Hello everyone
It’s a real pleasure to be here and present this project for you, which is
a client testimony for a project
that we undertook at Airbus to visualize
the complexity of the architecture of
our airplanes using
Tom Sawyer Perspectives
I’ll just need a remote.
There we go. So, briefly, I’ll introduce this airplane,
the 380, the largest commercial airplane in the world
which defines big data
simply put. So some numbers, which you
may know
the plane can carry 800 to 850 passengers
so it’s very large, very heavy.
It only uses 3 liters of fuel per passenger.
So that’s the data, let’s say, for the airlines.
If we look at it from the standpoint of
our technical teams, we also have some very
striking data, for example the wingspan, which measures
850 square meters, and about 3 million parts to assemble
and, um
an operational reliability,
I can’t remember the word in French,
which lasts around 98% for the airline
which is of course a very important factor,
and most important of all is the quantity of software
and systems that are embedded in our airplanes. So overall
we’re talking about around 100 million lines of code
and in fact it increases from plane to plane
so we’d consider that it’s about 5 times more than in older planes
which creates a lot of complexity.
So all of this generates, for example, about
500 kilometers of cables in the airplane.
The airplane itself is 80 meters long
so that gives you an idea of what’s happening inside.
So some numbers to help you understand
the aeronautic market a little, because,
we’ll talk about quite a few things, but I just want to give you some
background information. Airbus primarily
operates in Europe, from design to manufacturing
to assembly and integration
we have 9 thousand airplanes in operation, which of course
have to be maintained, and we have a back log, so a lot of
orders already in the system
6,800, which will have us in production mode for the next
ten or so years. We also have market forecasts,
which are very important, since we estimate
that we’ll see a market increase close to 70%
notably in Asia, which will generate
a global demand of around 33 thousand planes.
So for Airbus, the challenge will be to
increase our production and our production rate
and to develop the
next generation of planes more rapidly.
Now I’d like to get into more details. So of course
this subject is fairly technical, but I’ll do my best
to make it approachable and easy to explain.
When we have an airplane like the 350
which came out a few years ago
there’s an architect that needs to, let’s say
guarantee the coherence of the plane
and the performance promised when it was sold.
So their mission is as follows, which is to validate
the architecture of the equipment
and confirm that it’s optimized.
They need to verify that everything is coherent in the plane
and they also have to find
appropriate compromises
for a plane that’s both light
not too expensive, quick to manufacture
fuel efficient
and all of these technical criteria which are very important
but can sometimes be contradictory. So they’ll have to
make decisions and trade-offs.
So to do this
of course there’s a design office that designs the plane
So, so you can imagine that’s thousands of people spread across
the different Airbus centers across Europe.
And there are thousands of data models that give us
different business logic layers
at different stages of maturity, because
of course all of this takes time, and grows in maturity, and there are different levels of
granularity, it can either be at the level of the global plane
or it can be down to the level of smaller systems,
from the landing system to the motor et cetera.
Of course we have thousands of tools that allow us to configure the plane
in its different definitions, notably the geometric definition of its systems
and we’re also talking about simulations
so all of the physical simulations
as well as the planning process.
So all of this, all of these models and tools, will store data
all over the place in our design office. And so
it’s not easy for the architect to ensure that
all of this is coherent, because they’re confronting a highly heterogeneous environment
and well, they’ll of course
spend a huge amount of time tracking down the data
to ensure that at a given time
they’re able to make a decision - is this the right choice
in this moment, the right system
the right door in the right spot, and so on.
And so that’s our big data project
to try to use
all of our data in our design office
which within each field
has its own coherence and its own logic, and its own simulations and objectives
and we want to make it accessible
for exchanges, and be able to
construct views and visualizations
that will allow us to understand their coherence.
Our architecture is very complex
which gives you another idea of why we need this.
Here you’ll see a few images of
what it’s like inside the plane. It’s complex
of course because the product itself is fairly narrow
so there are a huge number of things
that we have to take into consideration.
We have client regulations, so that’s thousands of regulations to follow
there are segregation rules for the safety of the plane
we have targets to meet in terms of the weight and price
and then there are many other fields that run in parallel
like for example the structure of the airplane, the people who run avionics
the electricity onboard, the people who tend the cabin for passengers
the distribution of air, climate control, fuel, oil, pressure and so on.
And because of all of this
each field has its own complex architecture.
And the question is
how do we ensure that at every moment
everything is coherent?
So the architect’s job is to work on the integration
to ensure that there’s a logic between
the fields, and that they’re coherent amongst themselves
with the knowledge that of course they’re
interdependent because they’re all part of the same plane.
