Hi,
I'm
Jerry
Fish.
I'm
a
support
engineer
with
JMP,
helping
customers
in
the
central
part
of
the
United
States.
Today's
talk
is
entitled
My
Gauge
Isn't
as
Good
as
It
Could
Beâ
Will
Its
Errors
Cost
us
Money
and
how
much?
I'm
Jason
Wiggins,
also
a
senior
systems
engineer,
and
I
support
semiconductor
users
in
the
Western
United
States.
This
talk
is
a
follow
on
to
one
we
did
for
discovery
Americas
in
2022.
In
our
first
talk,
we
introduced
the
notion
that
measurement
systems
are
integral
to
our
businesses.
In
fact,
we
have
many
measurement
systems
we
interact
with
in
our
daily
lives.
Along
with
that
idea,
we
introduced
the
notion
that
measurement
system
or
gauge
variation
can
impact
decisions
in
real
world
inspection
situations.
We
introduced
gauge
performance
curves
as
a
way
of
visualizing
gauge
variation
and relative
to
specification
limits.
In
this
talk,
we'll
extend
that
and
explore
the
costs
associated
with
gauge
variation
through
a
fun
role
play
conversation
between
a
quality
manager
of
an
automobile
manufacturing
plant,
that'll
be
Jerry,
and
I'll
be
acting
as
a
quality
consultant.
To
kick
things
off,
I'll
get
on
a
quick
team
call
with
Jerry.
Hi,
Jerry.
Hi,
Jason.
How
are
you
doing?
I'm
doing
pretty
good.
Thanks
for
spending
a
few
minutes
with
me.
As
a
quality
consultant,
I
help
quality
stakeholders
like
yourself
understand
and
improve
processes.
Now,
I
prefer
using
JMP
as
it's
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
improvement,
so
more
time
can
be
dedicated
to
actually
improving
the
process.
Well,
it
is
nice
to
meet
you,
Jason.
Just
to
let
you
know,
though,
we
already
have
software
in
place
for
our
internal
quality
programs,
so
I'm
not
really
sure
what
your
software
can
do
that
we
cannot
already
do.
Can
we
make
this
quick?
I
understand
completely,
Jerry.
I'll
try
to
make
the
most
of
your
time
today.
First,
can
you
tell
me
a
little
bit
about
your
company
and
your
quality
program?
Sure,
happy
to.
Acme
Motors
has
built
a
reputation
with
our
customers
of
manufacturing
the
highest
quality
cars.
We're
always
concerned
with
quality.
We
have
various
gauges
that
we
use
to
ensure
our
quality
stays
high.
We've
been
doing
this
for
years,
and
frankly,
we
think
we're
pretty
good
at
it.
I'm
familiar w
ith
Acme
Motors
and
your
high
quality
reputation.
My
consulting
team
and
I
have
recently
been
working
with
manufacturing
companies
like
yours
to
advance
the
use
and
effectiveness
of
gauge
studies.
Measurement
systems
analysis,
another
way
of
saying
that.
One
of
the
things
we
seek
to
understand
are
the
monetary
costs
associated
with
the
gauges
used
to
measure
process
quality
characteristics
in
your
manufacturing
plant.
Have
you
quantified
how
much
any
of
your
gauges
are
costing
your
business?
I'm
not
sure
what
you
mean.
Well,
gauges
are
not
perfect.
They
make
mistakes.
Sometimes
they'll
throw
away
good
parts
and
sometimes
they'll
pass
bad
parts.
Unless
you
have
a
perfect
gauge
and
really
no
one
has
these,
these
mistakes
are
inevitable.
I
suppose
so,
but
we've
done
gauge
studies
that
say
our
gauges
are
good.
Well,
some
of
them
are
actually
categorized
as
adequate
by
the
AI
AG
guidelines.
Doesn't
that
mean
we're
okay
to
use
them?
Well,
possibly,
but
there
is
a
lot
more
to
the
story
than
just
using
good,
adequate,
and
poor
AI
AG
gauge
assessment
criteria.
For
example,
have
you
seen
a
gauge
performance
curve?
I
can't
say
that
I
have.
No.
Now,
this
is
what
one
looks
like.
The
X
axis
shows
the
true
part
values
and
the
lower
and
upper
specification
limits
are
shown
with
these
lines.
