I 'm  incredibly  honored  to  be  here  for  JMP  US  Discovery  Summit .

I  will  be  presenting  the  project  titled  Industry  Innovation  Blending  Modern  JMP

and  Classical  Six  Sigma Applied  Engineering  Statistics .

This  is  otherwise  referred to  as  a  Lean  Six  Sigma  JMP  Based

Black Belt  Program , which  is  a  novel  program

really  championed  by  Charles  Chen , my  co -author  and  co -presenter .

Let 's  dive  right  into  it .

First  of  all ,

what  is  the  high -level  roadmap for  this  program ?

It  really  starts  with  transforming a  global  quality  culture

and  ends  with  connecting  local  JMP  SMEs  and   Master Black Belts .

The  way  that  we 've  done  it is  through  a  segmentation  of  programs ,

A  plus ,  A  minus ,  and  A .

Really ,  we 're  focusing   on  the  A  plus  program

being  the  elite  program ,

and  we 're  going  to  get into  that  much  more .

But  the  outcome  is  really  for  a  focus on  the  entire  global  quality  initiative .

This  program  covers  a  2 -3  year  span ,

and  it 's  really  based on  a  2 -3  year  outcome .

But  the  ultimate  convergence  of  it  is  that this  organization  use  case  is  that

we 've  decided  to  use  JMP for  all  the  programs ,

including  the  Six  Sigma  program , and  always  with  the  intent  to  deliver  it

with  the  highest  quality  in  alignment with  the  global  quality  strategy .

A  key  feature  of  this  program is  encouraging  healthy  competition

through  hosting  JMP  forum  events , which  we 'll  showcase  a  little  bit .

What  we 've  shown  here is  that  we 're  using  a  Six  Sigma  tool

called  the  SWOT  Assessment, s trength , weaknesses ,  opportunity ,  and  threats,

as  you  can  see  in  this  matrix .

The  things  I  want to  emphasize  here  are  that

using  JMP  as  both  the  external  curriculum

and  the  internal  curriculum is  fundamental  to  this  program .

What  I  mean  by  that  is ,

which  I 'll  show  later , is  that  we 've  really  combined

JMP  education 's  curriculum ,

which  they 've  provided to  the  customer  base ,

for  a  nondisclosure  agreement with  an  internal  Six  Sigma  curriculum .

An  applied  statistics  lean  with  JMP ,

plus  a  rigorous  Internal  Six  Sigma applied  engineering  program .

It 's  important  that  our  leaders focus  on  opportunities .

This  methodology  really  focuses on  building  details ,

but  if  we  go  from  details up  to  the  nucleus ,

we 're  talking  about  leaders who  focus  on  opportunity .

We  want  to  start  small , think  big ,  and  act  fast .

This  is  one  of  our  main  tenets .

On  the  opportunity  side ,

really  looking  at  how  we  can  synergize the  JMP  program ,  the  JMP  16 /17  curriculum

with  the  BB  curriculum , and  create  an  optimum  recipe

for  leadership  development ,  learning, and  deployment  across  the  organization .

The  other  thing  I  want to  mention  here  is  that  this  slide

highlights  a  migration  from  Minitab .

While  it  might  indicate a  shorter -term  productivity  loss ,

it 's  a  huge  return  in  medium to  longer -term  productivity  gain .

As  everybody  probably who  attends  Discovery  knows ,

JMP 's  interactive  graphing  capabilities and  multiplatform  interaction

and  flexible  and  powerful statistical  modeling  capabilities

with  a  scripting  engine and  JSL  really  make  JMP  far  more  powerful

than  Minitab  in  today 's  analytics  era .

This  is  a  key  milestone  for  us that  we  achieved  early  on .

How  does  this  A ,  A  plus ,  A  minus  tiered  program  design  work ?

Basically ,  we 're  using  the  three  levels

to  effectively  segment  our  trainees , our  stakeholders ,  our  customers ,

if  you  will .

This  segmentation  strategy was  developed  by  Dr .  Chen

to  maximize  the  potential for  really  getting  the  knowledge  out  there

through  the  organization in  a  practical  and  applied  way .

What  you  can  see  is  most  of  the  users within  this  case  study  organization ,

over  5 ,000  are  tiered to  the  A  minus  program .

We  wanted  to  highlight there 's  a  20 %  growth  rate  in  2023 .

So  we  continue  to  expect  growth .

We 're  seeing  a  lot  of  engagement from  the  trainee  stakeholders .

The  A  program  really  focuses on  quality  engineers ,

quality  and  reliability . That 's  over  300  users .

Then  really  the  cornerstone of  this  program ,

the   Master Black Belts and  the   Black Belts ,  highly  trained ,

highly  educated ,  many  PhDs , probably  more  than  50  people .

These  people  are  really  being  funneled through  the  A  plus  program ,

which  is  really  a  mentor and  an  instructor  program .

These  people  are  really  critical for  the  success  of  this  program  because

they 're  the  local  site  champions and  they 're  not  just  given  knowledge ,

but  they 're  given  the  tools  to  be  able to  apply  knowledge  with  JMP  as  the  tool .

This  is ,  in  my  mind ,

probably  the  most  important  slide of  this  presentation

because  it  really  focuses  on  the  strategy and  vision  of  this  program ,

which  just  really  funnels through  all t he  details .