So, we can’t just consider them as just calculations
that we superimpose, and in the end make a plane.
It’s essential to make sure that all of this is coherent.
So, they have to take into consideration the configuration of the airplane, which is to say that
there are a variety of options that are offered to the airline
and each option will have an impact on
what goes on board.
There’s a field that will take into consideration the operations
and functioning of the plane. So in terms
of the tasks involved in making the plane
and then there’s the world of the systems
on board the plane, the flight commands
which are either hardware or software
for example, the flight programs, and in this category of course there’s quite a lot to deal with.
There’s the world of the structure, so the people who will come to verify
that the plane has the right frame and
shell thicknesses, to be able to hold and manage
the flight throughout the trip.
We also have the people in the cabin
as well, who verify that the cabin is as it should be for the airline.
The people who conceive the global vision, which we call the 3D architecture
the 3D vision. And finally,
the people who take care of the industrial organization
since Airbus is an integrator
and we rely on a very large chain of suppliers.
And so very early in our design
of the plane we have a decision to make
about what the industrial system will be
and who will do which tasks.
So that’s the work of the architect, who has a hard time
maintaining all this across the entire design target.
And these are the choices they have to make.
So, we said to ourselves, let’s make a project
which we called GAIA, which I’ll explain
a little later, it’s a multi-view system dedicated
specifically to the architects, which will allow us to have
a systems engineering approach
to really follow the plane in its globality
as well as the industrial system that goes with it.
And for this we tried to create four types of viewpoints.
The first is really to focus in on the tasks
and solutions that we can have for a structure, so
all of the environments of customer requirements
What are my alternatives? What really are
my solutions? And to see clearly
all the options that we’ll have to evaluate
during the design cycle.
After that there’s another domain that’s really about the product:
How do I segment it? And what are the interfaces that I’ll have in this product?
The third component is about the industrial system.
What are, let’s say, the overall concepts?
Will we be cutting the plane
in big pieces?  Will we cut it horizontally?  What will be
the big concepts in this cutting up?
And who will we be entrusting
with these different what we call ‘work packages’ in our jargon?
And finally, we’ll want to make decisions, so we’ll have to
be able to assemble all of this data and these viewpoints
to make a decision.
So for this, we turned to our partner
Tom Sawyer Software, which is a
pioneer in graph visualization
and layouts, and has been doing it for
20-something years, and has a deep understanding
of this capacity to create views
and who developed a product called Tom Sawyer Perspectives
which brings all of the technological bricks needed to create these views
in addition to the ability to integrate data
using numerous technologies
which are mentioned here.
And so, it allows us to do this without having to worry too much
about the technology, about where the data is
within our structures across Airbus.
So, Tom Sawyer Software already works for
a number of other large accounts
and we were delighted, I’ll let you know in confidence
to be working collaboratively with them to develop this solution.
So, what does this look like? The three pillars
of our project were the following:
The first was to say that what we want is to
create a convivial and multi-view environment
that will allow us to interact with this data.
We know that we’re going to be manipulating complex architectures
and we’re not trying to make them simple, but what we want is
to be able to display what really makes sense
in order to make this decision. So, to create an environment
like before when architects had their drawing boards, what we want is to
reproduce this environment, where they’ll be able to really understand what’s happening.
The second element is to be able to use all of Airbus’
available data to its maximum capacity.
With all of the problems of heterogeneity, the formatting problems
and the language problems, where the people
in the factories don’t have the same language as the people in the design office
or drafting stage, and so on.
And the third point was to
be able to use these tools to have a systems engineering approach
that will allow us to display these views
using what we call ‘meta modeling’, in other words
making a kind of layer of abstraction
over the objects in question.
There we go.
So for the data integration element - at the beginning what we have is
all these different fields that exchange amongst themselves, and so just imagine
the operations and the systems layers.
There’s a correlation between each of these fields
with tools that help us verify how
the systems respond to one another and so on.
And so it’s an enormous number of tools, of pseudo-data
that’s intertwined and a lot of quite complex
computational architecture. And so what we want is to
draw from this, and assemble
and consolidate it. So that’s what we did
with data integration software provided by Tom Sawyer Perspectives.
It allowed us to do this mapping
and achieve what we wanted, which was to display how the data
is interdependent.
The second point was the visualization.
This was an important aspect, so that users really have
an intuitive relationship with this tool
and understand immediately the added value that it brings them.
And what’s important is to be able to navigate, research
and contextualize the search. Which means that
at a given time I should be able to say
‘I’m going to focus on this zone’, or on an electric problem,
or on an object, or on a problem from a supplier.
