Hello,  my  name  is  Laura  Lancaster

and  I'm  here  with  my  colleague, Annie   Dudley Zangi,

to  talk  about  recent  developments in   JMP quality  and  SPC.

The  first  thing  I  want  to  talk  about is some  improvements  that  we've  made

to  the  distribution  platform

specifically  related to  limits  of  detection.

So  limited  detection   is  when  we're  unable  to  measure

above  or  below  a  certain  threshold.

And  in  JMP  Pro  16,  some  functionality was  added  for  limits  of  detection.

Specifically  in  the  DOE  platform, we  added  the  ability

to  account  for  limits  of  detection   and  a  Detection  Limits  column  property

was  added  that's  used  by  the  Generalized  Regression  platform

to  specify  censoring  for  responses.

However,  what  was  left  unaddressed was  a  problem  with  process  capability

and  limits  of  detection.

The  problem  is  that when  you  ignore  limits  of  detection

when  analyzing  process  capability, it  can  give  misleading  results.

And  there  was  no  way  to  do process  capability  with  censored  data.

But  in   JMP Pro  17,   and  I  just  wanted  to  note

that  this  is  the  only  feature that  we're  going  to  talk  about

that's  JMP Pro.

Everything  else is  regular  JMP in  this  talk.

So  in  JMP  Pro  17, now  in  the  Distribution  Platform,

we  recognize  that  Detection  Limits column  property

and  we  can  adjust  the  fitters for   censored data.

That  means  that the  Process  Capability  report

that's  within  those  fitters that  use  the  adjusted  fit

to  account  for  censored  data will  give  more  accurate  results.

And  the  available adjusted  distribution  fitters

are  Normal,  Log normal,  Gamma, Weibull,  Exponential,  and  Beta.

And  before  I  go  to  the  example,

I  just  wanted  to  give  a  shout  out to  check  out  the  poster  session

Introducing  Limits  of  Detection   in  the  Distribution  Platform

that  Clay  Barker  and  I  worked  on if  you  want  to  learn  more  about  this.

Let's  go  ahead  and  go  to  JMP.

Here  I  have  some  drug  impurity  data

where  I  have  an  issue   with  being  able  to  detect  impurities

below  a  value  of  one.

And  this  data  that  I've  recorded   is  actually  in  the  second  column

and  anywhere  that I  wasn't  able  to  record  an  impurity

because  it  was  below  one, I've  simply  recorded  it  as  a  one.

So  this  is  censored  data.

This  first  column   is  really  the  true  impurity  values

that  I'm  unable  to  know, unable  to  detect  with  my  detection.

So  let's  go  ahead   and  compare  both  of  these  columns

using  distribution.

So  if  I  go  to  Analyze,  Distribution, and  I  look  at  both  of  these  columns,

you  can  clearly  see there's  a  pretty  big  difference

between  having  true  impurity  values   which  I'm  unable  to  know,

and  the  censored  data.

Ultimately,  what  I  want  to  do is  I  want  to  do  a  log normal  fit

and  run  a  process  capability  analysis on  this  data.

So  I'm  going  to  go  ahead  and  do  that for  both  of  these  distributions.

So  I'm  going  to  do  log normal  fit   for  both  of  them.

You  can  see  that  I  get...

Obviously  the  histograms look  pretty  different

and  my  fits  look  pretty  different  too, which  isn't  surprising.

Now,  I  want  to  do  Process  Capability on  both  of  these.

I've  already  added  an  upper  spec  limit   as  a  column  property,

and  you  can  see  that when  I  have  my  true  data,

which  I'm  unable  to  know,

my  capability  analysis looks  pretty  different

from  having  the  censored  data.

With  the  true  data, my  capability  looks  pretty  bad.

There's  probably  something I  need  to  address.

But  because  I'm  not  able  to  see the  true  data,

and  I  only  have  the  censored  data that  I  can  analyze  in  JMP,

the  PPK  value  is  a  lot  better.

It's  above  one, and  I  may  blissfully  move  along

thinking  that  my  process  is  capable

when  in  actuality, it  really  isn't  so  good.

