Hello,
and
welcome,
everybody,
to
my
today's
presentation.
Thank
you
for
joining.
I
will
give
you
a
short
talk
about
the
concept
of
SVEM,
or self-
validated
ensemble
modeling,
and
how
this
concept
can
be
a
path
towards
DOEs
with
complex
models
and
very
few
runs.
I
hope
I
can
show
you
that
on
some
use
cases,
that
this
concept
is
actually
the
solution
towards
these DOEs
with
complex
models
and
very
few
runs.
But
before
I
start
going
into
detail
of
my
presentation,
I
want
to
give
you
a
short
presentation
of
the
company
I'm
working
for.
It's
Lohmann.
Lohmann
is
the
producer
of
adhesive
tapes,
so
mainly
pressure-
sensitive
adhesive
tapes,
but
also
structural
adhesive
tapes.
The
Lohmann
Tape
Group
made
a
turnover
in
2020
of
about
â¬670 million.
It
consists
of
two
parts.
It's
the
Lohmann T
ape
Group,
that's
who
I'm
working
for,
and
the
joint venture,
Lohmann & Rauscher
.
The
Tape
Group
had
a
turnover
in
2020
of
about
â¬300
billion,
and
last
year,
we
celebrated
our
170th
birthday.
Basically,
the
Tape
Group is divided
in
two
major
parts.
That's
the
technical
products,
where
I'm
working
for.
We
offer
mainly
double-
sided
adhesive tapes,
pressure-sensitive adhesive tapes,
and
structural
adhesive
tapes.
We
are
solely
B2
B
company.
And
the
other
part
is
our
hygiene
brand.
Ninety percent
of
our
products
are
customized
products.
We
are
active
worldwide.
We
have
24
sites
around
the
globe
and
about
1,800
employees.
Who are we?
We
are
the
bonding
engineers,
and
as
I
mentioned
before,
90 percent
of
our
products
are
customized,
so our
main
goal
is
to
offer
our
customers
the
best
possible
solution,
or
to
offer
him
a
customized
solution
very
specific
for
his
problem
or
to
solve
his
problem.
In
order
to
do
that,
we
have
a
quite
large
toolbox
in order to do that,
so that consists of a...
We
are
able
to
do
our
own
base
polymers
for
pressure-
sensitive
adhesive
tapes.
We
can
do
our
own
formulation
to
formulate
either
pressure-
sensitive
or
structural
adhesive.
We
can
do
our
own
coating
that
needs
to
form
the
final
adhesive
tape.
But
we
can
also
do
all
the
lamination,
die
cutting,
processing,
doing
spools,
rolls,
so
offering
him
the
adhesive
solution
in
the
form
he
actually
needs
or
he
actually
wants,
in
order
to
solve
or
to
best
satisfy
his
needs.
But
we
can
also
do
all
the
testing
and
also
help
the
customer
in
integrating
our
adhesive
solution
into
his
process.
Having
this
quite
large
toolbox
or
these
many
tools
in
order
to
fulfill
the
customer's
needs
also
comes
together
with
a
lot
of
degrees
of
freedom
in
order
to
do
that.
That
means
degrees
of
freedom
or
this
huge
amount
of
degrees
of
freedom,
the
large
value
change
which
we
have
and
dealing
with
that
complexity.
One
of
the
solution
to
deal
with
this
complexity
for
us
is
to
use
DOE,
or
design
of
experiments,
for
us
in
the
development
department
mainly
for
the
polymerization,
formulation,
or
coating.
That
brings
me
back
to
the
topic
of
my
talk.
Since
we
use
our DOE
to
tackle
this
problem
or
to
tackle
this l
arge
complexity,
and
being
able
to
use
all
the
tools
in
the
best
possible
and
most
efficient
way
to
fulfill
our
customer's
demand,
we
use
DOE,
but
let's
say
we
want
to
have
that
as
efficient
as
possible
to
tackle
complex
problems
with
the
lowest
amount
of
experimental
effort.
