Welcome,
everybody.
My
name
is
Jason
Wiggins.
I'm
a
Systems
Engineer
with
JMP.
For
this
presentation,
I'll
be
playing
the
role
of
a
Quality
Consultant.
My
name
is
Jerry
Fish.
I'm
a
Systems
Engineer
with
JMP.
I'll
be
playing
the
role
of
a
Quality
Manager
at
a
manufacturing
plant.
Just
a
couple
of
references
for
this
presentation
upfront.
We're,
Jerry
and
I,
building
on
a
paper
that
we
presented
at
Discovery
Europe
2023.
That's
the
paper
number.
You
can
go
to
community.j
mp.com
and
search
for
that
paper.
Jerry
will
also
be
presenting
an
on-demand
video
for
Discovery
on
Demand.
That's
the
paper
number
to
search
as
well.
Throughout
the
presentation,
we
will
be
referencing
Dr. Donald
Wheeler's
book,
EMP
III,
Evaluating
the
Measurement
Process
and
using
imperfect
data.
All
right,
Jerry,
ready
to
kick
this
off?
Absolutely,
go
for
it.
All
right,
I'm
calling
you
up.
Hi,
Jerry.
Thanks
for
spending
a
few
minutes
with
me.
As
a
quality
consultant,
I
help
quality
stakeholders
like
yourself
understand
and
improve
processes.
Hi,
Jason.
Nice
to
meet
you.
I
need
to
let
you
know,
I
don't
have
much
time
to
spend
with
you
right
now.
We've
got
an
emergency
situation
on
our
production
line
that
I
don't
really
need
to
go
address.
I
know
we've
got
30
minutes
scheduled
and
I'm
interested
in
discussing
gages,
but
can
we
try
to
make
this
quick?
I
understand
completely.
I'll
try
to
make
the
most
of
our
time
today.
To
kick
things
off,
can
you
tell
me
a
little
bit
about
your
company
and
your
quality
program?
Sure.
You
probably
know
that
Acme
has
built
a
reputation
with
our
customers
for
manufacturing
highest
quality
products.
We're
always
concerned
with
quality.
We
have
various
gages
that
we
use
to
ensure
our
quality
stays
high,
and
we've
been
doing
this
for
many
years,
so
we
think
we're
pretty
good
at
it.
I'm
familiar
with
Acme's
high
quality
reputation.
My
consulting
team
and
I
have
been
working
with
manufacturing
companies
like
yourself
that
have
a
high
focus
on
quality
to
advance
the
use
and
effectiveness
of
great
gage
studies.
One
of
the
things
we
seek
to
understand
is
the
monetary
cost
associated
with
the
gages
used
to
measure
process
quality
characteristics.
Have
you
quantified
how
much
any
of
your
gages
are
costing
your
business?
I'm
not
sure
I
know
what
you
mean.
Well,
gages
aren't
perfect.
They
make
mistakes.
Sometimes
they'll
throw
away
good
parts
and
sometimes
they'll
pass
bad
parts.
Unless
you
have
a
perfect
gage
and
no
one
has
these,
mistakes
are
inevitable.
Yeah,
I
get
that,
I
suppose
so.
We've
done
traditional
gage
studies
that
say
our
gages
are
good.
Well,
some
of
them
are
actually
categorized as adequate
for the
AIAG
Automotive
Industry
Action
Group,
which
we
use
for
our
testing.
Doesn't
that
mean
they're
okay
to
use?
There's
a
lot
more
to
the
story
than
just
using
the
good,
adequate,
and
poor
AIAG
assessment.
In
fact,
we
could
have
a
long
discussion
about
the
differences
between
AIAG
results
and
a
newer
method
called
EMP
3.
I
know
you
said
you
have
30
minutes.
It
turns
out
that
AIAG,
we
were
trying
to
sum
it
up,
it's
just
much
more
critical
of
gages
than
it
needs
to
be.
The
EMP
method,
which
is
pioneered
by
a
guy
named
Dr. Wheeler,
is
a
more
realistic
way
to
evaluate
gages.
I
appreciate
you
skipping
over
that
part.
We
recognize
no
gage
is
perfect.
