Hello,
everybody.
My
name
is
Ole
Lühn
and
I
am
member
of
Global
Quality
Assurance
at
BASF.
Today
at
the
JMP
Discovery
Summit
in
Europe
2023
in
Sitges
in
Spain,
I
want
to
talk
about
my
topic,
which
is
called
Don't
Lose
Your
Time
Anymore,
Automatic
Assess,
Visualisation
and
Evaluation
of
Product
Environmental.
So
how
to
get
fast
access
to
your
production
sites.
And
here
in
the
background,
we
see
the
site
I'm
working
at,
which
is
BASF
Schwarz
heide
in
Germany.
On
this
picture
and
on
this
slide,
you
see
all
the
sites
of
BASF
worldwide.
So
there
are
Verbund
sites,
which
is
a
combination
of
different
production
environments.
We
have
R&D
centers,
production
sites,
and
regional
centers.
And
for
Europe,
I
am
located
in
Schwartzheide.
And
on
the
next
slide,
I
will
show
you
a
little
bit
more
detail
where
Schwartzheide
is
located.
I
am
working,
in
fact,
with
different
sites
closely
together,
which
are
Ludwig
shafen,
which
is
the
main
part
or
the
main
site
of
our
company
with
approximately
39,000
employees.
And
Schwartzheide
is
located
roughly
140
kilometre
south
of
Berlin,
and
we
have
about
2,000
employees.
Today
at
the
JMP
Discovery
Summit
in
Sitges,
which
is
located
a
little
bit
north
of
Tarragona
and
south
of
Barcelona,
we
are
having
the
Discovery
Summit. I
explicitly
showed
Tarragona
here
on
the
slide
because
I
have
also
worked
with
them
to
do
where
we
formulate
our
end
-use
product,
which
we
sell
to
the
market.
And
on
the
upper
right
corner,
you
see
a
glance
of
Europe
side
from
BASF,
and
for
the
color
code,
please
see
the
previous
slide.
I
am
working
in
Schwartzheide,
and
here
you
can
see
the
picture
of
the
site
as
of
today.
So
the
plant
where
I
am
working
at
and
where
I
am
working
for
is
in
the
orange
rectangular
shown
here
on
this
picture,
and
on
the
lower
left,
this
picture
shows
the
view
from
my
former
office.
I
had
a
look
out
of
the
window
and
could
see
the
plant
where
I
was
working
for.
So
at
that
time,
I
was
still
located
in
a
different
office.
Of
course,
I
want
to
know
more
about
what
is
happening
in
the
plant,
and
that
is
what
my
talk
is
about,
to
have
access
to
data
in
the
plant
when
you're
not
currently
working
there.
So
the
introduction
of
BASF
and
me,
I
am
a
member
in
the
business
unit,
Agricultural
Solutions,
in
the
team
of
the
Global
Quality
Assurance.
And
I
am
a
JMP
user
since
roughly
JMP
9,
so
it
must
be
something
2010.
My
task
is
to
assure
production
processes
and
times
of quality, so
quality
assurance.
Also,
I
am
working
in
quality
management
and
I
am
a
ud
itor
for
different
ISO
norms,
and
I
am
involved
in
nonconformance
management
and
deviation
management.
For
the
final
product,
I'm
responsible
for
the
release,
and
I
also
have
to
sign
each
COA
that
we
pack
and
that
we
pack
to
the
product
shippings
around
the
world,
and
on
each
COA,
I
have
to
sign.
So
I
want
to
know
what
is
happening
in
the
production.
So
from
a
statistical
point
of
view,
I
want
to
know
if
there
are
differences
in
the
production
or
in
the
production
process
and
the
production
environment.
From
a
practical
point
of
view,
I
need
to
evaluate
if
these
differences
do
really
matter.
I
want
to
know
the
details
of
the
production
as
fast
as
possible
and
as
easily
available
as
possible,
and
that's
why
I
started
this
topic
a
while
ago.
My
goal
is
to
have
a
proper
root
cause
analysis,
preventive
and
corrective
actions
in
nonconformance
management.
The
work
started
roughly
2020
when
we
all
realized
that
the
corona
pandemic
was
developing.
And
like
in
many
companies,
there
was
a
restrictive
access
to
the
plant.
So
while
I
was
sitting
at
home,
I
was
asking
myself
how
to
get
access
to
the
data
from
the
plant
without
being
on
site.
