Hello,
everyone.
This
is
Anudeep
Maripi.
I'm
a
senior
process
engineer
for
optics
lithography
at
Magic Leap.
Today,
I'm
going
to
demonstrate
fault
detection
by
upgrading
control
charts
with
change
point
detection
in
JMP,
and
show
you
how
the
change
point
detection
integration
with
control
charts
is
highly
useful
on
our
manufacturing
shop
floor.
I'll
start
the
presentation
with
a
brief
introduction
about
our
company,
Magic Leap,
and
the
optics
manufacturing,
the
DMAIC
problem-
solving
approach
and
the
JMP
tools
that
we
use
in
the
manufacturing,
our
challenges
that
we
face
in
the
control
phase
of
DMAIC,
upgrading
existing
control
charts
with
change
point,
and
by
doing
so,
detecting
and
diagnosing
the
faults
in
a
process
some
case
studies
that
we
regularly
use
at
Magic Leap
and
a
JMP
demonstration
on
how
to
create
a
change
point
dashboard
and
integrate
change
point
into
your
control
charts,
and
few
takeaways.
To
start
with,
at
Magic Leap,
we
envision
a
world
with
physical
and
digital
are one.
Our
mission
is
to
amplify
human
potential
by
delivering
a
most
immersive
AR wearable
platform,
so
people
can
intuitively
see,
hear,
and
touch
digital
content
in
the
physical
world.
Our wearable
device
transforms
enterprise
productivity
by
providing
AR
solutions.
For
example,
collaboration
and
co-
presence
between
remote
teams
to
work
together
as
if
they
are
in
the
same
room,
3D
visualizations
to
optimize
processes,
augmented
workforce
that
helps
train
and
upskill
workers
using
see-
what-
I-
see
capability.
These
are
only
a
few
examples.
The
device
has
industry's
leading
optics
with
large
field
of
view,
best
image
quality,
high
color
uniformity,
and
dynamic
dimming
which
helps
display
a
high-
quality
virtual
content
in
our
real
world.
The
optical
lens
that
is
used
in
this
device
is
a
small
component
but
a
very
critical
component
of
Magic
Leap
2,
enabling
best-
in-
class
image
performance.
The
optical
lens
goes
through
25
plus
complex
manufacturing
process
and
metrology
stations
during
the
manufacturing.
It
is
tested
over
100
plus
critical-
to-
quality
parameters.
Each
individual
process
needs
to
reach
high
99%
yield
targets
to
attain
90%
Rolled
Throughput
Yield
targets.
We
cannot
achieve
these
high
yields
and
process
capability
without
the
help
of
JMP
statistic
tools.
One
of
these
high-
yielding
processes
is
imprint
lithography
where
I'
m
responsible
for
quality
and
delivery
tools.
This
process
has
many
steps,
and
that
is
visualized
on
the
right-
hand
side
of
yourâ¦
The first
step
is
where
we
align
the
mask
to
the
template
and
then
dispense fo
r
resist,
lower
the
template
and
EUV
cure
to
replicate
the
exact
pattern
from
the
mask
template
to
the
wafer.
This
imprint
process
alone
has
22
critical-
to-
quality
parameters
that
operates
between
tight
control
limits
such
as
just
20
nanometers
film
thickness
and
100
microns
drop
accuracy.
As
you
could
see,
these
tolerances
are
extremely
small.
For
reference,
human
hair
is
just
20
microns
to
100
microns.
Thus,
the
imprint
lithography
requires
a
precision
placement
and
alignment
that
are
indistinguishable
to
the
human
eye.
Meaning
we
rely
a
lot
upon
onboard
metrology
and
vision
systems
that
generate
tons
of
data
from
large
numbers
of
input
parameters.
JMP
helps
us
to
easily
analyze
these
large
data
sets
in
our
problem-
solving
processes.
We
use
DMAIC
approach
in
our
problem-
solving
process.
Following
are
the
JMP
tools
that
we
use
in
every
phase
of
our DMAIC
processors,
respectively.
Like
many
manufacturing
teams,
we
found
out
Control
phase
as
most
challenging
and
overlooked
phase,
because
at
a
start
up
like
Magic
Leap,
we
continuously
evolve,
make
improvements,
and
iterate
several
new
designs
and
revisions.
