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From YouTube: INDUS Industry 4.NOW - Intelligent Factory with connected assets and automated processes
Description
In today’s time, all resources are at a premium. with COVID hitting us hard, we have realized that our businesses can be disrupted by any unforeseen circumstances. The shortage of manpower, skilled or otherwise has always been looming large on us.
All of this means that the manufacturing environment is changing. The focus is no longer on capacity, but, on the ability to respond quickly to customer needs and market changes while still controlling costs and quality.
Manufacturing companies are taking a number of initiatives with the help of the latest technologies to optimize manufacturing processes to minimize negative environmental impacts while conserving energy and natural resources and establishing safe working conditions has become a key objective of most companies.
In addition, supply chain and manufacturing are at the center - not only for business success but also for a company’s differentiation strategy.
This session will focus on how do we drive digitalization by adopting Industry 4.0 technologies to significantly improve the manufacturing operations making our factories “Intelligent Factories”.
We will see how “Intelligent factories” use advanced technologies to run autonomously and deliver mass produced or individualised products at scale.
A
A
So
we
are
live
yes
again
good
morning.
Everyone
thanks
a
lot
for
taking
time
and
joining
us
in
in
the
last
of
the
five
part
series
that
we
have
run
around
industry
4..
Now
we
started
off
this
series
by
handing
a
panel
discussion
with
very
eminent
panelists
talking
about
what
is
industry
4.0?
Why
is
it
important?
What
are
some
of
the
things
that
we
need
to
consider
before
we
embark
on
this
journey,
and
then
we
looked
at
how
asset
manage
how
intelligent
asset
maintenance
can
actually
help
us?
A
A
So
now
we
come
to
the
last,
but
not
the
most
exciting
part
of
the
series,
which
is
smart
factories,
how
product
now
can
actually
enable
smart
factories
and
what
that
means
for
our
businesses.
So
for
that
we
have
sahu
the
senior
director
foreign
as
part
of
the
innovation
office
in
nsap
in
singapore.
He
will
take
us
through
the
first
part
of
the
session
so
satya
over
to
you.
B
Hey
thank
you
mukesh
for
giving
us
an
opportunity
and
for
both
sap
and
our
customers
in
india.
This
gives
us
an
opportunity
to
share
what
is
that
sap
is
doing
in
the
space
of
industry
4-0
to
digitize
the
manufacturing
plants
so
good
morning,
and
thank
you
all
for
joining
today's
webinar.
A
warm
welcome
from
my
side-
and
I
hope
you
all
are
doing
well
see
we.
So
I
have
got
a
few
ghost
speakers
with
me
myself.
B
Satya
saw-
and
I
have
with
me
vivian
gandhi,
so
he's
india,
innovation,
advisory,
lead,
and
I
have
with
me
marvin,
who
is
the
director
of
excessive
experience
center
in
singapore,
so
jamarbin
is
joined
with
jarl,
so
somehow
we
missed
to
put
his
picture.
That's
that's
our
bad
thing
but,
however,
like
you
know,
we
have
got
a
team
which,
which
will
make
the
session
not
only
too
much
of
knowledge,
but
will
make
it
more
fun
as
well
during
the
course.
B
Okay,
let's
get
started
so
we
all
know
that
the
fourth
industrial
revolution
is
fundamentally
going
to
change
how
the
products
are
designed
manufactured
and
delivered
delivered
in
the
sense
not
as
a
product
as
it
is,
but
but
you
know,
delivering
the
product
as
a
service
as
well
and,
most
importantly,
what
we
are
seeing
is
how
the
stakeholders
are
making
informed
decision
on
real
time
and
in
today's
world.
What
is
constant
is
the
supply
chain
disruption
and
we
have
seen
the
pandemic,
the
trade
wars
and
the
semiconductor
cheap
shortages.
B
How
it
has
you
know,
created
a
imbalance
in
the
supply
chain.
So,
under
such
circumstances,
the
critical
priority
for
manufacturing
companies
across
the
globe
is
to
be
more
flexible
or
more
agile,
and
you
know,
increase
the
productivity
with
the
technologies
coming
from
industry
photo
and
today's
webinar
as
much
said,
we'll
be
focusing
on
the
smart
manufacturing
how
the
smart
manufacturing
drives
the
agility
or
the
flexibility,
productivity
and
sustainability.
That
is
going
to
be
our
focus
on
today's
webinar,
and
we
strongly
believe
in
saying
is
believing
okay.
B
So,
during
the
presentation
we
will
showcase
a
typical
industry.
