Hello.
My
name
is
Laura
Lancaster.
I'm
a
statistical
developer
in
the
JMP
group,
and
today
I'm
here
to
talk
about
finding
optimal
operating
regions
for
critical
quality
attributes
with
the
design space profiler.
The
first
thing
I
wanted
to
talk
about
is
the
general
quality
paradigm
called
quality by design,
also
commonly
referred
to
as
QbD
for
short.
Quality by design
is
a
systematic
approach
for
incorporating
quality
into
the
entire
product
lifecycle
beginning
at
the
design
phase.
It
was
first
introduced
by
Joseph
Juran,
and
it
was
made
popular
in
his
book,
Juran
on
Q
uality by Design
way
back
in
1992.
It
was
a
very
popular
book,
and
a
few
years
after
it
was
published,
the
FDA
and
the
International
Conference
on
Harmonization,
also
referred
to
as
ICH,
adopted
these
quality by design
principles
for
the
development,
manufacturing,
and
regulation
of
drugs.
They
published
several
guidelines
for
implementing
quality by design
in
the
pharmaceutical
industry
called
ICH
Q8-
Q12,
and
we're
going
to
focus
on
ICH
Q8
guidelines.
What
exactly
do
we
mean
by
design
space?
Well,
this
is
a
very
important
concept
in
the
pharmaceutical
industry
for
quality
by
design,
and
it's
defined
in
the
ICH
Q8(
R2)
guidelines
as
the
multidimensional
combination
and
interaction of
material
attributes
and
process
parameters
that
have
been
demonstrated
to
provide
assurance
of
quality.
Essentially,
the
design
space
is
what
identifies
your
optimal
operating
region
that's
going
to
give
you
maximal
flexibility
in
your
production
while
still
assuring
that
you
get
a
quality
product.
JMP's
new
design space profiler,
new
in
JMP
17,
helps
us
find
this
design
space,
this
optimal
operating
region.
Now
there's
actually
several
steps
that
need
to
be
taken
before
you
can
use
the
design space profiler
to
determine
your
design
space.
These
are
outlined
in
the
Q8
guidelines,
and
so
we're
going
to
run
through
those
steps.
The
first
step
that
you
want
to
take
is
you
want
to
define
your
quality
target
product
profile,
and
this
is
defined
as
a
prospective
summary
of
the
quality
characteristics
of
a
drug
product
that
ideally
will
be
achieved
to
ensure
the
desired
quality,
taking
into
account
safety
and
efficacy
of
your
drug.
Next,
you
want
to
determine
what
are
the
critical
quality
attributes
and
what
are
their
appropriate
specification
limits.
A
critical
quality
attribute,
also
referred
to
as
a
CQA,
is
defined
as
a
physical,
chemical,
biological
or
microbiological
property
or
characteristic
that
should
be
within
an
appropriate
limit,
range,
or
distribution
to
ensure
the
desired
product
quality.
Once
you've
determined
those,
then
you're
going
to
design
experiments
to
determine
what
are
the
critical
manufacturing
process
parameters
that
affect
these
critical
quality
attributes.
A
critical
process
parameter,
also
referred
to
as
a
CPP
for
short,
is
a
process
parameter
whose
variability
has
impact
on
a
critical
quality
attribute
and
therefore
should
be
monitored
or
controlled
to
ensure
the
process
produces
the
desired
quality
product.
Once
you've
determined
all
of
that,
then
you're
going
to
find
a
good
prediction
model
for
your
critical
quality
attributes
in
terms
of
your
critical
process
parameters.
Once
you've
done
that,
then
you
can
use
the
design space profiler
in
JMP
to
determine
your
design
space
for
your
optimal
operating
region.
Let's
talk
a
little
more
specifically
about
the
design space profiler
in
JMP.
First
of
all,
as
I
mentioned,
it's
new
in
JMP
17,
and
it
resides
within
the
Prediction
Profiler.
Hopefully
everyone
is
familiar
with
the
Prediction
Profiler
in
JMP.
