Hi
,
this
is
Stone
Cheng
,
I
'm
a
technical
director
in
Henkel
Corporation
.
I
've
been
using
JMP
for
more
than
five
years
in
product
development
.
Today
I
'm
happy
to
share
with
our
best
practice
in
utilizing
JMP
data
analytics
in
product
development
of
adhesive
and
sealants
.
My
presentation
has
two
parts
.
The
part
one
is
application
gallery
where
JMP
used
in
various
stage
in
product
development
will
be
discussed,
and
in
part
two
I
will
focus
on
using
JMP
as
a
formulation
worksheet
with
the
demonstration
.
In
my
organization
,
folks
either
have
not
heard
of
JMP
or
have
an
impression
that
JMP
is
an advanced
DOE
software
.
For
the
latter,
it
is
true
,
but
it
's
not
the
whole
truth
.
Since
there
are
other
DOE
software
options
,
it
is
hard
to
persuade
folks
to
switch
away
from
tool
they
are
familiar
with
.
JMP
is
an
excellent
all-
around
data
analytics
tool
.
To
promote
JMP
adoption
,
we
need
example
to
demonstrate
its
power
in
the
data
analytics
.
In
my
presentation
,
the
example
are
taken
from
my
experience
in
adhesive
and
sealants
.
About
Henkel
.
Henkel
is
a
22-
billion
enterprise
with
two
business
unit
:
adhesive
technology
and
consumer
brand
of
laundry
,
home,
and
beauty
care
.
I
'm
in
the
adhesive
technology
unit
.
We
are
global
adhesive
leader
serving
800
industries
with
more
than
20
,000
products
.
Let
's
start
with
case
number
1
.
One
customer
has
a
need
,
they
may
call
a
service
center
asking
for
product
recommendation
.
For
example
,
a
customer
may
need
a
room
temperature
adhesive
hook
with
the
shear
strength
between
1
,500
-3
,000
and
a
work
life
between
15
-30
minutes
and
in
the
package
of
10
.
What
we
need
is
a
searchable
product
selected
guide
,
what
is
showing
in
our
case
1
.
Once
the
product
information
are
entered
into
the
JMP
table
,
one
can
use
the
tabulate
and
local
data
filter
to
downselect
the
product
based
on
the
customer
requirement
as
shown
in
here
.
This
is
a
very,
very
powerful
tool
in
the
preformation
stage
.
My
team
has
been
applying
this
tool
to
search
for
formulation
,
pattern
,
literature
and
the
raw
material
specifications
.
In
case
number
2
,
multivariate
analysis
is
applied
to
a
silver
filler
,
which
are
used
in
making
conductive
adhesive
.
From
multivariable
analysis
,
if
we
focus
on
the
surface
area
,
it
has
a
moderate
negative
correlation
with
the
tap
density
and
then
it
has
a
stronger
positive
correlation
to
lubricant
amount
as
measure
at
the
weight
loss
at
530
AC
.
There
is
a
graphical
way
here
and
it
's
a
table
format
here
.
With
this
analogy
,
chemistry
can
select
the
right
silver
package
for
the
application
.
Case
3
is
the
literature
.
Literature
is
a
great
place
for
learning
the
chemistry
and
formulation
and
this
particular
cited
literature
illustrates
how
epoxy
tensile
strength
are
affected
by
the
mixture
of
two
amine
hardener
.
The
results
are
described
qualitatively
by
a
table
and
a
graph
of
the
stress
train
curve
here
.
For
formulator
quantitative
description
is
good
,
but
learning
via
quantitative
modeling
is
even
better
for
the
prediction
purpose
.
In
case
number
3
,
we
take
the
literature
data
and
then
create
a
two
-factor
column
right
here
.
These
are
the
epoxy
/amine
stoichiometry
,
or
we
call
it
the
index
.
The
other
factor
is
the
fraction
of
one
of
the
amine
PAE
to
the
total
amine
fraction
.
With
the
Fit
Model
platform,
and
we
use
the
ISM
model
,
it
showed
that
the
quadratic
effect
of
the
index,
together
with
two
main
effects,
are
all
significant
.
The
prediction
provider
and
the
contour
profiler
are
used
to
quantify
the
learning
and
give
the
prediction
.
In
case
number
4
,
my
group
was
assigned
to
support
a
technology
platform
that
include
about
30
products
.
