Hello
all.
I'm
David
Paige,
I'm
the
Digital
Champion
of
the
Global
Business
Unit
of
Soda Ash & Derivatives
at
Solvay
.
Together
with
me,
we
have
Carlos
Perez,
who
is
Industrial
Data
Scientist
at
Solvay
at
corporate
level,
who
will
be
co-
presenter
of
this
presentation.
This
presentation
is
about
the
scaling
up
of
the
use
of
machine
learning
techniques
in
the
chemical
process
and
concretely
at
Solvay.
Here
in
this
slide,
we
have
the
agenda
of
this
presentation.
First
of
all,
I
will
share
with
you
a
brief
introduction
of
our
multinational
company,
Solvay. T
hen
some
words
also
about
our
general
business
unit,
Soda Ash & Derivatives.
Then
here
in
the
point
number
three,
we
will
enter
in
discussion
about
how
machine
learning
techniques
helps
improve
our
production
process.
Then
we
will
go
a
little
bit
deeper
about
the
usage
of
JMP
in
our
GBU.
I
will
explain
to
you
the
awareness
sessions
and
the
training
that
we
provided
to
our
population
of
engineers.
A
lso,
we
will
see
a
couple
of
practical
use
cases.
Then
my
colleague
Carlos
will
share
with
you
one
add-
in
that
they
developed
internally
at
corporate
level
at
Solvay,
which
is
very
useful
for
us,
for
the
final
users
to
connect
to
our
main
source
of
data,
which
is
the
MES,
manufacturing execution systems.
Finally,
I
will
share
with
you
the
main
challenges
that
we
faced
during
this
journey
and
also
the
lessons
learned.
Brief
introduction
of
our
group,
Solvay.
We
are
a
science
company
founded
in
1863
whose
technologies
bring
benefits
to
many
aspects
of
daily
life.
Our
innovative
solutions
contribute
to
a
safer,
cleaner,
and
more
sustainable
product
found in homes,
food,
and
consumer
goods,
planes,
cars,
batteries,
smart
devices,
health
care
applications,
and
water
and
air
purification
systems.
Very
important,
our
group
seeks
to
create
sustainable
shared
value
for
all.
Notably,
through
its
Solvay
One
Planet
program,
we
have
three
pillars:
protecting
the
climate,
preserving
natural
resources,
and
fostering
better
life.
Here
at
the
bottom
of
the
slides,
you
can
see
some
key
figures
of
the
group
in
2021.
As
you
can
see,
we
have
a
little
bit
more
of
21,000
employees
all
over
the
world.
We
have
presence
in
63
countries
and
we
have
98
industrial
manufacturing
sites.
Now,
if
we
jump
to
our
general
business
unit
of
Soda Ash & Derivatives,
which
is
the
business
unit
that
I
work
for,
coordinating
the
implementation
of
what
we
call
the
digital
transformation
initiatives.
As
you
can
see,
we
have
11
production
sites
distributed
around
the
world.
We
have
six
production
sites
here
in
Europe,
but
also
we
have
two
production
sites
in
North
America
and
one
production
site
in
Asia
out
of
other
locations
around
the
world
like
warehousing
and
buildings.
Also,
we
have
three
R&I
centers
located
in
Brussels,
in
Dombasle,
which
is
our
manufacturing
site
in
France,
and
in
Torrelavega
in
the
north
of
Spain.
Globally,
we
are
3,200
employees
all
over
the
world.
Our
products.
These
are
the
two
main
products
that
we
produce
in
our
general
business
unit.
We
produce
soda ash
and
we
produce
sodium
bicarbonate.
The
soda ash
is
mainly
used
for
the
glass
manufacturing
production,
different
types
of
glass
for
building,
but
also
for
photovoltaics
panels
or
for
containers,
as
you
can
see
here,
the
example
of
the
bottles.
Also, soda ash
is
used
to
produce
detergents
and
very
important
and
very
new
with
the making of the
[inaudible 00:04:44]
of
electrification
that
we
are
seeing
around
the
world.
Soda
ash
is
also
used
for
the
production
of
helium
for
the
batteries.
Bicarbonate.
Our
sodium
bicarbonate,
it's
used
for
different
markets.
First
one,
for
the
exhaust
gas
industry
cleaning,
which
is
our
SOLVAir
market.
A
lso
a
very
new
application
for
the
same
purpose,
gas
cleaning,
but
for
the
ships.