So I have to be able to, myself, very simply
with just my fingers, or in a way, let’s say
that doesn’t require training, be able to
start from the sum of the data, contextualize
my problem, and be able to follow the thread to
try to understand why something was done,
why it was decided to put something in a certain location,
to initiate a solution with a certain equipment and so on.
And so in this way it creates
an ensemble of views
that the user can arrange however they’d like and interact with.
And so there’s an environment that can configure
almost independently different views
whether those are configuration views, functional views
logical views, product views, and be able to
organize products in clusters
to reorganize a test cycle,
understand the 2-D architecture,
and how the industrial schema is organized.
I’ll show you a quick example.
So just before this example that I’ll show you, of course
GAIA is a platform, so we have the capability to
integrate data, to create views, and to
display and map them, and we have to, depending on usage
configure the application so that it fulfills a precise usage
for a particular job or architect.
So the example I’m going to show you has to do with the systems station
and it’s particularly pertinent for two reasons.
The first is that the data in this example
that I’m going to show you is based on
real data from the planes, so it’s not something that we made up.
It’s difficult to prove that, but
it’s what allowed us to show the data in this application.
Really the systems station data is different from plane to plane
according to the customization of the plane. The cabling
will be different
and we need to take for a certain airplane
all of the cabling data, where it was developed
which factory, and what cables and so on
and also what I’d call the functional and logical data
which is really the architectural data
of the plane. So, what we were able to do is put all of this
together.
And the other point that this example illustrates
is that the assembly of systems is truly
the encounter between the people in systems
and those in structure. Because we do have to assemble the harness
in the plane. And sometimes in the information structures
we lose this relationship between the system
and the structure, because they’re in two
very separate fields within Airbus.
And so being able to show and emphasize
how we manage this dependence between the systems and the structure
shows the capabilities of this tool.
So, if you could play the video please.
So, what you see here is the tool
and we see for example in this demonstration
we as architects are focused on a harness.
I’ll take a harness, that’s the little image that we saw before
and a harness is a pretty important element
it’s something that
we’d put together locally during an assembly.
And so in this view, which is a kind of 2D view
so not a
defining view but rather just an architectural one
we’ll load all of the data that’s involved with this harness.
Here it kind of looks like
a forest here, but it’s still much more telling
than just a chart that I could get in a production system.
And I can see where the data is
so that’s already an achievement, to be able to assemble
and map all the elements of this harness
and their position, coming from two different sources
and being able to display it very simply.
Then what I want is to reduce this. So, this harness is made of
a huge number of systems, and I want to focus, for example
just on what we call the ATAs, in other words the fields
and here I filtered onto the air conditioning field.
So, we do this filtering
and we see right away that there are many fewer lines
and so we can see, on the left hand view, which is more so the view of the product
what equipment is impacted
by this harness, and which elements
are part of the air conditioner. So, I can take a piece of equipment
you can see it’s a little internal sessonquine
so something quite small
and I can say I’d like to know
what function of the airplane this component contributes to.
So, this left hand view
allows me to take this element, or any other, I can take one from wherever,
and say, well, tell me what you do.
And so it creates a little automatic view, because I have
a functional architectural cutout that tells me the function
of each of my elements.
So, I see this is an element that helps provide
power to the ventilation system.
And the other view, in parallel
is the industrial view. So, I can see where
the equipment is assembled. Ok, so I can see that
normally it’s assembled in Toulouse,
in a station called M15, and using this I can
make a decision, if I ever want to change a harness
I can anticipate all of the parameters
of change. So, I can see where the change will need to be made
who will be the supplier,
and where it’ll take place. Because maybe in Toulouse
they’ll have trouble assembling it, or there’ll be a pacing issue that’ll need to be solved.
Voila
So, GAIA was a very successful collaboration
in our work between leading
an MBSE approach at Airbus
and the Tom Sawyer Perspectives solution
which really allowed us to carry this out very quickly
and customize it on demand for very strong requirements
from our users, to be able to create these views
and exchange them and adapt the display to make a decision.
So, we were able to
show, in the last few months
our capacity to take available data
and display it directly.
So, this was an important
message that we were able to convey
and will allow us to go on
to deployments for pilot cases, and for plane improvement projects.
So, I greatly encourage you, if you’re interested
to come and see our GAIA demo
at the Tom Sawyer Software stand, which is number 253.
Thanks so much.
Thank you.
So, Yes. Keith Haag is here with me
and I’ll hand it off to him.