So
how
can
I
see
what
happens
in
the
plant
when
I'm
not
really
sitting
in
the
plant?
And
how
can
I
be
part
of
the
production
and
the
production
teams
when
I'm
not
available
around?
So
the
idea
is
that
I
go
digital
to
the
plant
and
realize
what
happens.
Without
the
data
access
control
V
and
control
V,
which
takes
everybody
notices
in
our
community
days,
maybe,
or
hours
to
get
a
real
evaluation
done.
So
my
idea
came
up
while
I
was
participating
on
a
seminar
on
the
tablet
production
process
in
the
JMP
lecture
courses,
and
my
goal
was
to
have
the
highest
degree
of
automation
in
my
data
access
in
seconds.
Here
is
what
it
is
all
about.
So
here
on
this
picture,
you
see
a
general
overview
of
our
plants.
In
fact,
we
have
three
plants
which
are
producing
parallel
our
product.
And
the
very
general
value
stream
is
shown
on
the
top
of
this
graph.
We
start
with
raw
materials,
we
have
intermediates
in
our
process,
and
we
go
to
the
final
product.
So
this
data
of
the
last
three
processes
is
available
via
a
REST-API
interface
where
I
can
get
the
data
from.
All
the
data
is
prepared
in
one
table.
I
have
the
time,
I
have
my
final
lot,
I
have
the
QC
data
of
my
final
product,
and
for
my
intermediates,
I
have
different
factors
and
different
responses
available.
What
I
mean
with
factors
and
responses
is
shown
on
the
upper
right
picture,
which
I
took
from
the
JMPs
course.
This
work
was
started
with
a
colleague
from
me,
Bernd
Heinen,
who
helped
me
out
in
writing
the
scripts
and
preparing
my
data
access.
This
is
what
I
want
to
show
you
now
in
JMP.
We
prepared
our
scripts
in
a
very
easy
way.
We
put
the
REST-API
address
here
in
our
scripts.
We
do
some
scripting
instructions.
I
had
a
problem
with
the
data
time
format
when
this
table
was
imported
into
JMP.
I
was
asking
in
the
community
and
someone
gave
me
the
solution
for
my
time
formatting
problem.
I
add
some
specs
to
my
columns
and
I
save
it
on
a
drive.
I
always
save
it
as
an
Excel
file
as
well.
The
data
which
is
shown
here
is
the
data
for
one
of
my
plants.
I
opened
this
table
now.
It's
table
number
two.
Here
I
have
my
time
of
different
processes
in
the
plant.
I
have
the
lot
of
my
final
product.
I
have
the
response,
the QC
data
of
my
final
product,
responses
and
factors
of
previous
processes.
Firstly,
I'm
interested
in
the
final
product.
I
started
to
plot
the
data
in
the
control
chart
of
one
of
my
responses
for
my
final
product.
However,
it
is
an
X
-bar
and
R
-chart.
The
reason
for
this
is
that
from
my
last
intermediate,
five
lots
are
put
together
in
the
final
reactor,
and
therefore,
the
X
-bar
and
R
-chart
is
created,
and
not
the
individual
and
moving
range
chart,
which
I
am
interested
in.
So
I
need
to
do
a
little
bit
of
data
preparation
work.
The
outcome
of
the
data
preparation
work
is
shown
on
the
lower
part
of
the
slide
where
I
have
the
access
of
all
three
plants
parallel,
I
can
do
my
data
preparation.
I
use
the
function
in
JMP
to
concatenate
these
data
tables
and
I
can
plot
easily
an
overview
of
my
final
QC
data
in
this
video.
I
use
the
Fit Y
-by
-X
platform
for
the
three
plans,
use
the
column
switcher
and
recorded
a
video.
Everything
is
possible
within
seconds.
The
idea
of
my
work
started
when
I
had
a
look
at
the
tablet
production
processes
or
the
tablet
production
process
from
the
JMP
lecture.
Here
we
have
this
red
triangle
which
shows
us
suspicious
values
in
a
production
trend.
My
idea
was
to
also
create
in
my
data
table
a
column
where
I
add
suspicious
data
or
suspicious
results
and
color
it
in
a
binary
format.
I
changed
my
continuous
variable
into
a
binary
one,
like
good
or
bad.
For
me,
it
is
more
easy
to
discuss
this
with
the
team
from
statistical
point
of
view
and
everybody
in
our
community
knows
this
as
well.