The
sodium
bicarbonate,
it's
also
used
for
the
pharmaceutical
industry
and
for
the
food
industry.
In
this
slide,
I
would
like
just
to
show
you
about
the
complexity
of
our
production
processes
to
manufacture
our
final
products.
As
we
said,
our
final
products
can
be
soda ash,
light
or
dense,
and
also
the
refined
bicarbonate.
To
produce
them,
we
need
to
consume
different
raw
materials
such
as
the
limestone,
the
brine,
and
also
we
use
the
coke and anthracite in
our
lime
fields.
As
you
can
see,
the
the
production
process
is
quite
complex
because
we
are
using
different
assets
like
the
absorbers,
like
distillation
in
the
distillation
sector,
dissolvers
,
precipitation
columns,
filters,
compressors.
We
have
a
long
variety
of
assets
used
in
the
manufacturing
process
and
very
complex
chemical
reactions,
mixing
gasses,
liquids,
solids.
We
need
to
take
into
account
thousands
of
parameters
in
terms
of
temperature,
pressure,
flows,
and
so
on.
It's
very
important
the
use
of
advanced
analytics
and
machine
learning
techniques
in
order
to
improve
this
production
process.
Here
we
are
entering
in
the
chapter
for
how
machine
learning
can
help
to
improve
our
production
process.
First
of
all,
to
share
with
you
our
strategy.
Clearly,
in
the
soda ash
and
bicarbonate,
our
strategy
is
to
be
competitive
and
keep
our
leadership
worldwide
position
in
this
global
commodity
market,
which
is
soda
ash,
but
also
in
the
premium
market
as
the
buyer.
What
is
our
objective
to
reach
this
ambition
in
our
strategy?
Our
objective
is
to
reduce
as
much
as
possible
the
variable
and
fixed
cost
in
our
manufacturing
sites
while
ensuring
the
overall
equipment
efficiencies, so
the
OEE,
and
the
quality
of
our
products.
Let
me
put
some
examples
of
how
we
can
impact
in
our
variable
cost
and
fixed
cost.
In
the
variable
cost
side,
clearly,
one
of
the
levers
that
we
can
improve
is
the
yield.
If
we
are
able
to
increase
our
sodium
precipitation
yield
in
our
carbonation
sector,
clearly
what
we
are
going
to
do
is
to
reduce
the
need
of
raw
material
and
energy
in
our
production
process
to
produce
the
same
quantity
of
soda ash.
The
same
for
the
topic
related
with
energy
efficiency.
In
the
previous
slide,
I
showed
you
the
complexity
of
the
production
process
and
the
energy
that
we
need
to
use
in
the
different
sector
such
as
the
distillation
sector,
calcination
sector,
or
lime
kilns.
If
we
are
able
to
improve
the
main
parameters
on
these
sectors,
we
will
be
able
to
reduce
the
specific
consumption
of
energy
in
our
production
process.
In
terms
of
fixed
costs,
one
of
our
main
fixed
cost
in
our
production
process
is
the
maintenance
cost.
We
have
unplanned
events,
unplanned
mechanical
breakdowns
in
our
industrial
assets,
and
also
we
perform
regular
maintenance
activity
and
cleaning
of
our
assets.
If
with
these
machine
learning
techniques
we
are
able
to
anticipate,
to
predict
these
unplanned
events,
we
could
also
potentially
reduce
our
fixed
costs.
Our
idea,
our
ambition
is
to
combine
the
deep
expertise
that
our
process
and
control
engineers
have
on
the
domain,
the
soda ash
production
process,
together
with
the
IT
and
computer
science
skills
and
math
and
statistics
skills.
This
is
our
ambition.
Traditional
method.
Traditionally,
what
our
engineers
is
doing
is
to
use
the
inputs
of
our
process
using
a
theoretical
model.
For
example,
the
thermodynamics
or
the
chemistry
in
order
to
understand
the
process
and
to
get
an
output.
This
is
the
traditional
method.
But
now
with
the
machine
learning
techniques,
what
we
can
benefit
for
is
about
the
historical
data.
In
our
site,
as
I
explained
before
in
this
very
complex
environment
of
the
production
of
soda
ash,
we
store
thousands
of
different
sensors
data
in
our
MES
systems,
in
the
manufacturing execution systems.