40
use
case
deployment
in
one
of
our
modern
plant
setups,
so
what
we
have
done
for
today's
webinar
is
this:
is
the
agenda?
Okay,
since
this
is
a
small
team,
we
will
be
in
a
workshop
mode
and
sharing
the
presentation
and
sharing
things
live
from
experience
center.
B
We
will
be
asking
few
questions
very,
very
simple
questions
to
get
your
input
and
we
will
be
using
the
mentee
meter
and,
as
you
know,
20
meter
don't
register
your
identity,
so
feel
free
to
give
your
feedback,
and
nobody
will
know
where
the
feedback
has
come
from,
so
why
we
are
taking
the
feedback.
Is
this
will
help
us
to
come
up
with
new
topics
to
share
with
you
in
future?
Okay,
so
that's
first
like
getting
to
know
each
other,
and
next
is
we
will
be
doing
a
white
coating
to
share
a
like.
B
The
trained
is
trend
of
adoption
of
ai
in
manufacturing
processes.
We
have
been
seeing.
This
is
going
to
a
great
extent
and
would
like
to
share
with
you
and
would
like
to
take
your
feedback
as
well,
and
the
last
is
that
you
know
how
you
can
begin
your
industry
portal
journey,
so
we
have
got
a
format
or
process.
We
will
be
sharing
that
with
you
and
where
you
can
get
more
information.
B
B
So
this
is
the
lengthy
meter.
So
there's
two
ways
you
can
participate
in
the
multimeter
one
is:
if
you
go
to
mendy.com
and
enter
the
code,
then
then
you
can
participate
in
the
menu
meter
or
one
simple
way
is
use
your
mobile
phone's
camera
and
scan
this
code,
and
this
will
take
you
to
directly
to
the
multimeter.
B
B
Okay,
most
of
the
interpretation
is
smart
factory,
but
with
the
evolution
of
artificial
intelligence,
we
are
seeing
the
number
of
initiatives
manufacturing
companies
are
taking
with
respect
to
predictive
maintenance.
Quality
digitization
of
production
flow
could
be.
Anything
would
like
to
hear
from
you.
What
is
how
you
see
the
industry
40
with
respect
to?
What
is
that
you
can
implement.
B
B
B
B
Okay.
So
what
are
the
typical
challenges
you
face?
Like
you
know,
to
begin
the
industry
photo
initiative
like
many
years
ago.
It
used
to
be
that
you
know
manufacturers
used
to
say
my
equipment
is
really
old.
Connectivity
is
the
issue,
and
some
some
says
that
you
know.
Where
can
I
see
the
value
coming
from
because
it's
it's
not
a
technical
project
or
technical
initiative?
It's
often
driven
by
the
business
that
you
know.
Once
you
see
the
value,
only
you
can
budget
it
where
to
start.
B
B
So
thank
you
for
sharing
your
feedback
excellent.
So
with
this,
let
me
let's,
let's
look
at
like
how
industry
photo
is
transforming
the
manufacturing
industries
and
so,
as
a
part
of
this
section,
what
we're
going
to
cover
is
what
are
the
typical
market
drivers
and
dynamics?
That's
one,
and
what
are
the
typical
challenges
the
manufacturing
companies
face
to
achieve?
B
B
I'm
sorry,
I'm
sorry,
okay,
thanks,
okay,
so
what
we
know
is
the
manufacturing
environment
is
changing
and
the
focus
is
no
longer
the
capacity
alone,
because
if
it's
a
continuous
manufacturing
capacity
makes
a
lot
of
sense.
If
it's
not
continuous
that
you
know
if
it's
a
batch
or
you
know
discrete
manufacturing
having
like
manufacturers,
don't
focus
only
on
capacity
in
today's
world,
okay,
but
on
the
ability
to
respond
quickly
to
customer
needs
and
market
chains,
while
still
controlling
the
cost
and
the
quality.
That's
the
focus.
B
What
currently,
what
we
are
seeing
from
our
customer
base?
In
addition
to
this,
what
we
are
saying
is
companies
are
taking
a
number
of
initiatives
to
optimize
the
manufacturing
processes,
to
minimize
the
negative
environmental
impact
and
establishing
a
safe
working
condition.
This
has
become
a
key
objective
for
many
manufacturing
companies
and
in
today's
environment.
B
Okay,
not
only
by
selling
the
product,
but
biscuit
manufacturing.
We
are
seeing
the
companies
are
offering
product
as
a
service
and
they
share
the
like.
You
know,
risk
and
reward
by.