Since
we
are
not
involved
in
the
original
formulation
development
,
how
to
study
the
formulation
family
in
this
case
is
not
trivial
.
Looking
at
a
big
Excel
table
with
all
the
formulation
certainly
is
not
effective
either
.
Case
number
4
is
the
example
of
addressing
this
challenge
.
I
select
the
three
top
most
used
ingredient
in
these
29
formulation
:
monomer
1
,
2,
and
then
oligomer
.
Then
by
using
the
hierarchical
clustering
analysis
right
here
,
right
here
our
formula
was
identified
to
have
very
,
very
small
distance
assumed
right
here
,
implying
that
they
are
in
cross
related
.
Actually
they
are
only
different
in
the
photo
initiator
for
different
wavelength
in
this
case
.
We
can
add
more
ingredients
one
by
one
in
this
hierarchical
clustering
and
then
learn
the
formulation
family
by
using
this
method
.
Most
of
the
chemist
analyze
the
formulation
performance
in
Excel
.
Case
number
5
is
a
JMP
tabulate
that
has
the
same
data
format
as
Excel
.
Basically
,
various
information
of
a
formula
are
displaced
in
the
same
column
.
Like
what
you
see
here
has
a
heading
,
has
a
recipe
,
has
a
processing
material
characteristic,
and
all
the
results
.
To
create
such
data
structure
,
we
need
to
enter
data
in
JMP
table
in
a
special
format
and
that
will
be
discussed
in
detail
in
part
two
of
my
presentation
.
Case
6
is
a
silicone
study
involved
23
formulations
and
more
than
10
measurement
property
.
It
is
quite
overwhelming
trying
to
analyze
the
raw
data
in
such
big
system
.
We
first
construct
a
series
of
graph
with
a
property
retention
in
Y
and
then
the
initial
property
in
X
.
For
example
,
if
you
look
at
the
first
graph
here
,
the
adhesion
retention
is
in
Y
and
initial
adhesions
in
X
.
We
also
give
the
reference
line
,
acceptance
line
for
each
axis
.
When
a
formulation
is
selected
,
for
example
,
I
choose
this
point
,
they
are
in
the
quadrant
with
acceptable
initial
adhesion
and
retention
,
then
all
its
associate
property
such
as
the
tensile
strength
,
elongation
,
hardness
,
all
show
up
,
also
formulation
all
show
up
at
the
same
time
,
these
are
all
thanks
to
automatic
highlight
.
This
is
all
thanks
to
JMP
dynamic
link
capability
.
Visualization
analysis
in
such
a
way
is
very
effective
for
chemists
to
know
the
overall
behavior
of
this
system
.
In
polymer
science
,
we
measure
the
modulus
of
polymer
as
function
of
the
temperature
with
an
instrument
called
dynamic
mechanical
analyzer
,
DMA
.
DMA
data
has
a
temperature
modulus
and
then
attend
delta
are
typically
transferred
to
Excel
in
a
wide
format
for
plotting
.
To
overlay
several
DMA
curve
for
comparison
it
is
durable
in
Excel
,
but
it
's
not
a
trivial
effort
.
In
case
number
7
,
we
stack
74
DMA
results
together
and
by
using
the
graph
builder
,
we
can
compare
DMA
results
very
,
very
quickly
just
by
clicking
.
I
cannot
imagine
doing
the
same
in
Excel
that
has
a
222
column
.
It
's
basically
74
sample
times
3
signal
per
sample
.
It
's
going
to
be
very
difficult
to
handle
in
Excel
environment
.
A
graph
builder
is
excellent
in
turning
a
very
complicated
graph
seen
in
Excel
into
a
visually
digestible
analysis
.
In
case
number
8
,
the
needle
pull
strength
is
illustrated
in
graph
builder
using
four
variable
.
We
have
eight
adhesive
on
the
top
,
we
have
three
different
radiation
system
on
the
Y
here
,
and
we
also
have
a
four
radiation
time
and
then
two
needle
hub
combination
.
See
how
easy
it
is
to
understand
this
JMP
graph
as
compared
to
the
Excel
graph
right
here
.
Good
.
In
case
number
9
,
we
are
conducting
accelerated
aging
study
for
four
epoxy
prototype
formulation
by
measuring
their
initial
adhesion
on
three
substrate
and
with
a
three
replica
.