It's
a
wonderful
tool
for
exploring,
visualizing
and
optimizing
any
model
that
you
produce
within
JMP.
The
goal
of
the
design space profiler
is
to
determine
a
good
design
space
by
finding
the
largest
hyper-rectangle
that
fits
into
that
acceptable
region
that's
defined
by
your
critical
quality
attribute
spec
limits
applied
to
your
prediction
model.
Now,
once
you
have
that
hyper-rectangle,
that
gives
you
your
lower
and
upper
limits
of
your
critical
process
parameters
to
determine
a
good
design
space
for
maintaining
a
quality
product.
Now,
I
just
wanted
to
mention
very
quickly
that
a
design
space
does
not
have
to
be
a
rectangular,
but
having
a
rectangular
design
space
makes
it
really
convenient
for
checking
each
factor
one
at
a
time.
Now,
the
problem
with
this
approach
is
that
the
acceptable
region
is
usually
nonlinear,
and
finding
the
largest
hyper-rectangle
in
a
nonlinear
region
is
a
very
difficult
mathematical
problem.
How
does
design space profiler
work
then?
Well,
instead
of
using
the
mathematical
approach
to
finding
the
largest
hyper-rectangle,
it
uses
a
simulated
approach.
It
generates
thousands
of
uniformly
distributed
points
throughout
the
space
defined
by
your
initial
critical
process
perimeter
factor
limits.
Then
it
uses
a
prediction
model
to
simulate
your
responses
for
your
critical
quality
attributes.
Note
that
it's
always
best
to
include
some
error
for
your
prediction
model
because
your
prediction
model
is
not
perfect,
it's
not
without
error.
Then
once
you
have
that
simulated
set
of
data,
you're
going
to
take
that
set
and
calculate
the
in-spec
portion
by
counting
the
total
number
of
points
that
are
in-
spec
for
all
the
responses,
all
your
critical
quality
attributes
from
the
points
that
are
within
the
space
defined
by
the
current
CPP
factor
limit
settings of
your
current
design
space.
Now
the
easiest
way
to
see
how
this
works
is
to
go
to
an
example
and
go
to
JMP.
That's
what
we're
going
to
do.
We're
going
to
look
at
an
example
for
a
pain
cream
study.
The
goal
of
this
study
is
to
repurpose
a
habit-forming
oral
opioid
drug
into
a
topical
pain
cream
that
provides
the
same
relief
as
the
oral
drug.
The
first
thing
they
did
was
figure
out
their
quality
target
product
profile,
and
then
they
were
able
to
determine
what
their
critical
quality
attributes
were.
It
turns
out
there
were
three
of
them:
entrapment
efficiency,
vesicle
size,
and
in
vitro
release,
and
these
are
the
spec
limits
that
give
a
quality
product
for
these
critical
quality
attributes.
Next,
they
ran
experiments
to
determine
what
are
the
process
parameters
that
affect
these
critical
quality
attributes.
It
turns
out
there
were
three
critical
process
parameters:
emulsifier,
lipid,
and
lecithin.
These
are
the
initial
factor
limit
settings.
Once
they
did
that,
they
used
the
Custom
Designer
in
JMP
to
design
response
surface
model
designs.
They
ran
the
experiment,
and
then
they
used
Fit
Least
Squares
to
fit
response
surface
models
for
the
three
critical
quality
attributes
in
terms
of
the
three
critical
process
parameters.
Once
they
did
all
of
that,
now
we
can
go
to
the
design space profiler
in
JMP
to
determine
the
design
space.
Let's
go
to
JMP.
This
is
my
data
table
that
was
created
from
Custom
Designer
when
I
created
a
design
for
the
response
surface
models
of
my
three
critical
quality
attributes
as
responses
in
my
three
critical
process
parameters
as
factors.
It's
what
I
have
right
here.
I
wanted
to
note
that
I
went
ahead
and
I
added
the
critical
quality
attribute
spec
limits
as
column
properties
because
the
design space profiler
has
to
know
what
the
spec
limits
are
to
work.