You
know
billing,
based
on
the
outcome,
and
some
of
the
key
technologies
which
has
been
widely
adopted
by
industries
are
definitely
one
is
the
internet
of
things.
Age
computing
is
getting
in
more
and
more
adoption
today
and
going
forward
as
well
cloud
computing.
B
Companies
have
got
a
global
or
headquarter
and
the
plant
which
is
distributed
across
different
geographies
and
the
systems
runs.
The
plant
is
completely
different
from
the
system,
which
runs
your
planning
process
or
in
other
r
d
process,
and
we
see
the
information
is
scattered
across
different
systems.
B
So
the
first
key
challenge
the
companies
are
facing:
the
silos
of
information,
information
scattered
across
many
dis
jointed
systems.
A
simple
example
is
production
planning,
system
and
manufacturing
execution
systems.
Don't
talk
to
each
other,
which
leads
to
that.
You
know
like
whatever
I
am
planning
it's
not
manufactured
accordingly,
and
due
to
this,
the
company
landed
up
in
a
either
excess
inventory
or,
like
you
know,
other
stock
situation
in
the
market
and
second
issue.
What
we
see
is
the
manual
and
inconsistency,
inconsistent
processes
across
different
pros
different,
you
know,
processes
and
different
plants
as
well.
B
So
the
silos
of
information
make
strategy
operations
and
execution
completely
disjoint
jointed,
and
what
we
see
is
the
traditional
boundaries
between
the
rnd,
the
manufacturing,
logistics
and
operations
are
dissolving
within
the
enterprise
and
after
the
supply
chain,
that's
fun
and
with
industry
40,
the
companies
look
to
break
the
silos
in
their
processes
to
gain
the
visibility
and
drive
the
customers
centricities
to
achieve
much
higher
level
of
efficiency,
optimization
and
ultimately
deliver
on
new
business
models.
Okay,
that's
what
we
are
seeing
like
you
know.
B
B
B
B
So
the
smart
factory
is
the
center
of
industry
boro
and
it's
just
a
part
of
it
to
make
the
factory
smart.
This
has
to
understand.
Like
you
know,
what
is
the
need
of
the
market?
So
we
are
talking
about
the
planning
think
about
before
we
build
the
product.
Companies
typically
decide
where
to
build
in
a
distributed
located.
Plants
companies
has
to
decide
where
to
build
and
how
many
to
build
and
when
to
build.
B
Next
comes
the
design
okay,
so
so
the
product
information
comes
from
the
design
phase
by
combining
the
x
and
o
data.
The
x
data
is
the
experience
data.
What
you
get
from
your
customers,
the
odata
is
the
operational
data,
so
you
get
it
in
in
like
a
machines,
the
machines
comes
with
a
number
of
you
know,
embedded
sensors,
and
if
the
r
d
team
get
those
sensor
data,
they
will
know
that
you
know
what
point
or
what
are
the
scenarios
where
the
product
fails
or
products
stop
to
work?
Okay
and
things
like
this
helps
them.
B
You
know
this
is
good
input
for
them
to
redesign
the
product
in
the
next
phase
when
they
release
it.
Okay,
so,
to
summarize,
like
you
know,
the
rnd
team,
because
the
customer
experience
data
the
product
experience,
that
is,
o
data
requirement
and
spec
in
a
make
to
order
or,
like
you
know,
engineered
to
other
type
of
scenario
and
the
kick
of
a
project
project,
design,
the
product
and-
and
you
know,
create
the
needed
master
data.
That
is
the
bill
of
material
or
routing
or
the
receivable
data
in
the
in
the
process.
B
So
so
far
we
have
seen
the
smart
factory
industry
photo
is
not
only
the
smart
factor
and
the
smart
factory
has
to
be
connected
with
your
sales
and
planning
system
and
design
system
to
ensure
it
manufactures
as
per
the
need.
That's
one
thing,
and
you
know,
helps
the
companies
to
actually
manufacture
new
product
at
scale.
B
B
So
after
we
build
the
product,
we
need
to
ensure
on
time
delivery
of
the
product
to
the
customer
location
and
what
companies
do
is
they
connect
with
the
logistics
service
provider,
optimize
the
transport
plan
and
monitor
the
transportation
through
blockchain
till
its
receives
at
the
customer
place,
and
this
is
helping
the
companies
to
improve
the
logistics
efficiency
and
ensure
the
genuine
product
reached
the
customer
all
the
time
and
now
next
is
the
operate
once
the
ones
which,
once
the
customer
receives
the
product.
Okay,
now
companies
are
thinking.