From  the  top  level ,  this  goes through  all  the  details  of  the  program .

I  want  to  emphasize  these  four  called  key  visions .

Cross -functional  leadership and  vision  team  building

through  a  process  that  actually was  codified  in  ASQ

and  other  Six  Sigma  literature,

forming,  storming , norming ,  and  performing,

and  being  data -driven ,  truly  data -driven ,

not  just  data -driven  in  lip  service or  through  maybe  basic  tools  like  Excel .

Then  having— really,  this  is  probably the  most  important  one—

having  a  long- term  vision .

These  are  the  visions .

The  audience  can  look at  these  bullet  points .

But  what  I  wanted  to  mention  is  that  many  people try  to  embrace  these  visions ,

but  they  really  lack  their  embodiment .

If  this  will  resonate ,  I  think ,

with  many  viewers ,  is  that  oftentimes meetings  might  be  cross -functional ,

but  the  communication  style isn 't  necessarily  cross -functional .

Many  teams , in  my  experience  in  industry  as  well,

jumped  to  this  norming  phase of  this  team  building

where  they  skip  the  forming and  the  storming  phase .

Really,  this  tendency  to  jump  to  norming is  driven  by  the  fact  that

everybody  assumes  that  they  already  know what  they  need  to  do,

and  many  people  come from  a  highly  educated  PhD  background .

Just  because  we  have these  highly  educated  people

doesn 't  mean  we  have  a  strong  team .

Bypassing  this  forming  and  storming  phase

where  people  really  take  the  time to  work  through  what  their  roles

and  responsibilities  are and  understand  the  expectations,

this  shortcut  ends  up  actually giving  us  really  a  non -long- term  payoff .

In  fact ,  it  really  hurts  teams .

The  other  thing  is  I  mentioned this  briefly ,  but  I 'll  say  it  again,

the  notion  of  being  data -driven is  often  very  misunderstood .

Many  people  think  that  if  they  present data  in  Excel  that  that 's  good  enough .

But  it 's  one  thing to  present  the  data  in  Excel ,

but  it 's  another  thing   to  actually  meaningfully  present  the  data

in  Excel  or  any  other  tool .

Oftentimes ,  when  people  assume they  already  know  what  they  don 't  know ,

then  this  reflects a  miss  of  important  details

that  they  can  use to  solve  their  problem  better .

The  final  thing  I  want  to  say , which  I  may  not  mention ,

but  it 's  peppered  through  the  rest of  the  presentation ,  is  that

it 's  fundamental  that  we  recognize the  achievements  of  every  individual ,

especially  the  MBBDV  mentor  candidates ,

because  it 's  through  this  recognition  that we  cultivate  passion  in  the  candidate .

The  program  no  longer  becomes an  obligation  or  an  assignment

from  management ,  but  really  an  honor and  an  empowerment  mechanism

for  these  individuals  to  become more  capable  themselves ,

investing  in  them,  and  also give  back  more  to  the  company .

This  is  the  roadmap .

There 's  a  lot  here ,  but  the  key elements  here  are ,

again ,  that  we 're  grouping  internal and  external  material .

When  I  say  internal  material ,

I 'm  talking  about the  material  in  the  blue .

This  is  well -vetted , well -thought -out  material

that  comes  from  the  product of  years  of  engineering ,

applied  engineering  experience within  this  organization

and  within  the  program  champion's knowledge  base

from  previous  organizations ,

which  is  a  Six  Sigma   Black Belt  focus .

Then  in  the  red ,  we  have  all the  JMP  education  training  materials

which  have  been  acquired  through  NDA by  this  test  case  organization .

You  can  see  how  strategically , through  these  A ,  B ,  C ,  D ,  E,

and  scripting  language  main  modules , we 're  looking  at  disseminating

and  developing  leaders  for  this  program .

Obviously ,  for  any  questions about  this ,  please  reach  Dr .  Chen ,

my  co -author  presenter  here .

I 'm  also  happy  to  take  questions .

One  other  thing  is  that  it 's  about a  50 -50  split  between  the  internal

and  external  curriculum , if  it 's  not  obvious  from  this  slide .

I  just  want  to  make sure  that  that 's  clear .

Then  what  I 'll  show  in  subsequent  slides is  you 'll  see  this  0 .5  nomenclature

and  I 'll  explain  what  that  means  later .

This  is  a  beautiful  slide .

There 's  a  lovely  story  here , and  it 's  really  about  connecting  ideas .

But  let  me  say  first  that we 're  going  to  basically  start  down  here

at  the  foundation .

The  A0 ,  A1 ,  A15 ,  come  up  to  the  A2 ,

which  is  the  nucleus , and  I 'm  going  to  mention  this  again,

and  then  go  up  to  A3  and  A4 ,

and  then  go  to  B1  to  B4 , and  then  down  to  C1  to  C4 .

It  starts  on  the  lower  left ,

and  then  it  goes  around ,  counterclockwise, down,  and  ends  back  at  the  lower  left .

What  are  we  trying  to  communicate  here ?

The  focus ,  really , as  the  title  of  this  talk  implies ,

is  on  applied  engineering  statistics .

There 's  five  stages . There 's  foundation ,  A0 ,  A1 ,  and  A1 .5 .

The  0 .5  was  identified  as  a  bridge to  help  bridge  the  knowledge  gap

between  the  A1  and  the  A2  curriculum , which  we  identified .