The
aging
condition
are
two
different
temperature
and
then
eight
week
aging
time
with
two
weeks
testing
interval
.
This
aging
design
and
the
data
was
initially
recorded
in
Excel
and
we
converted
the
Excel
data
into
JMP
table
with
seven
column
,
seven
column
only
and
the
stack
format
and
then
we
make
a
graph
.
You
will
agree
that
the
visualization
in
JMP
graph
builder
in
this
case
is
much
easier
to
see
the
aging
performance
than
looking
at
the
busy
Excel
table
here
.
In
formulation
stage
,
we
frequently
need
to
optimize
composition
.
Case
number
10
is
example
where
two
catalysts
in
polyurethane
are
optimized
with
the
DOE
design
.
The
factor
are
:
catalyst
ratio
and
the
catalyst
total
amount
.
There
is
a
10
-run
face
-center
central
composite
design
,
the
predictor
provider
indicating
that
the
catalyst
total
amount
factor
has
a
long
linear
effect
on
the
work
life
.
The
white
area
in
the
contour
profiler
is
actually
the
suite
design
space
which
desire
work
life
.
In
this
case
,
20
-28
.
It
is
important
for
chemistry
to
select
this
green
highlighted
white
area
for
better
production
robustness
than
the
area
[inaudible 00:12:10]
in
blue
,
it
has
a
higher
tolerance
for
the
amount
change
there
just
in
case
operator
makes
some
minor
mistake
.
That
's
why
it
has
a
better
production
robustness
.
We
routinely
see
chemistry
perform
statistics
analysis
of
adhesion
data
like
what
you
see
here
,
but
we
hardly
see
anybody
presenting
the
results
about
the
failure
mode
analysis
.
In
JMP
the
failure
mode
analysis
can
be
performed
in
two
places
.
One
is
in
the
contingency
analysis
in
Y
by
X
platform
and
the
second
one
is
using
the
graph
builder
.
The
case
number
11
is
the
example
applying
to
the
silicone
sealant
where
the
failure
mode
change
before
and
after
high
temperature
aging
,
before
and
after
is
clearly
shown
.
Clearly
shown
,
Beside
a
good
adhesion
,
adhesive
rheology
will
need
to
be
formulated
so
that
it
can
be
effectively
applied
to
the
substrate
.
We
have
a
project
to
develop
a
seam
sealant
to
protect
the
metal
joint
by
spraying
process
.
Case
number
12
is
the
example
that
illustrating
the
complication
of
spraying
process
with
respect
to
the
sealant
viscosity
variation
.
We
have
three
spraying
variable
.
They
are
the
pressure
,
nozzle
type
,
and
then
the
head
travel
speed
and
then
we
have
one
material
factor
in
this
case
is
the
viscosity
.
Initially
,
we
plan
the
experiment
in
factorial
DOE
fashion
,
but
one
of
the
factor
,
the
sparing
pressure
are
very
very
hard
to
control
.
We
end
up
performing
regression
of
the
40
round
with
ISM
model
using
the
strain
width
and
strain
thickness
as
the
response
.
We
get
a
good
model
with
R
-squared
about
0
.91
for
width
and
0
.81
for
the
thickness
.
The
modeling
result
tell
us
that
the
spraying
condition
will
need
to
be
adjusted
dependent
on
the
sealant
viscosity
.
This
is
illustrated
in
this
prediction
profile
here
.
Each
processing
parameter
has
their
own
machine
limit
and
also
desirable
operation
limit
.
If
this
predicted
processing
variable
are
outside
those
limits
,
then
the
chemist
will
need
to
redesign
formulation
reality
and
making
sure
that
manufacturing
has
the
processing
capability
to
meet
the
viscosity
requirement
.
This
example
show
that
the
formulation
design
and
application
constraint
will
need
to
be
considered
side
by
side
and
JMP
is
actually
a
very
good
tool
in
facilitating
this
type
of
study
.
Case
number
13
is
example
that
JMP
is
used
to
handle
huge
instrumentation
data
sets
.
In
testing
thermal
interface
material
,
the
temperature
at
a
different
location
and
the
power
consumption
data
are
collected
and
then
uploaded
to
the
JMP
.
Once
the
data
are
in
JMP
table
,
visualization
of
the
data
and
data
analysis
of
data
set
as
much
as
500
,000
row
are
still
very
manageable
and
has
a
fast
response
.
