Hi,  everyone.

Thank  you  for  joining  us   for  our  presentation

of  From  Data  to  Story:

Using  visualization   to  drive  continuous  improvement.

My  name  is  Allison  Bankaitis, and  my  co- presenter  is  Scott  W ise.

A  little  bit  about  myself.

I  currently  supervise  a  small  team of process engineers

at  Coherent  Incorporated,

but  I'm  still  very  involved  in  the  daily  process  engineering  efforts.

Previously,  I  held  various  process  engineering  roles

at  Corney  Incorporated, and  I'm  very  excited  to  show  a  case  study

of  how  we  view  some  of  these  JMP  tools  in  our  process  engineering  work.

A  little  bit  for  Scott.

Thank  you,  Allison.

I'm Scott  Wise.

I'm  from  JMP  in  support Allison's  JMP  usage,

as  well  as  other  customers in  the  Northern  California  area.

And  I'm  just  real  excited  to  be  a  part   of  this  really  cool  case  study.

Hopefully,  you'll  pick  up  a  lot  of  best  practices

and  tips  from  some  of  the  things  that  helped  us.

All  right.

I  placed  the  abstract  here   for  future  reference,

but  just  wanted  to  highlight  a  few  things.

Coherent  has  placed a  recent  focus  on  Lean,

which  aims  to  eliminate  waste.

Tools  from  JMP   have  aided  data  visualization,

which  in  turn has  enabled  reduction  of  waste.

Another  advantage  of  these  tools  is  the  ability  to  engage

and  empower  employees  throughout  the  organization.

These  areas  will  be  the  focus  of  this  presentation.

Our  first  section  is  about  eliminating   waste  in  the  data  collection  process.

In  this  case  study,

we  had  a  data  collection  process with  unnecessary  complexity.

It  used  to  take  20  minutes  to  process  one  part.

So  to  do  this,  we  had  built  a  data  query  and  access.

This  is  just  a  screenshot  here  showing  an  example  of  a  few  data  tables

where  we  combined   variables  from  various  tables

to  get  the  output   that  we  are  looking  for.

And  then  we  used  a  macro  to  pull  data for  an  individual  part  into  Excel.

This is again,

just  a  screenshot  of  an  example  database  connection  in  Excel

and  the  code that  we  would  write  in  Excel.

This  was,  again,  done  for  each  individual  part.

From  that  data,   then  we  could   attribute  data  in  Excel.

We  could  then  calculate  average  values  of  each  attribute.

We  would  then  pull  additional  data  from  our  MES  website,

such  as  part  type  or  other  items  listed  here.

Then  all  that  data  was  copied  into  an  Excel  summary  log.

So  we  maintain  the  log,

but  we  weren't  really  doing  anything  to  track  or  analyze  the  data.

With  JMP,  I  was  able  to  streamline  the  process.

I  built  this  framework  in  about  an  hour

and  reduced  process  time  to  five  minutes  per  part.

And  in  this  case,

this  is  just  for  one  engineer  myself, on  one  product  that  I've  worked  on.

But  if  we  can  extend  this  to  multiple  products

and  multiple  engineers,

we  could  really  gain  a  lot  large  savings  of  time.

So  to  do  this,  I  built  a  data  query in  JMP,

which  included  both  the  attribute  and  MES  data  in  one  location.

Just  a  screenshot  of  there'd  be several tables  here  pulling  in  the  data,

the  different  variables  here,

we  can  do  some  initial  filtering  in  the  data  query.

So  in  this  case  I  selected  a  time  frame  that  I  wanted  to  focus  on

and  then  a  subset   of  variable  that  I  down  selected,

so  I  don't  manage  both  data  set.

And  then  I  can  build  the  data  table  here

and  always  clean  up   more  of  the  data  later  on  as  necessary.

After  I  had  the  data,

I  replicated  some  of  the  charts  that  we  already  had  in  Excel,

just  made  them  very  similar  so  that  people  could  see

what  they're  used to  dealing  with  for  the  time  being.

After  that,  again,

I  built  the  summary  table  to  replicate   what  they  were  used  to  seeing.

I  calculated  the  average  attribute  data and  merged  the  original  table

and  the  tabulated  table  into  one  summary  table

so  that  they  could  have  an  output  what  they  are  used  to  seeing.

The  next  thing   is  to  take  this  data

and  move  from  data  to  story.