If
you
don't
add
them
as
column
properties,
you'll
be
prompted
to
add
them
when
you
launch
the
design space profiler.
But
I've
already
done
that.
I've
also
already
saved
the
script
for
my
models
that
I
created
using
Fit
Least
Square.
I'm
going
to
go
ahead
and
run
that.
It
automatically
launches
Fit
Least
Squares.
I
have
the
models
closed
and
just
the
Prediction
Profiler
open.
Notice
that
it
looks
like
the
Prediction
Profiler
that
you're
used
to
seeing.
I've
also
added
the
spec
limits
as
reference
lines
just
so
I
can
see
them
in
relation
to
my
models.
To
get
to
the
design space profiler,
all
you
do
is
go
to
the
Prediction
Profiler
menu,
turn
it
on,
and
a few
down,
you'll
see
there's
a
new
option
called
design space profiler.
If
I
click
on
that,
the
design space profiler
will
appear
right
below
the
Prediction
Profiler.
Now
notice
that
because
I
had
added
the
spec
limits
as
column
properties,
it
automatically
brought
those
in.
If
you
go
to
the
bottom
right-hand
side,
you'll
see
where
it's
brought
in
my
spec
limits
for
my
three
responses
or
three
critical
quality
attributes.
You'll
also
notice
that
it's
brought
in
some
error,
which
it's
going
to
use
as
my
error
for
my
prediction
model.
This
has
come
in
from
the
Least
Square
model's
root
mean
squared
error.
For
example,
up
here,
if
I
go
to
my
in-vitro
release
model,
you
can
see
the
root
mean
square
error
is
1.2972,
and
that's
the
same
value
that's
listed
down
here
as
the
error
standard
deviation.
Now
you
can
change
these
if
you
think
these
are
not
accurate,
these are too
big
or
whatever.
You
can
even
completely
remove
them
and
have
no
error,
but
we
highly
recommend
that
you
do
add
some
error
to
your
models.
Okay,
there
are
several
things
that
you
might
notice
are
different
about
this
design space profiler
than
other
profilers.
One
of
the
first
things
you'll
notice
is
that
over
here
on
the
Y-axis,
instead
of
having
a
value
like
you
normally
do,
like
up
here
in
the
Prediction
Profiler,
it
just
says
in-spec portion.
But
the
actual
value
for
the
Y-
axis
is
actually
over
here
to
the
right.
My
in-spec
portion
for
all
three
critical
quality
attributes
when
I
have
the
initial
factor
limits
set
at
the
full
range
is
71.2%.
Then
you
can
also
see
over
here
that
it
says
the
volume
portion
is
100%.
That's
because
I'm
using
100%
of
the
entire
simulated
data
set
starting
up
because
everything's
at
the
full
factor
range.
Another
thing
that's
different
is
for
each
factor
cell,
you'll
see
that
there's
two
curves
instead
of
the
usual
one
curve.
That's
because
we're
looking
for
factor
limits,
so
a
lower
and
upper
limit
for
each
factor.
The
blue
curve
represents
the
in-spec
portion
as
the
lower
limit
changes.
There's
a
handy
legend
over
here
to
help
me.
The
red
curve
represents
the
in-spec
portion
as
the
upper
limit
changes.
I
can
interact
with
this
profiler
to
change
my
factor
limit
settings
or
my
design
space.
I
can
move
these
markers.
I
want
to
obviously
move
this
to
get
a
higher
in-spec portion,
and
so
you
would
want
to
find
a curve
that
has
an
upward
slope.
I
can
move
this
one
inward
by
dragging
this
marker.
I
could
also
just
enter
values
down
here
below
the
cells.
I
could
enter
values
here
in
these
fields
next
to
the
factors.
Or
there's
also
another
way,
which
is
to
use
these
buttons,
move
inward
and
move
outward.
This
move
inward
button,
if
I
click
on
it,
it's
going
to
give
the
largest...
It's going to make the move,
it's going to give the largest increase
in
inspect
portion.
It's
going
to
look
for
the
curve
with
the
steepest
path.