I 'll  just  say  that  here to  make  that  clear .

The  A2 ,  which  really  focuses on  basic  statistics

and  modeling  ANOVA  and  regression .

That 's  up  here .

Then  connecting  the  dots , we 're  going  to  the  DMAIC  curriculum ,

B1  to  B4 , which  really  focus  on  the  DOE  material ,

and  then  from  there, progressing  on  to  the  data  mining

and  text  mining  material ,

which  is  really what  the  course  content  entails .

The  progression  here , there 's  an  analogy  here  drawn

between  this  training  curriculum and  kung  fu  or  Chinese  kung  fu.

Let 's  talk  about  that .

Kung  fu  really  embodies  this  idea  that excellence  and  mastery  in  any  endeavor

require  persistent  effort , dedication ,  and  time .

It 's  not  a  quick  fix .

There's no you  learn  everything and  you 're  an  expert .

That 's  the  idea  here .

The  emphasis  here  is  that the  foundation  must  be  very  solid ,

so  skill  achieved  through  hard  work .

We  go  through  the  A0 ,  A1 , the  foundational  study ,

we  go  through  the  1 .5 to  help  bridge  up  to  A2 .

By  doing  the  A0 ,  A1  and  A 1 .5 , we 're  going  diverse .

There  are  72  skills  in  Chinese  kung  fu that  have  to  be  learned .

I  think  this  illustrates  that .

We  go  diverse  in  order to  build  a  foundation ,

and  then  we  strengthen the  foundation  through  the  A2 ,

through  the  ANOVA  and  regression modeling  techniques .

Then  from  here , we  get  to  the  A3  and  A4 ,

where  we 're  talking about  the   central limit theorem

and  rational  subgrouping , which  are  really  fundamental ,

not  just  in  statistical  thinking , but  in  applied  engineering  thinking ,

because  data  is  part  of  engineering  now .

What  we 're  showing  here  is  that

there 's  basically  two  key  maybe  Chakras or  acupuncture  points ,  if  you  will ,

that  require  opening  up in this  martial  arts  tradition .

Those  two  points  are  drawn  parallel

to   central limit theorem and  rational  subgrouping .

What  I  will  say  is  that  these  concepts ,

while  theoretically  many  people understand  practically ,  they  may  not .

The   central limit theorem and  rational  subgrouping  are  actually

closely  connected  to  a  process engineers  use  all  the  time ,

the  CPK  and  PPK  and  understanding  that is  a  great  launching  point .

I  think  if  anyone  has  any  questions about  the  importance  of  those ,

we 'd  be  happy  to  discuss  that .

Rational  subgrouping  is  so  important

because  it  really  refers to  how  we 're  going  to  understand  within

versus  between  variation  and  how  we 're  going  to  meaningfully

sample  our  data  so  that we  can  extract  practical  insights  from  it .

So  many  times  in  an  industry ,

I 've  seen  examples  where  sampling is  done  non -systematically

and  in  a  way  that  produces meaningless  information .

So  rational  sampling , rational  subgrouping  is  really  critical .

All  this ,  up  to  this  point , [inaudible 00:17:21]

the  product  of  developing a  very  strong  foundation ,

connecting  the  dots ,

and  only  then  do  we  really  move  over

to  the  more  advanced DOE  and  regression  tools ,

which  is  the  B1  to  B4  program ,

and  not  just  learning  the  tools , but  becoming  effective  and  fast

and  using  the  systematic  DMAIC  framework to  drive  them .

After  the  stakeholder  works  through the  fruit  of  this  endeavor ,

through  project  work  and  coursework ,

they  can  become  a  master  and  they  can start  employing  more  advanced  tools .

Really ,  the  analysis  effort becomes  more  of  a  creative  integration

of  different  tools  and  techniques

and  with  an  understanding of  their  limitations

to  be  able  to  solve a  problem  holistically .

There 's  a  lot  there ,  but  I  really  wanted  to  summarize  the  power  there .

Now ,  here  this  slide , we 're  talking  really  about

the  high -level  deployment of  the   Black Belt  training  curriculum .

You  can  see  that  most  of  people ,  again ,

like  as  shown  in  the  previous  slide , are  trained  in  the  A0 ,  A1 ,  and  A2

for  the  foundation over  800 ,  over  600 ,  over  300 .

Then  you 've  got  the  MBB, BB  mentors   who  are  required  to  do  the  project  work ,

starting  to  be  trained to  be  in  A3  and  A4 .

These  programs  ultimately  need to  be  driven  by  local  leaders

who  promote  and  empower  the  folks that  are  taking  these  courses  under  them .

This  is  the  only  way  that the  program  becomes  truly  impactful

in  the  organization , because  the  leaders  drive  the  change ,

and  through  their  knowledge and  experience ,

they  teach  people who  came  before  them .

The  key  thing  here  is  the  participation and  application  in  projects  is  fundamental

for  these  leaders because  only  through  applied  learning

in  a  project  context

can  one  truly  become  an  effective   applied  engineering  statistician .

This  slide ,  in  essence , maps  the  JMP  tools  to  the  DMAIC  steps .

For  each  DMAIC  step ,   we  pair  it  with  the  JMP  platform

so  that  the  stakeholder  can  consider and  learn  to  solve  a  problem

in  a  more  systematic  manner .