Data
from
temperature,
flows,
pressure
in
different
parts
of
the
process.
We
have
this
historical
of
data,
so
we
can
provide
with
the
machine
learning
algorithms,
inputs
and
outputs
of
our
process.
Creating
machine
learning,
big
data
models
that
could
help
us
to
improve
our
process
and
to
understand
better
our
process
for
the
future
inputs.
Now,
here
in
this
slide,
just
to
share
some
publications,
also
from
Dow,
another
important
multinational
chemical
company,
that
is
sharing
with
us
here
that
a
chemical
company
must
invest
to
create
a
critical
mass
of
chemical
engineers
with
technical
skills
in
statistics,
mathematics,
modeling,
optimization,
process
control,
visualization,
simulation,
and
programming.
But
it's
much
easier
to
train
chemical
engineers
on
data
analytics
topics,
rather
than
to
train
data
scientists
on
chemical
engineering
topics.
We
completely
agree
on
this
statement,
and
this
is
what
we
want
to
do
at
Solvay.
We
have
a
lot
of
very
skilled
chemical
engineers,
and
we
want
to
train
them with
this
advanced
analytics
techniques.
T
his
is
the
main
reason
why
we
launched
the
program
of
Machine
Learning
Techniques
with
JMP
in
our
GBU,
our
Global
Business
Unit.
It
was
a
program
that
started
in
2021.
The
target
population
of
this
program
was
47
engineers
in
our
GBU. I
t
was
led
by
the
industrial
data
science
team
at
corporate
level.
What
was
the
content
of
this
program?
We
had
one-
hour
session,
first
of
all,
one
day,
where
we
explained
why
we
want
to
use
the
machine
learning
techniques
with
JMP
in
order
to
improve
our
production
processes,
as
I
explained
you
before.
Then
during
seven
days,
we
made
an
individual
online
course
for
each
of
the
47
engineers
related
to
a
statistical
thinking
part.
It
was
just
an
introduction
of
a
statistical
thinking
methodology.
Then
we
enter
it
on
the
JMP
introduction
part,
explaining
the
tool,
explaining
the
main
feature,
the
benefits
of
using
JMP,
and
the
main
tips
to
start
creating
some
graphics,
some
statistical
reports,
and
some
basic
things.
We
combine
theoretical
lessons
with
practical
exercises
and
planarizations
of
the
web.
Then
during
15
days,
we
enter
it
in
more
details
about
what
we
can
do
related
with
machine
learning
techniques,
which
we
did
the
same
individual
online
course
and
also
practical
exercises
and
plenary
session.
All
of
this
program
training
last,
let's
say,
around
one
month.
But
then
the
most
important
part
was
the
selection
of
the
real
cases
to
solve
in
the
different
manufacturing
sites
for
the
different
participants
of
the
training.
We
made
this
selection
and
we
provide
a
license
of
JMP,
of
course,
and
a
regular
support
with
weekly,
linear
meetings
and
individual
coaching.
Let
me
put
two
examples
of
practical
use
cases
about
this
selection
that
we
did
afterwards
this
training
session.
The
first
one
is
about
increasing
the
sodium
precipitation
yield
in
the
production
processes
of
Rheinberg,
our
manufacturing
site
in
Germany,
and
Torrelavega,
our
site
in
Spain.
How
we
use
the
JMP
on
this
project?
First
of
all,
to
screen
the
multiple
variables
that
we
think
that
can
impact
our
main
target
variable,
which
is
the
sodium
precipitation
yield,
in
order
to
explain
the
variability
of
this
target.
The
goal
was
to
investigate
what
are
the
variables
that
can
explain
better
the
variability
of
our
target.
For
this,
we
use
one
of
the
tools
that
we're
learning
during
this
one
month
course,
which
is
the
predictor
screen.
This
is
very
important,
because,
as
I
explained
to
you
before,
we
have
hundreds
of
variables
impacting
this
output,
which
is
the
yield
in
the
process, s
o
it's
very
difficult
to
analyze
one
by
one.
This
tool
allow
us
to,
in
a
very
quick
way,
in
a
very
fast
way
and
intuitive
way,
to
understand
what
are
the
main
contributors
explaining
the
variability
that
we
have
in
our
tool.
Also,
we
need
to
say
that
JMP
is
a
very
intuitive
and
code-
free
advanced
analytics
tool,
and
this
is
very
important
because
the
production
engineers,
not
all
of
them
have
the
knowledge
to
use
these
programming
code
tools.