So  to  do  this,  the  first  thing  I  was  curious  to  know

was  what  does  the  data tell  us  about  current  performance.

So  I  plotted  the  data  over  time  as  my  first  aspect

and  I  will  show  that  to  you  in  JMP.

So  just  using  this  graph  builder

and  the  timestamp  that  I  chose

and  then  the  main  output  that  I  started  looking  at,

added  that  to  the  chart  here.

What  I  used  to  do  is  manually  go  in  here

and  add  reference  lines  using  this  field  here.

But  what  Scott  showed  me,  which  is  really  neat

and  then  extends  to  all  of  the  graphs,

is  that  you  can  add  it   directly  to  the  data  table,

you  can  add  the  spec  limits.

You  just  go  into  the  Variable  of  Interest, Column  Properties

and  go  down  to  Spec  Limits  and  add  the  values  in  here.

This  is  checked  so  that  you  can  see the  graph  reference  lines  on  each  graph.

So  once  I  had  that  output,

I  could  see  that  there  is  a  large  amount   of  variation  in  the  data

and  many  of  the  values   were  outside  of  the  spec  limit.

So  the  next  thing  I  wanted  to  do was  compare  additional  variables.

So  to  do  that  pretty  quickly,  I  was  able  to  just  add

the  column  switcher   to  this  graph  I  already  had

by  going  here  and  selecting  the  variable  I  wanted  to  change

along  with  the  other variables  that  I  trust.

Then  from  here  I  can  quickly  click  through  all  these  variables

and  see  the  variation  in  each  one.

Next  for  me, I  have  some  process  knowledge

and  I'm  sure  you  would  have  process  knowledge  of  your  situation  as  well.

Based  on  this  process  knowledge, I  was  able  to  select  a  variable

that  I  thought   might  be  driving  some  of  the  variation.

In  my  case,

I  thought  that  X3   might  be  responsible

for  driving  some  of  these  trends  that  I  was  seeing.

I  put  that  into  our  graph  here.

The  other  piece   of  process  knowledge  that  I  have

is  that  our  spec is  based  on  the  average  value  for  each  part.

I  changed  this  to  mean  and  then  I  was  curious  to  see

the  line  of  fit  over  time, so  I  put  that  and  added  that  here  as  well.

The  next  thing  I  was  interested  in  seeing

is  a  little  bit  more  about  performance by  looking  at  a  control  chart.

To  see  the  control  chart,  analyze  quality   and  Process  Control  chart  builder,

and  I  was  curious  to  see  it  against  X2 , which  is  a  part  number

and  that  same  variable  that  I've  been  looking  at.

A gain,  I  was  going  to  split  it  out  by X3.

From  here,  we  can  see  there's  a  shift  in  the  average

based  on  which  subset  of  X3 .

A lso  the  thing  that  was  obvious  to  me is  that  the  sample  sizes  were  uneven.

To  me,  knowing  the  process,

I  know  they  should  each have  10  collections  of  data  for  each  part.

So  based  on  our  process,  I  said,

well, to  get  an  initial  look  at  the  performance,

I'm  going  to  limit  to  only   parts  that  have  10  measurements  per.

To  do  that,   we  made  a  new  data  table,

cleaned  up  the  data  again, and  once  I  had  this,

I  recreated  the  control  chart

with  just  a  small  change.

Then  here  I  added  a  local  data  filter

to  have  X3 split  out  on  two  separate  graphs.

That  was  my  learning.

Now  I  can  see  these  upper  and  lower  control  limits

and  this  process  capability  chart,

since  now  I  have   the  even  subgroup  sample  size  of  tech.

That's  where  I  will  hand  it  over  to  Scott.

Thank  you  very  much.

All  right.

I'm  going  to  pick  up  with  the  rest  of  the  story.

Allison  has  done  a  great  job  of  understanding

where  the  current  performance  of  her  process  was.

But  we  also  thought  there  might  be some   other  key  variables  within  her  data

that  could  be  useful  for  explaining  these   differences  we're  seeing  in  the  output.

One  of  the  things   that  we  tried  was  actually  a  modeling  tool

that's  very  simple  and  often  used  to  screen  for  important  variables.

It's  called  a  partition.

In  this  partition,

all  you  have  to  do  is  of  course,

you're  going  to  pull  up  your  data

and  then  it  is  under  predictive  modeling.

People  call  this  a  decision  tree,

and  I'll  show  you  why  when  we  start  to  fill  it  out.