We
also
recognize
that
using
a
poor
gage
risks
accepting
bad
parts,
which
is
bad
for
my
business.
The
worse
the
gage,
the
higher
the
risk.
We've
got
a
way
that
we handle
this
problem.
Oh,
what's
that?
Well,
we
instruct
our
manufacturing
group
to
use
inspection
limits
that
are
inside
of
the
specifications.
If
we
set
them
far
enough
inside
these
spec
limits,
we
reduce
and
essentially
we
can
eliminate
shipping
bad
parts.
Doesn't
that
fix
our
problem?
Using
inspection
limits
that
are
inside
the
spec
limits
is
a
good
way
to
reduce
the
risk
of
passing
bad
parts
when the
gage
measures
apart
near
the
specification
limits.
This
is
a
common
strategy,
something
that
companies
call
a guard banding
or
specifying
manufacturing
instructions.
How
do
you
choose
how
much
to
narrow
your
inspection
limits?
Well,
frankly,
I'm
not
sure
we
put
that
much
rigor
into
it.
We
just
move
our
inspection
limits
in
board
of
the
specs
by some amount
that
our
subject
matter
experts
have found
work
well
over
the
years.
Well,
you're
in
good
company.
Many
others
like
you
employ
the
same
approach.
There
is
a
downside,
though.
Oh,
what's
that?
By
arbitrarily
choosing
how
much
to
narrow
inspection
limits,
you
may
be
unnecessarily
throwing
out
good
parts.
That's
just
assuming
they're
too
narrow.
Or
you
could
still
have
bad
parts
that
escape
inspection
if
the
limits
aren't
narrow
enough.
Have
you
considered
taking
a
more
statistical
approach?
We
have
not.
I've
always
thought
we
should
have
a
better
way
to
justify
our
manufacturing
instructions,
but
we
don't
know
how
to
do
that.
Well,
Dr. Wheeler
proposes
a
method
of
setting
manufacturing
instructions
based
on
the
statistics
we
get
from
doing
a
measurement
systems
analysis
in
his
book,
EMP
3.
I
honestly
haven't
heard
of
Wheeler's
EMP
method
before.
Is
it
new?
It's
been
around
for
a
couple
of
decades
now.
More
importantly
to
my
knowledge, Dr.
Wheeler
was
the
first
person
to
publish
a
solution
to
the
problem
of
objectively
setting
these
inspection
limits.
His
approach
uses
outputs
from
MSA,
like
we
were
mentioning.
Some
of
these
are
probable
error,
along
with
expectations,
your
expectations
were
conformance
to
specifications.
Do
any
of
the
modern
statistical
analysis
package
that
are
on
the
market
today
support
this
EMP
method?
I'm
glad
you
asked.
Jmp,
which
is
my
preferred
data
analytics
tool,
JMP
is
a
general
purpose,
easy
to
use
data
analytics
package
that
has
many
quality
and
process
control
features.
Jmp
makes
quick
work
of
the
analytics
part
of
process
improvements,
so
more
time
can
be
dedicated
to
improving
the
process
itself.
You
know
that
that's
not
a
trivial
amount
of
work,
typically.
Jmp's
EMP
personality
and
the
measurement
systems
analysis
platform
is
based
on
Wheeler's
book.
All
that
stuff
is
in
there.
The
calculations
for
manufacturing
instructions
are
not
available
in
JMP
yet.
We
built
an
add-in
that
does
them
for
you
and
provides
some
tools
for
cost
trade-off
analysis.
Okay,
good.
I
was
afraid
I
might
have
to
come
up
with
my
own
equations.
I'll
order
a
copy
of
that
book
for
my
team.
If
you've
already
got
an
add-in
that
does
that
for
us,
I'm
definitely
interested.
What
does
it
cost
and
how
does
it
work?
Well,
good
news,
it's
free.
You
can
find
it
in
the
file
exchange
under
community.Jmp.com
online
or
JMP
User
Community.
Let
me
show
you
how
it
works.
Great.
All
right.
Here
on
the
left
side,
we
have
to
enter
a
little
bit
of
information
about
our
product.
First
is
the
specification
limits.
You
know,
this
is
what
our
customer
wants.
This
is
voice
of
the
customer.
An
upper
and
lower
specification
limit.