Also,
important
to
say
that
to
visualize
the
long-
term
variability
of
the
target,
but
also
its
relationship
between
the
most
important
variables
is
a
very
important
feature
that
JMP
has.
Finally,
also,
we
use
JMP
to
elaborate
the
statistical
reports
about
the
performance
of
the
different
approaches,
different
trials
that
we
perform
along
the
project.
This
is
the
first
example
where
we
use
JMP
in
order
to
understand
better
our
process
and
improve
our
yield
in
both
of
our
management
designs.
Second
one,
in
this
case,
we
are
talking
about
finding
the
root
causes
for
the
variability
of
one
important
parameter
of
the
final
product,
which
is
the
carbonate
content
in
the
sodium
bicarbonate.
Here
we
use
JMP
similarly,
like
in
the
project
that
I
explained
before,
we
screen
the
multiple
variables
and
select
the
most
important
ones
to
explain
the
variability
of
this
target.
For
this,
of
course,
we
use
to
use
it
again,
the
predictor
is
giving
input,
as
you
can
see
here
in
the
right-
hand
side
of
the
slide.
Also
here,
it
was
very,
very
important,
the
visualization
in
a
graphical
way,
the
interaction
of
the
main
variables
that
we
identified
thanks
to
the
predictor screening.
You
need
to
understand
that
on
this
type
of
projects,
we
need
to
collaborate
with
different
stakeholders.
The
production
engineers
cannot
solve
this
type
of
very
complex
projects
alone.
Here
we
need
to
align
and
speak
and
generate
debates
with
the
production
operators
on
the
field,
with
production
operators
in
the
control
room,
with
site
managers,
other
engineers
in
other
plans,
experts
at
corporate
level,
and
so
on.
It's
very
important
to
translate
what
we
analyzed
in
analytical
way,
in
a
graphical
way
to
make
and
generate
these
debates.
Finally,
also
very
important
to
help
on
the
decision-
making
process
in
order
to,
at
the
end,
of
course,
taking
decision
on
these
main
variables
that
we
demonstrated
in
an
objective
way
to
the
people
that
at
the
end
decides
to
make
a
modification
in
the
process
to
take
these
decisions.
This
is
what
we
have
done
also
in
this
project.
At
the
end,
it's
about
make
a
modification,
make
a
small
investment
to
modify
a
part
of
our
installation
in
order
to
reduce
the
variability
of
this
carbon
content
of
the
final
product.
That's
all
for
my
side
for
the
moment.
Now,
I
will
give
the
floor
to
my
colleague,
Carlos,
data
scientist
at
the
corporate
level,
who
will
explain
to
you
an
add-
in
that
we
developed
internally at
Solvay
that
allow
us
to
connect
the
data
from
our
MES,
which
is
the
player,
as
I
explained
before,
where
we
store
all
the
data
into
JMP.
Thank
you,
David.
I
will
go
ahead
and
share
my
screen. Can you all
see?
Yes.
Okay,
I
want
to
get
started.
Do
you
see
this
ribbon
or
it's
only
me?
Yes,
it's visible
.
Yes,
thank
you,
David.
In
this
section
of
the
presentation,
I
will
show
you
one
tool,
one
add-
in
to
demonstrate,
an
open
source
add-in
that
we
have
created
in
the
team
of
industrial
data
scientists
at
corporate
level
in
Solvay. T
his
is
a
team
that
supports
all
of
the
global
business
units.
That
means
that
we
have
to
provide
for
solutions
for
all
of
the
different
MES
that
exists
in
Solvay.
We
did
the
automation
of
this
task
because
we
saw
the
situation
that
was
happening
before
where
we
had
to
download
the
data
in
a
spreadsheet
sometimes
without
a
lot
of
advanced
capabilities.
Then
we
had
to
import
this
data,
treat
it,
and
then
finally
be
able
to
use
it
without...
Sometimes
it
was
not
even
clearly
identified
because
it
was
only
the
name
of
the
sensor,
but
sometimes
the
name
of
the
sensor
is
not
very
clear,
because
it's
not
very
well
standardized,
the
notation.
To
leverage
the
power
of
data,
we
say,
"Okay,
let's
make
the
process
of
extracting
the
data
as
automated
as
possible
so
that
all
the
process
engineers
can
use
it."