That
means
the
geometry
actually
can
be
used
to
handle
instrumentation
data
.
We
have
a
project
to
apply
adhesive
to
software
by
the
sensor
printing
and
this
application
is
challenging
with
pinhole
defect
issue
.
Process
engineer
changed
six
processing
variable
randomly
and
then
collect
21
wrong
results
.
His
data
analysis
did
not
reveal
any
special
trend
,
so
the
JMP
was
then
used
for
the
troubleshooting
in
this
case
and
the
prediction
partition
analysis
has
identified
factor
F
as
the
key
factor
.
Later
on
we
apply
the
predictor
screening
analysis
and
then
identify
additional
factor
D
that
need
a
further
investigation
.
For
JMP
training
,
we
learned
that
the
predictor
screening
can
identify
predictor
,
they
may
be
weak
alone
,
but
strong
when
they
are
used
in
combination
with
other
predictor
.
In
the
scaling
up
and
the
manufacturing
stage
production
,
when
the
batch
run
into
the
issue
,
the
raw
material
lot
-to
-lot
analysis
is
one
of
the
troubleshooting
item
in
order
to
isolate
the
potential
raw
material
effect
.
This
exercise
is
typically
done
in
the
Excel
table
.
But
when
the
multiple
raw
material
and
multiple
lots
of
each
raw
material
are
involved
,
it
is
difficult
to
look
at
a
huge
Excel
table
to
analyze
the
raw
material
effect
.
In
case
number
15
,
a
polyester
formulation
with
three
raw
material
and
about
45
separate
lots
are
plotted
verses
the
date
of
manufacturing
with
the
color
scale
of
the
gel
time
.
This
heat
map
plot
provide
a
visual
analysis
for
the
production
engineer
to
determine
whether
a
particular
loss
of
raw
material
is
the
major
cause
of
the
out
of
spec
batch
.
We
turn
the
Excel
table
into
a
visual
way
for
better
analysis
.
Statistics
comparison
in
T
-test
or
ANOVA
analysis
are
performed
routinely
in
the
product
development
.
A
product
benchmark
exercise
typically
involves
multiple
product
running
under
various
testing
protocol
,
aiming
to
have
a
very
comprehensive
the
product
comparison
learning
here
.
Case
16
is
an
example
of
statistic
analysis
involved
large
combination
of
23
products
and
then
more
than
10
testing
protocol
.
In
JMP
,
a
large
volume
statistics
analysis
is
not
a
challenge
since
creating
of
the
sub
-table
is
not
required
in
this
case
,
as
compared
to
other
software
.
One
can
utilize
the
column
switchers
and
the
local
data
filter
to
create
all
the
combination
of
property
and
adhesive
for
statistics
analysis
.
Plus
the
results
of
each
analysis
can
be
copied
into
a
JMP
journal
to
streamline
the
reporting
.
For
case
number
17
,
the
needle
bonding
testing
of
light
cured
,
historically
,
have
a
high
data
variance
.
Case
17
use
JMP
to
summarize
18
reports
of
needle
-bound
testing
which
involve
multiple
lots
of
adhesive
,
and
those
are
tested
in
various
time
.
The
needle
pore
strand
,
its
the
COV
,
are
plotted
in
graph
builder
under
various
lighting
,
radiation
condition
,
as
well
as
the
substrate
combination
.
With
the
local
data
filter
here
,
one
can
easily
change
the
criteria
selection
to
have
a
comprehensive
comparison
of
this
adhesive
and
their
consistency
performance
.
When
this
result
was
presented
,
everyone
was
amazed
with
the
JMP
capability
.
It
is
so
versatile
and
so
powerful
.
This
is
the
last
case
for
the
application
gallery
.
In
this
case
,
number
18
,
we
use
the
parallel
plot
feature
in
the
graph
builder
to
demonstrate
visual
comparison
of
15
performance
items
and
10
adhesives
.
Each
performance
has
its
own
unit
and
scale
which
provide
a
visual
comparison
more
quantitatively
in
contrast
to
the
qualitatively
comparison
in
spider
chart
which
is
used
in
Excel
.
So
far
,
in
the
18
application
gallery
examples
,
the
data
are
coming
from
literature
,
instrumentation
,
processing
,
and
not
much
emphasis
on
formulated
.
