning is concerned with accurately
knowing what your business is doing in real time,
and artificial intelligence is more concerned
with producing optimal decisions, okay.
The second step that we have to do is we have to define
how machine learning and AI can help my business.
So, in this case, our definition is going to be,
it can help my business by accurately telling me
how efficient my machine is running right now,
how efficient my area, that's a group of machines,
is running right now,
and then how efficient my plant is running right now.
In most cases, that efficiency is limited to
the performance of the actual equipment itself.
But, efficiency of a business is actually a lot more
than just how efficient the business is running.
Or, how efficient the equipment's running.
It's really how efficient the business is running.
So, when I'm calculating OEE, for example,
which is an efficiency calculation,
of equipment, areas, and plants,
one of the things that I ignore,
I'm only doing those calculations based on
the machines, the equipment,
that live inside of my OEE production model.
But there's a lot of other things that
I don't put in there, like for example,
my test lab, my quality test lab is never a machine
inside of my OEE calculation.
My warehouse,
which houses all of my raw materials and my finished goods,
is never an area or a machine.
But yet,
the efficiency of those operations plays into the overall,
how efficient my plant is operating at this time.
How quickly it takes a material handler
to get raw materials resupplied to a piece of equipment,
that's an important calculation, right,
but it's never part of the OEE calculation,
unless the reason my machine is down,
the availability is down,
is because I'm waiting on raw materials,
unless you put a reason in the model for that.
We would never know that the material handler
is running inefficiently, okay.
So, what we're gonna say is that,
we're gonna define machine learning and AI
can help my business by accurately determining
what my plant efficiency is,
and then AI is gonna make recommendations
on how we can improve that efficiency.
Here's that decision,
so that we can get that efficiency number up, okay.
So, the first thing we do once we've determined
how we can help my business
is we're gonna connect our data.
That's the very next step.
So, in order for us to accurately predict
machine efficiency, area efficiency, and predict,
and plant efficiency, not predict it,
but acquire that knowledge,
is we have to A: Know how all of our equipment's running,
and how all those groups are running.
So the first thing we're gonna do is
we're gonna connect the data.
So we have to get data from the edge,
we've gotta get it from our SCADA system, our MES system,
and our ERP system.
In just the OEE calculation we care about that stuff.
So, we're gonna pipe all of that into a unified name space.
If you need to know what the unified name space is,
then you need to go back and watch
all of our previous videos where we talk about MQTT, IIOT,
and the value of the unified name space.
We talked about unified name space in last month's videos,
okay.
The first thing we want to do is connect our data.
Which data do we want to connect?
The answer is, all data.
We do not want to make any assumptions about
how the data's gonna be consumed.
So, what we want to do is we want to get
all of our data points throughout the entire enterprise,
we want to get it mapped into a unified name space.
So, we're concerned with raw values,
we're concerned with scaled values,
we're concerned with all data points, okay.
Think about it, it's all right,
what the PLC firmware revision that
we're running in a specific PLC,
what is the serial number,
who was the mechanic who installed it,
what was the name of the OEM, all of that information.
This is part of the reason that the whole IIOT
infrastructure component matters.
When I install a sensor, I want to know,
ideally I would have the lot information,
and the serial number from that specific sensor
being published over MQTT into the unified name space,
so that is information I could consume.
Why?
Because machine learning is going to acquire knowledge
about our business, okay,
and it's gonna try and acquire more and more accurate
knowledge, okay, and then,
artificial intelligence is going to give us
optimal decisions.
The machine learning algorithm will determine,
we don't make the decisions about what information
is not important.
The machine learning and AI make those decisions.
Here's an example of some optimal decisions that an AI
algorithm could give us.
AI makes a recommendation to us,
stop using Pepperl and Fuchs inclination sensors.
Here's why, because when we've compared,
when machine learning has compared,
acquired the knowledge about the accuracy
of the inclination sensors,
when we're comparing Banner's inclination sensor
to Pepperl and Fuch,
what we've discovered is that Pepperl and Fuch's
higher error rate is giving us bad information
which is causing bad results,
as opposed to Banner's lower error rate
is giving us better information,
which is giving us better results.