The
control
phase
is
overlooked
because
it
takes
longer
time
for
the
confirmation.
For
example,
we
find
a
problem
or
an
issue,
say
like
process
exceeded
control
limits
and
giving
low
CPK
values,
resulting
in
low
CPK
during
our
Control
phase.
We
define
the
problem,
we
measure
the
historical
data,
analyze
the
problem
by
multivariate
methods
and
correlation
matrices,
improve
the
problem
by
implementing
the
corrective
actions,
by
conducting
DOEs
and
response
screening.
But
once
this
is
resolved,
we
move
on
to
our
next
severe
problem
and
move
on
to
severity
3
problem
once
you
resolve
the
severity, severity
2
problem,
and
the
cycle
continues,
overlooking
the
most
important
phase
of
DMAIC,
which
is
the
control
phase.
This
was
the
reason
we
came
up
with
the
change
point
detection
dashboard,
which
is
fast
and
efficient.
It
pinpoints
issues
faster,
easier
to
visualize
faults
and
changes.
Even
operators
who
are
not
proficient
with
the
statistics
can
use
this
dashboard
to
diagnose
the
issues.
For
example,
before
introducing
this
change
point
dashboard
on
our
manufacturing
shop
floor,
the
escalation
progress
is
like
the
left
sideway,
where
the
operator
monitors
the
yield
dashboard
and
identifies
the
yield
losses
and
faults.
Operator
reports
to
technician
to
look
into
the
faults.
If
technician
unable
to
resolve
escalates
the
engineer,
engineer
analyzes
the
control
charts
data
using
control
charts
alarms
and
trends,
and
then
manually
join
these
outputs
to
inputs
and
do
the
correlation
and
find
out
and
diagnose
the
fault.
And
then
engineer
implements
the
corrective
actions.
But
after
introducing
our
change
point
dashboard
on
the
manufacturing
floor,
the
operator,
technician,
and
engineers
monitor
the
change
point
dashboard
on
the
shop
floor.
If
change
is
found,
they
look
into
correlation
of
inputs
before
the
change
and
after
the
change
and
easily
diagnose
the
issue,
and
then
easily
improve
the
corrective
actions.
This
helped
us
easily
to
transition
into
our
TPM
model,
where
operators,
technicians
and
engineers
are
all
coming
together
to
minimize
the
faults.
The
change
point
dashboard
makes
our
control
phase
very
efficient.
I'm
going
to
demonstrate
in
the
following
slides
on
how
this
is
efficient
as
well
as
how
to
make
this
change
point
dashboards.
Before
showing
the
change
point
dashboard,
I
want
to
show
you
our
journey
from
traditional
control
charts
monitoring
to
change
point
detection
dashboard.
While
the
traditional
control
charts
are
very
helpful
to
monitor
excursions
in
our
process
using
Westgard
rules,
they
show
the
immediate
out
of
control
points,
but
they
often
miss
the
subtle
drifts
in
the
process,
which
are
very
gradual
but
still
significant.
These
control
charts
are
very
helpful
to
monitor
the
time
based
and
also
give
us
what
parts
are
impacted
by
this.
But
as
I
mentioned
before,
we
have
22
critical
parameters
in
this
process
alone
and
there
are
tons
of
input
parameters
that
impacts
these
22
critical
parameters,
which
means
we
need
to
have
many
control
charts
monitoring,
at
the
same
time,
to
have
a
stable
process.
But
then
we
move
on
to
model-
driven
multi
variate
control
charts.
These
control
charts,
we
monitor
both
inputs
and
outputs
together,
and
we
immediately
get
a
correlation
between
them.
But
unfortunately,
these
doesn't
have
any
time
series
or
port IDs
to
help
and
deploy
this
in
the
manufacturing
shop
floor.
The
multivariate
correlation
is
very
important
for
us,
giving
the
relationship
between
our
outputs
and
inputs
in
the
process.
And
this
again
doesn't
have
the
time
series
data
like
the
control
charts
to
identify
when
the
issue
has
started.
The
change
point
detection,
whereas
we
found
very
helpful
to
easily
detect
when
that
fault
or
change
or
significant
abrupt
drift
happened
in
our
process.
But
it
only
gives
a
value
when
a
change
point
occurred,
it
doesn't
have
the
time
series
and
not
very
valuable
as
a
monitoring
tool
on
our
manufacturing
shop
floor.