There,
I
think,
where
SVEM
as
a
concept
comes
in.
As
you
all
know,
during
the
process
development
or
product
development,
you
run
over
the
same
stages.
We
start in
the
lab,
doing
our
experiment
on
a
laboratory
scale
and
switch
to
the
pilot
scale,
then
final scale of
step
when going
to
the
production
in
order
to
produce
the
final
product.
Doing
this
way,
let's
say,
the
effort
per
experiment,
might
be
time
or
cost,
dramatically
increases.
In
order
to
minimize
these
costs,
we
use
DOE
in
order
to
minimize
the
experimental
effort.
But
also,
if
you
go
the
steps
from
the
lab
over
the
pilot
to
the
production,
the
higher
the
effort
per
experiment
is,
the
more
critical
are
the
number
of
experiments.
Situations
where
the
number
of
experiments
is
critical
might
be,
let's
say
for
us,
but
also
let's
apply
for
other
industries,
is
if
you
have
to
do
experiments
on
a
pilot
or
a
production
scale.
Or
even if
you
do
experiments
on
a
laboratory
scale,
if
you
have,
for
example,
long-
running
experiments
over the year,
let's say the
analysis
of
an
experiment
takes
very
long,
that
might
be
a
situation
where
the
number
of
experiments
is
very
critical.
But
also
in
combination
with
that,
if
you
have
complex
models
or complex
problems
which
you
have
to
address,
if
you,
for
example,
need
a
full
RSM
model
or
if
you
want
to
do
an
optimization,
you
will
run
into
this
problem
where
you
always
might
have
a
large
amount
of
experiments
in
order
to
model
this
complex
problem.
The
best
situation
would
be
that
you
can
generate
DOEs,
which
allows
you
to trace
a
very
complex
problem
or
to
apply
complex
models,
but
at
the
same
time,
keep
the
number
of
experiments
as
low
as
possible.
Just
to
give
you
an
example
in
our
case,
the
production
of
adhesive
tapes.
If
I
do
experiments
on
a
laboratory
scale,
I
need
less
than
a
kilogram
of
adhesive
per
experiment,
and
I
definitely
don't
need
more
than
one
or
two
square meters
of
adhesive
tape
in
order
to
do
all
the
full
analysis
which
I
want.
If
I
then move
to
the
pilot
scale,
depending
on
the
experiment
we
want
to
do,
you
might
need
between
10
or
50
kilogram
of
adhesive,
and
your cost
maybe
25-
100 square meters
of
adhesive
tape
per
experiment.
I
f
you
go
even
further
into
the
production
scale,
you
might
need even
more
than
100
kilogram
per
experiment
of a
dhesive
and
you
r cost
maybe
1,000
square
meter
of
adhesive
tape
per
experiment.
But
at
the
same
time,
I
still
only
need
about
one square meter
or
two
square
meter
to
do
the
full
analysis
of
my
experiment.
If
you
are
in
an
unfortunate
situation,
I have
to
waste
99.9 percent
of
my
product
is
basically
waste.
That's
a
lot
of
material
used
for
one
experiment,
and it
also
comes
along
with
quite
an
enormous
amount
of
cost
per
experiment.
Just
to give
you
an
illustration,
that's
a
picture
of
our
current
pilot
line.
T
hat's
the
size
of
a
door.
Even
for
a
pilot
line,
it's
quite
large,
so you
can
imagine
the
amount
of
material
you
need
is
quite
large.
And
that's
numbers
per
experiment.
But
even
on
a
laboratory
scale,
you
might
run
into
the
situations
where the
number
of
runs
is
critical.
It's
either
if
you
have
complex
models
or
large
amount
of
factors,
and
I've
shown
you
that
in
the
last
use
case.
