Welcome,
and
thank
you
for
joining
my
poster
presentation
at
this
year's
JMP C
onference.
My
name
is
Kaitlin Shorkey
,
and
I'm
a
senior
statistical
engineer
at
Corning
Incorporated.
How
do
you
get
a
glimpse
of
a
product
quality
before
it
completes
the
production
process?
We
chose
to
build
a
model
that
will
predict
the
product
quality
outcome
before
it
has
completed
the
entire
process.
There
are
two
major
benefits
of
this
approach.
One
is
the
operations
team
receives
instant
feedback
on
how
the
parts
will
perform
and
can
adjust
the
process
in
real
time.
The
second
is
that
we
can
dem
the
product
acceptable
or
not.
Like
I
just
mentioned,
the
main
objective
of
this
work
is
to
build
a
predictive
model
using
a
few
modeling
approaches
to
understand
and
predict
product
quality
based
on
certain
product
measurements.
Our
major
steps
in
building
this
model
are
data
collection,
model
development,
testing
and
implementation.
First off,
for
the
data
collection
phase,
parts
are
collected
at
the
end
of
the
production
line
and
appropriate
product
measurements
are
completed.
The
parts
are
then
subjected
to
the
quality
test.
The
product
measurements
and
quality
measurement
results
are
combined
into
a
data
set
and
used
for
building
the
model.
In
this
case,
the
quality
measurement
results
are
combined
into
a
data
set
and
used
for
building
the
model.
In
this
case,
the
quality
measurement
is
the
response
and
all
the
product
measurements
are
the
predictors.
The
dataset
consists
767
predictors
and
990
observations
or
parts.
This
stuff
can
take
a
long
time
to
execute.
Since
we're
building
a
model,
it's
important
to
get
as
large
of
a
range
of
product
measurements
and
quality
measurement
results
as
we
can.
If
we
leave
this
the
accuracy
and
model
predictions
are
more
consistent
across
the
range.
Essentially,
this
allows
the
model
to
accurately
predict
at
all
levels
of
the
product
quality
results.
Once
the
dataset
is
compiled,
it
is
thoroughly
examined
to
ensure
it
is
as
clean
as
possible.
After
the
data
collection
and
cleaning,
the
second
phase
of
model
development
is
started.
For
this,
we
begin
with
variable
clustering.
Step
by
regression,
remove
highly
correlated
variables
and
select
the
most
important
ones.
With
so
many
predictors
we
first apply
variable
clustering.
This
method
allows
for
the
reduction
in
the
number
of
variables.
Variable
clustering
groups
the
predictors,
the
variables
into
clusters
that
share
common
characteristics.
Each
cluster
can
be
represented
by
a
single
component
or
variable.
A
snippet
of
the
cluster
summary
from
JMP
is
shown,
which
indicates
that
85
%
of
the
variation
is
explained
by
clustering.
Cluster
12
has
49
members,
and
V
232
is
the
most
representative
of
that
cluster.
The
variables
that
are
identified
as
the
most
representative
ones
are
then
used
in
the
next
method
of
stepwise
regression.
Stepwise
regression
is
used
on
the
identified
clustered
variables
to
select
the
most
important
one
to
us
in
the
model,
and
further
reduces
the
number
of
variables.
For
this,
the
forward
direction
and
minimum
corrected
AIC
stopping
rule
is
used.
The
direction
controls
how
variables
enter
and
leave
the
model.
The
forward
direction
means
that
terms
are
entered
into
the
model
that
have
the
smallest
p-value.
The
stopping
rule
is
used
to
determine
which
model
is
selected.
The
corrected
AIC
is
based
on
negative
two,
law
of
likelihood,
and
the
model
with
the
smallest
corrected
AIC
is
a
preferred
model.
From
this,
51
variables
are
entered
into
the
model
of
the
99
available
variables
from
the
variable
clustering
step.
At
this
point,
we
have
reduced
the
number
of
variables
from
767
to
51
using
variable
clustering
and
stepwise
regression.
The
final
method
is
to
fit
a
generalized
regression
model.
For
this,
the
law
of
normal
distribution
is
used
with
an
adaptive
lasso
estimation
method.
For
this,
the
long
normal
distribution
is
used
with
an
adaptive
lasso
estimation
method.
The
law
of
normal
distribution
is
the
best
fit
for
the
response,
so
is
chosen
to
use
in
the
regression
model.
The
adaptive lasso
estimation
method
is
a
penalized
regression
technique
which
shrinks
the
size
of
the
regression
coefficient
and
reduces
the
variance
in
the
estimate.
This
helps
to
improve
predictive
ability
of
the
model.