But  all  you  got  to  do  right  now  is  give  it  an  output  that's  our  21 there,

X 21,

and  get  it  the  inputs  we  want.

I'm  going  to  put  all  the  inputs  in  except  for  X2 ,

which  was  the  kind  of  a  part  ID.

I'm  going  to  remove  that  one.

There  was  another  one  that  Allison  recommended  I  removed,

given  her  process  knowledge,   and  that  was  X8.

But  we'll  leave  all  the  others  in.

When  I  say  Okay,

it  brings  up  the  start  of  a  decision  tree.

What  it's  doing  is  saying,  I  can  make  a  bunch  of  splits

and  I'm  going  to  look  at  all  the  inputs and  I'm  going  to  try  to  find  a  cut  point.

We'll  breaking  basically   any  of  those  variables  into  two  groups.

Will  that  give  any  explanatory  value

toward  the  differences I'm  seeing  in  the  output?

In  this  case,  X 21.

So  if  you  make  the  first  split, it's  saying  that  I've  explained  27%

of  all  the  difference you're  seeing  in  the  output

via  just  splitting  X 19  at  500.

If  it's  greater  or  equal  to  500,

I'm  going  to  have  a  much  lower  mean  of  12. 67.

If  it's  less  than  500, watch  out,  it  jumps  up  to  13.

This  is  really  cool  to  find  other  things   I  might  want  to  split,

break,  view  on  my  graphs.

You  can  continue  splitting  and  it  will  look  at  other  variables

like  X3  came  into  place  here

and  Allison  already  knew

that  was  going  to  be an  important  variable.

A s  you  keep  splitting,

you  can  see  it  starts  to  add in  terms  of  the  predictability.

This   RSquare, the  closer  to  one,  the  more  predictable.

So it's  like  56%  predictability  here.

I've  gone  ahead  and  done  that, I'll  show  you  what  that  view  looks  like.

Here's  the  finished  view   I  came  up  with.

I've  got  these  nice  big  column  contribution  bars  here  at  the  bottom.

You  can  see  that  X 19  got  split.

Actually  found  five  cut  points  for  X 19,

but  52%  of  all  the  splits  it  was  doing  involved  X 19,

so  it  gave  it  a  nice  big  bar.

The  next  three  would  be  next.

Then  everybody  else   was  very  small  contribution

or  no  contribution.

It leads  us  to  say,

"Hey,  X 19  might  be  important   and  it  reinforces  X3  being  important."

Now  that  we  have  that  information,

well,  how  confident  are  we  that  these  things  do  belong

in  our  study?

Here  it  would  be  nice to  look  at  X 21  by  X 19  broken  out.

This  one,  of  course,

is  going  to  be  just  simply  going  back  into  our

graph  builder.

This  chance  we  can  put the  X 19  down  on  the  bottom  axis

so that would be the only X.

Let's  go  ahead  and  put  our  X 21  right  there  on  the  Y.

We  can  break  that  out  by  the  X3  variable,  which  is  pretty  cool.

Now,  one  thing  we  might  want  to  do, X2 was  the  part  ID.

We  can  give  it  some  color  or  some  overlay.

Either  way,  I  think  I  will  just  go  ahead and  give  it  some  color  here

and  I  will  turn  off  the  line.

That's  helpful.

But  what  would  be  helpful is  to  use  that  local  data  filter

that  Allison  showed,

in  case  they  want  to  really  look   at  a  specific   sequence  of  parts.

I'll  go  under  the  red  hotspot  there, that  red  triangle.

I'll  go  local  data  filter  and  then  we'll  add  the  X2,

and  beautiful.

Now  we  can  go  and  just  change  up our  view  by  that  local  data  filter.

That  was  a  cool  view  that  we've  got.

I  can  see  that  it's  making  a  lot  of  differences  there.

Now  one  thing  you  might  ask  is  could  we even  model  this?

Before  I  even  go  and  model  it   so  we  can  make  some  predictions,

how  sure  am  I  that  X3 and  X 19  really  are  affecting  X 21?

Well,  we  can  actually do  a  statistical  test.

We  can  test  means.

The  way  we're  going  to  do  that  here is  we  are  going  to  go  back  to  our  data.

We're  just  going  to  go  to  Analyze,

fit  Y  by  X

and  now  we're  going  to  go  into  our  output.

We  want  to  look  at  things  either  the  effect  on  X 21

by  those  things  we  care  about,   X3  and  X 19.