Because
it's
a
chemical
reaction where
we want
to
vary
the
ingredients,
but
also
the
process
parameters
if
you
have
long-
running
experiments
where
experiments
run
for
more
than
one
day,
or
the
analysis
of
the
experiment
takes
very
long,
and
at
the
same
time,
you
have
very
limited
project
time
or
project
budget.
In
all
these
situations,
it
is
very
desirable
to
minimize
the
experimental
effort
as
much
as
possible,
so to
decrease
the
number
of
runs
as
much
as
possible.
In
2021,
last
year's
Discovery
Summit,
I came
across
two
presentations.
One by
Ramsey,
Gottwalt,
and
Lemkus,
and
the
other
one
by
Kay
and
Hersh,
talking
about
the
concept
of
SVEMs
or
self-
validated
ensemble
modeling,
and
for
me
that
raised
the
question of,
"Can
that
concept
be
actually
the
solution
for
DOEs
with
few
experiments,
but
at
the
same
time,
keep the
complexity
of
your
model
for
the
problem
which
you
have?"
With
that
said,
I
want
to
switch
over
now
to
my
use
cases
in
order
to
hopefully
show
you
that
this
concept
is
actually
or
might
be
actually
a
solution
to
that
problem.
I want to switch over to JMP.
The
first
example
I
want
to
show
you
today
is
where
we
had
to
do
a
design
of
experiments
on an actual
production
scale,
on
production
equipment.
The
second
one
is
done
on
a
pilot
plant,
and
the
third
one
was
done
on
the
lab.
I have some
information
for
you
where the
first
two
examples
were
done
or
the
design
was
created
without
knowing
the
concept of SVEM,
but
the
analysis
was
done
knowing
then
the
concept
of
SVEM.
The
third
example
wasn't
actually
done
after
I
knew
about
the
concept
of
SVEM,
and
then
I
applied
or
I
designed
the
DOE
specifically
knowing
about
the
analysis
concept
of
SVEM.
So I'll go
to
the
first
example.
In
the
first
example,
we
want
to
do
a
product
development
by
means
of
a
process
optimization.
We
could
only
do
that
process
optimization
on
the
actual
production
equipment,
so we had
to
do
all
the
experiments
on
a
production
scale.
If
you
imagine
though,
you
always
have
to
find
production
slots,
and
you
have
to
always
compete
with
the actual
production
orders,
so your
normal
daily
operation
of
business.
So
the
number
of
runs
which
you
can
do
on
actual
production
equipment
is
always
very
limited
and
it's
always
very
costly,
and
you
always
have
to
fit
in
the
normal
production.
What
you
want
to
do
at
that
point,
for
the
first
example,
we
had
five
continuous
process
factors,
we
know
we
expected
a
nonlinear
behavior,
so
we
knew
we
had
to
use
the
full
quadratic
models
and
the
expected
interactions,
and
we
also
know
that
we
had
a
maximum
of
50
run
due
to
limited
production
time,
time,
and
money.
What we
came
up?
Again,
as
I've
told
before,
that
was
done
before
I
knew
about
the
concept
of
SVEM.
When
we
created
the
design,
we
put
all
the
linear
and
quadratic
effects
that
we
need
necessary
in
the
custom
design
platform
and
we
set
the
interactions
to
optional,
and
that's
the
15
runs
which
we
ended
up.
And
then
we
started
with
the
experiments.
Unfortunately,
only
12
runs
could
be
accomplished
because
we
had
limited
time
and
capacity,
and
we
were
running
out
of
time
in
order
to
do
the
last
three
remaining
runs.
After 12 runs,
we
basically
had
to
stop
and
then
do
the
analysis,
but
fortunately
for
us,
we
could
do
the
remaining
three
experiments
after
the
analysis
was
done.
That
brought
us
into
the
position
where
these
extra
three
experiments
which
were
conducted
after
the
analysis
was
an
actually
test
set
where
we
could
check
the
validity
of
our
model,
which
we
created
using
the
concept
of
SVEM.