That 's  really  the  power of  the  DMAIC  framework .

Our  approach  is ,  I  would  say , more  difficult  in  the  beginning

because  the  candidate   has  to  get  the  data  first .

That 's  difficult  at  first ,

especially  for  somebody  who  isn 't  trained in  real  applied  engineering  data  analysis .

But  it  ends  up  being  smoother  in  the  end

because  once  they  have  the  DMAIC  tool , they  can  drive  the  project  to  completion .

They 're  much  more  likely  to  drive  it to  completion  in  a  systematic  manner .

One  thing  to  mention  here   is  that

a  lot  of  people  in  today 's  engineering technology  environment

want  to  get  certification  in   Black Belt .

But  oftentimes ,   especially  in  high -tech  industries ,

the  engineering  function   isn 't  well  defined .

With  a  paper  certification ,

it 's  probably  not  of  any  real  help to  the  trainee .

They  won 't  really  learn  anything

to  be  able  to  actually  make  an  impact   in  their  organization .

The  goal  of  this  program  is  really to  build  impact ,  create  impact .

In  this  slide ,  really  the  emphasis  here   is  that  the  A2  class ,

the  ANOVA  and  regression ,

as  I  talked  about  as  being   the  fundamental  and  the  bridge ,

it 's  important  for  the  global  vision , and  it  really ,  truly  is  a  global  vision .

There 's  deployment  across  the  world .

So  far  in  this  case  study , we 've  deployed  over  30  A2  instructors

ranging  from  geographies   like  Germany ,  Israel ,

even  China ,  bridging  over  to  Japan .

We 've  offered  20  of  the  A2 in  2023,  20  of  the  classes .

The  first  120  trainees have  to  do  projects .

The  remaining  trainees ,  over  180 , have  an  optional  project  component ,

but  they  can 't  continue   beyond  the  A2  program  as  a  result  of  that .

I  talked  a  little  bit  about the  A  minus  or  the  0 .5 .

and  so  this  slide  is  really   to  speak  to  that .

What  are  the  objectives in  creating  this  A  minus  program ?

It 's  really  to  bridge  the  gap

between  the  original  A0  and  A4  modules   that  were  developed .

The  project  component is  they  don 't  need  to  do .

Of  course ,  as  I  mentioned ,  not  having   the  data  initially  makes  it  difficult

for  the  training  to  get  started .

This  covers  that  limitation ,  if  you  will ,   for  certain  people

and  addresses  that  knowledge  gap   that  we  identified .

Once  they  finish  this  training ,

they  can  go  more  deeper   into  advanced  subjects .

Many  people  come  to  learn  JMP

and  they  like  DMAIC   as  a  problem -solving  methodology .

This  is  a  good  mechanism  to  catch  them   and  find  them  where  they  are .

Actually ,  this  program   is  extremely  popular  right  now .

It 's  well  overbooked .

The  plan  was  to  book  only  12  people in  three  of  the  1 .5  trainings ,  for  example

and  there 's  already over  23  or  over  25  people .

Again ,  the  local  leader , the  local  MBB  leader  is  facilitating

and  maintaining  and  following  up and  continuing  to  generate  interest .

That 's  actually  really  fundamental to  this  program

and  the  success  of  this  program .

Also ,  there 's  some  feedback  from  people

who  already  took the  more  advanced   program ,

who  are  now  taking  the  1 .5 , and  they  like  it  very  much .

I  think  many  of  them  find  that   it  helps  them  connect  the  dots  more

with  what  they 've  already   journeyed  through .

This  slide  just  gives  you  a  feel   for  the  contents  of  this  program .

You  can  see  that  obviously   there 's  a  Getting  Started  component .

Graphical  analysis   and  outliers, box  plot  analysis

is  really  fundamental  if  done  properly .

Then  the  A2 ,  A3 ,  the  ANOVA,  regression ,

the  MSA  measurement  systems  analysis,   and  Gauge  R&R .

Then ,  in  the  A4 , we  introduce  SPC  and  Multivariate  SPC .

Then  in  the  C  program , we  get  into  the  advanced  data  mining ,

text  mining ,  PCA ,  multivariate  methods , all  through  the  JMP 's  platforms .

Actually ,  this  type  of  a  program , the  goal  would  ultimately  be

aspirationally  to  develop  it   at  the  supplier  level

so  we  can  create  a  synergy  with  suppliers

and  improve  quality  using  this  true   data -driven  approach  with  JMP .

How  would  we  deliver   A  minus  training  classes ?

Well ,  this  slide  gives  you   a  high -level  overview  of  how  this  happens .

This  reflects  the  training  style .

We  deliver  the  subject  in  five  steps .

The  example  isn 't  critical  in  this  context but  we  use  Choice  Design .

In  this  case ,  we  use  Choice  Design   to  conduct  an  Attribute  GRR

which  was  also  featured  later .

We  go  through  the  launch  window

and  demonstrate  how  to  populate  it in  Choice  Design  in  the  DOE .

We  conduct  the  analysis and  look  at  the  top -level  statistics  here ,

the  marginal  probability  and  utility .

We  interpret  the  analysis

by  looking  at  these  statistics and  the  probability  profiler .

Then  based  on  that ,  we  take   appropriate  improvement  actions .