We
have
leveraged
this
power
in
JMP,
in
GBU
soda
ash,
and
also
in
other
GBUs
with
an
add-in
that
is
able
to
connect
to
the
two
most
common
data
bases
in
Solvay
that
are
the
MES
historians,
IP21
and
PI
from
Aztec
and
from
AVEVA,
respectively.
This
add-in
connects
directly
to
the
databases
if
we
are
in
the
local
network
and
is
able
to
fetch
with
a
query
whatever
information
is
stored
there.
We
have
automated
the
task
of
connecting
to
the
server,
selecting
the
query
parameter,
downloading
the
sensor
data
table
in
a regular
format
with
the
description
and
units.
A
lso,
we
have
integrated
other
functions.
It's
also
worth
mentioning
that
in
this
case,
we
are
dealing
with
a
lot
of
sites.
The
sites
of
soda a
sh
are
among
these,
of
course,
where
we
use,
as
I
mentioned,
two
main
databases
for
historian MES,
and
where
this
is
more
or
less
the
range
of
sensors
that
we
have
to
take
into
account.
This
is
how
it
looks
today
the
add-in
which
is
available
from
the
menu
add-ins.
It
was
developed
from
an
application
menu,
and
it
has
also
some
script
on
the
background.
I
will
show
a
clear
demo
with
this,
so
bear
with
me.
It's
a
focus
on
process
engineers.
As
I
mentioned,
it
integrates
a
description
and
engineering
unit,
which
is
very
useful
for
identifying
what
data
you
are
using
in
your
analysis.
A
t
the
end
of
the
day
you
get
two
data
tables.
One
of
them,
so
what
I
see,
what
you
have
with
typical
statistics,
and
the
other
one,
which
is
a
time
series
with
all
the
details
of
every
sensor
according
to
your
extraction.
But
above
that,
the
functions
that
I
mentioned.
I
will
go
out
of
this
script
to
be
able
to
show
you
the
demo
of
this
tool.
Just
bear
with
me.
This
is
JMP,
as
you
know
it.
Here
we
have
an
add-in
that
when
it
is
connected
to
the
database,
you
are
able
to
see
all
the
list
of
servers
here
in
the
list.
It
is
connected
to
a
server
list
that
is
maintained
by
another
team,
which
is
an
IT
team.
In
this
server
list,
it
is
also
possible
to
modify
the
details
in
case
one
of
the
servers
is
not
available.
For
example,
one
can
put
the
IP
address
and
domain
here
to
add
a
new
server
that
is
connected
to
the
internal
network
of
Solvay.
After
that,
after
selecting
your
server,
the
next
step
is
to
go
ahead
and
filter
your
sensor
by
name
or
description.
This
is
important
because
as
we
mentioned,
we
have
in
the
order
of
thousands
of
sensors,
which
means
that
if
you
are going
to
go
ahead
and
try
to
see
everything
that
is
available,
it
might
take
a
long
time
or
the
server
might
crash.
For
that
reason,
we
have
this
filter
so
that
you
can
see
what's
relevant
to
you
in
case
you
want
to
see
flow
sensors,
temperature
sensors,
pressure
sensor,
or
you
want
to
look
the
three
sensors
by
description
or
both.
After
you
are
done
with
this
filter,
what
you
need
to
do
is
select
the
relevant
tags
from
this
other
list
which
are
given
in the
presentation,
just
an
example.
But
in
this
case,
I'm
not
connected
to
a
local
network,
but
you
can
see
an
example
here.
You
will
see
here
all
the
list
of
available
sensors,
and
what
you
need
to
do
is
to
add
them
to
right
-hand
side,
which
is
in
this
way.
The
right-
hand
side
list
means
that
these
sensors
are
ready
to
be
extracted
for
you.
Now
what
you
need
to
choose
is
the
start
time
and
end
time
for
your
extraction.
You
select
that,
it
perhaps
is
one
day,
one
month,
one
year,
I
don't
know.
Then
you
have
to
choose
what
type
of
method
you
want.
The
most
common
is
interpolated
because
it
means
that
you
will
have
evenly
spaced
data
by
minute,
by
second,
by
hour
or
by
day.
Also,
we
offer
an
aggregation
that
is
in
this
case
is
the
average.
A
lso,
we
offer
to
extract
the
actual
data
as
it
was
recorded
by
the
sensor.
