Welcome
to
this
post
of
presentation
in
the
applications
of
MSA
platform
tools
in
JMP
17.
Before
we
go
into
it,
I
just
would
like
to
give
a
brief
description
about
Measurement
System
Analysis,
short
for
MSA.
So
when
we
look
at
the
total
observed
variation
in
the
process,
we
use
measurement
system
analysis
to
try
to
identify
and
manage
the
sources
of
variation
that
can
influence
the
measurement
system
being
used.
It's
a
combination
of
measurement
devices,
people,
procedures,
standards,
etc.
So
we
can
decompose
that
total
observed
variation
into
two
other
components,
the
process
component,
or
sometimes
called
part
to
part
variation.
But
what
we're
really
interested
with
measurement
system
analysis
is
in
this
measurement
system
variation
component.
Looking
at
the
measurement
error
that
is
associated
with
this
measurement
system
variation,
that
can
be
broken
down
into
other
components,
precision
and
accuracy.
For
the
purpose
of
this
poster,
I
will
concentrate
on
the
tools
that
enable
us
to
identify
sources
of
variation
within
precision
and
specifically
repeatability
and
a
little
bit
about
bias
component
under
the
accuracy
component.
What
we
tend
to
do
when
looking
at
measurement
system
analysis,
there
are
several
methods
involved,
normally
and
particularly
for
continuous
data.
We
start
by
examining
the
accuracy
and
consistency
of
the
measurement
device
alone
using
a
technique
called
type 1 gage study.
This
is
sometimes
also
named
as
analyzing
the
pure
repeatability
of
the
system.
So
we
have
one
single
part,
one
single
device
if
the
measurement
system
requires
a
manual
intervention,
we
can
have
one
operator.
But
the
idea
here
is
that
we
start
evaluating
the
pure
repeatability
of
the
system
before
going
into
more
complex
analysis
where
other
sources
of
variation
may
be
part
of
the
measurement
system,
which
essentially
is
the
second
step
around
what
is
called
Full Gage R&R,
which
examines
both
repeatability
and
reproducibility.
Last
but
not
least,
we
have
continuous
gage
linearity
and
bias
study,
but
it's
not
going
to
be
covered
specifically
in
this
poster.
So
let's
have
a
look
of
what
this
means
in
terms
of
JMP
17.
In
the
new
version
of
JMP,
we
do
have
a
new
MSA
method,
type
1
gage
study,
that
essentially
is
going
to
help
us
identify
that
initial
phase
of
the
analysis
with
regards
to
the
pure
repeatability
of
the
system.
So
I'll
show
you
a
quick
example
of
that
of
an
output
report
with
the
Type
1
Gage
R&R.
And
what
you
can
see
here
is
that
by
default,
the
report
shows
a
run
chart.
So
this
looks
at
30
repeats
of
the
same
part
using
the
same
device
or
equipment
in
order,
so
this
timeline
really
helps
us
identify
any
special
situation,
any
special
measurements
that
didn't
work
very
well.
There
is
a
reference
that
we're
on
the
nominal.
If
you're
using
a
reference
part,
for
example,
we
can
definitely
identify
whether
the
average
of
those
measurements
are
in
line
with
the
reference
part.
That
mean
value
can
be
added
to
the
graph
if
we
want
to.
As
you
can
see,
it's
going
to
be
on
top
of
it.
But
if
I
remove
the
reference
line,
then
you
can
see
average
and
the
reference
are
very
similar
for
this
example.
We
also
want
to
have
a
look
at
this
Type 1 Gage R&R study
and
have
a
reference
around
20
%
of
our
tolerance.
What
in
the
type
1
gage
study
we're
doing,
we
are
limiting
the
analysis
to
only
20
%
of
the
total
tolerance
in
order
to
assess
whether
the
pure
repeatability
is
acceptable
or
not.
But
this
specification,
if
you
will,
for
the
type
1
can
be
all
consented
in
the
settings
of
this
tool.
It
provides
some
summary
and
capability
statistics,
so
the
normal
reference
location
and
spread
references,
particularly
when
it
comes
to
six
standard
deviations,
the
number
of
measurements
taken
and
the
tolerance.
T
hen
here
are
the
two
limits
above
reference
on
the
graph
for
the
20
%
of
the
tolerance,
so
plus
or
minus
10
%
of
that
tolerance.
If
you
use
to
the
process
capability
indexes,
what
you
will
see
now
for
capability
of
the
gage,
CG
and
CGK,
they
are
exactly
the
same.
The
biggest
difference
here
is
that
obviously
we're
looking
at
the
capability
of
the
gage
and
assessing
this
variation
with
regards
to,
in
this
case,
the
20
%
of
the
tolerance,
but
suddenly
we
can
have
a
look
at
both
the
variation
relative
to
those
spec
limits
and
also
variation
and
location
for
CGK.
So
this
gives
us
a
summary
of
some
metrics
to
evaluate
the
Type 1 Gage R&R study
results.
There are
some
percentage
being
calculated
as
well
in
terms
of
percentage
of
variation
with
regards
to
repeatability,
but
obviously,
if
you're
using
a
reference
part
that
you
already
know
its
nominal
value,
we
can
evaluate
not
only
the
pure
repeatability
of
the
system
in
this
case,
but
also
the
bias.
So
the
difference
between
the
reference
value
and
the
nominal
value.