Each
one
has
its
own
disadvantages
and
advantages.
We
combine
all
of
these
control
charts
into
one
dashboard
and
leveraging
the
control
point
detection
functions
values
into
these.
Our
next
logical
step
is
regression
which
is
cause
and
effect
analysis.
Find
out
the
cause
and
effect
and
interlock
the
tool
inputs,
stopping
the
failure
on
your
CTQs
critical-
to-
quality
parameters
or
on
your
outputs
and
also
move
to
prediction
analysis.
This
is
one
example
to
show
you
how
the
control
charts
looks
different
when
you
integrate
the
change
point
detection.
For
example,
on
the
left,
I
have
a
control
chart
without
the
change
point
detection.
You
can
see
there
are
few
ups
and
downs
between
the
control
limits.
But
when
I
take
this
value
of
256,
which
the
change
point
detection
gives
you
when
you
run
for
the
same
data,
and
take
this
and
integrate
into
the
same
control
chart,
you
could
see
the
visualization
got
better.
In
this
case,
before
the
change
point,
you
have
a
demo
of
variation,
you
have
a
baseline,
you
have
the
mean
is
below
your
target
line,
and
after
the
change
point,
your
mean
is
above
the
target
line
now
and
your
variation
has
reduced.
In
this
way,
you
could
easily
find
out
and
visualize
what
is
the
change
in
your
existing
control
charts.
Once
we
found
out
how
valuable
this
change
point
is,
quickly
telling
us
where
the
change
happened
or
where
the
fault
happened
in
your
process.
We
started
using
some
use
cases.
We
started
with
some
use
cases
such
as
this
where
we
introduced
a
change
and
we
want
to
validate
the
change.
In
this
example,
we
have
a
process
where
it
is
giving
low
Cpk
value
because
of
the
population
is
mostly
distributed
towards
your
positive
side.
We
identified
it as
a
mean
shift.
It
needs
to
mean
shift,
and
we
introduced
the
known
change
mean
shift.
When
we
introduced
this
mean shift,
you
could
see
the
process
became
more
capable,
about
2.3
or
1.5.
You
could
see
when
we
deploy
the
change
point
detection
function
for
the
same
data,
the
change
point
gives
you
value
and
the
direction
whether
your
change
has
taken
place
or
not
in
your
process.
This
is
highly
visual
for
us
to
evaluate
whether
my
known
change,
which
I
just
introduced,
is
impacting
my
process,
the
positive
or negative.
The
second
use
cases,
as
I
mentioned,
we
have
lot
of
onboard
metrology
tools
in
our
process,
which
is
giving
a
lot
of
data.
We
do
periodical
measurements
and
testing
in
these
metrology
tools,
but
we
always
rely
upon
the
repeatability
and
reproducibility
data,
which
is
Gase
R&R
data
from
these
charts.
But
the
Gase R&R
data
is
not
finding
us any
faults
in
the
process
or
when
the
metrologist
tools
started
drifting
in
either
positive
or
negative.
But
for
the
same
data,
when
we
deployed
the
change
point
function,
it
immediately
tells
us
when
the
subtle
changes
and
abrupt
significant
drifts
happened
in
this
metrology,
too.
That
is
very
high
or
highly
helpful
data
to
identify
and
make
sure
our
metrologist
tools
are
still
stable
and
not
drifting
the
data.
The
third
and
most
important
one
is
to
detect
and
diagnose
the
faults
with ease.
This
is
a
dashboard
that
we
worked
on
to
deploy
on
our
manufacturing
shop
floor.
Our
workflow
would
be,
we
monitor
multiple
response
parameters
or
critical-
to-
quality
parameters
as
outputs,
and
then
we
identify
if
there
are
any
changes
using
the
change
point
dashboard.
At
the
same
time,
we
also
look
into
model-
driven
multivariate
change
point
dashboard
where
it
will
tell
us
what
are
the
excursions
and
easily
correlate
that
excursions
to
the
input
parameters.
But
the
true
value
came
in
when
we
take
this
change
point
values
and
integrate
them
into
our
existing
control
charts.
In
this
example,
we
have
a
baseline
data
before
the
change
and
after
the
change,
which
means
when
the
fault
occurred,
you
could
see
there
is
a
slight
mean
shift
and
there's
a
huge
variation
in
our
process.