But  thankfully,  in  JMP Pro  17,

I  can  add  a  detection  limits column  property  in my  data.

So  this  third  column   is  the  same  as  my  second  column,

except  that  I've  added a  detection  limits  column  property.

So  I've  added  that  I  have a  lower  detection  limit  of  one.

And  now  when  I  run  Distribution  platform on  this  third  column

with  a  detection  limit  column  property,

and  I  do  my  logn ormal  fit   and  notice  because  I  have  censored  data,

I  have  a  limited  number of  distributions  available,

I'm  going  to  do  my  log normal  fit,

and  it's  telling  me  it  detected that  detection  limit  column  property,

and  it  knows  I  have   a  lower  detection  limit  of  one.

And  when  I  do  Process  Capability,

you  can  see  that  my  capability  analysis is more  in  line  with  when  I  had

the  true  data   because  my  PPK is 0.546,

doesn't  look  so  good.

And  I  realize  that   there's  probably  something

that  I  need  to  address  with  this  process.

It's  not  very  capable.

All  right,  so  let's  move along  to  the  next  topic.

The  next  thing  I  want  to  talk  about

is some  improvements in  Measurement  Systems  Analysis,

specifically  the  Type 1 Gauge  Analysis  platform.

A  Type  1  Gauge  Analysis  platform is  a  basic  measurement  study

that  analyzes  the  repeatability and  bias  of  a  gauge

to  measure  one  part relative  to  a  reference  standard.

It's  usually  performed   before  more  complex  types  of  MSA  studies

such  as  EMP  or  Gauge  R&R that  are  already  in  JMP.

It's  required   by  some  standard  organizations

such  as  VDA  in  Germany,

and  this  has  been  requested by  our  customers

for  quite  a  while, but  we  believe  it's  useful  for  anyone,

whether  it's  required by  a  standard  organization  or  not.

It's  located  in  JMP  17

in  the  Measurement  Systems  Analysis launch  dialog

as  an  MSA  Method  type.

It  requires  a  reference  standard  value

to  compare  your  measurements  against, and  a  tolerance  range

where  you  want  your  measurements   to  be  within  20%  of  your  tolerance  range.

Produces  a  run  chart, metrics  such  as  Cg, Cgk,

which  are  comparable   to  capability  statistics,  bias  analysis,

and  a  histogram  for  analyzing  normality.

Let's  go  ahead and  look  at  this  new  platform  in  JMP.

Here  is  my  Type  1  Gauge  data.

It's  simply  measurements of  one  part  with  one  gauge.

So  to  get  to  the  platform,

I  go  to  Analyze,  Quality  and  Process, Measurement  Systems  Analysis.

And  the  first  thing  I  want  to  do is  change  the  method

from  EMP  to  Type  1  Gauge.

I'm  going  to  move  my  measurements as  the  response

I'm going to  leave  everything  else at  default,  and  I'm  going  to  click  OK.

But  before  I  can  proceed  to  get  my  report, I  have  to  enter  that  metadata

that I  mentioned  earlier,

the  reference  value and  the  tolerance  range.

So  I'm  going  to  go  ahead and  enter  that  information.

I'm  going  to  enter  it as  a  tolerance  range

and my  reference  value.

I'm going to skip  resolution because  that's  optional.

Click  OK.

And  this  is  the  default  report  that  I  get.

I  get  a  run  chart  on  my  measurements graphed  against  my  reference  line.

And  I  also  get  the  20% tolerance  range  lines.

One's  10%  tolerance  range  above  reference and  one  is  10%  below.

So  you  get  some  default  capability  statistics.

Now  notice  that  my  measurements are  well  within  the  20%

of  my  tolerance  range, which  is  really  good.

I  could  also  do  a  Bias  Test to  see  if  my  measurements  are  biased.

It  looks  okay.

And  I  could  also  turn  on  a  histogram to  test  for  normality.

And  before  we  move  on,

I  wanted  to  find  out  one  more  thing, and  that's  that  in  this  top  outline  menu,

if  I  click  on  that, there's  an  option  to  save  that  metadata

that  I  had  to  enter to  be  able  to  get  this  report.