If
I
click
on
move
outward,
if
I
were
trying
to
increase
the
sizes
of
my
design
space,
it
would
give
me
the
least
decrease.
I
would
look
for
the
least
steep path
downward.
Let
me
go
ahead
and
click
move
inward.
I
noticed
when
I
click
that,
the
lower
limit
for
a
emulsifier
went
from
700-
705,
and
the
in-spec
portion
went
up.
Now,
if
I
want
to
look
at...
This
is
the
in-spec
portion
for
all
three
critical
quality
attributes,
but
if
I
want
to
look
for
them
individually,
they're
to
the
far
right-hand
side
of
each
response
or
each
critical
quality
attribute.
I
can
see
the
in-spec
portion
for
each
one
individually.
But
up
here
it's
finding
it
corporately
for
all
of
them.
Let's
click
move
inward
again
and
notice
that
a
emulsifier
went
in
again
and
now
it's
up
to
710
and
my
in-spec
portion
is
up
to
78.
The
volume
portion
now
is
down
to
89.79%.
Click
it
again.
Now
my
lecithin
lower
limit
went
up.
My
goal,
I'm
going
to
see
if
I
can
get
my
in-spec
portion
all
the
way
up
to
100%,
if
I
still
can
maintain
factor
limits
that
seem
reasonable
and
realistic.
But
before
I
do
that,
there
are
several
options
from
the
menu
of
the
design
space
profiler
that
I
really
like
to
use
while
I'm
doing
this.
The
first
one
is
I
like
to
turn
on
make
and
connect
random
table.
This
is
going
to
create
a
new
table
of
random
data.
It's
going
to
do
10,000
by
default,
I'm
going
to
leave
that.
I'm
going
to
add
random
noise.
It's
the
same
random
noise
added
to
the
design space profiler
based
on
these
bare
standard
deviations.
Click
okay
and
I
get
a
new
set
of
10,000
data
points,
uniformly
distributed
throughout
the
factor
space.
These
data
points
are
color
coded.
The
green
points
are
in-spec
for
all
of
my
critical
quality
attributes
and
the
red
are
out.
Anything
that's
selected
is
still
within
my
design
space
or
my
current
factor
limit
settings.
What
I
really
like
about
this
table
is
the
graphs
that
are
created
by
the
scripts
that
are
automatically
saved
to
the
table.
I
really
like
to
look
at
the
scatterplot
matrix
Y.
Let
me
turn
that
on.
This
gives
me
a
view
of
my
data
with
all
combinations
of
my
response variables
and
my
critical
quality
attributes.
It
has
the
spec
limits
drawn
on
and
it
has
the
in-spec portion
shaded
green
and
all
the
red
points
are
out
of
spec
and
the
green
points
are
in-
spec.
I
also
like
to
look
at
the
factor
space,
and
so
I
could
do
that
by
looking
at
the
scatter
plot
matrix
X.
This
is
going
to
give
me
my
factor
space
or
my
critical
process
parameter
space.
Once
again,
same
color
coding
and
what's
shaded
is
within
the
current
factor
settings.
See
if
I
can
situate
this
so
we
can
look
at
both
of
these
while
I'm
adjusting
my
factor
limits
over
here.
Okay,
you
can
see
as
I
move
inward,
you
can
see
how
the
shaded
area
is
shrinking
for
my
factor
space,
my
design
space,
and
the
number
of
out-of-spec
points
are
also
shrinking.
Now,
I
really
like
to
also
turn
on
the
connect/hide
mode,
and
what
that
does
is
it
just
hides
any
points
that
are
no
longer
within
my
current
factor
limit
settings
or
my
current
design
space.
Now
if
I
keep
clicking
move
inward,
you
can
see
how
the
red
points
are
starting
to
disappear.
One
other
option
you
can
use,
if
you
prefer,
is
you
can
choose
to
only
look
at
the
points
that
are
in-spec
or
only
look
at
the
points
that
are
out
of
spec.
I'm
going
to
turn
Y
out
of
spec
on
to
only
look
at
the
points
that
are
still
out
of
spec.