This  is  also  more  about  interpretation

then  we  take  appropriate improvement  actions .

When  the  trainees  go  through  this  process ,

it  actually  really  helps  them  identify   gaps  in  their  learning  and  fill  them  in .

This  is  where  the  applied  style   is  really  important ,

even  in  the  non -project   required  curriculum .

This  is  an  expansion   of  this  A -minus  program

and  emphasizes  a  really  thoughtful and  strategic  vision where the yellow…

These  are  actually  modules   as  summarized  in  previous  slides

in  a  similar  manner .

The  yellow  are  the  modules  that  really we 're  in  the  process  of  developing .

The  green  ones  have  already  been  developed by  the  program  champion ,

mostly ,  I  think ,  or  exclusively   by  Dr .  Chen .

If  you  jump  to  the  E ,  it 's  really   about  reliability  and  marketing .

I  mentioned  B  is  the  DOE ,   and  the  custom  DOE  really  is  the  emphasis .

C  is  the  abbreviated  data  mining ,   and  the  A1 ,  A2  are  really  the  foundations .

The  emphasis  here  is  that   after  each  MBB  gets  certified ,

it 's  really  not  the  end , it 's  really  the  beginning  for  them .

There 's  an  emphasis  and  careful  thought  in  customizing  each  of  their  functions ,

developing  them .

Some ,  for  example ,  may  go  into  DOE . Some  might  take  data  mining .

Some  will  become  a  leader in  the  A1 .5  curriculum  and  so  on .

The  goal  is  for  us  to  make  sure   that  all  these  leaders

ultimately  become  not  only  certified ,   but  capable  of  training

and  developing  other  local  leaders .

In  the  paper  certification  anyway   by  the  Global  VP  of  Quality  and  the  CEO

is  a  testament  that   we  really  follow  these  people

in  their  development  path   after  certification .

If  it 's  not  obvious  already , their  strength  is  JMP  here

and using  JMP  to  solve  their  problems .

This  is  a  fun  slide .

This  is  me  and  my  daughter , just  over  a  year  old .

But  what  we 're  showing  here  is  that

probably  a  record  number   of  internal  people  in  this  organization

took  the  STIPS  exam  at  the  same  time .

There  was  100 %  passing  rate .

These  are  a  median  score  of  915 , an  average  of  899 .

Top  two  scores  for  these  two  individuals , very  close  to  100 %.

There 's ,  of  course ,  me  and  my  daughter .

Dr .  Chen  is  here  and  here 's  his  son .

We  have  this  little  JMP  girl   and  JMP  boy  thing  going .

It 's  fun .

I 'll  credit  and  thank  Sarah  Springer

for  her  really  wonderful  collaboration at  the  end  of  the  presentation .

But  here  she  is  here .

A  key  component  of  this  program ,   just  like  today ,

is  participating  in  JMP  Discovery  Summit .

This  highlights   our  Discovery  Summit  achievements .

The  fruits  of  real  work

that  are  being  recognized by  Discovery  Committee  members .

I  think  this  is  from  a  June  12th   Gloucester  Forum  event .

The  two  key  event  ingredients  I  mentioned

are  the  forum  events   and  the  Discovery  Summit  participation .

This  is  the  June  12th Gloucester  JMP  event  here .

We  have  asked  JMP  to  support JMP  17  new  features .

We 'll  continue  to  seek  their  support

during  an  upcoming  September  11   Hillsborough  event .

We 're  engaging  with  basically all of  the  JMP  Discovery  Summit  events .

US ,  Japan ,  China ,  Europe .

Let  me  see .

I  forgot  to  mention, this  is  Agatha  Debris .  This  is  Sarah .

This  is  one  of  our  candidates , or  actually  leader  at  this  point .

This  is  Don  McCormick ,  of  course ,  of  JMP .

We  anticipate  quite  a  bit  of  engagement in  the  2024  Europe .

Actually ,  there  are  many  reports   that  are  available .

Half  of  the  80  that  have  been  developed   are  confidential ,

so  those  will  probably  be  off  the  table .

But  we 're  definitely  expecting   some  good  likely  acceptance

based  on  the  work  that 's  been  done .

Here ,  we  see  the  Singapore  event ,

the  2022  September  9  Singapore Elite Eight  Tournament  event .

This  just  highlights  our  competition .

It  was  a  very  competitive and  successful  event .

Talks  were  very  diverse .

I  actually  presented  a  talk  here .

You 'll  see  me  in  here   on  box  plot  statistics ,

which  was  high -quality  enough

to  be  accepted  at   US  Discovery  Summit  last  year .

I 'll  probably  link  that on  the  Discovery  page  for  this  project .

But  a  key  aspect  of  having   these  forum  events  is  soliciting  feedback

on  the  presentation  quality .

That 's  where  JMP 's  participation comes  in  the  organization .

We  seek  engagement  from  JMP  stakeholders who  can  review  the  material

because  it  garners  enthusiasm and  engagement  for  those  presentations .

It  makes  them  more  concise ,   more  effectively  delivered ,

and  it  moves  to  a  feedback -forward  model

where  continuous  improvement is  obtained  through  continuous  feedback .

There 's  this  mindset  of  technology  facing , moving  to  service  facing .