One
more
thing,
if
for
some
reason
you
already
know
the
list
of
sensors
that
you
want
to
download,
and
you
don't
want
to
browse
by
name
or
description,
you
can
directly
paste
also
this
list
from
a
CSV
format
that
you
have
available.
When
you
have
all
these
parameters
ready,
what
you
have
to
do
is
heat
from
extraction.
T
his
will
take
the
time
it
takes
the
SQL
query
to
go
to
IP
21
or
PI.
W
hen
it
finishes,
you
will
get
two
tables
with
what
I
mentioned
before.
One
with
summary,
which
will
allow
you
to
understand
the
typical
statistically
values
for
each
sensor,
row
by
row.
In
this
case,
you
have
the
name
of
the
sensor,
description
units,
and
also
the
mean,
standard
deviation,
max,
mean
range
of
the
sensor.
In
this
way,
you
can
understand
if
the
sensor
is
perhaps
not
working
or
something
odd
is
going
on
so
that
you
don't
need
to
extract.
Furthermore,
you
will
also
get
the
time
series
data,
which
in
this
case
looks
like
this.
You
get
a
column
with
the
time
stamp,
then
also
one
column
by
sensor
with
the
proper
format.
For
example,
this
is
a
continuous
amount,
this
is
a
discrete
amount,
and
everything
is
properly
formatted.
On
top
of
that,
you
get
the
description
of
the
sensor
as
I
mentioned,
and
the
units,
which
is
very
useful
for
processing
units.
This
allows
you
already
to
apply
all
the
methods
from
JMP.
Here
you
have
an
automated
version
of
the
add-in
that
allows
you
to
extract
data
directly
to
JMP.
It's
also
open
source,
so
if
you
are
interested
in
contributing,
you
can
go
to
the
community
in
JMP
or
in
GitHub
and
contribute
your
own
developments.
On
top
of
that,
we
also
offer
three
functionalities,
which
are
the
update
table.
The
update
table
will
make
sure
that
when
you
are
done
extracting
your
data
and
you
perform
one
analysis,
you
can
keep
updating
the
same
analysis
the
next
day.
For
example,
let's
say,
yesterday
I
downloaded
this
data
and
I
created
one
column
for
calculating
some
value.
L
et's
say
that
today
I
want
to
also
see
how
this
calculated
value
is.
That
means
I
just
have
to
hit
this
button
and
this
data
will
be
updated
with
the
newest
data
from
yesterday
to
today.
Also,
we
offer
a
refresh
functionality
in
which
it's
meant
to
work
as
some
dashboard.
This
means
that
it
will
fix
time
window
and
you
will
be
able
to
see
your
analysis
with
respect
to
the
current
time.
That
means
that
if
I
perform
an
analysis
yesterday
and
I
have
a
new
column
with
a
new
formula,
I
can
hit
this
button
and
only
see
the
relevant
data
table
for
the
actual
period
for
getting
the
past.
That
means
that
as
I
said,
some
fix
window
is
fixed,
and
then
you
can
see
the
same
analysis
with
the
current
time
instead
of
with
the
old
time.
One
of
them
will
only
see
one
time
window
and
the
other
one
will
update
the
full
time
window.
Furthermore,
you
also
have
the
add
new
tags,
which
means
that
for
some
reason
you
forgot
to
add
a
tag
and
you
are
remembering
that
it's
very
important,
you
can
add
a
new
functionality.
With
all
this
said,
I
will
go
to
the
next
slide.
That
means
that
by
this
point
you
have
already
a
nice
data
table
in
JMP,
which
with
all
these
functionalities
that
we
mentioned,
update
table,
refresh
table
and
add
new
tags.
This
allows
you
already
to
use
the
typical
methods
for
advanced
analytics
in
JMP.
For
example,
this
one
I
am
showing
here
both
the
JMP
and
JMP
Pro
version,
but
this
is
up
to
you.
We
also
empower
the
user
to
use
another
add-in
that
we
also
developed
that
is
called
Predictor
Explainer,
which
will
be
presented
in
another
Discovery
talk.
We
also
have
other
types
of
analysis.
This
will
allow
us
to
perform
the
typical
task
in
data
analytics,
which
could
be
root
cause
analysis,
anomaly
detection,
process
optimization,
and
others.
With
this,
I
will
let
David
to
conclude
on
the
presentation.
Yes,
thank
you
very
much,
Carlos.