We
need
to
enter
what
we
know
about
our
measurement
error.
We're
going
to
use
the
standard
deviation
of
our
measurement
error.
This
is
something
that
we
can
get
from
a
measurement
systems
analysis.
We
also
need
to
supply
some
information
about
what
we
expect
the
true
part
distribution
to
look
like.
The
parameters
mean
and
standard
deviation,
give
us
a
description
of
what
the
true
part
distribution
is
for
our
process.
Wait
a
minute.
I
don't
know
my
true
part
distribution.
That's
the
problem
here.
All
I've
got
is
my
measured
part
distribution.
Okay,
let's
for
the
moment,
assume
that
the
true
part
distribution
is
normally
distributed,
and
the
gage
are
also
normally
distributed.
We
can
use
a
simple
relationship
to
get
the
true
part
variance.
If
I
did
a
measurement
systems
analysis,
I
have
the
variance
associated
with
my
gage.
If
I
did
a
bunch
of
measurements
of
my
process,
I
have
a
measured
part
distribution
of
my
process.
I
just
have
to
subtract
those,
and
then
I
can
get
the
true
part
variance.
From
that,
I
can
get
the
standard
deviation
for
my
true
part
distribution.
Mean's
simple
also.
We're
just
going
to
assume
that
that
mean
is
the
measured
part
distribution
less
any
bias
that
we
have
with
the
gage.
Okay.
I
think
I'm
with
you
so
far.
We
are
still
moving
towards
how
much
my
gage
is
costing
my
business,
right?
My
line
still
has
that
problem
that
I
need
to
address.
Got
you.
We
are.
If
you'll
indulge
me
just
a
little
bit
longer.
We
have
more
work
to
do.
First,
let's
look
at
the
statistics
summary.
Probably
layer,
interclass
correlation and
precision
to
tolerance
are
ways
of
describing
gage
variation
that
we
get
by
conducting
Wheeler's
measurement
systems
analysis.
These
values
are
computed
using
specification
limits
and
gage
sigma
inputs.
I'll
let
you
read
about
these
calculations
when
you
get
Wheeler's
book.
Okay.
The
capability
indices
are
used
to
describe
how
much
of
the
spec
limit
range
is
consumed
by
process
variation.
This
is
the
variation
of
the
true-part
values.
Capability
is
calculated
using
spec
limits
and
our
true-part
mean
in
sigma.
Yes,
at
Acne,
we
regularly
use
CP
and
CPK.
Nice.
Collectively,
you're
probably
getting
an
idea
that
these
values
provide
us
a
nice
snapshot
of
gage
and
process
variation.
It's
going
to
help
us.
For
this
example,
an
ICC
of
0.86
is
telling
us
that
14
%
of
our
measurement
variation
is
a
result
of
gage
error.
But
our
process
is
not
performing
so
well
at
a
capability
of
only
about
67%.
The
last
thing
that
we
need
is
expected
conformance
at
manufacturing
instructions.
That's
a
mouthful.
What
do
you
mean
by
that?
It
is.
Let
me
see
if
I
can
unravel
that.
This
is
the
probability
that
a
measured
part
will
truly
conform
to
specifications
if
the
gage
measures
exactly
at
specification
limits
that
we
discussed
earlier.
Exactly
at
55
or
75.
For
example,
let's
say
that
we
want
99%
conformance.
To
achieve
this,
we
need
to
set
our
manufacturing
instructions.
Let
me
make
this
a
little
bit
bigger
for
us
here.
We
need
to
make
our
manufacturing
instructions
57.3
and
72.7.
That's
actually
narrower
than
the
55
and
75 that
are
our
spec
limits.
Then
if
we
do
that,
we
can
expect
a
99%
chance
that
will
reject
or
accurately
accept
a
part
that's
near
those
specification
limits.
Okay,
let
me
echo
that
back
to
you
to
see
if
I've
got
that
right.
If
I
set
my
inspection
limits
for
this
example
to
57.3
and
72.7,
and
then
if
I
measure
a
part
with
my
gage
that
measures
exactly
57.3,
there's
a
99%
chance
that
it
meets
my
product
specifications
of
55
and
75.
That's
right.