Remember,  I  had  to  enter   the  reference  value

and  the  tolerance  range.

So  I  could  either  save  this  metadata as a  column  property  and  we've  introduced

a  new  column  property  called  MSA, or  I  could  save  it  to  a  table.

I'm  going  to  go  ahead  and  save  it   as  a  column  property

so  I  can  show  you the  new  MSA  column  properties.

If  I  go  back  to  the  data  table, this  is  the  new  MSA  column  property.

You  can  see  it's  storing my  tolerance  range

and  my  reference  value,

and  it  also  can  hold  other  metadata for  other  types  of  MSA  analysis.

Let's  move  along  to  the  next  topic.

I  also  want  to  talk  about some  improvements

to  existing  MSA  platforms, the  EMP  MSA  platform.

EMP  stands  for   Evaluating  the  Measurement  Process,

and  this  is  the  platform   based  on  Don Wheeler's  approach

and  a  variability  chart  platform.

So  in  both  of  these  platforms,

we've  improved  the  usability when  analyzing  multiple  measurements

at  one  time.

We  have  better  handling  of  the  metadata,

such  as   [inaudible 00:10:16]  or  tolerance  values

or  process  Sigma.

So  this  has  been  improved in  variability  charts

and it's added  to  the  EMP  MSA  platform.

We've  also  reorganized  the  reports

so that  they  work  better with  data  filters.

In  addition,  we've  filled  in  the  gaps

between  the  EMP  MSA  platform and  the  Variability  Chart.

We've  done  this   by  adding  some  reports

to  the  EMP  MSA  platform.

We've  added   the  Misclassification P robability  Report,

the  AIAG  Gauge  R&R  report, and  a  Linearity  and  Bias  report.

In  addition, we've  modernized  the  Linearity  report

and  the  Variability  Chart to  match  the  new  Linearity  report

in  the  EMP  MSA  platform.

So  let's  go  ahead and  look  at  some  of  these  changes.

So  here  I  have  some  measurement  systems analysis  data  for  some  tablets

where  I've  measured   two  different  attributes

with  multiple  operators.

And  I  want  to  analyze  this   using  the EMP  platform.

So  I'm  going  to  go  to  Analyze,   Quality  and  Process

Measurement  Systems  Analysis.

First  thing  I  want  to  do is  change  the  method  back  to  EMP,

take  my  measurements  as  response, Tablet as  Part,  Operator  as Grouping.

Notice  there's  now  this  standard  role,

if  I  were  doing  a  linearity  and  bias  study,

I  would  use  that, but  I'm  not  in  this  example.

Also some  new  options  down  here in  the  dialogue,

but  the  one  I  want  to  point  out is the  Show  EMP  Metadata  Entry  Dialogue.

I  want  to  set  that  to  Yes so  I  can  enter  tolerance  values

and a  historical  Sigma   for  the  AIAG  Gauge  R&R  report.

So  I'm  going  to  click  OK and  this  dialogue  pops  up.

I  don't  have  to  enter  this  data   during  the  launch,

but  I'm  going  to   because  I  think  it's  easier.

So  I'm  going  to  go  ahead and  enter  the  data,

and  when  I  click  OK,

my  report  looks  similar  to  how  it's  always  looked

when  I've  had  multiple  measurements.

But  I  also  have   an  additional  outline  at  the  top,

and  we'll  look  at  that  in  a  minute.

But  the  first  thing  I  want  to  do is  I  want  to  turn  on

the  Misclassification  Probabilities  report for  both  of  these  analyses.

So  I'm  going  to  choose Misclassification  Probabilities,

and  you  can  see,  I  get  a  new misclassification  probability  report

for  both  of  these  and  it's  available without  a  prompt

because  I've  already  entered   my  lower  and  upper  tolerance  values.

Now,  if  I  had  not  already  entered  that  information,

I  would  have  been  prompted.

Or  I  could  use  the  new  option, Edit  MSA  Metadata,

to  either  enter  or  edit any  of  that  information,

which  would  automatically  update any  of  the  corresponding  reports.