The
actual
data
analysis,
it's
shown
in
these
two
presentations
from
last
year's
Discovery
Summit,
and
I
put
in
the
code
so
you
can
find it
very
easily
on
the
community
page.
The
data
was
analyzed
using
SVEM,
so I
did
100
iterations.
I
used
a
full
quadratic
model,
used
all
the
interactions
possible
with
these
continuous
process
factors,
and
I
used
the
LASSO
as
the
estimation
method.
I
just
put
in
for
the
script
how
it's
done
this
analysis
using
SVEM,
but
also,
I
would
want
to
refer
to
these
two
presentations
where
it's
all
written
down
or
explained
in
great
detail.
Coming
to
the
results,
I
want
to
show
you
the
profiler,
and what you
can
actually
see
that
we
actually
need.
Second
order
terms,
so
we
have
a
quadratic
dependency.
But
we
also
had
interactions.
So
everything
what
we
expected
was
basically
present.
But
let's
go
ahead
and
check
for
the
validity.
You
might
say,
"Okay,
you
just
over
fitted
the
model.
You
just
used
too
much
model
terms.
It's
too
complex.
That's
why
you
have
second
order
terms.
That's
why
you
have
interactions."
Well,
let's
look
at
the
predicted
versus
actual
plot.
For
that
example,
we're in the good
position
where
we
had
actually
a
test
set,
so these
three
remaining
experiment
which
the
model
has
never
seen,
and
the 12
,
let's
say,
that's
the
red
dots.
A
s
you
can
see
here
though,
if
you
watch
it,
the
predicted
versus
actual
plot,
that's
not
too
bad.
T
he
prediction
of
these
three
remaining
ones
is
pretty
good.
If
I
check
and
see
if,
for
example,
another
response,
the
prediction
is
very, very
accurate.
A
s
I
mentioned
before,
these
runs,
the
model
has
never
seen.
For
us,
that's
what's
quite
amazing.
To
have
only
12
runs
may have
crippled
this sort
of
experiment
because
three
of
the
runs
were
missing,
and
still
having
this
very
good
predictive
capability
of
that
method
SVEM.
So in
general,
prediction
was
very
good.
We
could
predict
the
remaining
three
runs
very
accurately
except
one
response.
Everything
fitted except
one
response,
it
didn't
fit
at
all.
We
thought,
"That can't be.
We
can't
just
predict
10
of
the
11
responses
almost
perfectly,
and
the
11th
one
doesn't
fit."
And we thought,
"Okay,
something
has
to
be
wrong.
Our
model
is
good
because it
fitted
10 times,
so
it
has
to
fit
also
the
11th
time."
And we
thought, "Okay,
something
with
the
measurement
has
to
be
wrong,
because
the
experiment
can't
be,
because
the
other
responses,
they
fit.
Our
prediction
was
very
good."
We
said
to the
expert,
"S
omething
in
the
measurement
had
to
be
wrong."
So our
expert
digged
a
little
bit
deeper
into
the
measurement
equipment,
and
they
actually
found
deviations
in
the
measurement.
T
he
measurements
on
that
last
response
was
a little
bit
different
than
the
first
12 runs
.
A
fter
the
deviations
were
corrected,
the
prediction
was
again
very
good.
That
means
that
this
analysis
or
the
prediction
was
so
good
that
we
even
found
deviations
in
the
measurements.
From
that
first
example,
I
would
say
SVEM
works
perfectly,
and
gives
you
very
great
insight
in
a
very
good
and
very
accurate
model
with
a
very
limited
amount
of
experimental
effort.
But
you
could
say
,
"Okay, you
could have
also
used
a
different
screening
design
and
you
would
have,
for
example,
11
runs,
and
you
would
have
got
exactly
the
same
result
of
that."
Might
be
true.
But
then
I
refer
to
the
third
example.
Second
example
is
very
similar.