Now
we
will
switch
gear
to
discuss
formulation
creation
,
use
worksheets
,
and
it
's
a
JMP
-based
worksheet
,
not
a
traditional
one
using
Excel
.
Before
we
show
you
the
JMP
worksheet
,
let
's
discuss
about
adhesive
type
.
Broadly
speaking
,
adhesive
can
be
divided
in
two
categories
:
one
component
adhesives
or
the
two
component
adhesives
,
or
1K
or
2K
.
A
1K
system
like
the
Super
Glue
everybody
knows
require
no
mixing
and
it
can
be
cured
by
moisture
,
by
light
,
by
heat
,
or
by
other
method
.
In
case
we
are
dealing
with
one
component
but
heat
cure
adhesive
such
as
epoxy
,
then
we
will
need
to
design
and
then
calculate
the
stoichiometry
or
the
index
to
balance
the
proportion
of
the
epoxy
to
the
amine
hardener
.
Then
for
the
two
component
system
,
2K
system
,
their
mixture
will
react
at
NDM
temperature
so
that
they
are
kept
apart
before
use
.
In
a
2K
system
,
their
stoichiometry
will
need
to
be
designed
and
calculated
based
upon
the
desired
mixing
ratio
,
either
by
weight
or
by
volume
.
There
are
some
formulation
calculation
here
we
need
to
perform
.
This
type
of
calculation
design
historically
been
done
in
Excel
.
This
is
the
Excel
.
Everybody
know
that
Excel
spreadsheet
allow
mixed
data
type
in
the
same
column
and
its
formulas
can
be
applied
to
individual
sales
level
that
make
it
very
flexible
as
a
formulation
calculation
worksheet
.
Formula
are
typically
organized
in
column
format
like
this
.
Each
column
has
a
full
group
of
formulation
information
such
as
their
heading
,
which
is
the
ID
,
their
recipe
ingredient
,
the
formulation
characteristic
or
processing
parameter
,
and
followed
by
the
result
.
What
about
the
result
?
Excel
-based
worksheet
is
very
useful
.
Everybody
using
that
because
it
's
easy
to
learn
,
but
it
does
come
with
some
shortcoming
such
as
first
of
all
the
row
matching
.
When
you
have
a
new
ingredient
or
new
testing
results
,
you
need
to
match
to
the
right
row
,
and
they
take
time
.
Then
one
may
need
to
hide
or
unhide
a
column
for
comparison
.
Then
third
thing
is
it
's
harder
to
analyze
the
data
when
the
results
are
put
in
different
tab
.
It
's
a
tab
-to
-tab
format
.
It
's
also
very
difficult
to
make
a
graph
in
such
kind
of
a
data
structure
.
JMP
offer
webinars
to
go
beyond
the
Excel
spreadsheet
in
various
features
as
listed
here
.
But
the
worksheet
calculation
is
not
emphasized
.
Perhaps
this
is
due
to
the
inherent
data
structure
that
each
column
cannot
have
a
mixed
data
type
and
the
column
formulas
is
applied
to
the
entire
column
which
is
not
as
versatile
or
flexible
as
compared
to
the
Excel
.
Despite
of
these
constraints
,
we
have
developed
JMP
worksheet
with
the
following
objectives
in
mind
.
It
should
have
a
broader
capability
for
formulation
design
,
calculation
,
recording
,
and
analysis
.
It
is
all
in
one
and
we
want
to
minimize
cross
-platform
copy
-pasting
.
It
should
be
easy
to
operate
,
easy
data
entry
and
use
the
JSL
for
a
lot
of
the
automation
.
Then
the
final
data
set
is
ready
for
machine
learning
exercise
.
Let
's
look
at
our
Gen1
,
and
that
is
for
one
component
system
.
This
includes
four
data
group
.
We
have
a
formulation
ID
,
we
have
a
recipe
,
we
also
have
a
material
processing
characteristic
,
and
then
we
have
a
testing
result
right
there
.
The
four
data
group
are
the
same
as
what
you
see
in
the
earlier
Excel
worksheet
,
but
layer
structure
was
organized
in
the
column
from
the
left
to
right
.
This
is
different
from
the
Excel
which
is
from
top
to
the
bottom
.
The
data
of
the
three
group
,
2
,
3
,
and
4
are
shared
and
recorded
in
the
same
column
,
which
has
a
numeric
data
type
.