The
Y
axis
shows
the
probability
of
passing
a
part.
If
you
have
a
part
that
is
truly
good
but
very
close
to
the
lower
spec
limit,
there's
almost
a
50
%
chance
the
gauge
will
recommend
that
you
throw
it
away.
That's
one
way
of
thinking
about
it.
But
also,
there's
nearly
a
50
%
chance
that
you
will
accept
a
part
that
is
truly
bad
and
near
the
lower
spec.
Very
interesting.
What
happens
if
we
could
change
the
variation
of
the
gauge
then?
Well,
the
shape
of
this
curve
definitely
depends
on
how
good
your
gauge
is.
Let's
play
with
this
just
a
little
bit.
What
if
we
could
reduce
the
variation
by
a
factor
of
10?
Make
a
quick
change
here
and
replot
our
gauge
performance
curve.
If
this
is
possible,
we
will
correctly
accept
or
reject
more
of
the
parts.
We're
moving
from
incorrect
to
correct
when
we
do
this.
Let
me
break
from
the
role
play
for
just
a
moment.
The
gauge
performance
curve
I
am
showing
is
an
add
in
that
we
made
for
our
2022
Discovery
Americas
presentation.
The
add
in
is
available
on
the
community.
Back
to
you,
Jerry.
That
is
really
an
interesting
chart,
Jason.
I
don't
think
we
do
anything
like
that.
What
you're
saying
is
that
the
gauge
errors
contaminate
the
measurements,
but
all
I
have
is
the
imperfect
measurement.
Your
gauge
performance
curve
is
plotted
versus
true
part
values.
I
wish
I
knew
those
true
part
values,
then
I
could
know
exactly
which
parts
to
keep
and
which
to
throw
away.
Is
there
a
way
I
can
know
the
true
part
value?
We
could
know
that
directly
if
we
had
that
ever
elusive
perfect
gauge,
which
we
don't.
We
really
can't
ever
get
to
the
level
of
knowing
the
true
value
of
an
individual
part,
but
we
can
estimate
the
true
part
distribution
given
our
knowledge
of
the
gauge
characteristics
and
the
measured
part
distribution.
How
would
you
do
that?
Well,
if
we
assume
that
gauge
errors
are
normally
distributed,
and
for
the
moment,
let's
ignore
any
bias
or
linearity
problems
that
you
might
have,
and
that
we
have
the
measured
part
distribution.
If
we
have
that,
we
can
back
out
the
variance
of
the
true
part
distribution
using
a
simple
equation.
That
simple
equation
is
just
the
difference
between
the
measured
part
variance
and
the
gauge
variance.
The
plot
on
the
right
is
shown
for
a
situation
where
the
measured
variance
is
25,
the
gauge
variance
is
16,
and
if
we
subtract
those
two,
the
true
part
variance
is
nine.
We're
beginning
to
get
the
parameters
for
that
distribution
because
we
know
the
results
of
our
gauge
study
and
we
understand
the
variance
associated
with
our
gauge.
Now,
we
would,
from
this,
build
a
normal
distribution
that
centered
on
the
measured
distribution
mean
with
the
new
standard
deviation.
A
gain,
the
result
of
which
is
going
to
look
like
the
plot
on
the
right.
In
the
plot,
the
blue
bars
represent
your
measured
part
distribution.
The
areas
above
and
below
the
spec
are
are
shadeded
in
pink.
Notice
that
the
measured
part
distribution
is
much
wider
than
the
true
part
distribution.
The
measured
distribution
is
what
you
get
when
you
run
the
true
part
distribution
through
your
imperfect
gauge.
Okay,
that
makes
sense,
at
least
a
simple
case.
How
do
you
relate
this
to
what
it's
costing
my
company?
Well,
we
can
use
this
information
in
a
numeric
simulation
to
characterize
the
mistakes
that
our
gauge
is
going
to
make.
When
we
do
that,
we
can
generate
a
part
inspection
table
like
this.
Let
me
study
this
table
for
a
minute.
My
eye
is
immediately
drawn
to
the
center,
the
green
box
that
says
95.4
%.
Am
I
interpreting
this
right?
95.4
%
of
my
total
production
is
truly
good
and
we're
shipping
it.
Correct.
That's
a
good
thing.
Now,
looking
at
the
first
and
last
columns,
if
I
add
18
and
25,
let's
see,
that's
about
0.043
%
of
my
production
parts
are
truly
low
parts.