You
said
it
even
better
than
I
could.
It's
really,
in
my
mind,
it's
useful
to
think
about
measurement
risk
in
this
way.
As
measurements
that
we
make
near
spec
limits
are
the
riskiest
that
we
encounter.
It
also
exposes
trade-offs
between
gage
effectiveness
and
process
capability.
Think
about
this,
Jerry.
If
the
process
is
highly
capable,
you
can
get
away
with
the
marginal
gage
even
when
measuring
near
the
spec
limits.
However,
if
you
have
a
poor
CPK,
it
may
be
smart
to
adjust
the
manufacturing
instructions
to
mitigate
risk,
even
if
the
gage
is
effective.
That
sounds
like
what
my
subject
matter
experts
have
been
trying
to
do,
but
you're
saying
that
we
can
put
a
lot
more
statistical
rigor
behind
setting
those
manufacturing
instructions.
Your
example
sets
manufacturing
instructions
to
give
99%
conformance.
I
assume
if
I
want
99.9%
conformance,
those
manufacturing
instructions
will
narrow
further?
They
will.
In
fact,
let's
just
take
a
look
at
how
much.
Wow,
that's
quite
a
bit.
Yeah.
58,
8,
and
71.1.
Okay,
I
think
I'm
getting
it.
Narrowing
those
manufacturing
instructions
is
great
for
guaranteeing
that
I'm
shipping
good
parts,
but
I
don't
necessarily
like
that.
Why
not?
In
fact,
I'll
switch
it
back.
Why
don't
you
like
that,
Jerry?
Well,
the
narrower
we
set
those
manufacturing
instructions,
the
more
good
product
I'm
going
to
throw
away,
aren't
I?
Yes,
that's
right.
I
think
you're
beginning
to
see
the
trade-offs
associated
with
setting
manufacturing
instructions.
It's
pretty
cool.
Yeah.
Can
you
tell
me
how
much
it's
going
to
cost
me
when
I
throw
that good product
away
given
the
selected
manufacturing
instructions?
Yes.
That's
given
in
the
profit
loss
explorer
outline
of
the
add-in.
Before
I
can
answer
that,
though,
we
have
to
enter
some
values.
Hypothetically,
when
we're
talking
about
one
of
your
parts,
how
much
revenue
can
you
expect
per
part?
Okay,
well,
that
number
looks
good.
For
the
sake
of
this
demo
or
this
example,
let's
just
say
$100
per
part.
Okay.
How
many
parts
do
you
expect
to
make?
100,000
sounds
good.
That's
typical,
at
least
for
maybe
a
year's
production.
Nice.
Good
part
run.
How
much
does
it
cost
you
to
make
a
part?
Pretty
easy
to
figure
that
out.
Out
of
the
$100
that
we
sell
a
part
for,
it
costs
us
about
$70
to
make
it,
so
we
make
a
$30
profit.
Last
but
not
least,
what
is
the
cost
of
shipping
a
bad
part?
What's
the
penalty
if
a
part
escapes
your
inspection
process?
I
see
what
you're
saying.
That's
a
little
tougher.
There's,
of
course,
the
potential
cost
of
return
and
the
associated
cost
of
repairs.
Those
are
fairly
easy
to
calculate.
There's
also
damage
to
Acme's
reputation.
Our
customers
demand
quality,
and
if
we
start
putting
bad
product
out
the
door,
it
can
quickly
get
out
of
hand
in
a
hurry
and
result
in
lost
future
sales.
That's
a
lot
more
difficult
to
calculate.
Really,
it
would
take
us
a
while
to
scratch
our
heads
and
figure
that
out.
For
the
sake
of
argument,
let's
say
that
amounts
to
about
$200
per
bad
part
that
we
let
out
of
the
plant
and
sell.
All
right,
great.
Now,
I
see
one
more
entry
box.
Another
word,
salad.
Number
of
PEs,
manufacturing
instructions
lie
from
WSLs.
What
in
the
world
does
that
mean?
All
right,
you
definitely
have
exposed
another
worthy
of
explanation
set
of
terminology,
Jerry.
Let
me
take
a
stab
at
this.
What
we're
referring
to
are
integer
multiples
of
probable
error.