There
are
more
powerful
ways
in
order
to
get
this
data
analyzed
for
regression,
for
example.
However,
I
choose
this
way.
I
will
show
you
what
I
mean.
I
prepared
this
in
my
slides.
I
wanted
to
have
these
indicators,
good
or
bad,
or
suspicious
or
not
suspicious
in
my
data
table,
so
this
data
table
is
taken
from
the
JMP
library,
the
tablet
production
process.
And
in
the
end,
I
had
my
data
prepared
like
this.
I
added
a
column
to
my
data
table.
Here,
because
I
had
five
lots
of
my
intermediates,
I
needed
to
split
my
data
first
and
to
evaluate
only
the
final
product.
I
added
a
column
where
I
evaluated
on
my
individual
and
moving
range
charts
how
is
the
data
distributed?
And
here
we
can
see
that
my
internal
specification
is
far
above
my
data.
However,
there
are
suspicious
events
in
the
production
process.
And
this
data
I
can
also
plot
in
a
dashboard
here
where
I
cut
off
above
the
upper
control
limit
my
data.
With
this
data
prepared,
I
can
have
easy
discussions
with
my
team
and
tell
them,
"Look,
here,
what
happened
here?
I
just
added
time
and
say
roughly
around
this
time,
we
had
a
suspicious
event
here
in
our
factory.
What
happened?"
Let's
go
back
to
the
JMP
table
or
to
the
presentation.
I
created
this
column,
good
or
bad.
And
from
a
statistical
point
of
view,
we
know
that
regression
is
better.
The
script
in
order
to
evaluate
all
this
data
at
once
within
20
seconds
of
time
is
containing
actions
like
to
split
the
data,
make
a
summary,
analyze
the
control
limit,
summarize
my
control
limit
in
an
extra
table,
introduce
this
column.
First
I
did
it
manually,
but
I
now
had
a
nice
script
to
do
it
automatically.
I
merged
my
data,
my
summary
back
into
my
first
data
table,
which
I
extracted
from
my
REST-API
interface
here,
and
it's
done.
Everything
was
in
seconds,
and
from
the
JMP
team,
Florian
helped
me
to
do
this
here.
Here
is
just
the
proposal
for
me
and
for
people
who
work
in
the
quality
department,
how
to
discuss
these
graphs
with
the
team.
Of
course,
there
are
different
ways
how
to
analyze
data
in
JMP.
I
like
most
the
Fit
Y
-by
-X
platform,
and
I
also
like
the
hypothesis
testing
ways
which
are
shown
on
the
video
here
on
slide
seven,
where
I
plot
the
three
plants
parallel
to
each
other
and
go
through
all
my
QC
data
of
interest
and
compare
it.
Furthermore,
I
also
like
the
process
performance
graph
which
is
available
in
JMP.
But
one
way
of
analyzing
my
data
is
also
that
I
can
easily
plot
Pareto
plots
of
my
out
-of
-control
events.
I
have
the
data
prepared.
We
go
to
the
table.
We
have
only
the
values
which
are
not
okay
or
above
the
control
limit
selected.
I
click
on
Pareto
Plots
and
I
can
discuss
this
easily
with
the
team
and
tell
them
these
two
variables
are
most
of
the
time
out-of-control.
What
do
we
do?
I
can
also
compare
three
plants
at
once
immediately.
It's
more
or
less
the
same,
but
I
can
have
a
look
here
and
create
meetings
and
discuss
about
suspiciencies
between
the
three
factories.
So
obviously
the
factory
which
is
colored
red,
there
the
less
out-of-control
events
are
created.
This
is
part
of
my
story
to
have
fast
data
available
here
to
to
be
discussed
in
the
teams.
Of
course,
I
know
that
there
are
different
options
in
JMP,
so
we
can
do
regression,
we
can
do
predictor
screenings,
and
the
data
table
for
the
predictor
screening
is
also
shown
here.
In
fact,
I
have
my
final
data
table
which
I
extracted
from
the
REST
-API
interface,
and
I
have
this
out-of-control
column
added
to
my
previous
data,
and
I
can
do
a
predictor
screening
and
I
can
have
a
look.
Where
are
the
factors
in
the
plant
which
influence
the
outcome
in
my
final
product
first? Of
course.
Okay,
he's
doing
the
calculation
at
the
moment.