I'm
going
to
see
if
I
can
hit
100.
Move
inward.
I'm
going
to
keep
going.
Still
looks
good.
Keep
going.
Okay,
now
I've
hit
100
%,
and
I
think
this
design
space,
these
factor
limits
do
look
like
they're
probably
reasonable.
You
might
notice
that
I
still
have
a
red
point
over
here
because
this
is
a
separate
set
of
simulated
data,
but
I'm
not
worried
about
that
one
random
point.
Okay,
let's
examine
this
design
space
that
I
have
set
here.
What
I
want
to
do
is
I
want
to
send
the
midpoint
of
this
design
space
back
to
the
Prediction
Profiler
to
see
what
that
looks
like.
I
can
easily
do
that
with
this
option
that
says,
"Send
midpoints
to
profiler."
When
I
do
that,
it
automatically
sends
the
midpoint
of
this
current
design
space
back
to
the
profiler so
I
can
look
at
it.
I'm
going
to
turn
on
the
desirability,
and
I
think
that
looks
quite
good.
I'm
going
to
go
ahead
and
save
this
setting.
I
want
to
do
that
just
so
I
can
compare
this
against
the
optimal
setting
if
I
were
to
optimize
by
maximizing
all
of
my
critical
quality
attributes,
which
is
something
you
may
or
not
want
to
compare
against.
But
it's
very
easy
to
do
and
I
can
look
at
the
difference.
You
can
see
there's
not
a
huge
difference
in
the
desirability
between
the
center
of
my
design
space
and
the
optimal
value
if
I
maximized
all
my
critical
quality
attributes.
I'm
pretty
good
with
this
center
of
this
design
space.
I'm
going
to
turn
it
back
to
that
point.
Another
thing
that
you
can
do
is...
Well,
I
don't
really
believe...
The
design space profiler
uses
uniformly
distributed
points,
but
I
think
that
my
critical
process
parameters
actually
follow
a
normal
distribution.
It's
very
easy
to
send
these
limits
back
to
the
profiler
and
back
to
the
simulator
so
I
can
simulate
using
the
normal
distributions.
If
I
click
Send
Limits
to
Simulator
and
choose
normal
with
limits
at
three
sigma,
what
it's
going
to
do
is
it's
going
to
send
the
limits
back
to
the
simulator
and
figure
out
what
my
standard
deviation
would
be
if
that
design
space
was
set
at
three
sigma.
Of
course,
you
can
change
these
values,
you
can
change
the
distribution.
I'm
going
to
use
these
settings
and
see
when
I
simulate
what
my
defect
rate
looks
like.
Looks
like
it's
at
zero
every
time
I'm
clicking
this,
which
is
great.
I
also
want
to
see
what
happens
way
out
at
the
tails.
I
can
easily
do
that
by
using
the
normal
weighted
distribution.
Let
me
turn
that
on
for
each
of
these.
This
is
a
way
to
check
what
happens
to
the
tails.
Now
when
I
click
simulate,
it's
not
quite
zero
because
I'm
testing
way
at
the
tails,
but
it's
still
very
low.
I'm
very
happy
with
this.
I
also
like
to
run
a
sanity
check
for
capability,
and
I
can
easily
do
that
by
using
the
Simulate
to
Table
option,
which
is
going
to
simulate
using
the
normal
distribution
for
my
critical
process
parameters
and
the
prediction
models
with
the
error,
the
same
error
I
used
before.
I
click
Make
Table,
I
get
a
simulated
table
and
it
has
a
save
distribution
script
that
if
I
run,
will
automatically
open
up
capability
because
I
saved
my
spec
limits
as
column
properties.
When
I
check
these
capability
reports
for
the
three
critical
quality
attributes,
it
looks
very
good.
Of
course,
this
is
simulated
data,
so
you
want
to
check
it
on
real
data,
but
I'm
pretty
happy
with
what
I'm
seeing
about
my
design
space
right
now.