I'm  going  to  put  them  both  in  here

and  it's  going  to  give  me  some  different  views.

It's  going  to  enable  me   to  compare  means  in  this  one  way  analysis.

I'm  going  to  right  click, I'm  going  to  turn  on  the  means  test.

I'm  going  to  right  click  here.

I  even  like  this  all  pairs  too  key.

I'm  going  to  adjust  our  axis  here.

It's  got  these   cool  means  diamonds.

The  middle  of  your  diamond  is  the  mean.

The  edges  is  your  95% confidence  around  the  mean.

The  way  it  works,

if  you  would  slide  these  things  over, would  they  overlap?

It  looks  like  they  would  pass like  ships  in  the  night.

There's  no  overlap.

As  well  as   you  got  these  comparison  circles,

you  can  click  on  one  and  see  if  the  other  one  turns  a  different  color.

A ll  this  is  based  off  a  0.5  alpha.

What  does  that  mean?

That's  your  confidence so  that's  95%  confident.

We'd  be  right  95  times  out  of  100  to  say input  X3  does  have  the  level  there,

is  having  an  effect   on  what  my  observed  measures  are  for  X 21.

Given  that  before  I  go   and  try  to  fit  a  line  or  a  curve  line,

I  can  go  under  this  red  triangle  hotspot.

I  can  go,  you  know  what, let's  go  ahead  and  group  by  X3 .

Now  when  I  go  back  under  this  triangle  option

and  I  go  to  fit  a  line,  or  in  this  case,  I  know  there's  a  little  curvature,

so  I'm  going  to  fit a  quadratic  line or  polynomial  line.

Now  it  broke  it  out  by   X-3

so  I'm  really, really  excited about  that  one.

The  blue  line,  which  is  his  first  version, 3-0,  there's  the  formula  for  it.

It  only  has  20%  explainability.

It's  not  a  great  fit,

but  you  can  see  that  jumped  up to  near  70%  predictability for the 3_1.

It's  telling  me  that  I've  got  not  only   significance  in  saying  X3  is  different

and  I'm  seeing  a  difference when  it  comes  to  X 19  by  X 21,

but  it  matters  for  X 19   what  level  of  X3  we're  talking  about.

That's  why  the  red  line  and  the  blue  line  are  not  on  top  of  each  other.

Therefore,  that's  an  interaction.

If  I'm  going  to  try  to  predict  something,   I  need  to  include  that.

So  at  this  point,

I  think  I  have  all  that  we're  going  to  need

to  do  to  get  in  the  hands   of  Allison  Spears,

a  really  cool  tool  that  can  help  them predict  what  the  output  is  going  to  be

based  on  settings  of  X3  and  X 19.

You're  seeing  on  the  screen  a  profiler that  comes  off  our  modeling  platform,

and  it's  very  easy  to  go  and  set  up.

If  we  go  back,  I'm  going  to  go  to  the  fit  model  here.

We'll  do  our  output  for   X21  again.

Under  my  inputs,  I  know  X3   and  X 19,

there  it  is,  are  very  important.

I  told  myself  X3  and  X 19  might  need  to  be  crossed,

I  might  need  to  see  those  interactions.

I  know  for   X19,  there's  some  curvature.

The  way  I  would  check  for  this  is  I  go,  I'd  select  X 19,

I  go  under  this  macros,   and  I'd  say  polynomial  two  degree.

I  have  it  set  at  two  so  I  would  get  this  curve  term,  polynomial  term  here.

There's  the  interaction, and  these  are  the  main  effects,

so  it's  really  two  factors, but  it's  the  four  things  in  my  model.

So  I'm  just  going  to  run  it   and  it's  going  to  try  to  fit  a  line.

This  should  look   very  much  like  the  fit  Y  by  X.

It's  only  really  explaining  52%.

This  model  is  only  explaining  52% of  the  differences  I'm  seeing  in  x 21.

Not  perfect,  but  it's  pretty  much,

think  about  it,

just  for  having  two  factors   and  their  interaction

in  one  of  their  curve  terms,  that's  pretty  good.

But  what  I  can  do  now  under  that  red  hotspot  is  turn  on  the  profiler.

This  is  worth  the  price  of  admission.

This  right  here  is  going  to  enable  Allison  and  her  team

to  sit  there  and  talk  about   what  settings  we  should  have.

S hould  I  be  at

v3_1 ?