All
the
recipe
,
all
the
testing
results
,
and
all
the
formulation
characteristics
all
in
the
numerical
data
type
,
and
they
are
documented
in
the
same
column
here
.
With
this
kind
of
a
format
â¦
The
data
was
also
stacked
together
.
I
have
formulation
1
here
,
formulation
2
here
.
With
a
stacking
format
,
one
can
freely
enter
the
new
ingredient
or
new
testing
item
without
needed
to
match
the
role
as
needed
in
Excel
.
JSL
was
also
created
to
enable
data
analysis
in
either
tabular
way
or
in
a
graph
format
.
This
is
in
a
tabular
way
.
Chemist
can
pick
several
formulation
ID
and
compare
their
recipe
characteristic
and
performance
in
a
very
,
very
condensed
format
here
.
This
is
very
different
from
Excel
without
needing
to
hide
/unhide
columns
to
bring
formulation
to
be
adjacent
to
each
other
.
Much
,
much
easier
under
the
JMP
format
here
.
Besides
tabulation
,
one
can
make
a
graph
of
the
property
versus
the
property
comments
or
the
sample
ID
,
but
not
the
ingredient
percentage
.
This
graph
can
be
combined
with
the
recipe
table
here
into
a
group
under
the
dashboard
operation
.
This
make
it
as
a
very
effective
visualization
analysis
.
As
for
testing
involves
multiple
replicates
.
We
typically
just
record
the
average
result
.
But
one
can
enter
the
individual
replicate
data
in
the
property
column
,
and
then
perform
the
T
test
,
the
all
-over
test
,
using
this
worksheet
here
.
In
case
people
doesn
't
want
to
enter
data
in
this
way
,
there
is
the
other
way
to
virtually
link
the
data
file
with
the
replication
result
with
the
worksheet
.
That
will
be
shown
later
in
the
presentation
.
So
far
,
what
you
see
is
our
Gen1
worksheet
which
involves
no
formulation
calculation
.
Chemists
in
my
group
has
been
using
this
tool
for
more
than
one
year
.
They
get
used
it
its
easy
data
entry
and
very
,
very
powerful
tabulation
analysis
.
Next
we
're
going
to
look
at
the
Gen
2
worksheet
that
can
overtake
the
Gen1
feature
.
It
has
an
additional
feature
for
the
formulation
calculation
for
the
1k
and
2k
system
.
This
worksheet
also
link
with
the
other
JMP
file
that
has
additional
raw
material
information
needed
for
calculation
.
We
have
the
other
worksheets
,
we
call
Gen
3
,
that
are
designed
to
deal
with
the
solvent
borne
system
.
It
also
allow
formulator
to
incorporate
master
batches
,
but
due
to
the
time
constraint
it
will
not
be
discussed
here
.
This
is
our
Gen2
worksheet
.
There
are
three
sections
.
We
have
a
heading
and
then
the
formulation
input
section
right
here
.
The
middle
one
,
we
have
a
calculation
output
.
The
third
section
is
the
processing
material
characteristic
and
also
the
testing
results
.
Section
1
and
section
3
are
like
the
one
in
Gen1
,
but
the
section
2
here
is
newly
added
.
The
column
row
name
is
used
to
link
the
reference
file
that
has
additional
data
information
needed
for
calculation
.
You
can
see
the
symbol
for
the
virtual
link
right
here
.
After
chemist
enter
the
formulation
ID
,
they
will
specify
for
columns
,
parts
,
row
,
name
,
and
initial
weight
.
If
they
are
doing
the
2K
system
,
they
need
to
also
specify
the
mixing
ratio
either
by
index
,
by
volume
,
or
by
weight
ratio
.
Then
the
worksheet
will
output
the
mixing
ratio
characteristic
here
again
by
index
,
by
volume
,
or
by
weight
.
They
also
provide
a
normalized
composition
,
either
by
part
.
By
part
means
A
and
B
sum
up
together
by
themselves
and
equal
to
100
,
or
A
and
B
mixed
together
.
We
call
it
normalized
by
total
here
.
After
seeing
this
one
and
the
chemist
can
perform
the
experiment
and
then
come
back
to
enter
the
results
right
here
.
The
other
thing
is
in
the
property
material
characteristic
,
we
have
the
other
column
called
Lookup
.