Let's  go  ahead  and  turn  on   the  AIAG Gauge R&R  report

for  both  of  these  as  well.

And  you  can  see  I  get   an  AI AG  Gauge  R&R  report

that  looks  a  lot  like what's  in  the  Variability  C hart  platform,

and  it  includes   that  percent  tolerance  column

because  I  entered  tolerance  values and  percent  process,

because  I  entered  historical  Sigma.

I  could  also  turn  on   the  discrimination  ratio  if  I  desired.

And  before  we  move  on, I  just  want  to  point  out

at  this  top  outline  menu,  once  again, we  have  an  option  to  save  the  metadata.

I  can  save  the  metadata,

which  includes  not  only  the  MSA  metadata, but  also  I  can  save  out  measurement  Sigma,

which  is  a  result  of  my  MSA  analysis,

which  can  be  consumed by  the  Process  Screening  platform.

So  it's  going  to  be  considered process  screening  metadata,

and  there's  actually  a  new process  screening

column  property  for  that.

But  I'm  going  to  save  this as a  table  just  so  we  can  look  at  it.

I can  see  I  have  my  MSA  metadata, plus  I've  saved  out  the  measurement  Sigma

once  I've  computed  those variance  components.

So  let's  go  on  to  the  next  topic,

my  final  topic before  I  hand  this  over  to  Annie,

the  last  thing  I  wanted  to  talk  about

was some  improvements to  the  Manage  Spec  Limits  utility.

In  fact,   the  name  has  been  changed

to  the  Manage  Limits  utility  because  now it  handles  more  than  just  spec  limits.

It  still  handles  spec  limits

and  anything  related to  process  capability.

But  now  it  also  can  handle   Process  Screening  metadata,

which  includes  centerline, specified  Sigma,

and  measurement  Sigma,

MSA  metadata,  and  Detection  Limits.

So  now  I'm  going  to   hand  this  over  to  Annie.

Hi,  everyone. I  am  Annie  Dudley  Zengi,

and  I  am  the  developer  responsible for  control  charts  in  JMP.

I'm  here  to  talk  with  you about  some  of  the  new  features

that  I  added  for  Control   Chart Builder in  version  17.

So  I  added  a  Label  Role   in  addition  to  the  Y,

the  subgroup,  and  the  phase  role, there's  now  a  label  role.

I've  added  a  button  so  that  you  can  switch an   XBar  and  R  chart  to  an  IMR  chart.

I  added  a  row  legend,

a  Connect Thru  Missing  Command, and  I've  done  some  Dialog U pdates.

I'll  start  with  this  data  table  diameter,

which  you  can  find  in  the  sample  data.

And  let's  start  with  the  label  role.

So  I'm  going  to  alternate   between  using  the  interface

and  using  the  dialogs so that  everybody  can  get  a  feel  for  both.

If  I  start  with  the  interface,   and  I  drag D iameter  in to  the  graph,

we  immediately  see  we  get an  Individual and  Moving  Range  chart.

Now,  one  thing  that  you'll  notice that's new  here

is  this  new  role   in  the  lower  left- hand  corner  of  the  chart

for  the  label.

Now  I  can  drag  Day  in.

Now  I  want  to  take  a  look at  Day  here  in  the  data  table.

So  you  might  notice  that there  are  six  different  rows

that  are  associated  with  May  1st,  1998.

There  are  six  rows  associated with  every  date

in  this  particular  data  table.

And  we  know  that  if  we  were  to  drag  that to  the  Subgroup  role,

then   Control  Chart  Builder will  automatically  aggregate.

But  sometimes  we  don't  want  that.

So  for  this  example,  I'm  going to  drag  this  to  the  Label  role.

You  notice  we  still  have an  Individual  and Moving  Range  chart.

It  did  not  switch and  it  did  not  aggregate  the  data.

We  can  see  that  it's  a  regular  axis.

We  currently  have  an  increment  of  24.

We  can  change  the  increment  to  six.

We  can  see  every  date  on  the  x- axis and  we  can  still  see  that

we  have  an  Individual   and Moving  Range  chart  of  Diameter.

So  there's  the  Label  role.