Remember,
we
talked
about
that
as
being
one
of
the
outputs
from
an
EMP
measurement
systems
analysis,
talking
about
the
integer
multiples
of
probable
error
away
from
watershed
limits.
This
is
how
we
will
describe
the
location
of
manufacturing
instructions
relative
to
watershed
limits
for
the
simulation
that
we're
running.
Turns
out
Wheeler
uses
the
same
approach
in
EMP
3.
When
you
get
the
book,
take
a
look
at
that.
It
should
be
pretty
consistent.
He
uses
watershed
limits
as
opposed
to
spec
limits
to
account
for
granularity
of
measurements.
If
we
performed
a
measurement
systems
analysis
using
the
EMP
method
on
this
gage,
we'd
be
given
a
measurement
increment.
Watershed
limits
are
essentially
half
a
measurement
increment
outside
the
specs.
From
a
practical
point
of
view,
bottom
line,
manufacturing
instructions
should
be
set
from
watershed
limits
as
opposed
to
specification
limits.
That
just
helps
us
get
around
that
granularity
problem.
Now,
it's
also
worth
noting
that
manufacturing
instructions
can
be
set
inside
or
outside
the
watershed
limits.
Now,
wait
a
minute.
That
sounds
crazy.
Are
you
saying
that
there
are
times
when
I
might
want
to
set
my
manufacturing
instructions
outside
of
my
product
specs?
Why
would
I
ever
want
to
do
that?
Yeah,
it
seems
counter
to
it,
and
I'd
argue
that
it's
relatively
rare.
There
are
some
conditions
where
for
strictly
economic
reasons,
you
might
be
better
off
choosing
an
even
wider
set
of
manufacturing
instructions
than
your
product
specs.
We
can
talk
through
an
example
in
just
a
second.
Remember
when
we
were
exploring
what
the
statistics
meant,
and
we
talked
about
this
idea
that
if
I
have
a
process
that
is
very
capable,
but
we
have
an
imperfect
gage.
It
may
be
smart
for
us
economically
to
actually
push
our
spec
limits
wide
and
adjust
the
inspection
process
accordingly.
There
are
circumstances
where
that
might
happen,
but
yeah,
I'd
argue
that
they're
pretty
rare.
I
see.
I
don't
know.
For
now,
for
the
sake
of
the
simulation,
let's
set
the
number
of
probable
errors
to
minus
one.
What
this
means
is
that
we're
narrowing
our
manufacturing
instructions.
Let
me
make
this
a
little
bit
wider
here.
We're
talking
about
narrowing
the
manufacturing
instructions
inside
the
specification
limits.
When
we
do
that,
we
can
focus
your
attention
down
to
the
expected
net
profit
at
manufacturing
instructions.
Here
we
can
see
that
the
selected
expected
net
is
the
net
profit,
and
we
can
stand
to
make
about
$1.9
million
for
100,000
parts
for
the
parameters
that
we've
entered.
Okay,
well,
at
least
that's a
profit.
I
see
a
maximum
expected
net
that
shows
an
even
higher
profit.
Is
that
something
I
should
consider
or
can
I
achieve
it?
Yes,
definitely.
The
maximum
profit
that
you
can
achieve
for
this
measurement
process
and
this
economic
set
of
circumstances
is
about
$2.1
million.
Now,
what
this
is
telling
us
is
that
we
can
achieve
it
by
making
our
manufacturing
instructions
equal
to
the
watershed
limits.
If
I
change
this
to
zero,
we can
see
that
we've
hit
that
maximum
expected
net.
We've
just
done
that
by
moving
the
manufacturing
instructions
to
the
watershed
limits.
This
is
optimal
for
the
conditions
that
we've
entered.
That's
really
interesting.
With
any
combination
like
this,
potentially,
I
could
find
an
optimal
where
I
need
to
set
these
manufacturing
instructions.
to
get
me
the
maximum
profit.
Yeah,
you're
nailing
it.
In
fact,
for
the
add-in,
there's
an
asterisk
on
the
graph
that
identifies
what
the
optimal
profit
you
can
achieve
is.
Cool.
When
you're
doing
this
optimum,
where
are
you
getting
these
numbers?