A
nother
0.041
%
on
the
last
column
are
truly
high.
This
is
bad.
It
says
my
process
is
making
bad
parts
that
must
be
thrown
away
or
reworked.
I
see
another
problem.
If
I
look
at
that
center
column
and
I
add
those
all
together,
I
get
99.9
%
of
my
production
parts
that
truly
are
good.
It
says
that
the
gauge
is
identifying
2.3
%
of
those
as
too
low
and
2.3
%
is
too
high
as
well.
Now,
the
customer
doesn't
care
about
that.
They're
still
getting
good
parts, b
ut
I
certainly
do.
I'm
making
good
product
and
I'm
throwing
it
away.
Worst
yet,
look
at
that
center
row,
those
red
squares.
There's
another
0.036,
18
and
18,
of
truly
bad
parts,
parts
that
are
too
low
or
too
high
that
are
being
accepted
by
this
measurement
gauge.
This
is
serious.
I
do
not
want
to
ship
bad
parts
to
my
customer
if
I
can
help
it.
That's
right.
This
is
cool.
You're
beginning
to
see
the
cost
of
having
an
imperfect
gauge.
This
is
really
interesting.
It
shows
that
if
I
don't
do
something
about
my
imperfect
gauge,
I'll
risk
accepting
bad
parts
and
throwing
away
good
parts,
both
of
which
are
bad
for
my
business.
On
the
other
hand,
I
think
we've
got
a
way
to
handle
this,
Jason.
Okay,
what's
that,
Jerry?
Well,
we
use
something
called
guard
bands.
These
are
our
bands
that
are
set
inside
the
specification
limits.
If
we
set
them
far
enough
inside
the
spec
limits,
we
can
reduce
and
essentially
eliminate
shipping
bad
parts.
Doesn't
that
fix
at
least
part
of
our
problem?
At
least,
guard
bands
are
definitely
a
good
way
to
reduce
the
percentage
of
bad
parts
that
make
it
through
your
inspection
process.
A
lot
of
companies
use
them.
Have
you
considered
the
fact
that
improving
quality
using
guard
bands
comes
at
the
expense
of
throwing
away
good
parts?
Honestly,
that
has
occurred
to
us,
but
we
haven't
tried
to
quantify
that
damage.
Well,
let's
extend
this
example
out
a
little
bit
more
and
let's
just
assume
that
we
bring
those
specifications
in
by
one
unit
of
measure.
We'll
call
these
guard
band
limits.
Our
lower
guard
band
limit
would
be
41
and
our
upper
guard
band
limit
would
be
59.
We're
going
to
use
this
as
our
inspection
screening
values
instead
of
the
original
upper
and
lower
specs.
Now,
we
can
do
the
same
numerical
simulation
and
update
the
results.
Let's
just
take
a
look
at
the
differences
between
the
tables.
Can
you
see
how
the
percentages
have
changed?
We
went
from
shipping
roughly
0.04
%
of
parts
that
were
truly
bad
to
only
shipping
0.03
%
of
bad
parts.
That
looks
successful.
Maybe
we
could
even
squeeze
our
guard
bands
in
further
and
improve
that.
Especially
given
our
high
production
volume.
We're
talking
real
bucks
here.
It
is.
Also
notice
how
many
truly
good
parts
are
now
being
screened
out.
Every
time
you
screen
out
and
throw
away
good
part,
it
is
costing
your
company
money.
Well,
you're
right
about
that,
Jason.
Is
there
a
way
to
look
at
this
monetarily?
What
if
we
assume
that
a
bad
part
in
the
simulation
results
in
a
bad
car?
Can
we
input
the
cost
of
scrapping
the
car
and
see
how
that
affects
the
bottom
line?
Absolutely,
we
can
do
that.
I'll
need
to
get
a
little
information
from
you,
though.
First,
how
much
does
it
cost
to
make
the
car?
Yeah,
let's
say
for
the
sake
of
this
demonstration,
$35,000.
Okay,
great.
That
means
for
each
rejected
car,
it
costs
your
company
$35,000.
You
might
manufacture
in
rework
costs
here,
but
let's
say,
for
example,
we
just
throw
the
car
away.
Now
we
need
production
quantity.
I
don't
know.