Of
course,
these
kind of things
are
difficult
to
discuss,
and
it's
also
not
part
of
my
job
to
discuss
here
about
process
suspiciencies.
My
topic
is
quality
and
I
want
to
reduce
the
number
of
off
-spec
events
in
the
plant.
I'm
talking
about
off
-spec
and
not
out-of-control
anymore.
We
had
a
time...
I
go
to
the
right
part
of
the
slide.
We
had
a
time
in
the
plant
where
we
created
off-spec
events.
I
did
a
partition
with
the
data
and
the
data
told
me,
"Look
here,
this
is
suspicious,
below
12,000
or
13,000.
Below
this
value,
it
is
most
probably
that
we
create
off-spec
events."
Here
in
the
graph
builder,
these
events
are
marked
red.
Obviously,
this
is
important.
It
is
a
summary
of
chemicals
that
we
put
on
a
filter
unit
in
the
plant.
I
will
show
you
the
video
soon.
The
second
step
is
that
in
the
partition,
we
came
to
the
conclusion
that
above
a
certain
solven
t
dosing,
it
is
most
probable
or
it
is
likely
that
we
produce
off-spec
lots.
There
are
two
results
from
this
evaluation.
Don't
put
too
much
and
be
careful
with
the
solven
t
dosing.
Now
I
want
to
show
you
the
video.
During
the
this
time,
we
sent
a
colleague
to
the
plant
and
he
took
videos
out
of
this
reactor.
The
red
rectangular
is
actually
more
or
less
the
amount
of
what
we
put
here
on
the
filter
unit.
The
green
rectangular
is
the
amount
of
solvent,
which
is
dosed
afterwards.
If
it's
too
few
put
on
the
filter,
and
too
much
solvent,
obviously
we
created
here
a
problem.
After
we
discussed
this
with
the
team,
it
was
about
one
-and
-a
-half
years
ago,
we
did
not
have
these
events
anymore
in
the
factory
or
in
the
plant.
So
for
me,
it
was
a
quite
successful
story
how
we
came
here
to
the
conclusion
that
we
can
be
more
careful
in
our
plant
about
our
processes.
My
primary
result
of
this
first
part
of
my
talk
is
that
via
the
scripts
and
the
way
I
imported
data
to
JMP
via
the
REST-API
interface,
it
is
possible
to
sharpen
the
awareness
in
the
production
with
respect
to
deviations.
Of
course,
there
are
more
powerful
regression
possibilities
in
the
tool
which
we
are
using,
but
it
is
not
my
job
as
a
quality
manager.
The
goal
is
to
hand
over
here
a
powerful
tool
which
we
can
use
in
our
factory
and
plant
for
deviation
management
and
root
cause
analysis.
My
idea
is
if
someone
is
not
wanting
to
use
our
tool
JMP
or
wants
to
use
a
different
tool,
I
can
save
always
the
scripts
or
the
files
as
Excel
tables.
However,
only
the
last
three
processes
are
available
here,
and
it
is
limited
because
this
is
a
project
which
was
started
2017
to
prepare
all
this
data
correlation
and
time
between
these
three
processes.
However,
what
do
I
do
if
I'm
interested
in
previous
processes
like
the
raw
material
or
my
first
intermediate?
How
can
I
get
this
data
in
to
jump
from
the
outside
of
the
plant?
Therefore,
I
started
to
use
and
to
investigate
the
interface
and
the
possibilities
to
import
data
via
a
connection
between
Python
and
Azure.
It
started
all
when
we
had
the
manufacturing
execution
system
changed
by
the
end
of
2020.
The
way
I
tried
to
go
was
stopped
because
this
manufacturing
execution
system
was
stopped.
I
thought,
"Was
everything
for
nothing
now?"
No.
I
found
a
paper
which
was
written
about
the
synergies
from
JMP
and
JMP
Pro
with
Python.
The
concept
is
shown
here
on
the
left
side
of
the
slide.
In
fact,
I
have
a
JMP
script,
I
have
a
Python
part,
and
I
have
an
option
in
this
Azure
where
I
do
my
data
access.
Everything
is
also
possible
within
seconds
to
get
the
data
out
of
the
plant.
All
is
written
here
in
the
scripting
guide,
page
786.
These
are
only
a
few
pages.
If
questions
come
up,
Emanuel
from
the
JMP
team
helped
me
here
to
do
this.
Here's
the
concept.
Let
me
move
this
Zoom
picture.