At
this
point,
we
go
to
our
correlation
matrix.
It
is
correlating
our
output
parameters
to
our
input
parameters
in
the
process.
The
correlation
matrix
will
immediately
tell
you
which
factor
has
a
strong
positive
or
a
negative
correlation
during
this
phase
when
compared
to
during
your
normal
phase.
That
will
immediately
tell
you
what
input
parameter
has
gone
wrong
or
that
might
have
contributed
a
lot
to
this fault
area.
That
is
how
we
are
able
to
quickly
find
the
faults
and
diagnose
the
issues.
From
here
on,
I'm
going
to
demonstrate
in
JMP
on
how
to
create
these
kind
of
dashboards,
at
the
same
time,
how
to
create
multiple
change
points.
As
you
could
see
in
our
previous
slides,
the
change
point
function
is
giving
only
one
value.
Let
me
turn
on
to
JMP.
In
this
example,
I'm
using
Boston Housing
data
which
is
from
1978.
This
is
one
good
example
as
a
beginner
learner
for
correlation
and
regression.
Many
tutorials
use
this
example.
So
I
thought
I
could
use
this
example
which
is
readily
available.
Everybody's
JMP
sample
data
sets
and
see
how
the
correlation
is,
and
if
there
are
any
multiple
change
points
in
the
data,
at
the
same
time,
correlate
to
the
practice.
The
sample
data
table
looks
like
this.
In
our
case,
I
want
to
see
the
median
market
value
or
median
prices
of
the
owner-occupied
homes
in
Boston
and
see
how
it
is
variating
with
time.
At
the
same
time,
there
are
corresponding
factors
like
what
is
the
crime
rate
in
the
town
or
what's
the
pollution
rate
in
the
town
or
the
age
of
other
factors
such
as
age
of
the
homes
or
dis
tance
in
these
regions.
You
can
read
the
data
about
these
column
names
in
the
column
notes
that
is
presented
over
here.
I'm
going
to
run
the
script
which
gives
usâ¦
Before
running
the
script,
I
also
want
to
tell
you
that
I'm
attaching
the
entire
script
as
part
of
my
presentation,
where
you
could
go
through
the
script
or
run
the
script
and
it
will
give
you
a
nice
dashboard
about
Boston Housing
values
variation
and
also
a
correlation.
You
could
download
the
script
and
look
into
my
score
and
you
could
use
the
same
methodology
and
the
logic
on
how
I'm
able
to
detect
multiple
change
points
from
the
dashboard
using
the
JS
Subscript.
When
I
run
the
script,
runs
and
I
get
a
nice
dashboard
like
this.
As
I
mentioned,
our
problem-
solving
framework
would
be
like
look
at
the
change
points
in
our
median
value
of
the
homes
in
Boston.
You
could
see
it
gave
us
the
median
value
that
there
are
a
big
change
here
and
it
tells
you
the
change
point
appears
to
be
on
row
373,
and
also
there
are
several
second of
small
change.
When
you
look
at
the
model-
driven
control
chart
data,
you
could
see
there
are
high
excursions
in
this
medium
value
of
the
housing
markets.
You
could
easily
tell
what
caused
this
excursion
by
looking
into
your
inputs
parameter,
that
is
crime
rate.
You
could
hover
on
any
of
these
and
find
out
what
is
impacting
these
huge
excursions
in
your
monitoring
variable,
which
is
median
value
of
the
houses.
As
I
mentioned,
the
real
value
comes
out
when
we
take
these
and
integrate
into
our
control
charts.
See
this
one,
this
control
chart
will
give
you
these
integrated
change
points
from
here.
We'll
give
you
a
good
visual.
You could see
now
in
Boston Housing
market
example,
we
have
a
baseline
housing
median
value
changes,
a
baseline
value
at
20
million.
And
then
when
the
housing
boom
happened,
or
when
the
change
point
1
occurred,
you
could
see
there
is
a
good
amount
of
variation
in
the
housing
market.
At
the
same
time,
there
is
a
mean
shift,
too.
But
soon,
there
is
another
change
happen
and
the
housing
market
crash
resulting
in
your
median
value
is
below
your
baseline.