This
can
extract
the
information
from
the
calculation
and
also
the
raw
material
fraction
percentage
ratio
and
automatically
displays
right
here
.
Then
chemists
just
need
to
copy
parameter
in
the
value
enter
column
and
then
this
will
be
automatically
transferred
to
the
two
normalized
percentage
column
for
display
purpose
.
We
also
have
three
JSL
there
to
facilitate
in
analysis
.
The
first
one
is
showing
you
normalization
,
normalized
by
total
.
That
means
A
and
B
being
mixed
together
and
sum
up
to
100
.
Here
,
I
showed
you
the
formula
,
showed
you
the
characteristic
and
showed
you
the
result
.
You
have
a
second
JSL
that
's
normalized
by
part
.
In
this
case
,
you
can
see
your
part
A
formulation
and
part
B
formulation
,
and
then
A
and
B
all
have
been
normalized
to
100
by
themselves
.
With
the
other
JSL
,
we
can
change
the
formulation
worksheet
format
from
the
stacked
to
the
white
format
.
In
this
case
their
ID
performance
,
individual
ingredient
,
and
then
the
characteristic
will
all
have
their
own
individual
columns
.
With
this
format
,
one
can
make
the
graph
with
the
property
versus
the
ingredient
percentage
which
cannot
be
done
under
the
stack
format
.
One
can
also
looking
for
the
correlation
between
the
performance
or
the
performance
with
the
formulation
characteristic
.
At
this
moment
,
I
like
to
show
you
the
live
demonstration
.
This
is
the
formulation
worksheet
I
just
showed
you
in
the
PowerPoint
.
Basically
,
we
have
the
heading
.
Then
we
have
a
formulation
input
section
.
We
have
a
calculation
between
n1
and
n2
.
Anything
here
is
for
calculation
.
Then
we
have
the
last
section
here
,
that
is
a
performance
and
then
the
property
material
characteristic
.
I
mentioned
that
we
have
a
JSL
,
allow
people
to
look
at
this
result
easily
.
Let
's
look
at
this
one
,
JSL
by
total
.
We
can
easily
highlight
any
formulation
or
compare
2
and
8
,
and
then
compare
their
formulation
and
their
result
.
These
are
mixed
together
.
We
can
look
at
it
by
part
.
Part
A
here
and
then
part
B
here
.
They
all
sum
up
to
a
hundred
by
themselves
.
Easily
,
we
can
compare
â¦
Oh
no
,
I
need
to
remove
this
one
first
.
I
can
compare
formulation
easily
by
manipulating
the
local
data
filter
.
Again
with
the
JSL
,
we
click
the
Join
All
.
We
are
turning
the
stack
format
into
a
wider
format
.
Each
row
belong
to
one
formulation
with
the
heading
here
,
with
their
property
,
with
their
formulation
,
and
with
their
formulation
characteristics
showing
right
here
.
For
machine
learning
,
we
can
highlight
a
role
ingredient
and
then
just
manually
add
zero
so
that
each
ingredient
has
zero
or
whatever
,
and
then
now
we
can
do
this
one
.
We
can
create
a
summation
or
something
,
easy
to
operate
in
this
.
I
'm
going
to
show
you
next
how
this
one
work
in
the
sense
that
assuming
that
we
're
going
to
create
a
formulation
.
I
'm
going
to
copy
the
heading
.
Sorry
,
I
'm
going
to
delete
everything
here
because
I
create
this
one
already
before
.
I
'm
going
to
delete
the
demonstration
one
.
I
'm
going
to
create
it
from
scratch
by
copying
the
heading
here
.
I
change
the
name
to
Demonstration
here
.
I
will
copy
the
formulation
because
I
'm
going
to
modify
formulation
from
this
one
,
the
DOE
8
.
Then
the
DOE
8
is
based
on
one
-to
-one
mixing
ratio
by
volume
.
But
in
this
new
one
,
we
could
change
it
to
one
-to
-two
mixing
.
A
divided
by
B
is
one
divided
by
two
,
so
it
will
be
0
.5
.
Then
I
copy
the
heading
including
the
mixing
ratio
all
the
way
down
.
Now
all
the
calculation
has
been
done
here
.
With
this
weight
percentage
I
'm
entering
,
it
showed
that
the
material
has
an
index
model
ratio
A
to
B
to
be
0
.65
,
which
is
too
low
.
