>> You're watching now
the third part of our mini series about inertial iodine.
The first two ones,
we presented the context,
set the stage, introduced a fictitious customer,
Parnell Aerospace, and the key problems they are facing,
as well as the major business outcomes they're looking into.
This part we'll look at one of these business outcome,
which is operational and asset visibility,
and it will demo how they can achieve that.
Let's discuss the business outcomes.
The first of them is operational and asset visibility,
which is about improving visibility
across connected factory assets and processes.
Offering the workers the data they need when they need it,
increasing their productivity as well
as the productivity of the equipment across all the sites.
Learning from one site,
applying in another one.
People building solutions that offer this asset visibility.
Our process engineers, line workers,
IT architects, and developers.
The people using this kind
of visibility could be process engineer,
again line workers shifts to providers,
plants managers, operators, assembly workers.
Going back to this diagram,
let's look at one of the services
that is being used to offer that asset visibility.
I want to show you the final result here,
and how a plant manager in look
into the overall efficiency of their equipment's.
Now I'm wearing the original manager for production,
for Parnell Aerospace,
and I want to see how the factories are doing and
if the production lines are being efficient in their production.
Usually the way you do that,
is by calling in the factory and
having the reports about how things happened last week,
number of parts have been created,
number of efficient parts,
and doing after the facts type of thing.
Now that we have all the assets connected through Azure,
it's essential, we'll discuss exactly how later today.
But now they have all this data in there,
what I can do is visualize that data that has been transformed,
exported from Azure Iot central into Azure Data Explorer,
which is an out of service,
meant to manage and work with time series data sets,
and I'm able to create dashboards with
rich query language that Azure detects for exposes.
Here you can see below is embedded into Iot Central,
the Azure Data Explorer dashboard that has been created.
Here we can see for
the Everett plant the production line, Number 1,
we can see that things are pretty healthy and that
we have some detailed data about
defective parts about I think it's going on.
If I want to investigate what's going on in Portland,
for example, I can switch to Portland and see all of that.
This is a very rich dashboard has been built in here,
but obviously you might want to have it
integrated into your business applications.
If I'm the power BI person,
if I'm a refiner person,
if I already have bits and pieces in an actual application,
I can leverage the integration
of these or the use of these query into
Azure data or into a richer, even richer dashboard.
A chance to refine away and you see you here are powered BI 1.
That way I'm in my business tools,
the usual ones I have my power BI dashboard for my month's sales,
and I can then correlate with stats dashboard here,
which is about the overall efficiency of my equipment's.
Here I can navigate to that data the same
way it was doing in Data Explorer,
I can switch to another in a factory,
change to production line to Number 1,
and see if everything is going okay here.
At your fingertips here,
you have all the data that's been already digested for you,
and exposed to me as a regional manager for production,
in the tools that I'm familiar with.