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Hi, I'm Colin Masson.
I'm the Industry Solution Director for Manufacturing for
Microsoft Cloud and Enterprise Team.
And I really wanna talk to you today about how advanced analytics is at
the core of the digital transformation that's happening in
manufacturing.
If we really look at what is happening in manufacturing,
we're moving from kind of traditional push
linear supply chains to really putting the customer and a whole new
generation of smart products at the core of what is often
been called the Fourth Industrial Revolution in manufacturing.
And if I kind of net it out, there's a lot of words on the slide here,
and we kind of built out the whole picture.
But it's really about moving from selling products
to really selling products as a service.
And so almost every manufacturer that I'm talking to is in some shape
or form trying to work out how do they monetize service.
And they monetize the new insights that they're getting from deploying
smart products usually powered by the Internet of Things or
Industrial Internet of Things as it's often called.
And that leads us kind of into the role that advanced analytics has to
play in this whole transformation that's going on.
So not only is product as a service at the core of this digital
transformation.
And often people will talk about the servitization of manufacturing,
another term that you may hear.
If we look at this whole transformation, it's driven by
many of our needs to get these custom products and
deliver a custom service, increase our margins.
But the guys that really have to deliver on all of this,
are the core guys in manufacturing.
So now if we look at the role advanced analytics is playing in
this transformation, you probably see where I'm headed already.
We've kind of got all of this change that's been forced on manufacturing.
They're trying to get new customer insights from those smart
products that they've got out there.
They're trying to ensure that their workforce in the ground,
in the digital factory are given as much insight as possible
into what they need to make and how they need to make it.
And so, they're trying to do all of this with
what is still a growing skills gap in manufacturing.
And this is where advanced analytics and certainly our approach,
Microsoft specific approach to advanced analytics, can really help.
And we've kind of got two things that we're trying to
do to make sure that the skills gap in manufacturing, and the fact that
data scientists are in huge demand that we can basically address this
need to apply advanced analytics technology to address this digital
transformation in manufacturing with a couple of different approaches.
On the left hand side of the slide here,
we're looking at the delivery of what we call a solution template.
So we have Cortana Intelligence Solution templates
that give you a fast start.
You don't have to be an expert in data science
to leverage these, but they get you up and
running, let you get a proof of concept going very quickly.
And one of those examples is kind of in quality assurance and
I'll demonstrate that to you shortly.
Another key area is demand forecasting.
And demand forecasting is important because we kinda need to give
manufacturing as much notice as possible of what they need to make
and when.
What I'll also talk about is some finished solutions that our
partners of delivering where pretty much that's delivered as an app.
And I'm gonna highlight a couple of examples from Wipro
using advanced analytics for asset failure analysis, and
Hitachi who are doing something with IoT Predictive Analytics for
connected field service scenarios.
So taking a quick look at the quality assurance solution template,
this kinda quick start for
you to go after quality assurance problems in manufacturing.
What we've done here is basically provide a solution that really lets
you get at the root cause analysis of quality problems
in a complex manufacturing process,
where you may have multiple steps in manufacturing.
The product may pass your traditional tests at each step and
yet fail at the end or actually fail when it gets to the customer.
So actually getting to the root cause analysis of the problem and
what is causing that problem in each step of manufacturing,
whether that's machine tolerances.
Whether it's the operating conditions of the machine and
correlating that with quality data
is a technique that we've used with Jabil.
And I'm gonna demonstrate that solution to you in more depth.
Okay, let's take a quick look at the Quality Assurance Solution template
for manufacturing.
If we just click on Try It Now,
You'll see a Power BI dashboard here.
I'm just gonna click down on the left-hand side just to click into
the Introduction before I show you, and
this kind of tells you a little bit about the solution template itself.
It's really allowing us to take a deep
dive into a multi-step manufacturing process.
And actually build a model from the quality results that are taken from
each step, as well as final testing, and even
failures and quality results we may get after it's left manufacturing or
returns, and actually put together a model that allows us to predict
failures in a more amenable way.
And as I discussed earlier,
that means we can kind of predict a failure maybe at step two in this
five step process that we're showing here.
This happens to be based on some work that we did with Jabil,
the third largest contract manufacturer in the world.
If you look at the actual solution template,
we are simulating five steps in a process similar to tables.
And at each one we've kinda put together all the pieces that you
need to basically be able to do that kind of correlation of the IoT
data that you're getting from floor, the quality test data that you're
getting from the floor, and combine that to build
a model that actually predicts when a part is gonna fail.
So then you can stop a part going from step two to step three
which may be a very important step in the case of Jabil,
that is when they start to add the expensive components onto the board.
So that's something you can't reverse, you can't rework.
So if you know the board has already failed at step two,
you kinda want to be able to reject that.
And so we've put together a simulator here.
Not a very good production line in this particular case.
Cuz if we really had this kind of failure rate all the way through,
we'd be getting something like 3 to 5% managing to get to step five.
But for the purposes of you to basically be able to simulate and
then start to plug in your own IOT data.
And get a solution up and running very quickly
to kinda do this quality assurance in manufacturing and
avoid those kind of expensive scrap or rework situations.
This is a really good representative solution for manufacturing.