AI may spit out a report that says
we've determined that there is actually
not a negligible loss, but a measurable loss,
by using these specific sensors,
and even though the Banner sensor costs us
11 cents more per unit,
we are losing 14 cents per unit
by using the cheaper version,
therefore there's a three cent gain.
And a human being is never gonna be able to determine that.
That information is on such a small level,
a human being themselves is never gonna be able
to determine that information.
Those are optimal decisions that machine learning
and AI could spit out for us, okay.
So the first thing we want to do is
we want to connect our data into the unified name space.
This is a critical component.
You will not be able to deploy a wide-scale
enterprise machine learning AI without
doing the unified name space, okay,
this is why we drive it home so much.
The next step after that is we've gotta map that data,
all data, into the IIOT hub, or into the cloud.
Really, we're gonna use the term "cloud."
Eventually,
all this information needs to find its way into the cloud.
For example, Tesla's self-driving cars,
it is not just your Model S,
your Model S doesn't have a computer on it
that's storing data into a database,
and then making decisions about whether or not
the thing in front of you is a human being or a car,
based on just the information that's stored inside
the Model S.
What's happening is,
your Tesla uploads information it's collected to the cloud,
then you have AI algorithms that are processing
that information and downloading more accurate knowledge
about your environment from the cloud.
So, what ends up happening is,
your Tesla gets smarter, and smarter, and smarter,
based on information that, not just your Tesla's collected,
but all Teslas have collected, okay.
So, when you think about that,
that's the closed loop component of machine learning.
The machine learning algorithm that's determining
whether or not the reading that the sensor
in the front of my Tesla is getting, is a human,
a dog, a fox, or an inanimate object, or another vehicle,
or a semi, or whatever, that information,
the decision it's making, acquiring that knowledge,
is based, not just on the data that your Tesla's collected,
but on the data that all Teslas have collected.
And that information finds its way into the cloud.
Go ahead, Zack.
- [Zack] If you had like, 10 different operators
with 10 different machines,
they're only operating based on the data that they have
from that machine itself-
- Correct, only from the information that's coming
from that specific machine, and here, let's use this.
So, machine learning can do this.
I can take 10 identical machines who are being run
by 10 different operators.
And if I have a machine learning algorithm
that is running on my machine,
let's say there are only three operators that run
machine number one,
my machine learning algorithm is,
doesn't take into account the fact that
I'm limited to just those three operators.
So, let's say there are things that
three individual operators are doing,
that are ultimately affecting performance
of that piece of equipment, okay.
So, if I have the machine learning algorithm
just running on the machine itself,
the machine learning algorithm is gonna
determine differences between the three operators,
but it's also not gonna learn, effectively learn,
the true impact that those operators have,
unless you were to take that machine,
and compare it to nine other machines
that each have three different operators.
There's additional information that
the machine learning algorithm will be able to ignore,
to say that this is specifically,
these outcomes are always specifically operator driven,
not just operator driven from the three
that I have to select from.
All right, so then we get our data into the IOT hub,
and then we need to pilot a machine learning program.
So, in this case, what we want to do,
what we've determine how it can help
our business initially is,
I want to know how efficient I'm running.
So, let's say I want to acquire the knowledge
about how efficient I am at the machine level,
the area level, and the plant level.
Traditionally, we do this with OEE.
We say that we're gonna go ahead and calculate
availability of our machine,
the quality of the product our machine's producing,
and the performance of that machine,
and we're gonna give ourselves an overall OEE number.
Okay, so this is completely arbitrary.
It's totally arbitrary.
Efficiency is always, it's a range,
and it's always relative to whether or not
somebody else is more efficient than us.
Machine learning may not calculate efficiency
based on availability, quality, and performance.
Machine learning may, through its acquisition of knowledge,
may determine, so as it acquires knowledge,
it may determine that the most accurate acquisition,
or the most accurate determination of efficiency,
is not through availability, quality, and performance,
or, there are other factors at play that
we eliminate automatically, the human being eliminated,
by saying that our availability is always a function
of scheduled run time versus actual run time, right.
Line state.
- [Zack] If you're driving a car on a road trip,
it might be better to go at a slower speed, because,
or, you know-
- That's right.