Let's
just
choose
a
million
cars.
Okay,
great.
Now,
how
much
do
you
charge
for
a
truly
good
car
that
makes
it
to
a
dealership?
The
dealerships
buy...
Let's
just
say
they
buy
these
cars
from
us
for
$40,000.
Okay.
If
I
understand
this
right,
your
profit
per
car
is
that
40K
minus
the
35
K
and
your
profit
has
been
$5,000
per
car.
Right.
Last
thing,
do
you
know
the
cost
associated
with
selling
a
bad
car?
That's
a
little
tougher.
There
are
the
obvious
costs
of
repairs
to
the
bad
car
or
potential
cost
of
return.
Those
are
relatively
easy
to
calculate,
but
there's
also
damage
to
our
reputation.
Our
customers
demand
quality,
and
if
we
start
putting
bad
product
out
the
door,
it
can
quickly
get
out
of
hand
and
result
in
lost
future
sales.
That's
a
lot
more
difficult
to
calculate.
I
know
you
need
the
number.
For
the
sake
of
argument,
let's
just
say
that
totals
to
$50,000
per
bad
car
that
makes
it
out
into
the
market.
Excellent.
Let's
take
a
look
at
the
profits
and
losses.
Same
simulation.
Just
review,
make
sure
that
we're
looking
at
the
correct
values.
You
told
me
that
manufacturing
cost
per
car
is
$35,000.
You
then
sell
that
to
a
dealer
for
$40,000.
Our
profit
is
$5,000.
Cost
of
selling
a
bad
car
is
$50,000.
We're
going
to
look
at
this
across
a
1
million
car
production
run.
Have I
captured e
verything,
right?
I
think
that
looks
good.
All
right.
If
we
look
at
the
net
profits
and
losses,
you
stand
to
make
about
346
billion
from
the
1
million
cars
you
make.
That
sounds
good.
Not
bad.
The
total
profit
from
the
truly
good
cars
that
are
shipped
is
about
371
billion.
The
loss
due
to
making
truly
bad
cars
that
are
caught
in
your
inspection
is
199
million,
which
is
the
sum
of
98
million
plus
101
million.
Okay.
The
law...
I let
you
digest
for
a
second?
Yeah,
I'm
following.
Okay.
The
laws
due
to
shipping
truly
bad
cars,
this
is
the
one
you
are
really
concerned
about,
is
137
million,
which
is
68
million
plus
69
million.
Finally,
the
loss
from
scrapping
truly
good
cars,
this
is
what's
costing
your
business,
is
$25
billion.
That's
quite
a
lot.
That's
the
sum
of
$12.4
million
and
12.4
million.
That's
fascinating
and
also
a
little
depressing
that
we're
losing
that
much
money.
If
you
change
things,
let's
say
you
change
the
guard
band
settings,
will
the
total
net
profit
change?
That's
right.
That's
definitely
true.
You
could
see
that
change.
In
that
case,
then
could
there
be
an
optimum?
I
can
imagine
widening
the
guard
bands
or
narrowing
them
and
looking
at
the net
profit,
would
there
be
an
optimum
for
that
net
profit
peaks
out?
Yes,
you
can
definitely
explore
that
trade-
off
in
a
lot
of
different
ways.
You
could
answer
questions
like,
how
would
improving
my
gauge
by
a
factor
of
10,
like
we
showed
with
the
gauge
performance
curve,
improve
my
profitability?
Or
how
much
can
I
afford
to
spend
on
fixing
or
replacing
a
gauge?
If
we
know
what
the
costs
to
the
company
are
for
our
measurement
system,
then
we
can
justify
the
cost
of
fixing
or
replacing
gauge.
Also,
just
to
your
point,
what
if
I
adjusted
my
guard
bands?
We
can
definitely
answer
that
question.
A
nother
common
one
is
what
if
I
improve
my
process
capability?
I
just
tighten
the
variation
in
my
process,
what
does
that
do
to
my
profits
and
losses?
I
could
trade
that
off
against
the
cost
of
improving
that
process
capability.
Interesting.
Well,
I
must
say,
Jason,
I'm
impressed.
This
has
been
a
good
use
of
time,
but
I
think
I
owe
it
to
my
company
to
muddy
the
waters
just
a
little
bit.
This
is
all
great
for
normal
distributions
and
simple
gauge
errors
and
those
kinds
of
things.