So that
can
be
actually
added
as
an
additional
test
if
we
want
to,
so
this
is
really
looking
at
the
hypothesis
testing
case
in
terms
of
if
the
bias
is
equal
to
zero
so
either
the
average
and
the
reference
value
are
the
same
or
very
close
to
each
other,
and
as
you
can
see,
the
reported
P
value
there,
in
this
case,
statistically,
there
is
no
significant
difference
between
average
and
reference
value.
Another
useful
visualization
within
this
tool
is
the
history
realm,
so
we
can
have
a
look
at
the
distribution
of
the
values
of
those
measurements
taken,
in
this
case,
those
30
measurements
for
reference,
that
can
be
customized
in
your
report
as
we
go
along.
So
very
quickly,
we
just
have
a
great
tool
to
initialize
our
measurement
system
analysis
process
by
now
having
what
is
called
a
Type 1 Gage R&R Study
as
part
of
the
MSA
platform
in
JMP.
What
we
sometimes
do
is
we
also
have
a
second
step
before
we
go
into
what
is
called
a
Full Gage R&R
assessment,
both
repeatability
and
[inaudible 00:07:43]
,
which
can
be
called
a
Type
2
Study.
We
have
an
example
here
of
that,
so
the
only
difference
here
is
the
fact
that
in
between
the
30
measurements,
we've
removed
the
part
from
a
potential
holding
fixture
in
between
each
measurement.
As
you
would
expect,
by
doing
that
intermediate
step
in
between
all
the
measurements,
then
we
expect
to
have
more
variation,
and
this
is
what
we
can
see
here
now.
So
not
only
we
have
more
variation,
where
you
can
also
see
that
the
average
value
of
the
readings
are
also
much
lower
than
its
target
location.
By
turning
on
the
bias
test,
we
will
be
able
to
see
that
now
the
P
value
when
compared
to
the
Type
1
study
where
we
didn't
remove
the
part
in
between
measurements,
the
part
was
fixed
and
just
measured
30
times
consecutively,
we
now
have
a
low
P
value
showing
that
there
is
significant
difference
between
the
reference
value
and
the
average. T
his
can
be
built
into
several
increment
and
sources
of
variation
even
before
we
start
adding
multiple
parts
and
operators
or
equipment
into
this
analysis.
But
if
we
do,
JMP
already
had
a
gage
on
our
study
tool
in
previous
versions.
This
is
just
a
quick
example,
what
that
means
in
terms
of
variability
of
the
gage, a
nd
in
this
case,
we
use
the
gage
on
our
method
involved.
So
if
you
go
to
Analyze,
Quality
and
Process,
there's
an
updated
version
for
the
Measurement
System
Analysis.
In
the
MSA
method,
we
can
see
now
we
have
the
Type
1
Gage
study
that
I've
used
for
both
Type
1
and
Type
2.
The
only
difference
there
is
that
on
the
report,
the
output
report
for
the
Type
2,
I
just
edited
the
title
and
called
it
Type
2
just
to
differentiate
between
the
two
output
reports.
But
what
we're
seeing
here
for
the
Full Gage R&R
or
the
variability
analysis,
I've
used
the
Gage
R&R
method,
and
this
is
where
we
can
also
decide
the
type
of
model
used,
normally
used
as
cross,
so
we
can
see
all
the
effects
crossed
with
each
other
in
the
analysis,
as
well
as
some
additional
options
are
also
available.
But
in
this
report,
I'm
customizing,
in
this
case,
I've
added
some
specification
limits.
Here,
essentially,
we're
not
looking
at
the
reproducibility
of
the
system.
It's
just
another
increment
we
evaluate
repeatability
in
this
case.
We
are
using
not
one,
but
10
parts
in
this
case
and
evaluating
that
variation
for
five
repeats
of
each
part.
As
you
can
see,
in
the
Gage
R&R
report
and
table,
the
reproducibility
component
is
zero
because
we
don't
have
additional
equipment
or
operators
being
evaluated
in
this
analysis,
so
all
the
variation
in
this
study
is
due
to
repeatability.
So
this
is
the
traditional
output
that
you
would
get
from
the
Gage
R&R
method
inside
of
JMP
for
reference.
To
finalize
the
new
tools
involved
in
JMP
17
for
the MSA
platform,
what
I
would
like
to
highlight
as
well
is
that
as
part
of
the
planning
phase
for
any
Gage
R&R
study,
it's
important
to
understand
what
is
the
method
utilized,
of
course,
but
also
what
will
be
a
good
method
of
data
collection.
A
s
part
of
JMP,
we
now
have
as
part
of
DOE
special
purpose,
a
new
tool
called
MSA
Design,
and
this
enables
adding
factors
like
parts
and
operators.
If
I
quickly
show
adding
three
factors
there,
I
can
identify
what
is
the
MSA
role
involved
in
each
one
of
them,
for
example.
This
is
a
great
opportunity
during
the
planning
phase
to
start
to
come
up
with
the
design
that
will
help
you
doing
the
data
collection
even
before
any
analysis
is
done.
For
more
information
about
how
to
utilize
the
MSA
design
feature,
you
can
follow
this
link,
which
will
take
you
to
the
JMP
user
community
video
where
Hyde
Miller,
JMP
systems
engineer,
has
provided
more
information
about
this
tool.
Hope
this
was
useful
for
you.
Thank
you
very
much.