I
want
to
save
this
design
space,
and
you
can
easily
do
that
by
going
to
the
design space profiler
menu
and
checking
Save
X
Spec
Limits,
and
this
will
save
these
factor
limit
settings
back
to
the
original
data
table
as
spec
limits
in
your
critical
process
parameter
columns.
I
want
to
click
that
and
I
go
back
to
my
original
data
table
just
back
here.
Close
these.
Okay,
there
it
is.
When
I
get
back
to
my
original
data
table,
you
can
see
that
these
spec
limits
have
been
saved
to
my
critical
process
parameter
columns.
This
is
a
great
way
to
save
this
information
if
I
save
the
data
table.
It's
also
nice
because
if
I
do
that
and
I
save
my
predictions
as
formulas,
I
can
look
at
the
design
space
in
terms
of
the
contour
profiles.
I
can
do
that
by
using
the
graph
contour
profiler.
I've
already
gone
ahead
and
saved
a
script
for
this.
I'm
going
to
run
it
and
I
have
it
set
up
to
show
the
contour
profilers
in
terms
of
all
of
my
critical
process
parameters.
You
can
see
this
faint
rectangle
is my
design
space
and
the
shaded
contours
or
the
contours
are
my
spec
limits.
You
can
see
how
my
design
space
is
well
within...
It's
well
within
my
spec
limit
contours.
I
have
that
nice
buffer
because
I
added
the
error,
so
I
can
look
at
that
in
terms
of
all
of
my
critical
process
parameters.
I'm
very
happy
with
this
design
space.
Let's
go
ahead
and
look
at
a
different
example.
Okay,
this
is
an
example
that's
outside
of
the
realm
of
the
pharmaceutical
industry.
It's
a
polymer
manufacturing
study,
and
the
goal
of
this
study
is
to
improve
the
quality
of
their
white
polymer.
I
wanted
to
use
an
example
to
show
that
these
methods
can
be
generalized
in
any
industry.
It
doesn't
have
to
be
pharmaceutical.
This
example
was
inspired
by
an
example
that's
in
the
Visual
Six
Sigma
Second
Edition
book
that
uses
JMP.
It's
a
great
book.
It's
in
my
references.
I
highly
recommend
you
check
it
out
if
you
haven't
ever
looked
at
it.
They
want
to
improve
their
white
polymer
and
they
figured
out
that
they
had
two
critical
quality
attributes:
melt
flow
index
and
color
index.
These
are
the
spec
limits
which
will
ensure
quality
for
the
white
polymer.
They
ran
experiments
to
figure
out
which
process
parameters
affected
these
critical
quality
attributes.
There
were
three
of
them,
Amps
for
slurry
tank
stirrer,
viscosity
modifier percent
,
and
percent
of
filler.
These
were
the
initial
factor
limit
settings.
Another
thing
that
was
different
about
the
study
is
that
they
used
historical
data
to
find
prediction
models
for
their
critical
quality
attributes
in
terms
of
their
critical
process
parameters.
They
used
two
different
platforms
and
two
different
types
of
models
for
these
critical
quality
attributes.
They
used
generalized
regression
platform
and
the
Lasso
method
to
find
a
model
for
the
melt flow
index,
and
they
used
the
fit
neural
platform
to
find
a
neural
model
for
the
color
index.
Because
they've
used
two
different
platforms
and
two
different
types
of
models,
they
need
to
use
the
profiler
platform
that's
underneath
the
graph
menu
in
JMP.
To
use
that,
you
need
to
save
your
models
as
formula
columns.
I
want
to
show
you
how
you
can
still
use
the
Prediction
Profiler
platform
to
use
the
design space profiler.
Let's
go
back
to
JMP.
I'm
going
to
open
up
the
polymer
data.
This
is
historical
data.
I've
got
my
two
critical
quality
attribute
columns,
my
three
critical
process
parameter
columns.
I've
gone
ahead
and
saved
the
spec
limits
as
column
properties,
and
these
are
my
two
models.
This
is
the
model
I
created
with
generalized
regression.
This
is
the
model
I
created
with
neural.