I
don't
understand
where
they
might
come
from.
Sure.
They
result
from
trade
offs
between
revenue
per
part,
cost
per
part,
and
damage
to
your
reputation.
Those
are
the
things
that
we
enter.
When
we
sell
a
bad
part,
that's
the
reputation
component.
Given
the
process
and
gage
parameters.
That's
the
true
part
and
measurement
error
components
that
we
entered
earlier.
For
now,
like
I
said,
let's
keep
the
example
to
where
we're
narrowing
our
manufacturing
inspection
limits.
It's
so
funny.
We
get
all
these
different
limits.
We're
talking
about
manufacturing
instructions,
which
are
inside
the
watershed
limits.
Now
that
we've
got
this
and
we're
agreed
that
hey,
this
is
the
way
we
want
to
run
this
simulation.
Let's
take
a
look
at
the
profit
simulation
panel.
The
matrix
down
below
is
where
we're
going
to
start,
breaks
the
profit
loss
into
nine
different
categories.
Okay,
there's
a
lot
of
information
there.
I'm
going
to
need
to
study
this
for
a
minute.
Let's
start
in
the
center,
the
center
of
that
three
by
three
grid.
S
tarting
there,
of
the
100,000
parts
that
we'll
make,
about
91,000
are
truly
good
parts
that
our
gage
reads
as
good
and
that
will
ship.
That
produces
2.7
million
in
profits,
and
I
assume
that
works
out
to
91,000
parts
times
we
said
the
revenue
per
part
was
$100
minus
$70
to
make
the
part.
Is
that
right?
That's
right.
Okay.
Moving
straight
to
the
left,
you're
predicting
we'll
have
370
parts
that
we
will
ship
because
the
gage
says
they're
good
parts,
but
these
parts
are
truly
below
the
lower
spec.
That's
a
relatively
low
number,
but
it's
not
really
good.
You
say
it's
costing
me
$63,000.
Am
I
reading
that
right?
Yes,
indeed,
you
are.
Okay,
and
I'm
sure
that
has
to
do
with
that
$200
penalty
I'm
paying
for
letting
bad
parts
get
out.
In
this
case,
it
looks
like
I
may
lose
about
as
much
by
shipping
parts
that
are
bad
on
the
high
side.
That's
right.
You
made
2.7
million
selling
good
parts,
but
now
the
imperfect
gage
has
cost
you
a
total
of
140,000
because
of
the
letting
bad
parts
out
into
the
field.
Yeah.
Okay,
well,
that's
not
very
good.
Now
you've
got
several
more
cells
in
that
three
by
three
grid
that
show
losses.
What
are
those
about?
The
upper
left
cell
shows
parts
that
are
truly
bad
because
they
are
below
spec.
And
your
gage
is
catching
them.
That's
good.
It's
catching
them,
it's
throwing
them
out.
The
gage
is
doing
what
you
expected
to
do.
Downside
here
is
that
there
are
nearly
1,900
parts
that
you're
throwing
out,
and
each
of
those
are
costing
you
$70
each.
Same
thing
for
the
lower
rights,
similar
math
there.
That's
another,
what,
$260,
$270,000
coming
out
of
my
pocket?
I
can't
blame
that
on
the
gage.
Those
can
only
be
fixed
if
I
improve
my
production
process.
What
about
those
other
cells?
Let's
look
at
the
upper
center
cell.
It's
showing
that
we
made
a
little
over
2,200
good
parts
that
were
thrown
out
because
the
gage
measured
below
the
manufacturing
instructions.
How
do
you
feel
about
that?
Oh,
not
so
great.
We
have
a
separate
team
of
inspectors
that
reinspect
rejected
parts
in
hopes
of
reclaiming
some
of
our
losses.
This
is
making
me
think
that
even
with
that
reinspection,
we're
still
throwing
out
good
parts.
I
assume
the
bottom
center
cell
is
the
same,
good
parts
that
my
poor
gage
says
are
higher
than
spec.
Yeah,
that's
right.
Yet
there's
another
150-somewhat
thousand
dollars
you're
losing.
All
of
those
profits
and
losses
are
summarized
in
the
bar
chart.
We
get
a
graphic
picture
of
those
as
well.
I
see.