This
is
easily
telling
us,
giving
us
a
visual
into
parts
where
what
is
the
baseline
data,
when
the
first
change
occurred,
what is and
how
much
is
the
change?
The
second
change
occurred
and
how
much
is
the
change
now.
You
can
compare
with
your
previous
two
changes.
If
you
as
a
prospect
to homebuyer
in
Boston
and
you're
looking
into
this
kind
of
data,
you
want
to
know
what
might
have
happened
that
have
impacted
these
kinds
of
changes.
In
your
data
set,
you
have
some
nice
factors
about
the
crime
zone,
the
crime
rate
in
that
same
area
in
the
same
period
of
time,
and
the
pollution
rates,
et cetera.
If
you
look
at
our
before
change
you
could
see
the
market
value,
how
it
is
correlating
with
your
factors.
In
this
case,
you
can
see
there's
a
good
amount
of
negative
correlation
with
the
crime
and
the
negative
correlation
with
the
industry
area
and
also
pollution.
And
there
is
a
strong
positive
correlation
which
is
about
0.8
with
this
phase.
Now,
look
at
the
second
change
point
1
phase
where
the
housing
boom
occurred
[inaudible 00:22:46]
mean shifted
upwards.
You
could
see
in
this
time
frame
your
crime
rate
is
low
or
no
correlation
with
these
changes.
Your
pollution
is
low
again,
but
your
rooms
still
same
strong
positive
correlation
in
these
two
phases.
If
you
ask
me
what
would
have
happened
this
time,
I
would
say
maybe
the
crime
has
increased
in
the
area
or
the
pollution
has
increased
in
the
area,
such
kind
of
dominant
factors
that
are
visible
,
that
are
not
favorable
for
a
prospective
home.
That's
going
on
to
the
housing
crash
when
it
happened.
A comparison
between
this
phase
to this phase,
change point
to see
how
the
correlation
just
changed.
You
can
immediately
tell,
"Okay,
my
comparison
with
the
median
market
value
from
this
phase,
my
correlation
with
the
client
is
pretty
low,
my
rooms
has
very
positive
correlation,
and
there
is
lstat
and
other
factors."
But
if
you
come
to
the
change
point 2
time,
you
could
see
the
crime
rate
has
increased
again,
your
pollution
has
increased
again,
which
resulted
in
the
negative
linear
correlation
of
this
change.
When
you
look
at
your
rooms,
which
has
been
positively
contributing
for
your
change
has
fell
down
completely.
In
this
way,
this
is
telling
you
a
lot
of
information
data
about
these
changes
that
occurred
in
1978.
Also,
easy
correlationship
data
with
your
factors
that
might
have
impacted
these
changes.
I'll
repeat.
Our
framework
is
detecting,
for
any
time
series
data,
we
first
detect
number
of
changes,
if
it
is
one
change
or
two
change
or
more
changes,
and
then
integrate
these
change
points
into
our
control
chart
to
get
a
better
visualization
about
the
changes.
Is
it
a
mean
shift
or
variation shift,
or
what
is
happening?
And
then
look
into
your
factors
that
might
have
contributed
to
these
changes
and
see
what
factors
a
strong
contribution
for
this
change
or
weak
contribution
or
no
contribution
or
negative
contribution.
Now
that
you
know
the
crime
rate,
your
market
value
âthe
median
market
value
is
impacted
by
the
crime
rate,
pollution,
and
the
roomsâ
let's
look
at
a
simple
visualization.
When
you
go
here,
you
can
see
this
is
a
simple
graph
plot.
I'm
taking
my
change
point
and
visualizing
it
in
a
better
way.
I'm
comparing
my
median
value
with
the
nox,
the
pollution
value
in
the
same
towns,
in
the
same
time
period
and
the
rooms
and
the
crime
rate.
You
could
see
when
my
housing
median
market
values
increase
or
the
housing
booming
phase,
you
could
see
the
pollution
is
very
low
and
the
rooms
for
dwelling
has
increased
and
your
crime
rate
is
at
least
possible
state.
As
a
prospective
homebuyer,
these
are
all
favorable
factors
for
me,
no
pollution
area,
no
crime
area or
safe
area,
and
then
I'm
getting
a
bigger
home
with a
convenient
number
of
rooms.