On
the
left
part,
it's
what
everybody
in
the
chemical
industry
knows.
Raw
materials
are
delivered
in
trucks.
They
are
stored
in
a
tank
farm
and
the
production
has
to
use
raw
material,
of
course.
And
raw
material
is
consumed
during
the
production
processes
and
new
raw
materials
are
delivered
and
the
tanks
are
refilled
again.
So
we
have
a
certain
level
and
everything
is
fine.
It
starts
and
the
way
to
investigate
here
and
to
get
the
data
in
to
JMP
is
we
have
to
look
up
our
individual
process
of
interest.
Every
point
of
interest
in
the
plant
has
a
certain
number,
and
this
number
needs
to
be
fined.
So
you
can
ask
colleagues,
you
can
ask
SAP,
you
can
ask
the
automation
team,
you
can
have
a
look
by
yourself.
You
need
to
find
a
way
in
your
company
who
can
help
you.
When
you
found
the
number,
you
have
to
make
the
request
in
Azure.
This
number
I
was
talking
about
here,
it's
named
at
us,
it's
the
unique
ID.
I
type
in
here
how
many
days
back
I
want
to
see
the
data.
I
test
it
in
Azure.
He
creates
a
table
and
I
put
this
code
into
my
JMP
script
and
I
click
on
execute.
This
is
what
I
show
you
now.
It's
also
done
in
few
seconds
only
here.
This
first
part
you
need
to
get
from
colleagues
or
you
can
look
up
in
the
internet.
I
looked
up
in
the
internet
and
this
is
not
part
of
JMP
support.
Here
you
can
see
that
within
seconds
I
can
have
a
look
at
the
filling
level
of
some
of
our
raw
material
tanks
or
the
tank
farm.
This
is
the
concept
that
any
process
of
interest
can
be
imported
like
this
into
JMP
within
seconds.
I
wrote
a
manual
or
a
journal
how
this
can
be
done,
and
I
will
publish
it
also
with
my
work
in
the
community.
If
you
don't
know
how
to
do
it,
it's
written
here.
Where
you
can
find
it
in
the
scripting
index,
I
have
a
paper
here
how
you
test
your
system.
I
have
a
few
recommendations
for
the
help
in
the
scripting
index,
and
also
I
found
some
answers
which
I
placed
before
in
the
community,
so
I
hope
to
have
also
feedback
from
me
documented
there,
and
I
showed
you
how
to
extract
data
from
anywhere
in
the
FAB
or
in
the
plant.
And
this
brings
me
to
the
end
of
my
talk.
The
summary
is
that
product
deviations
happen
to
all
of
us.
So
here
I
show
you
some
quality
assurance
tools,
and
via
one
click,
I
have
all
my
necessary
graphs
and
information
prepared
like
this
to
discuss
with
the
team.
My
message
is
that
don't
be
afraid
from
using
the
JSL
scripting
language
that
I
was
before.
When
you
start,
you
learn
fast
and
you
get
the
job
done.
You
can
prepare
good
discussions
with
your
production
teams
and
you
can
start
continuous
improvement
plans
and
PDCAs,
and
everything
can
be
available
within
a
few
seconds.
I
also
have
some
ideas
how
to
go
on
here,
and
there
I
need
the
help
from
you,
so
from
the
colleagues
from
which
are
attending
the
conference.
Maybe
you
can
help
me
automate
and
schedule
my
evaluations.
I
know
that
this
can
be
done
in
JMP
Live
in
version
17.
If
you
don't
have
it,
I
need
to
use
the
Windows
Scheduler.
For
example,
I
need
to
improve
a
little
bit
by
creating
add
-ins,
and
the
data
cleaning
still
can
be
a
little
bit
bit
more
better
and
improved,
but
okay.
My
main
goal
was
to
make
the
members
talk
more
about
suspicious
events
in
our
factory
and
out-of-control
events,
which
is
the
lower
right
part
of
the
picture
of
the
slide,
and
less
about
off-spec
events
and
out-of-control
events.
From
quality
management
point
of
view,
I
really
am
a
fan
of
turtle
diagrams
and
turtle
tools
to
document
your
improvements.
This
brings
me
to
the
end
of
my
talk.
I
hope
you
enjoyed
it
as
well
as
me
when
I
was
preparing
this
work,
and
I'm
looking
forward
to
your
questions
and
to
see
you
in
Spain.
Thank
you.