Okay,
but
now
that
assumes
that
we're
using
manufacturing
instructions
that
are
one
probable
error
inside
the
watershed
limits.
What
if
we
change
the
manufacturing
instructions
to
the
optimum
you
were
talking
about?
How
does
that
change
the
profit
picture?
Let's
see
what
happens.
Just
watch
the
bars
change
off
to
the
right,
and
we'll
talk
about
those
a
little
bit.
We'll
bump
that
up
to
the
optimum,
and
what
did
you
see
happen?
Yeah,
so
it
looked
like
the
shorter
bars
got
shorter
yet,
and
the
long
bar
got
a
little
longer.
That's
good,
and
my
net
profit
went
up.
Good.
We're
making
more
money.
It's
probably
worth
thinking
about
that
this middle
bar
because
of
that
reputation
cost
that
you
talked
about.
Yeah,
sure,
we
may
be
making
more
as
a
business,
but
you
ought
to
at
least
be
talking
with
stakeholders
in
the
company
and
asking
how
we're
saving
a
little
bit
of
money
here
to
be
more
profitable,
but
is
that
the
right
thing
to
do?
Yeah,
I
get
it.
That's
interesting.
Yeah,
I
might
choose
to
use
the
narrower
manufacturing
instructions
because
that
cost
to
our
reputation,
you
really
got
to
think
about
that
$200
we
put
in
earlier,
that
could
be
a
much
higher
number.
We'd
have
to
sharpen
our
pencils
on
that.
Then
again,
I
guess
I
could
experiment
with
that,
with
this
add-in,
right?
I
could
try
different
values.
Yeah,
absolutely.
I
hope
you
will.
I
feel
like
that
would
be
valuable.
Great.
I
think
I'm
starting
to
see
now
how
changes
in
process
capability
and
gage
effectiveness
and
how
I
set
the
inspection
limits
may
have
a
big
impact
on
my
profitability.
That's
what
we
hoped
for
when
we
developed
this
add-in.
Considering
profitability
when
answering
questions
about
gage
and
process
capabilities
could
be
really
helpful
when
you're
trying
to
justify
improvement
projects,
or
it
could
also
give
you
a
lot
of
confidence
that
the
process
and
gage
are
delivering
the
results
that
you
desire.
I
must
say,
Jason,
I
am
impressed.
However,
I
feel
like
I
need
to
muddy
the
waters
a
little
bit.
This
is
all
great
for
normal
distributions,
simple
gage
errors,
et
cetera.
Those
calculations,
as
you've
shown
before,
are
easy.
What
if
I
have
linearity
or
bias
problems
with
my
gage?
Or
if
I
have
a
skewed
part
distribution,
getting
a
true
part
distribution
out
of
the
measured
part
distribution
becomes
a
lot
more
difficult
than
just
using
that
simple
formula
you
showed
earlier,
right?
Can
you
even
do
that?
Today,
no.
Really,
we're
limited
to
normal
distributions,
but
we're
almost
there.
It's
in
the
plan.
In
fact,
it's
the
next
step
in
our
development
plans
for
the
add-in.
I do have a
colleague
who's
presenting
at
JMP's
online
Discovery
Conference
who
has
done
an
on-demand
presentation
for
estimating
the
true
part
distribution
based
on
measured
part
distributions
that
may
be
non-normal
based
on
gage
characteristics.
It
handles
even
arbitrary
distributions,
has
some
common
ones
and
arbitrary
distributions.
We
will
be
incorporating
these
capabilities
into
the
add-in,
like
I
mentioned.
It's
going
to
be
sometime
soon.
You
should
definitely
right
now
go
check
out
the
talk.
Yeah,
it
sounds
like
an
interesting
talk
by
an
interesting
presenter.
This
all
sounds
interesting.
I
will,
Jason.
Now,
we've
covered
a
lot
of
material
today.
Would
you
mind
quickly
taking
me
back
through
the
add-in
again
to
make
sure
I've
got
it?
Absolutely.
Where
we
started
our
conversation
was
supplying
a
little
bit
of
information
about
our
measurement
process.
We
entered
specification
limits.
Again,
those
are
the
voice
of
the
customer
that
we
talked
about.