- [Zack] Because you're gonna have free time anyways
to run your machine,
so it doesn't make sense to run it at 100%,
you're gonna use more electricity,
when you have free time anyway in your schedule,
that it might make you more profitable to run it at 90%-
- Well, what is efficiency?
Efficiency is, in manufacturing,
it's making the most money with the least amount of effort.
Machine learning, machine learning gives us the ability,
through acquiring knowledge over time.
Our OEE calculation never changes.
So, let's say I acquire some knowledge, right,
as a human being, I'm looking at AQP and OEE,
and I'm acquiring knowledge over time,
and I'm saying, "you know what?
"our performance number is always 60%,
"it's never higher than 60."
Well, how is the performance number calculated?
What we say, is we have a standard rate
that that machine should run at.
And when the machine is actually running,
we are basically saying,
this is how fast you're running compared
to the rate we say you should be running at.
The knowledge that we acquire,
if we never go greater than 60%,
either we have crappy operators,
or our standard is too high.
- [Zack] The machine was installed incorrectly.
- That's right.
So, then we lower the standard,
so that the number moves up, okay.
That's acquisition of knowledge,
and that's what we do with that information.
But with machine learning,
machine learning is concerned with more accurately
acquiring that knowledge over time.
And so, machine learning is gonna automatically account
for the fact that we used the wrong standard
to compare our efficiency to begin with.
It's gonna automatically account for that
as it acquires knowledge over time.
So, the fundamental difference here
is that machine learning is focused on acquiring
the knowledge of efficiency over time,
and the algorithm itself uses the information
that it's learned over time to more accurately give you
this efficiency calculation, as opposed to OEE,
which is static.
When we first define the OEE calculation,
when we first define the inputs,
these are just static, recurring outcomes.
What we ultimately want from machine learning is,
tell me how efficient I am, okay.
What we want AI to do, what artificial intelligence to do,
with that efficiency number,
is we want artificial intelligence to
take that efficiency number,
and a whole bunch of other variables,
and tell us what we should change about our business,
so we can become more efficient.
The way we do this already, with OEE,
is we say if the availability number is low,
it means our equipment's down too much,
let's go talk to the maintenance group.
"Hey, you've gotta get equipment up and running,
"deploy a preventative maintenance program,
"change these bearing more frequently,"
that kind of stuff.
When it comes to quality, we've gotta say, hey,
we need to, if we see a low number,
there's a human being that looks at that,
if the human being doesn't look at the number,
doesn't look at it at all, and it just gets stored,
which is, by the way, very common,
that you may have really low quality numbers
and not actually know it, you may just be accepting it.
But, if we look at the number, we see the numbers are low,
then we do a deeper dive and we say, you know,
do I have poor raw materials, am I using crappy tooling,
do we have poor engineering in the equipment,
what's the reason we're producing bad quality parts?
And then the performance number is,
if we're running at low performance,
are our operators taking, I mean,
are they just shutting the machine off,
and taking long breaks,
or are the running the machine too slow,
all these types of things.
These are open loop decisions.
Machine learning is more concerned with accurately acquiring
the accurate knowledge about our efficiency.
AI is gonna go ahead and consume that information,
maybe consume this information too,
and spit out the optimal, "hey we need to change this."
A recommendation from AI may be something like,
"your standard rate's too high.
"the number you are comparing yourself against
"is not accurate,
"and you're just using the wrong standard rate.
"do you want to change it?"
So, anyway, these are the initial steps in machine learning,
so if I'm making the value proposition for machine learning,
right, if I'm making that value proposition,
this is the way that I'm pretty much
gonna make that argument.
- [Zack] What video should I watch next?
- The video that you should watch next is probably
the steps to AI,
as opposed to the steps to machine learning.
This is really the step to machine learning.
There's a higher level component here, I mean,
the reality is that you do machine learning
before you ever get to AI.
Right now there's this, machine learning and AI,
they're just kind of, they're used interchangeably,
but they're not.
We've established that machine learning
is concerned with the accurate acquisition of knowledge,
and I think over a period of time,
you have to acquire that accurate knowledge
before you can ever even think about leveraging AI, right.
We'll probably shoot that next, but yeah, yep.
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