This  collaboration  with  JMP and  this  partnership  with  JMP

helping  us  to  review  these  presentations   as  part  of  this  effort ,

not  only  improves  the  presentation  quality for  the  purpose  of  submitting  at  Discovery

but  it  improves  the  quality  of  the  work

that 's  being  done   at  the  organizational  level .

I 'm  going  to  briefly  cover   some  case  studies

just  to  give  you  a  flavor   of  what  we 're  doing  on  the  ground .

This  case  study  was  really  focused

on  comparing  Excel  versus  Minitab versus  JMP  Gauge  R&R  analysis .

It 's  about  how  do  we  manage   a  destructive  Gauge  R&R ?

First  of  all ,  how  do  you  know   a  test  is  destructive ?

On  the  left  here ,  we  basically  did ...

In  the  measure  phase , we  did  a  rigorous  comparison

and  showed  that  JMP  is  more  reliable for  the  decision -making  process .

The  key  is  because  it  considers  the  ANOVA ,  the  analysis  of  variance

with  an  interaction ,

and  that 's  really  the  most  comprehensive and  best  tool  out  there .

On  the  right -hand  side ,  we 're  talking   about  that  destructive  test  methodology

and  how  do  we  determine  that .

There 's  really  three  approaches .

We  can  assume  that  the  study   is  fully  crossed  with  no  degradation .

We  can  assume ...

When  I  say  degradation ,

I  mean  the  sample  doesn 't  change as  it 's  being  tested  repeatedly .

We  can  assume  the  test  is  nested , so  there 's  some  degradation  behavior .

We  have  a  third  choice .

We  can  use  the  crossed  methodology .

But  if  we  can  systematically  go  through a  decision -making  process  with  a  flowchart

to  show  that  the  destructive  quality on  the  sample  is  minimal

within  some  prescribed  limits ,

then  we  can  use  this  third  choice ,

and  this  flowchart  helps  manage   that  decision -making  process

to  decide  how  we  want  to  approach the  destructive  method .

I  forgot  to  highlight .

This  presenter  became  BB -certified and  scored  925  in  the  STIPS  exam .

This  project  owner .

The  second  case  study  achieved  third  place

in  the  annual  rankings   within  the  organization .

The  emphasis  here  is  on  conducting ,

which  I  alluded  to  before  conducting an  Attribute  Gauge  R&R

using  a  Choice  Design ,

which  is  really ,  in  my  mind ,  a  very   novel  application  for  Choice  Design .

It 's  very  exciting .

The  objective  overall  on  the  response

was  to  reduce  Failure  Analysis cycle  time  reduction .

This  presenter  presented in  2020  at  the  US  Discovery  Summit

and  scored  an  impressive   925  on  their  STIPS  exam .

Just  to  give  you  a  sense  here ,

the  question  we 're  trying  to  answer  is

how  do  we  know  that  our  team

can  make  a  consensus  decision   in  our  meetings  in  general ?

Considering  this  Attribute  GRR   in  our  model  for  that

is  what 's  been  done  here .

Briefly ,  if  all  the  members  achieve a  response  probability  of  100 %,

then  that  would  imply  that  the  team can  make  a  consensus  decision ,

as  you  can  see  these  numbers  here , these  response  probability  numbers .

But  what  this  is  showing  is  that   a  few  of  the  respondents

that  scored  higher  in  the  green and   at  the  end ,  actually ,  too ,

scored  lower .

These  respondents  who  scored  higher really  dominated  the  meeting .

They  were  the  most  talkative , the  most  experienced .

These  lower -scoring  respondents   are  the  people  that

either  they  weren 't  paying  attention or  they  weren 't  talking  intentionally ,

they  weren 't  engaging  in  the  meeting .

This  is  a  data -driven  way  to  demonstrate

this  team  is  not  ready  to  make a  consensus  decision  right  now .

This  is  another  case  study .

A  lovely  case  study  where  we  use text  mining  to  search  keywords .

Actually ,  you 'll  see ,

part  number  is  part  of  the  word  cloud that  we  identified  in  Text  Explorer .

The  novel  thing  here  is  we  saved   the  indicators  for  these  words .

By  doing  this  indicator  saving , we  put  part  number  into  the  model .

When  we  put  part  number  into  the  model ,

that  model  went  from  a  poor  model   to  actually  a  very  good  model .

Part  number  became  the  strongest ,

the  most  effective  factor   in  driving  the  response  here ,

failure  analysis  cycle  time  response .

It 's  a  very  powerful   and  elegant  application

of  using  Text  Explorer  bread -and -butter to  go  into  modeling

without  JMP  pro  actually .

What 's  great  about  it   is  it 's  very  easy  to  understand

how  to  work  through  that  workflow in  JMP .

One  thing  that 's  super  powerful  about  this

is  when  we  have  a  model   and  it  predicts  well ,

we  don 't  have  to  do  a  bunch of  other  root  cause  analysis

that  we  did  up  to  this  point

for  other ,  say ,  similar  part  numbers in  the  product  family

where  the  part  number  performance   would  vary

even  within  that  part  number   product  family .

This  is  another  quite exhaustive  case  study .

The  key  here  is  this  presenter  did  scored  very ,  very  well

among  the  top  in  the  STIPS .

They  passed  the  DOE  certification  exam

and  they  also  presented   at  Discovery  Summit  in  2022

and  were  BB  certified .

But  they  were  basically  measuring a  very  difficult  shape .