We
talked
about
getting
an
idea
of
what
our
measurement
error
is
from
a
measurement
systems
analysis,
and
then
using
the
standard
deviation
from
that
study
for
a
gage
error.
We
also
talked
about
supplying
some
information
about
the
true
part
distribution.
The
parameters
mean
and
sigma,
we
talked
about
ways
that
we
can
infer
that
from
the
measured
part
distribution.
We talked about
the
statistics
in
the
add-in
being
a
snapshot
into
our
gage
capability
as
well
as
our
process
capability.
I
think
that's
important
as
we
begin
to
have
these
conversations
about
how
much
the
gage
is
going
to
cost
us.
We
walked
through
ideas
around
moving
our
specification
limits
in
these
manufacturing
limits,
narrowing
them
or
even
expanding
them
to
reduce
risk
when
we're
measuring
at
the
specification
limits.
There
are
other
bells
and
whistles
in
the
add-in
that
we
didn't
have
time
to
show,
but
these
are
just
ways
of
really
illustrating
these
risks
by
now
overlaying
true-part
distributions,
assuming
we're
measuring
the
true-part
near
the
manufacturing
instructions.
Profit
Loss
Explorer
was
our
way
to
simulate
the
costs
associated
with
our
measurement
errors.
It
gives
us
the
opportunity
to
explore
what
ifs.
If
we
are
making
a
certain
product
and
product
has
certain
costs
associated
with
it,
how
much
profit
can
we
expect
if
we're
running
manufacturing
instructions
at
different
multiples
of
probable
error.
We
wrapped
up
our
conversation
with
an
in-depth
conversation
about
those
profits
and
losses
and
how
the
goal
of
our
business
and
the
reputation
of
our
business
should
be
considered
when
we're
making
those
trade-offs
as
well.
Nice.
Thank
you.
I
think
that
summarized
it
really
well.
Now,
besides
using
estimated
true-part
distributions,
is
there
anything
else
that
will
be
developed
for
the
add-in?
Well,
we
also
have
already
developed
this
misclassification
Explorer.
It's
just
another
way
of
looking
at
gage
errors
and
our
process
variation.
We
didn't
have
enough
time
to
go
into
this
component
of
the
add-in.
We
plan
on
explaining
this
a
little
bit
more
in
the
near
future,
very
likely
as
we
start
incorporating
some
of
those
non-normal
distributions
that
I
mentioned.
Cool.
That's
fantastic.
What
about
other
training
opportunities?
What
we've
shown
here
today
is
what
our
add-in
currently
does,
short
of
the
misclassification
explore
that
we
skip.
We'd
like
to
get
more
into
the
nuts
and
bolts
of
the
equations
behind
the
add-in
and
the
approaches.
We
plan
a
series
of
online
talks
coming
in
the
near
future
where
we
can
explore
some
of
those
differences
between
the
AIAG
classification
methods
and
EMP
and
how
EMP
can
give
us
more
realistic
and
useful
information
about
our
gage.
We'll
also
spend
some
more
time
talking
about
gage
performance
curves.
We
talked
about
this
classification
graphs,
but
we'll
introduce
you
to
some
traditional
gage
performance
curves
as
well
as
conformance
curves.
Actually,
it's
conformance
we
talked
about
classification
we'll
get
to.
Look
for
those
in
the
near
future.
Great.
Who
else
should
we
thank
for
this
effort
so
far?
Well,
Brady,
Brady.
We
should
have
a
picture
of
Brady right
here.
Yeah,
that
would
have
been
good.
Brady,
Brady
is
the
brains
behind
the
add-in.
Unfortunately,
he
couldn't
join
us,
but
definitely
is
heavily
involved,
and
we
have
the
add-in
and
the
code
to
thank.
Great.
Thanks
to
Brady,
and
thanks
to
all
of
you
for
attending
and
those
who
will
be
watching
this
video
and
using
the
add-in
later.
For
the
live
demo,
we'd
be
happy
to
answer
questions.
Otherwise,
if
you
put
your
questions
in
the
chat
area
below
the
presentation,
we'd
be
glad
to
get
back
with
you
as
soon
as
we
can.
Thank
you.
Thank
you
very
much
for
attending.