Those
calculations
that
you've
shown
are
easy.
But
what
if
I
have
gauge
linearity
or
bias
problems?
Or
what
if
I
have
a
skewed
distribution,
which
is
really
pretty
typical
in
my
company.
We
rarely
run
into
the
nice
bell
shape
curve.
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.
Can
you
even
can
do
that?
Absolutely.
We
are
writing
an
add-
in
that
will
make
you
able
to
define
the
shape
of
any
measured
part
distribution.
W
e
can
do
the
same
exercise
with
measured
part
distributions
that
are
normal
or
log-
normal,
uniform,
Weibull,
or
even
a
custom
distribution.
It's
an
add-
in
we're
working
on.
It's
a
work
in
progress.
All
right,
that's
fantastic.
I'm
ready
to
buy
in.
When
will
that
be
available?
We
have
the
basics
of
the
add
in
worked
out,
but
we
need
some
time
to
make
it
more
user
friendly.
We'll
be
working
on
that
in
the
coming
few
months.
Probably
before
midyear,
we'll
have
that
wrapped
up.
When
it's
done,
we'll
post
it
on
the
JMP
website
in
our
community
file
exchange.
A
few
months,
really?
I'll
forget
all
this
by
then.
That's
okay.
We
recognize
that.
Once
our
ad
in
is
ready
for
prime
time,
we'll
announce
a
series
of
open
to
the
public
seminars
where
we
will
go
into
detail
about
what
you've
seen
here,
as
well
as
other
aspects
like
relating
these
concepts
to
Donald
Wheeler's
EMP
methodology,
which
is
another
personality
in
the
Measurement
Systems
Analysis
platform
in
JMP.
Here's
a
quick
peek
at
the
topics
for
the
up
and
coming
talks.
We'll
spend
more
time
elaborating
on
how
gauge
studies
that
are
using
the
AI
AG
classification
that
we
talked
about
earlier,
we'll
talk
about
how
that
can
lead
to
unrealistic
gauge
assessments.
We'll
also
explore
how
Wheeler's
Evaluating
the
Measurement
Process,
the
EMP
method,
can
provide
us
more
realistic
gauge
classification.
We're
going
to
present
the
problem
with
AIG
and
present
the
solution
using
Wheeler's
methods.
We'll
also
show
how
EMP
method
can
advise
us
on
how
to
use
our
gauge.
How
do
we
use
it
in
the
production
process?
One
example
that
we'll
be
covering
is
objectively
setting
guard
bends.
The
remaining
topics,
hey,
we'll
spend
a
little
bit
more
time
interpreting
gauge
performance
curves,
talk
about
how
to
blend
performance
with
part
variation
to
determine
cost
associated
with
imperfect
gauges.
Really,
that
is
what
we're
talking
about
today,
but
we
feel
like
we
need
to
extend
that
a
little
bit
so
that
we
all
understand
how
that
works.
Final
two
topics,
how
can
Wheeler's
calculations
be
factored
into
this
gauge
cost
conversation,
and
how
to
understand
gauge
cost,
again,
to
the
point
of
non-
normal
part
distributions.
That's
perfect.
Can
you
make
sure
that
I'm
on
that
invitation
list?
I
want
to
make
sure
that
everyone
in
my
quality
department
attends
your
seminars.
Sure
thing,
Jerry.
Anything
else
I
can
do
for
you
today?
Yes.
Get
back
to
work
on
that
ad-
in.
The
sooner
it's
available,
the
better.
Will
do. All
right,
this
concludes
our
presentation.
I'll
say
that
as
we
were
doing
research
for
this
talk,
we
uncovered
many
concepts
that
are
important
to
understanding
how
to
use
measurement
systems.
We
feel
like one
of
these
concepts
deserve
more
time
than
we
had
in
our
talk
today.
We
look
forward
to
continuing
the
conversation
with
you
in
the
coming
months.
Any
closing
thoughts,
Jarry?
Just
that
if
you've
kind
folks
that
are
attending
today,
if
you're
interested
in
attending
those
upcoming
seminars,
please
let
your
local
JMP
support
person
know
or
your
support
team
know,
and
they'll
make
sure
that
you're
included
on
that
invitation
list.
Excellent.
With
that,
thank
you,
everyone,
for
attending.
Thanks,
all.