They  had  to  modify  their  recipe in  order  to  perform  that  measurement .

This  analysis ,  it 's  quite  small , but  in  effect ,  what  it  ended  up  showing

is  that  they  were  able  to  achieve a  significant  performance

in  their  precision- to- tolerance  ratio ,

so  there 's  a  P- to- TV  ratio ,   and  P- to- T  ratio .

This  shape  is  very  complex .

This  is  a  hallmark  in  high  tech .

Another  example  here  shows

that  there 's  non -uniformity in  dimension  on  the  basis  of  a  location .

We  have  to  perhaps  look at  the  distribution

of  the  average  difference from  location  to  location ,

from  center  to  edge , rather  than  just  trying  to  take

an  overall  aggregate  measurement to  capture  the  entire  surface .

That 's  what  some   of  this  analysis  highlights .

Here 's  another  really  lovely  case  study .

This  one  used  group  orthogonal super-saturated  design

to  block  the  first  failure  mode   from  the  second  failure  mode

in  this  problem .

It  was  a  two -step  process  optimization using  this  GO -SSD .

The  project  owner  had  to  figure  out

how  to  manage   seven  variables  in  this  context .

It  took  her  over  two  weeks  to  figure  that  out .

Basically  what  we 're  showing

is  the  design  process  through  different [inaudible 00:42:20] she might have considered.

Then  on  the  right,  we 're  showing some  Monte  Carlo  simulation  in  JMP ,

which  JMP  is  very  user- friendly  at  doing .

Then  essentially  validating the  optimal  process  settings

where  if  we  looked  at  the  simulation

were  the  means   within  the  confidence  interval

and  the  prediction   interval  range  of  the  model ,

and  that  was  the  tool  for  validation ,

graphical  visual  interactive  tool   for  validation .

Then  the  presenter  also  went  into  some  SVC to  identify  any  process  issues .

The  key  message  here  is  that  if  the  design  is  good ,

doesn 't  mean  the  process  is  stable .

You  can  see  this  process  is  drifting .

That 's  why  the  SVC  component

is  so  critical   in  the  trainees'  learning  path .

Here 's  another  really  lovely case  study  here .

The  emphasis  is  using  SIPOC

with  process  improvement  flowcharts   to  define  the  scope  of  the  project .

The  interesting  thing  about  this  project

is  the  project  scope  really  pertained   to  the  process  itself ,

so  we 're  in  effect  modeling  a  process .

The  approach  was  creating  a  model and  then  refining  it

and  working  on  cycle  time  reduction

and  improving  the  model 's   predictive  capabilities  for  cycle  time .

This  emphasizes  the  importance of  developing  a  robust  model ,

so  that  process  automation  predictions can  be  robust .

On  the  right  here , we 're  looking  at  leverage  plots .

This  is  quite  interesting

because  we  see  data  points  that  are  off the  residual  by  predicted  plot  here .

I  drew  some  lines  in  here .

They  actually  reveal  a  collinearity  issue .

This  pattern  reveals  a  collinearity  issue which  is  consistent  with  the  high  BIS

that  we  see   in  the  parameter  estimates  table .

This  reveals  a  combination    of  potential  things :

hardware  constraint ,  phase  constraint ,

a  data  acquisition  problem through  the  absence  of  a  cyclic  pattern,

and  that  we  can  see  in  some  time  series augmented  on  a  studentized  residual  plot .

There 's  the  opportunity  here  to  go into  a  more  detailed  time  series  analysis

to  understand  why  is  there this  time  shift  problem ?

Why  is  there  this   collinearity  problem  in  time

or  in  space ,  for  example , for  angular  depth  or  wafer  location ?

Autocorrelation  is  fundamental and  this  project  highlights  that ,

whether  it  be  multicollinearity in  space  or  autocorrelation  in  time .

This  slide  just  highlights  yet  another  wonderful  forum  event ,

2023  March  Madness .

It  was  extremely  close and  very  competitive

and  the  top  eight  people   were  invited  to  the  Singapore  forum  event .

Here  is  one  more  case  study .

In  this  case  study, we 're  using  item  analysis

to  focus  on  difficulty  and  discrimination ,

to  basically   strategically  assign  exam  questions ,

JMP  STIPS  questions  to  either a  Green  Belt  or  a   Black Belt  exam .

By  carefully  looking  at  discrimination and  difficulty  in  the  context

of  this  item  analysis  framework ,

we  can  effectively  categorize  the  questions .

You  can  see  on  the  right  how  we  did  that .

We  selected  four  questions that  showcased

our  rigorous  selection  criteria .

Actually,  I 'll  show  that in  the  next  slide .

But  here  what  you  can  see

is  that  discrimination  can  be  seen by  the  steepness  of  the  curve .

If  you  see  this  step ,

you  can  say ,  okay ,  this  is a  very  discriminatory  question .

Then  difficulty  refers to  the  translation  of  the  curve

and  these  colors  indicate  different  categories

where  we  used…

Actually,  this  is  very  important , we  use  this  flowchart .

We  can  dichotomize  difficulty   as  easy  and  hard

and  then  discrimination in  terms  of  the  location .

I  mentioned  that .

Then  we  can  dichotomize  discrimination

as  yes /no  in  terms  of  the  location  and  steepness .

The  colors  show  the  different  categories

and  this  goes into  actually  specific  examples

of  which  questions fall  into  each  category .