Should  I  be  at  v3_0

for  this  x_3  input?

Should  I  be  low  or  high?

A gain,  it  shows  that  interaction  live.

For  example,

I'll  shift  this  color  here.

Watch  what  happens  when  I'm  low.

I'm  sorry,   not  low  but  high  on  X 19,  I'm  way  out  here  to  500.

By  the  way,   you  can  type  in  what  you  care  about.

Maybe  I  want  to  see  what  it's  at  480.

Look  how  flat   that  line  is  between _0  and  _1.

It doesn't  really  matter   which  one  I  select.

I'm  going  to  get  the  same  kind  of  prediction.

The  red  is  my  prediction, and  the  blue  around  it  here

is  my  confidence  interval around  that  prediction.

Of  course,  this  wouldn't  be  good

because  I'm  right on  the  lower  spec  limits.

Watch  what  happens  when  I  start  to  pull  it.

Well,  I  might  be  happier  here   with  version  3-0  in  a  setting  around  350

because  that  gets  me  close  to  the  targets.

But  if  I  keep  going  up  here, you  see  how  steep  this  line  is  begin,

and  I  definitely   don't  want  to  be  on  version  3_1.

Because  it  has  a  steeper  line and  it  has  made  this  slope  very  steep.

It's  all  coming  out,   but  it's  interactive  in  this  profiler

and  now  we  can  play  with  what  would  be  the  right  settings for

if  I  had  to  stay  with  version  3-1.

If  I  go  to  version  3-0, what  would  be  the  right  settings  here?

They  might  be  different  settings.

There's  always  multiple  optimal  settings  you  can  select.

This  is  really  cool.

We  now  have  the  ability  to  predict.

All  right.

Continuous  process  improvements.

All  this  was  great.

We  now  have  a  faster  way to  get  our  analysis  done.

We've  gone  through  a  flow  that  enable s us  to  find  what's  important

and  see  what's  important.

But  what  if  we  want  to  use   that  information  to  monitor  over  time

and  continually  improve  our  process?

It  might  be  nice  to  have   for  different  levels  of  X-3  a  dashboard.

Allison  and  I  worked  to  create   a  standard  type  of  dashboard

that  her  team  is  used  to  seeing.

They're  used  to  seeing control  charts  first,

and  then  the  process  capability  around  their  specs.

Then  next  they  would  want  to  see the  output  over  time.

That's  the  top  chart  in  the  middle  there

and  then  below  that  if  there's anything   else  they  should  worry  about.

That  was  our  big  finding,  that, "Hey,  x 19  has  an  effect,"

so  they  would  want  to  see  that.

Lastly,  on  the  right  hand  side, we  put  a  table  with  the  average  means

for  the  output  of  interest,  plus  even  some more  outputs  they  like  to  take  a  look  at.

Of  course,   we  want  this  to  be  interactive.

So  how  can  we  build  this  dashboard for  level  zero  and  level  one?

We're  going  to  bring  up  our  data  here.

I  think  I  already  have  it  opened  up  here.

I  will  go  now   and  just  create  in  just  one  swoop.

This  is  why  it's  nice  to  be  able  to  save  your  graphs,  your  analysis  back  to  data.

I'm  just  going  to  click  and  create   a  whole  bunch  of  views  here

that  are  going  to  replicate  what  the  team  wants  to  see.

Here  is  that  control  chart  builder   for  the  X  bar  and  R.

Next,  we  have  the  process  capability.

Next,  we  have  that  output  over  time.

Next,  we  have  the  output   over  the  X 19  that  we  wanted  to  show.

Now  we  have  the  table.

I  have  all  the  elements, and  if  you  have  all  the  elements,

you  don't  have  to  save  them  back to  make  someone  run  them  one  at  a  time.

You  can  combine  them  into  a  dashboard  template

and  it's  under  File,  New  Dashboard.

It  will  allow  you  to  pick some  type  of  template  to  start  off  with.

I'm  just  going  to  pick this  blank  template.

Now  it's  got  all  my  reports,   all  the  graphs  and  tables

and  things  I've  opened  on  the  left.

Now  I  can  just  bring  into  the  body  of  the  dashboard  what  I  care  about

and  I  can  orient  things  the  way   I  would  like  to  see  them  on  my  dashboard.

When  I'm  done,   it's  easy  to  go  and  run  that  dashboard

and  then  later  save  that  dashboard  when  I'm  ready.