Okay, let's look at demand forecasting and
the solution template here.
This particular case what we're doing is really
taking a look at how we can actually make life easier for
manufacturers again by doing a better job of forecasting.
So, as I mentioned, there's a lot of change that is being
forced on manufacturing to switch between products because of
the proliferation of products or skews that they're expected to make.
In kind of a new assemble to order, configure to order or
even engineer to order that customers are demanding.
So what we're doing here is really allowing you to look at the prices
you should be charging for the products that you are making.
But also start to actually model how you could shape demand.
So how could you actually smooth out the number
of change overs potentially that you have to do back in manufacturing.
Or maybe not giving the right answer for manufacturing, but
doing the right thing for the business, it might be,
how do I optimize the product mix,
the right mix of products that is gonna give me the maximum profit.
And so, this is obviously a common problem.
It doesn't just apply to manufacturing,
it applies to consumer goods and other areas as well.
But it has particular relevance to manufacturing.
So I do wanna highlight this one as well in terms of being able to
smooth out some of that demand volatility that is kind of forcing
its way all the way back through the supply chain to core manufacturing.
Now, I wanna highlight a couple of finished solutions from partners
that are really helping us address both the examples I'm gonna show in
the next couple of slides are kinda in that servitization area,
where people are trying to drive improved
field service of products that they've already sold.
In this case, Wipro with their Asset Failure Analysis,
are really providing the complete tool set
that you need to really get at the root cause analysis of failures.
And so they're using a combination of logistic regression and
artificial neural networks to kinda get at the root cause
of failures of complex assets.
And so they can get right down to what are the part
failures that may be causing the problem.
Of course that then means they can either do a better job
of getting those parts at the right location in the supply chain for
where those failures are gonna occur or
hopefully do something more proactive, right?
In terms of either going out and
proactively maintaining those products for your customers,
or pushing that back into the design process, or
changing a supplier if it's a particular supplier's problem.
So this application really gives you that ability to
do all of that asset failure analysis.
And of course, then you can feed that into business processes.
The next example I'm going to look at is with Hitachi.
So Hitachi are doing something similar,
albeit very focused on IoT enabled products in the field.
And so with their finished solution,
what they're doing is providing everything that you need to
intelligently predict the failure before they occur.
So they're augmenting a lot of the things that we do as Dynamic 365
Connected Field service but they're expanding on the ability
to go beyond remote monitoring into this predictive maintenance.
And so they're actually doing, also, some form of root cause analysis.
And a couple of others, I don't have slides on, but
I kinda wanna call out.
An interesting example would be
work that's being done by Blue Granite on product mix optimization.
They've done a lot of work with chemical companies, and
kinda doing clustering of
all the different products that can be made on their facilities.
And optimizing what that product mix should be, either to
help production, or to actually increase profitability.
They're also doing some creative things around applying that
to say what products could I make on similar facilities?
So I wanted to call out some work that Blue Granite is doing on
solutions.
And then another one that's very public and you can go out and
search on Bing or Google for is really by New Signature and
work that they've done with Hersheys on their Twizzlers.
And that one gets back to that manufacturing core where by
building a multi-variant model on the tanks that hold all
of that liquor-ish that goes into Twizzlers.
They've been able to basically control or
advise on the control of 22 different sensors to make
sure that they produce the right specification of that Twizzler.
If you make it too fat, as in it's 1% heavier,
you're giving away a lot of money in terms of what you're packaging.
So by actually getting tighter control over those 22 senses,
they can reduce the amount of product giveaway.
And so that's another example of really how advanced
analytics is being applied in new ways in manufacturing.
And I wanna take a few minutes to say what's different about
Microsoft's approach.
What I think as you've heard, we're kinda focused on industry solutions,
and either working with partners on finished solutions or providing
the accelerators for common business scenarios that were seeing.
And we're also deliver it in a fashion
that means you don't have to have a whole team of data scientist,
you could be a small business, large business.
It doesn't really matter you can actually spin up these capabilities
on Azure and actually get to value very quickly.
So advanced analytics certainly isn't new to manufacturing.
Many of us have had experience in operations research and
we've done Six Sigma quality for years.
And we've done multi-variant modeling for years.
And so many of us have libraries that we've built up particularly if
we have complex assets, then we may have built libraries in R or Python
or other languages and our intention is that you can still leverage that.
You can leverage that with these solutions and
take that investment forward and expand and
address the significant skills gap we have in manufacturing, but
also in recruiting data scientists.
And it's easy to get started, right?
Just go to the Cortana Intelligence Gallery to find out more about
the manufacturing vertical solution templates I showed you, quality
assurance and demand forecasting and we keep adding to that library.
I also wanna point you at, if you really want SaaS solutions,
focused industry solutions for manufacturing or
verticals, then I also wanna point you at AppSource.microsoft.com or
you can just put in AppSource.com.
And you'll kind of find those finished solutions there, quick and
easy, many of them with trials that you can spin up just as quickly as I
showed you on the quality assurance solution template.
Well, I hope you are as passionate as I am about the opportunity
to leverage advanced analytics in powering the digital transformation
in manufacturing.
Go try these solutions out today.
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