I've
gone
ahead
and
saved
both
of
these
models
to
the
table
as
formulas.
I've
also
saved
the
Prediction
Profiler
platform
script,
which
I'm
going
to
run,
and
you
can
see
this
is
my
Prediction
Profiler
in
terms
of
these
two
models.
The
design space profiler,
once
again,
I
go
to
the
Prediction
Profiler
menu,
turn
on
the
design space profiler.
It's
slightly
slower
here
just
because
I'm
using
formulas
and
I
have
a
neural
model.
But
once
again,
it
opens
up
and
it
shows
me
that
with
the
initial
factor
limit
settings,
my
in-spec
portion
is
about
65
%.
It
looks
like
what
I'm
going
to
want
to
do
is
I'm
going
to
want
to
decrease
the
upper
limit
of
my
XF
factor
critical
process
parameter.
Indeed,
if
I
click
move
inward,
see
how
my
in-spec
portion
is
going
up
by
decreasing
the
XF
upper
factor
limit.
I
want
to
see
if
I
can
get
to
100
%.
If
I
can
get
there
and
have
reasonable
factor
limits,
see
what
things
look
like
if
I
keep
heading
for
that.
Okay,
I've
gone
to
100
%.
My
factor
limits
on
XF
are
a
little
tight,
so
let's
see
what
this
looks
like
in
the
prediction
profiler.
I'm
going
to
send
a
midpoint
up
there.
I
think
it
still
looks
relatively
reasonable.
But
one
thing
I
wanted
to
point
out
is
if
you
do
end
up
you're
not
happy
with
things,
you
can
actually
also
use
this
design
space
profiler
to
do
what
if
scenarios,
if
it
were
realistic
to
be
able
to
change
your
spec
limits,
let's
say
you
were
able
to
make
them
wider,
or
what
if
your
consumer
demands
that
they're
tighter?
You
can
check
out
what
if
scenarios
as
well
by
changing
these
down
here
in
these
fields.
But
in
this
case,
I
think
I'm
pretty
happy
with
this
design
space.
I
think
it
will
work.
I'm
going
to
go
ahead
and
save
the
spec
limits
to
the
original
data
table.
See,
they
got
saved
here.
Once
again,
I've
saved
a
script
for
the
contour
profiler.
I'm
going
to
run
that
really
quickly.
Once
again,
I
can
see
my
design
space
in
terms
of
my
contours.
That's
the
faint
rectangles
here,
and
the
shaded
regions
are
my
spec
limits.
I
could
see
how
the
design
space
is
well
within
my
specs,
so
I'm
pretty
happy
with
this.
Let's
go
back
to
PowerPoint
just
to
give
you
some
takeaways.
Okay,
some
quick
takeaways
about
the
design space profiler.
First
of
all,
the
in-spec
portion
that's
reported
in
the
design space profiler,
those
values
should
not
be
considered
probability
statement
unless
you
think
that
your
critical
process
parameter
factors
follow
a
uniform
distribution
within
the
limits
because
that's
what's
being
used
to
create
that
statement
there.
Also,
the
design space profiler
is
not
meant
for
models
that
have
large
number
of
factors
or
very
small
factor
ranges,
and
that
is
because
of
the
simulated
nature
of
the
approach
it
takes.
It's
also
recommended,
as
I
mentioned
a
couple
of
times
in
my
talk,
to
always
use
random
error
for
your
prediction
models,
for
your
responses
because
your
models
are
not
without
error.
Finally,
finding
a
good
design
space
is
applicable
to
more
than
just
the
pharmaceutical
industry,
even
that's
where
the
idea
came
from.
That
second
example
was
just
to
demonstrate
how
it
can
be
used
in
any
industry
where
you
care
about
having
a
robust
process
in
maintaining
quality.
These
are
my
references,
and
here's
contact
information.
I
wasn't
able
to
show
you
everything
about
the
design
space
profiler,
so
I
hope
that
you
will
check
it
out.
If
you
have
any
questions
or
if
you
have
any
feedback,
please
contact
me.
Thank
you
so
much.