But
let's
see,
in
this
phase,
when
the
housing
market
crashed,
at
the
same
time,
your
pollution
rate
has
increased.
The
number
of
rooms
for
dwelling
has
decreased.
And
then
there's
an
astronomically
high
crime
rate
which
is
impacting
the
same
thing.
This
is
a
clear
visual,
but
you
landed
up
in
this
problem
or
with
this
kind
of
conclusion
with
the
help
of
your
correlation
matrices.
Correlation
matrix
and
then
a
change
point.
Your
correlation
matrix
and
then
the
change
point
and
then
a
better
resolution
like
this.
Let
me
go
and
show
you
how
the
JSL
script
is
pooling
multiple
change
points.
You
could
find
the
change
point
data
in
your
control
charts,
multivariate
control
charts.
And
again,
this
is
a
multi
variate
control
chart.
You
could
use
as
many
as
variables
as
your
multivariate
control
chart
input
parameters.
But
in
my
case,
I'm
using
median
market
value,
and
I
get
a
multivariate
correlation
and
a
change
point.
Clearly,
see,
it
gives
me
373
as
a
number.
How
I
am
taking
or
extracting
this
373
number
is
using
this
kind
of
JMP
or
JSL
can
extract
any
of
the
text
box
columns
from
any
of
your
reports
and
then
put
it
into
your
data
table.
In
this
case,
I
collected
this
data
point.
You
could
see
the
equation
1
holds
the
phrase
call
or
the
text
box
call.
The
change
point
appears
to
be
373.
This
one
simple
code
will
hold
or
extract
that
value
from
the
report.
And
then
I
take
that
into
my
columns
in
the
change
point
1
column,
and
I
extract
the
numerical
value
out
of
it.
I
wrote
the
JSL
as
simple
as
possible.
I'm
very
beginner
with
scripting
and
coding.
You
could
see
I
used
simple
formulas
and
simple
methods
to
get this.
You
can
see
the
change
point
report
is
only
giving
you
one
change
point
value.
But
you
could
see
in
your
process,
there
are
other
changes,
too,
that
you
might
be
interested
in
and
we
want
to
find
them
out.
What
I
do
is
once
you
get
your
first
value,
you
hide
and
exclude
everything
after
your
change
point.
After
this
373,
you
exclude
everything.
Now
you're
left
with
this
phase.
You
want
to
see
what
is
my
change
in
this
phase,
and
the
cycle
continues.
For
example,
in
my
equation
2
from
my
report
2,
I'm
pulling
the
text
box
phrase
called
the
change
point.
Sorry,
this
is
hard
to
show.
When
you
hover
on
this,
you
can
see
that
variable
has
the
phrase
[inaudible 00:29:49].
Variable
has
the
phase
of
the
change
point
appears
at
row
157.
You
take
the
value
and
then
put
it
in
your
column
call,
CP
Text,
something
like
that,
where
you
could
extract
your
second
change
point.
Once
you
get
a
data
like
that,
it
is
very
easy
to
construct
a
dashboard
like
this,
where
you
create
your
control
chart
dashboard,
each
individual
components,
model-
driven
multivariate
control
chart,
and
create
the
dashboard
using
the
new
dashboard
application.
Once
you
create
a
nice
dashboard
like
this,
you
just
go
here
and
you
just
take
everything
into
the
script.
I'm
switching
down
to
my
presentation.
I
hope
the
Boston Housing
data
example
is
very
clear,
because
how
easily
you
can
detect
changes
in
any
time
series
data
and
correlate
those
with
your
input
parameters.
In
real
life
or
manufacturing
scenario,
you
would
have
several
input
parameters
like
this.
In
this
case,
in
my
dashboard,
we
look
into
around
50
input
parameters
comparing
with
our
output
parameters.
We
also
monitor
the
multivariate
control
charge.
You
could
see
I'm
monitoring
multiple
CTQs
together
instead
of
each
individual
traditional
control
chart.
I'm
exporting
these
values
into
my
existing
control
chart
and
then
immediately
diagnosing
my
faults.
This
dashboard,
as
you
can
see,
it's
pretty
self-
explanatory,
easy
to
read,
easy
to
deploy
on
your
manufacturing
floor.