We
wanted
to
do
DOE
on
a
pilot
plant,
and
we
had
four
continuous
factors.
Two
of
those
factors
required
the
production
o
f
specially
produced
ingredients
because
those
two,
they
represented
a
process
variation
of
our
window
to
operate
for
our
current
process.
We also
expected a
non-linear
behavior,
and
we
were
told
not
to
exceed
10
runs.
But
it was
strongly
recommended
to
us
that
fewer
runs
would
be
more
than
welcome
because
of
capacity
issues.
So we
end
up
with
a
hybrid
production/
laboratory
approach,
where we
ended
up
with
only
having
six
runs,
or
we
only
needed
six
runs
on
the
pilot
scale
or
the
production
scale,
that
they
boosted
to
10
runs
in
the
lab.
That's
basically
the
design
which
we
ended
up.
That's
the
way
I
created
the
design.
I
set
the
linear
and
some
interaction
terms
to
"necessary"
in
that
custom
design
platform,
and
the
remaining
interaction
and
quadratic
term,
we
set
them
to
"optional."
Again,
that
was
done,
the
creation
of
the
design,
before
I
knew
about
the
concept
of
SVEM,
but
in
essence
was
then
done
using
SVEM.
For
us,
in
that
particular
case,
the
goal
was
to...
Let's say factor
1
and
2
represent
the
process
variability.
What
we
wanted
to
achieve
is
we
wanted
to
minimize
a
certain
response,
in
that
case
response
number 7,
to
minimize
its
variability
without
changing
factor
1
and
2,
because
changing
the
process
variability
which
we
[inaudible 00:17:56]
have,
so it's
very
difficult.
But
we
had
two
additional
factors
that
were
param
eters
from
the
coating
equipment
which
we
could
change
very
easily.
The
goal
was
to
minimize
response
number
7
but
keep
all
the
rest
basically
in
the
window
which
we
wanted.
Again,
analysis
was
done
pretty
much
the
same
way
like
before.
We
used
SVEM like
we
presented
in
the
last
year's
presentations.
We
did
100
iterations.
We
again
use
the
full
quadratic
models
with
all
the
interactions,
and
we
used LASSO
as
the
estimation
method.
Just
showing
you
that
on
the
profile
again,
we
had
second
order
terms,
we
had
interactions
as
you
can
see
here,
for
example.
They
were
all present,
all what
we
expected
to
be
there
like
second
quadratic
dependencies
and
interactions
actually
were
there.
For
our
goal
was
to
minimize
this
response
and
without
changing
these
factor
1
and 2,
because
that's
the
process
windows
which
we're
currently
operating
in.
During
the
process
range,
if you
look
at
that
response
here,
it
changes
quite
a
bit.
Doing
all
the
optimization
and
having
this
model
fully
operational,
we
found
out
if
you
just
change
factor,
for
example,
number
3,
variability
basically vanishes
completely
without
changing
the
rest
of
the
factor.
So we
find
with
only
six
runs
on
a
production
scale,
optimal
settings
[inaudible 00:19:36]
to
enhance
or
optimize
a
process
quite
considerably.
You
might
again
say,
"Okay,
four
factors.
You
could
only
again
may
have
used
a
definite
screen
design."
Or
let's
say,
"Ten
runs
is
not
too
few
runs
in
order
to
do
that."
But
that
brings
me
to
my
last
and
final
example.
We
were
doing
a
design
of
experiments
on
the
laboratory
scale,
and
we
wanted
to
optimize
or
understand
in
more
detail
a
polymerization
process.
We
wanted
to
do
all
factors
and
variations
within
one
single
design
experiment.
We
ended
up
with
having
15
factors,
eight
ingredients,
and
seven
process
parameters.
We
didn't
have
a
mixed
design.
We
used
these
eight
ingredients,
fiddled
around
a
little
bit
so
we
didn't
end
up
with
a
mixed
design.