Now  the  next  option  is the  switch  to  the  IMR  chart.

This  option  was  made  available because  there's  now  a  Label  role.

To  switch  to  an  IMR  chart,  we  first have  to  have  an   XBar  on  our  chart.

So  I  will  create  an   XBar  on  our  chart through  the  dialog.

You can choose  Control  Chart and  then   XBar  Control  Chart.

Again  I'll  move  Diameter  to  the  Y.

And  this  time  I'm  going to  put  Day  in  as  a  Subgroup.

You  can  see  here  it's  aggregated  the  data

because  we  have  Day   as actually  the  subgroup.

But  if  I  show  the  control  panel and  I  scroll  down,

you'll  notice  there's  a  new  button  here

underneath  the  old  button of the  Three  Way  Control  Chart.

And  when  I  click  that  button,

it  moves  the  variable from  the  Subgroup  role

into  the  Label  role. So  you  see  we  now  have

an  Individual and  Moving  Range  Chart of  Diameter.

Now,  the  next  option  is a  Row  Legend.

Row  Legend  is  new for  Control  Chart  Builder.

And  I  have  a  little  note  here.

The  Row  Legend  option   is  only  going  to  appear

when  there's  only  one  row  per  subgroup.

So  if  you  right- click  like  you  do  in  a  lot of  other  graphs  in  many  other  platforms

in  JMP,  you'll  now  see  a  Row  Legend  here, but  only  if  you  have  one  row  per  subgroup.

And  the  Row  Legend   acts  like  a  row  legend  does  anywhere  else.

I  can  choose,  say,  for  example,  Operator, and  it  will  color  by  Operator  by  default.

And  now  you  have  your points  colored  accordingly.

The  next  option— I'm  going  to  close  this— is   Connect Thru  Missing.

Now, Connect Thru  Missing is going  to  involve  some  missing  data.

So  let's  open  up  Coding, which  happens  to  have  the  Weight

that you might  normally  be  measuring,

but  it  also  has  Weight  2 that  has  missing  data.

If  I  go  through  the  interface and  create  two  control  charts,

you  notice  we  have   a  good- looking  control  chart  here.

Everything  is  connected  and  so  forth.

But  if  we  scroll  down  to  the  second  one, we  see  some  gaps.

And  sometimes  management   doesn't  want  to  see  the  gaps,

so  we  need  to  connect  those.

So  there's  a  new  option under  the  red  triangle  menu

called  Connect Thru  Missing.

You  can  see  the  little  caption  there.

It  says,  "This  item  is  new as  of  version  17."

This  was  in  the  old  Legacy  platform.

And  so  I've  been  bringing  more  options into  Control  Chart  Builder

that  were  available in  the  old  Legacy  platform.

So  there's  your   Connect Thru  missing.

Now,  the  next  option— I'm  going  to  switch back  to  my  slides  here  for  a  moment—

so  the  next  option  is  the  Laney and  P  prime  control  charts.

This  is  a  bigger  option.

So  let's  think  about Control C harts  for  a  moment.

The  purpose  of  Control  Charts is to  show  the  stability  of  your  process.

If  your  process  is  not  stable, then  you  cannot  reliably  make

the  same  sized  part, which  is  going  to  be  a  problem

for  all  of  your  customers.

And  so  there's  lots  of  tests  involved in  making  sure  that  you  are  stable,

that  you're  reliably  able to  make  the  same  part.

Now,  if  you're  looking  at  attribute control  charts,  those  are  based  on  either

the  Binomial  or  the  Poisson  distribution, and  those  assume  a  constant  variance.

Now,  what  happens  if the  variance  changes  over  time?

Maybe  there's  humidity   or  there's  temperature  problems

or  there's  wear  and  tear  on  a  gear.

This  is  what  statisticians  refer  to as  over dispersion,

or  in  rare  instances,  under  dispersion.

And  one  parameter  distribution cannot  model  this.

So  Laney  proposes that  we  normalize  the  data

in  order  to  account  for  the  variant and  account  for  varying  subgroup  sizes.

And  David  Laney  wrote  a  paper  in  2002, Improved  Control  Charts  for  Quality.