But  I've  already  got  that  run  here.

So  I'm  going  to  close  down the  dashboard  builder.

I'm  going  to  show  you   the  dashboard  we  have  already  created

to  capture  all  this  information.

With  one  click  of  the  button here's,  my  dashboard.

And  boy,  beautiful  looking  dashboard  here, just  the  way  I  want  to  see  it.

Now,  the  thing  that  we  loved  about  this

was  your  ability  as  well to  still  use  the  junk  dynamic  linkage.

I  can  select  a  couple  of  high  points

and  I  can  see   where  they  will  flow  in  the  other  graphs.

I  can  even  see  down  here   where t hey're  highlighted  to  my  table.

So  this  is  great,   but  what  about  that  X3  variable?

We  knew  we  wanted  to  be  able  to  create separate  dashboards  for  each  of  those.

So  instead  of  using  a  local  data  filter, I'm  going  to  use  a  global  data  filter.

It's  actually  under  your  Rose  venue.

It's  right  at  the  bottom.

This  one  affects  all  graphs,  all  analysis.

It  affects  what's  hidden   and  selected  back  to  your  data  table.

On  this  one, I'll  just  go  ahead  and  put  X3 .

Now  when  I  click  on  Show  and  Include,

I'll  turn  off  the  select   so  I  can  make  my  own  selections.

Now  I  can  toggle  between   that _0  and  that  _1 .

Now  it  works  the  same  way.

I  can  see  things  that  were  out  of  control  or  out  of  spec  here  for  just  version  3-0,

then  I  can  do  the  same  thing  for  3-_1.

There  we  go.

We  have  a  nice  tool  that  can  be  really   used  to,  again,  not  get  data  just  quicker

and  not  just  do  one  analysis,

but  actually  make  this a  continuous  process  improvement  tool

that  we  can  use  day  in  and  day  out to  quickly  get  the  view  we  want

and  ask  the  questions we  need  to  drive  improvements.

All  right.

So  that  is  our  story  of  moving from  data  to  story,  I  should  say.

We  wanted  to  leave  you   with  where  to  learn  more,

where  to  get  more  information.

Of  course, we're  going  to  give  you  the  presentation.

We're  going  to  give  you  the  journal  we  use

so  you  can  replicate these  views  we're  seeing.

But  Allison  and  I  felt  that  if  you  were wanting  to  really  get  started  with  JMP,

go  to  the  Getting  Started  with  JMP  webinars  that  we  have.

So  it's  on  the  JMP  website, will  include  links  in  the  journal,

and  it  covers  about  everything we  showed  you  today.

We  had  a  few  more  tips  and  tricks,

but  the  new  user  welcome  kit  is  another  really  good  thing  to  take.

This  one   allows  you  to  work  with  a  data  set,

it gives  you  a  data  set  that  you  can  follow  along,

and  it's  really  nice  step- by- step  instructions.

We're  both  big  fans  of  the  Statistical   Thinking  for  Industrial  Problem Solving.

Free  online  learning, basically  E-learning  course,

and  you  have  so  many  different  places  you  can  do.

I've  used  this  to  do  just  in  time  learning,

and  I've  had  a  lot  of  people t ake  all  the  sections  just  to  get  up  to  speed

on  everything  JMP  can  do   to  help  you  compare  and  describe

and  predict  all  those  fun  things  you  want  to  do.

Don't  forget,  if  you  have   specific  things  you  want  to  do,

we  do  have  Mastering  JMP  webinars  that  are  available  here.

The  JMP community, communityjmp .com is a good place to look

for  just  in  time  learning,

and  as  well,  JMP  Education,

if  you  want  to  get  more   of  the  underlying  theory

on  how  a  lot  of  these  things  work.

We  do  a  lot  of  public  training   or  can  customize  training  for  you  as  well

Just  talk  to  JMP  Education.

All  right.

I  will  allow  Allison to  say  a  few  words  when  we  finish.

But  thanks,  everybody,  for  joining  us,

and  we  hope  you  picked  up  on  a  few  things you  would  like  to  try  within  JMP.

Thanks,  Scott.

Thanks,  everyone,  for  joining  us.

It  was  really  a  pleasure  to  share this  case  study  from  Coherent  with  you

and  to  share  all  the  new  cool  tricks that  Scott  has  taught  me

and  that  we've  learned  through  our  journey   with  JMP  at  Coherent.

So  thanks  again  and  take  care.

Bye.