We  can  even  go  a  step  further

by  looking  at  the  patterns   within  each  of  the  assigned  categories .

Actually ,  you  can  think  of  this  as  group  one .

The  group  one  questions  were  easy

with  no  discrimination   where  basically  everybody  got  it  right .

The  group  two  questions , easy  with  discrimination ,

basically,  most  people  got  it  right .

The  group  three ,  hard  with  discrimination , basically,  everybody  got  them  wrong .

Then  the  four  hard  with  discrimination ,

most  people  got  it  wrong ,  basically .

You  can  really  think  about  the  power of  this  methodology

for  segmenting  questions   and  classifying  them

and  giving  a  meaningful   and  challenging  exam  to  students

to  develop  them  in  the  right  way .

Then  these  cell  plots  added

an  additional  layer  of  analytic  immediacy in  JMP  where  we  can  see  the  proportion

of  respondents  that  answer the  question  correct  versus  incorrect .

It 's  a  very  nice  companion to  these  item  analysis  curves .

For  reference ,  blue  is  incorrect , red  is  correct .

It 's  a  little  flipped from  you  might  think .

Just  a  couple  more   quick  case  studies and  I 'll  wrap  up .

This  case  study ,

we  used  ANOVA  and  regression to  improve  supplier  quality  control .

This  is  one  of  our  most   prolific  student's  mentors .

Now  he  got  a  good  DOE  cert  exam  score ,

was   Black Belt  certified  over  900  on  the  STIPS  exam .

Basically ,   I  think  the  novelty  in  this  project

is  really  using  a  regression  algorithm— we 'll  just  jump  over  to  the  right—

to  handle  outlier  problems

in  the  sense  of  the  regression  itself  or  outlier  problems  in  general .

There 's  a  lot  of  thinking  in  here

with  respect   to  different  types  of  regression

and  how  those  different regression  methodologies  help  us

capture  outliers  on  the  low and  the  high  end .

I  think  one  thing   that 's  really  important

to  think  about  as  a  practitioner  is ,

is  the  low  end  more  important or  is  the  high  end  and  why ?

From  an  engineering  sense ,

in  this  case ,   the  low  end  was  most  important .

The  fitting  approach   had  to  be  tailored  to  the  low  end .

We 're  talking  about  understanding types  of  outliers

and  not  only  types  of  outliers ,

but  where  they  come  from ,  why  they  originate .

Just  a  couple  more  here .

This  one  is  quite  nice  because  it 's  a  VSM -focused  project .

It 's  very  unique  in  the  implementation of  the  VSM  approach

because  mostly  people  who  do  VSM ,

it 's  a  documentation  exercise .

It 's  quite  qualitative .

But  in  this  case ,  this  candidate

who 's  also  BB  certified   and  taking  the  BB  certification ,

they  really  went  into  all  sorts of  detailed  calculations  and  mapping .

As  a  result  of  that ,  they  actually

determined  that  they  didn 't  even  need  to  do  a  Gage  R&R

because  the  VSM  was  done .

The  entire  quality  metric in  this  case  was  VSM- based .

This  is  really  showcased  here

because  this  was  the  most  successful Lean  Six  Sigma  project  to  date .

Later  on  this  project  is  actually , I  think ,  still  being  extended  to  use  SBC

to  validate  process  stability   after  building  the  process  up  like  this .

One  final  case  study ,

I  think  I  just  want  to  highlight  here  that  more  in  the  flavor  of  SPC

using  Control  Chart  Builder ,

JMP 's  super  powerful and  flexible  control  charting  platform

to  verify  process   both  stability  and  capability.

You  can  see  from  here

that  there 's  a  certain  risk  of  the  process being  either  not  capable  or  stable  enough .

You  can  see  this  process   floating  on  the  upper  spec  limit .

The  idea  is  actually ,  I  believe , to  collect  more  data

to  characterize  the  process  better ,

to  define  the  spec  ranges  better ,   to  match  the  process  behavior .

We 're  practically  looking at  that  on  this  chart ,

in  addition  to  looking   at  the  control  limits

that  are  driven  by  the  SVC  methodology .

Here ,  this  just  showcases ,  this  was the  stakeholder ,  the  trainee ,  their  slide .

These  folks  are  really  promoting  JMP   at  the  local  level

and  they 're  really  thinking  creatively

about  how  to  promote  JMP  more  themselves , which  is  fantastic .

Here ,  this  is a  coming  mid -autumn…

Dr .  Chen  is  calling  it  a  mid -autumn  festival .

This  is  upcoming  here .

You  can  just  see  there 's  just  a  lot of  great  thought  going  into  what 's  coming ,

who 's  involved ,

and  it 's  really  a  group  effort  with  these  top  level  MBBs

working  with  the  program  champion to  drive  this  initiative .

This  slide  shows  an  emphasis  on  really  to  become  a  top  five  performer

in  this  healthy  competition  framework , it 's  really  not  easy .

All  these  people   are  really  close  right  now ,

but  the  requirements  are  very  multidisciplinary .

There 's  a  participation in  the  forum  events ,

there 's  participation  in  instruction and  mentorship ,

in  attending  Discovery  Summit  conferences, and  preparing  curriculum,

and  getting  the  certification .

Even  promoting  the  internal  initiatives