I
don't
know
if
you
are
seeing
now
my
screen.
If
you
stop
sharing,
maybe.
Yes,
stop share
.
Okay.
Good.
Let
me
reshare
the
screen.
Yeah.
Okay.
Could
you
see
now
my
screen?
Yeah.
Perfect.
Thanks,
Carlos.
Thanks
for
your
support
that
you
provided
to
our
GBU,
not
only
developing
this
add-in,
but
also
coaching
our
production
process
engineers
on
JMP, too
.
Last
slide
to
share
with
you
what
are
the
main
challenges
that
we
faced
during
this
journey
of
scaling
up
the
usage
of
JMP
in
our
GBU,
and
also
the
lessons
learned
and
the
next
steps.
Today,
let's
say,
that
around
20
%
of
the
target
population
that
two
years
ago
we
started
with
this
program,
is
continue
using
JMP
today
in
a
routine
basis.
The
main
blocking
points
that
we
found
are,
of
course,
resistance
of
change.
Some
people
are
more
comfortable
using
another
tools
like
Minitab
or
only
Excel
files.
In
any
project
or
initiative
that
requires
a
change
in
a
tool,
there
is
always
this
resistance
of
change
that
requires
time
and
efforts
to
change.
But
another
reason
is
also
the
lack
of
time.
Lack
of
time
that
is
linked
also
to
priorities.
The
priorities
of
the
role
of
production
and
process
engineers
is
not
always
fully
oriented
on
process
optimization
only,
because
sometimes
there
are
too
much
reporting
to
do
and
other
topics
to
cover
in
their
role.
The
main
points
to
keep
during
this
process
are
this
type
of
awareness
that
we
did
with
the
practical
industry
success
for
example.
This
is
very,
very
important.
In
order
to
convince
the
people
and
to
show
the
value
to
use
machine
learning
techniques
to
improve
our
process
and
to
reach
this
competitive
level
that
we
want
as
a
company,
as
a
business,
we
need
to
use
these
practical
industry
successful, for
examples.
Because
this
is
related
with
a
population
of
chemical
engineers
that
they
will
not
understand
if
we
start
to
talk
about
different
examples
in
marketing,
in
finance,
to
improve
all
of
these
other
areas
with
which
we
need
to
show
them
clear
and
concrete
examples
related
to
process
industry.
Then
also
an
important
point
about
the
importance
of
this
predictor screening
tool
as
a
kit
tool
for
us
for
the
variability
sourcing.
The
main
problem
that
we
have,
as
I
explained
it
before,
is
the
variability
that
we
have
is
certain
parameters
of
our
process
that
we
need
to
reduce.
If
we
are
able
to
reduce
this
variability
of
the
key
parameters,
we
are
going
to
really
reduce
our
variable
and
fixed
cost
in
our
production
manufacturing
sites.
This
tool
is
very
important
for
our
production
engineers
to
find
the
root
causes
of
this
variability
and
act
on
them.
Also
an
important
thing
is
this
combination
that
we
did
between
planarization,
so
all
together
sharing
thoughts
and
experiences,
but
also
with
individual
practice.
P
rovide
time
to
the
people
to
practice
by
their
own
and
then
exchange
in
a
common
call.
Finally,
the
points
that
we
identified
to
reinforce
and
to
implement
in
the
near
future
are,
first
of
all,
of
course,
in
order
to
tackle
this
problem
of
resistance
of
change,
we
need
to
convince
the
site
management
about
the
importance
of
analytics
for
the
production
and
process
engineers.
We
need
to
launch
a
series
of
awareness
sessions
dedicated
for
them.
This
is
in
the item
we
are
going
to
do
a
lot
of long
this
year.
Also
very
important
for
us,
we
identified
this
strong
individual
coaching
for
the
production
and
process
engineers
when
they
start
to
use
JMP
in
the
real
cases,
in
the
real
projects.
Because
JMP
requires
time,
the
different
tools
as
per
the
tool
screening
and
other
tools
requires
time.
It's
very
important
for
the
very
first
projects that
one
engineer
developed
using
JMP
to
have
a
good
coach,
a
good
trainer
to
have
a
company
during
the
process.
That's
all
from
our
side.
Thanks
a
lot
for
your
attention.
If
you
have
any
questions
for
me
or
for
Carlos,
we
are
available.
Thanks
a
lot.