Also
the
data
set
was
split
into
a
training
and
validation
set.
The
training
set
has
744
observations,
and
the
validation
set
has
246.
From
this,
the
resulting
model
produces
a
.81
generalized
R-square
for
the
training
set
and
.8
for
the
validation
set.
These
R-squares
are
acceptable
for
our
process
that
we
will
now
evaluate
the
accuracy
and
predictability
of
the
resulting
model.
Now
that
we
have
a
model,
we
need
to
review
its
accuracy
and
predictability
to
see
if
it
would
be
suitable
to
use
in
production.
In
doing
this,
a
graph
is
produced
that
compares
a
predicted
quality
measurement
for
a
specific
part
to
the
actual
quality
measurement.
In
the
graph
the xx
shows
the
predicted
value,
and
the
yx
shows
the
actual.
Also,
the
quality
measurement
is
bucketed
into
three
groups
based
on
its
value,
which
is
shown
by
the
three
colors
on
the
graph.
In
general,
the
model
predicts
the
quality
measurement well.
It
does
appear
that
the
model
may
fit
better
in
the
lower
product
quality
range
than
the
upper,
which
may
be
due
to
more
observations
in
the
lower
range.
As
mentioned,
the
quality
measurement
was
bucketed
into
three
different
categories
based
on
its
value.
This
was
also
done
for
the
predictive
quality
measurement.
For
each
observation,
if
the
quality
measurement
category
is
the
same
as
a
predicted
measurement
category,
it
is
assigned
to
one.
If
not,
it
is
assigned
to
zero.
For
both
the
training
and
validation
sets,
the
average
of
these
ones
and
zeros
is
calculated
and
is
used
as
the
accuracy to
measure.
We
see
that
training
set
has
an
accuracy
of
87.5%
and
the
validation
set
has
an
accuracy
of
84%.
For
the
model
to
be
moved
to
the
testing
phase,
accuracy
must
be
above
a
certain
limit,
and
both
of
these
accuracy
values
are.
This
will
allow
us
to
move
to
the
testing
phase
of
the
project.
In
addition,
we
look
at
the
confusion
matrix
to
visualize
the
accuracy
of
the
model
by
comparing
the
actual
to
the
predicted
categories.
Ideally,
the
off
diagonals
of
each
matrix
should
be
all
zeros,
with
the
diagonal
from
top
left
to
bottom
right
containing
all
the
counts.
The
matrices
show
on
the
poster
that
the
higher
counts
are
along
that
diagonal
with
lower
numbers
in
the
off
diagonal,
but
discrepancies
still
exist
among
the
three
categories.
For
example,
in
the
training
set,
there
are
29
instances
where
the
actual
quality
measurement
of
the
three
was
predicted
as
a
two.
In
the
same
case
for
the
validation
set,
there
are
12.
The
confusion
matrix
helps
to
understand
where
these
discrepancies
are
so
further
investigations
can
be
done
and
improvements
made.
Overall
though,
the
model
has
an
accuracy
above
that
requires
limit,
where
our
next
step
would
be
the
testing
and
implementation
phases.
Now
that
our
model
is
through
the
development
phase,
it's
time
to
test
it
in
live
situations.
For
this,
the
model
is
used
under
engineering
control
to
determine
how
well
it
predicts
the
quality
measurement
in
small,
controlled
experiment.
This
is
done
by
the
engineering
team
with
support
from
the
project
team
when
necessary.
Once
the
engineering
team
is
satisfied
with
this
testing,
the
model
is
fully
implemented
into
production
and
monitored
over
time.
In
conclusion,
this
model
development
process
has
allowed
us
to
build
predictive
models
for
the
production
process.
The
methods
of
variable
clustering,
stepwise
variable
selection
in
generalized
regression
were
the
most
appropriate
and
best
students
to
use
for
this
application.
With
further
research
and
investigation,
other
methods
could
be
potentially
applied
to
improve
model
performance
even
more.
From
a
production
standpoint,
the
benefit
of
this
model
is
that
the
operations
team
will
receive
instant
feedback
on
how
a
part
or
group
of
parts
will
perform,
and
can
ingest
the
tune
and
tune
the
process
in
real
time.
We
can
also
deem
the
product
acceptable
or
not.
If
rejected,
the
product
is
disposed
of
and
will
not
continue
through
the
process,
which
over
time
reduces
production
costs.
Lastly,
I'd
like
to
give
a
huge
special
thank
you
to
Zvouno and
Chova
and
the
entire
project
team
at
Corning Incorporated.
Thank
you
for
joining
and
listening
to
my
poster
presentation.