But
as
the
experiment
itself
was
very
time-
consuming,
the
number
of
experiments
should
be
low,
ideally
below
30.
Because
we
had
limited
project
times,
and
30
is
something
which
we
might
be
able
to
afford.
But
at
that
moment,
I
already
knew
about
the
concept
of
SVEM
so
I
did
the
design
of
the
experiment
specifically
knowing
about
or
applying
the
concept
of
SVEM.
So what
I
did,
I
chose
the Bayesian I-optimal
design.
We
knew
there
will
be
non-
linear
behavior,
so
all
the
second
order
terms
and
interactions
were
in
there.
What
we
ended
up
was
a
design
that
you
can
see
here,
having
only
23
runs,
and
having
15
factors
of ingredients
plus
process
parameters,
basically
a
full
RSM
model
and
only
have like
23
runs.
For
me,
it
was
pretty
amazing
to
be
able
to
do
that.
Let's
see
the
result
if
it
actually
did
work,
because
we
weren't
so
sure
that
it
actually
worked.
But
we
were
convinced
that
it
will
be
known
at
the
time
that
it
actually
does
work.
Analysis
was,
again,
done
the
same
way.
The
only
difference,
I
won't
show
you
a
profiler
for
that
example
because
having
15
factors,
quite
complex
models,
and
having
to
implement
it
in
JSL
makes
it
very, very
slow.
But
I
was
told
that
it's
going
to
be
implemented
in
JMP
17,
which
then
will
be
much
faster
so
I'm
really
hoping
to
get
JMP 17
as
fast
as
possible.
What
we
found
out
is
that
non-
linearity
was
present.
We
had
again
interactions,
and
we
had
second
order
terms
which
were
significant.
So again,
we
needed
the
full
model.
The
experimental
verification
is
currently
ongoing.
First results
look
very
promising.
I
haven't
put
them
in.
Just
in
contrast
to
a
classic
approach,
we
would
have
required
much
more
experiments
than
just
23.
And again,
to
show
you
that
we
had
very
good
prediction
capability,
to
just
plot
some
predicted
versus
actual.
That's
just
for
one
response,
it's
very
good.
Just
to
give
you
another
one,
it's
not
as
good
as
the
first
one,
but still,
for
us,
it's
good enough.
So
the
predictions here
are
very
good.
For
the
first
experiment,
for
the
verification
runs,
it
looks
very
promising.
Just
to
show
you
some
image
of
the
profiler.
It's
unfortunate
non-
interactive.
Second
order
terms
were
present,
interactions
were
present,
so
all
that
what
we
expected
was
basically
in
the
model.
With
that,
I'm
at
the
end
of
the
use
cases.
I
want
to
switch
back.
Just
to
give
you
short
summary.
Kay
and
Hersh,
they
put
in
their
last
year's D
iscovery
Summit
the
title
"Re-Thinking
the
Design
and Analysis
of
Experiments"
and
put
in
a
question
mark.
From
my
point
of
view,
you
don't
need
the
question
mark.
It's
like
that.
I'm
convinced
that
SVEM
will
change
the
way
DOE is
used,
especially
if
you
go
for
either
very
complex
designs,
or
large
number
of
factors,
or
if
you
have
to
go
through
very
costly
experiments.
At
least,
that's
my
opinion.
It
opens
a
new
way
towards
DOEs
with
minimalistic
experimental
runs
or
effort,
and
at
the
same
time,
you
can
gather
the
maximum
amount
of
information
and
insight
out
of
that
without
having
to
sacrifice
maybe
number
of
runs
or
number
of
factors
or
the
complexity
of
your
models.
I'm
convinced SVEM
will
change
the
way
at
least
I
use
DOE,
and
for
me,
it
was
pretty
amazing
to
see
that
in
action.
With
that,
thank
you
very
much
for
watching,
and
I
hope
I
could
show
you
that
this
concept
is
worth
trying.
Thank
you
very
much.