Technicians
even
not
comfortable
with
statistical
analysis,
and
doing
this
kind
of
building
control
charts
and
everything,
they'll
just
look
at
the
process
saying
that,
"O
kay,
I
have
two
changes
in
my
process,
and
let
me
see
what
are
the
input
parameters
that
are
correlating
these
changes
and
see
if
I
can
change
them
or
manipulate
them
so
that
my
process
back
to
stable
state like this.
Again,
the
correlation
coefficients
are
pretty
easy
to
explain
to
anyone
on
the
manufacturing
shop floor.
For
example,
a
strong
positive
correlation
would
give
you
a
blue
number
closer
to
1,
or
a
strong
negative
correlation
would
be
something
like
-0.7,
but
the
variation
would
be
something
like
this
when
you
increase
something.
With
subtle
bit
of
variation,
you
would
see
a
negative
correlation
there,
and
then
a
weak
negative
correlation
would
be
something
like
this,
a
lot
of
variation
instead
of
a
straight
line like this.
And
then
there's
no
correlation.
The
number
would
be
pretty
much
close
to
0.
You
would
see,
even
if
you
increase
one,
the
other
might
be
decreasing.
It
is
very
easy
to
explain.
It
is
very
visual
on
the
manufacturing
shop
floor
to
find
out
these
kind
of
numbers,
looking
to
see
what
input
has
changed
and
what
input
has
a
strong
correlation.
There
is
an
added
bonus
with
the
change
point
integration.
As
you
can
see,
these
are
all
numerical
data,
which
is
getting
a
numerical
correlation
like
this.
But
the
change
point
is
telling
you
when
the
fault
has
occurred
and
when
you
know
when
the
fault
has
occurred,
you
just
look
into
your
data
such
as
template
change
or
your
load
port
IDs,
and
you
will
get
a
correlation
with
your
non-
numerical
data
[inaudible 00:34:10]
.
You
would
start
a
discussion
on
your
manufacturing floor.
Hey,
when
this
time
occurred,
I
did
this
change
over
PM
or
the
tool
is
setting
idle,
or
I
did
a
prevent
day
to
maintenance
at
this
exact
time.
Maybe
that
prevent
day
to
maintenance
time,
some
issue
has
occurred.
This
is
giving
you
a
full
picture.
You're
looking
into
tons
of
50
plus
input
parameters
and
easily
getting
a
correlation
with
your
CTQs
and
easily
detecting
the
faults.
At
the
same
time,
you
have
non-
numerical
data
such
as
like
this,
and
you
find
out
and
you
relate
to like,
what
was
I
doing
when
this
change
or
fault
has
occurred
in
my
process?
The
final
takeaway
is
by
integrating
change
point
function
to
our
existing
control
charts.
We're
quickly
finding
faults
and
diagnosing
them
faster,
saving
millions
of
dollars
in
our
fast-
paced
manufacturing.
With
JSL
scripting,
we're
able
to
find
out
there
are
multiple
change
points
in
our
process
and
integrate
them
to
our
control
chart
dashboards.
This
change
point
and
multivariate
methods
can
be
used
in
any
time
series,
as
I
was
mentioning.
For
example,
when
I'm
preparing
for
this
presentation,
I
gave
a
small
set
of
our
monthly
savings
data
set
to
my
wife
who
is
not
familiar
with
statistics
and
control
charts.
Upon
tracking
this
dashboard,
my
wife
easily
found
there
are
change
points
in
our
savings.
But
at
the
same
time,
she
did
not
like
the
fact
that
when
the
dashboard
pointed
out
that
our
Amazon
spending
is
well
correlating
for
our
low
saving
months.
As
you
can
see,
like
this
time,
this
change
point,
you
could
use
it
on
any
time
series
data.
You
could
find
out
your
changes
in
your
time
series
and
when
the
change
has
occurred
or
when
the
significant
change
has
occurred,
and
then
you
can
correlate
to
your
input
parameters.
In
our
case,
we
are
just
correlating
your
Amazon
spending
or
take-
out
dinners
and
several
other
factors
[inaudible 00:36:18]
.
That's
pretty
much,
and
I
hope
you
found
this
presentation
very
useful,
and
you
also
learned
how
you
can
easily
find
multiple
change
points
through
JSL
scripting
and
create
easy
dashboards
to
where
demonstrators
to
find
out
faults
and
easily
diagnose
them.
Thank
you.