We
need
to
,
using
our
chemistry
knowledge
,
to
turn
this
around
.
In
this
case
,
for
example
,
I
make
this
one
2
.
I
can
easily
make
this
one
into
1
.05
.
That
is
the
range
I
'm
looking
for
.
Basically
,
assuming
it
is
the
design
that
we
want
,
formulation
we
want
,
the
next
thing
we
want
to
do
is
to
copy
some
of
the
testing
that
we
already
had
before
,
that
we
are
monitoring
before
,
but
without
the
results
,
of
course
.
We
have
a
new
result
here
,
so
I
'm
going
to
delete
that
one
.
But
we
also
want
to
add
additional
property
which
for
example
is
viscosity
measure
at
a
room
temperature
.
With
this
section
here
,
then
we
want
to
extend
our
heading
to
specify
those
are
belong
to
this
formulation
.
As
soon
as
I
specify
the
heading
,
the
Lookup
automatically
give
me
the
information
such
as
the
missing
characteristic
.
1
.5
or
0
.5
,
they
are
automatically
copied
to
here
through
the
Lookup
function
and
then
the
feeder
loading
in
the
formulation
normalized
to
Total
while
also
being
extracted
,
sum
up
together
and
put
it
right
here
.
Now
I
can
copy
this
information
,
put
them
in
value
enter
,
and
specify
my
mixer
is
number
2
,
and
then
start
to
enter
my
results
,
time
that
's
going
to
be
80
,
and
adhesion
450
assuming
,
viscosity
20
,000
.
I
'm
pretty
much
finished
everything
,
so
let
's
look
at
the
result
here
.
We
just
enter
Demonstration
.
This
one
was
based
on
the
DOE
number
five
.
DOE
number
five
is
one
to
one
mixing
and
this
Demo
is
only
one
to
two
mixing
,
and
we
added
the
viscosity
result
right
here
.
It
's
very
,
very
easy
.
One
click
you
see
the
result
and
in
the
format
it
's
very
easy
to
understand
for
comparison
.
This
is
the
end
of
my
demonstration
.
Let
me
go
back
to
the
presentation
here
.
We
consider
the
JMP
worksheet
that
I
'm
just
showing
you
is
an
integrated
platform
and
here
is
the
summary
.
The
worksheet
in
the
stack
format
,
here
,
is
used
for
formulation
design
,
calculation
and
for
recording
the
results
.
The
data
entry
of
raw
material
which
is
needed
for
the
worksheet
is
minimized
by
virtually
linked
with
the
other
file
that
has
additional
raw
material
information
.
JSL
was
widely
used
to
automate
the
worksheet
output
to
the
tabulate
,
to
graphic
,
to
the
statistic
analysis
,
and
also
to
create
a
table
with
wide
data
format
.
The
wide
data
format
,
they
already
have
a
data
structure
for
modeling
via
the
machine
learning
and
also
allow
the
graphical
analysis
using
the
ingredient
as
one
of
the
axis
.
Then
since
each
of
the
row
in
this
wide
format
is
a
unique
tool
formulation
ID
,
this
actually
can
be
used
as
a
reference
table
to
join
the
other
JMP
file
that
has
a
testing
result
that
has
a
replication
.
When
these
are
joined
together
,
then
we
can
plot
the
raw
data
and
do
statistic
analysis
,
either
as
function
of
the
ingredient
or
as
function
of
the
formulation
ID
.
This
JMP
Integrated
Worksheet
Platform
truly
illustrates
it
is
an
all
-in
-one
platform
,
very
,
very
capable
.
In
summary
,
JMP
is
not
just
an
advanced
DOE
software
.
JMP
's
data
analytics
has
been
effectively
utilized
in
my
group
for
product
development
at
various
stage
to
speed
up
the
innovation
process
.
JMP
-based
formulation
worksheet
is
an
integrated
platform
that
feature
broad
formulation
capability
,
all
in
one
,
easy
operation
,
and
machine
learning
ready
data
structure
,
and
more
and
more
waiting
to
be
further
explored
.
With
this
,
thanks
for
your
attention
and
I
also
like
to
acknowledge
the
people
I
work
with
and
learning
to
JMP
together
and
also
our
management
system
for
supporting
JMP
adoption
initiative
.
Thank
you
very
much
.