So  let's  take  a  look  at  the  Laney  charts.

Here  I  have  some  data also found  in  the  sample  data.

This  is  not  a  terribly  large  lot  size,

but  here  we  have  a  column for  teaching  purposes

that  has  a  varying  lot  size.

So  let's  explore  how  this  works.

If  we  were  to  look  at,  say,

a  P  chart  of  the  number  of  defective out  of  this  varying  lot  size.

I'm  going  to  use  the  menus, the  dialogs  again,

I'm going to create  a  P  chart   to  start  with

and  let's  see  how  that  performs.

Okay,  we're  going  to  look at  our  number  defective,

and  then  we  have  the  lot as  our  Subgroup  identifier.

Now,  I'm  going  to  use  Lot  Size  2 because  that's  the  varying  lot  size.

And  click  OK.

All  right, so  on  first  glance, yes,  we  expected  the  non- constant  limits

because  we  have the  varying  subgroup  sizes.

But  we  also  notice  immediately that  our  chart,

this  process  is  out  of  control because  we  have  these  points

that  are  beyond  the  limits and  we  can  turn  on

the  Test  Beyond  Limits and  they're  flagged.

And  so  this  process  would  probably  raise

all  kinds  of  alarms  and  people would  be  trying  to  retool  things.

Now,  if  I  show  the  control  panel when  I  have  the  statistics  set

to  proportion,  because Laney  only,  in  his  paper,

gave  formulas  for  the  P  and  the  NP  chart,  or the  P  and  the  U  chart.

So  currently  it's  only  implemented for  a  proportion.

But  when  you  have  your  statistics  set

to  proportion,  you  have  four  choices  now instead  of  just  two  on  your  Sigma.

So  we  could  switch   to  the  Laney  P  prime  chart

and  see  what  that  difference is  going  to  be.

And  suddenly  you  see  your  process is  not  nearly  as  problematic.

It's  not  out  of  control  at  all.

It  looks  like  this  process is actually  stable,  which  is  great  news.

Now,  is  this  is  this  really  okay,   you  might  ask,

or  is  this  cheating?

Let's  take  a  look  at  the  formulas and  help  us  figure  this  out.

So  Laney  suggested  that  we  compute a  moving  range,

Sigma  on  the  standardized  values.

So  these  Z's,  those  are our  standardized  values.

We  compute  an  average moving  range  on  that.

And  we  have  a  Sigma  sub z ,

which  is  the  average  moving  range divided  by  1.128.

And  then  we  take  that  Sigma  sub  z

and we  insert  it  into  the  exact  same formula  that  we  saw  for  our  P  limits.

And  so  what  you  can  see  from  this is  if  you  actually  have

a  constant  variance, this  Sigma  sub  z  is  going  to  approach  one.

Many argue, including Laney, that it is generally safe to use this

instead of the P chart since it's going to approach one

and  it's  going  to  be  the  same

anytime  you  actually  do  have constant  limits.

So  there's  the  Laney  P  prime  chart.

I  wanted  to  show  you  also, there's  a  few  dialog  updates.

Let  me  show  you  some  of  those  right  here.

So  I  hinted  a  little  bit  at  it.

You  can  see  the  Laney P  prime  and  U  prime.

Those  are  two  new  dialogs that  you  can  see  there.

The  IMR  chart  now  has  a  label  role   on  the  dialog.

The  XBar  and  our  Control  Chart  now  has a  Constant  Subgroup  Size  option

in  case  you  don't  have  a  subgroup that  you  want  to  specify.

There's  a  little  more  work  that  was  done on  the  Three  Way  Control  Charts.

So  that  now,  not  only  can  you  specify the  constant  subgroup  size

if  you  don't  have  a  subgroup  already  identified,

you  can  also  choose  your  Grouping  Method,

your  Between  and  Within  Sigmas for  your  control  chart.

So  there's  different  options that  are  added

on  the  Three W ay  Control  Chart  dialog.

And  I  want  to  thank  you very  much  for  your  time.

If  you  have  any  questions, please  feel  free  to  ask.

Thank  you.