B
Okay
and
finally,
what
we
are
saying
is
providing
a
predictive
maintenance
through
machine
learning
to
improve
the
operational
efficiency
of
the
equipment.
Okay,
so
this
is
the
complete
scope.
In
addition
to
this,
what
we
see
is
now
companies
are
looking
at
how
they
can
interact
with
their
partners
on
real
time.
So,
in
today's
economy
we
see
no
company
can
operate
in
silos.
They
need
to
collaborate
with
a
number
of
partners
in
the
ecosystem,
such
as
design
partners,
outsource
manufacturing
partners,
suppliers,
logistics
providers,
the
carriers,
transportations
transporters,
and
this
this
with
with
the
real-time
connectivity.
B
This
not
only
improves
the
collaboration,
but
also
helps
to
accelerate
the
business
process
as
well.
Okay,
so
now
why
companies
do?
But,
let's
look
at
you-
know
few
kpis
to
sell
the
companies
to
secure
revenue
in
the
new
product
launch
time
to
market.
This
improves
the
time
to
market
the
time
to
value
related
kpis,
and
this
helps
the
companies
in
terms
of
you
know,
cost
reduction
by
producing
the
right
product
at
the
right
time
and
optimizing
the
inventory
maintenance
point
of
view
by
applying
the
predictive
maintenance.
B
This
helps
to
optimize
the
cost
to
maintain
their
own
equipment
or
their
customers,
equipment
and
flash
to
the
energy.
So
if
you,
if
you
cannot
see
the
energy
higher
energy
energy
consumption
now,
then
you
lose
not
only
a
lot
of
money
by
paying
to
the
utility
companies,
but
also
this.
This
actually
increase
the
risk,
as
well
as
decrease
the
total
lifetime
of
the
equipment.
B
So
the
reality
here
is
the
business.
Benefits
cannot
be
achieved
by
any
single
solution.
The
only
way
to
do
is
to
have
a
an
end-to-end
perspective
or
approach
and
an
integrated
solution
which
helps
the
companies
to
optimize
the
entire
business
process
from
end
to
end-
and
this
is
the
new
scope
of
industry
40-
what
we
have
been
seeing
when
you
talk
to
the
customers
from
a
short,
mid
and
long
term
point
of
view-
and
there
are
many
companies
that
achieved
it.
B
Okay,
I'm
sharing
a
few
ansaldo
and
nichia
the
the
power
generation
leader
in
italy
and
they
manufacture
the
turbines
generators
and
also
they
take
a
turnkey
project
for
power
plant
construction
and
by
adopting
the
predictive
aspect
in
the
areas
of
maintenance.
This
is
helping
them
to
save
close
to
five
to
eight
percent
days
of
the
cost
in
the
equipment
maintenance.
That's
one
and
catl
the
world's
leading
producer
of
lithium-ion
batteries.
B
This
is
used
mainly
in
the
electric
vehicles
and
companies
from
china
and
to
meet
the
growing
demand.
The
company
wanted
to
expand
its
production
capacity
while
maintaining
the
stringent
safety
standard
and
high
product
quality
and
with
by
digitizing
the
factory
floor.
Okay,
they
have
increased
the
production
volume
by
25
days
and
they
actually,
during
the
course
of
digitization,
they
integrated
close
to
8
000
machines
on
a
single
platform
using
sap's
iot
solution,
which
is
providing
a
real
time,
visibility
to
the
manufacturing
performance
kpis
like
oee,
which
is
helping
them
to
further
find
out.
B
What
is
the
scope
of
improvement,
and
you
know-
and
this
also
helps
them
to
know
continuous
improvement
activities
which
the
production,
planner
production
supervisor
of
plant
plant
manager
techs,
and
this
also
helps
them
to
get
an
end-to-end
possibility
of
the
processes
across.
You
know
the
production
plants,
the
last
one
is
the
selenike
logistics.
It's
an
automotive
part
supplier
from
germany,
and
the
initiative
was
how
do
smart
container
speed
up
the
production?
Okay,
so
selenic
supplies
to
auto
oems,
the
auto
oems.
B
They
can
check
the
current
location
of
the
delivery
on
their
tablet
and
they
also
know
that
now
every
delivery
will
will
arrive
punctually
at
the
right
production
line.
So
the
strength
is
job
is
not
to
take
the
order
and
deliver
somewhere
but
to
deliver
at
the
right
production
line
and-
and
you
know
in
the
right
sequence,
how
it
is
being
ordered
by
their
oem,
and
this
is
helping
them
to
prevent
delays
in
the
production
production
plants.
B
There
are
three
use
cases
and
continuing
to
its
fire
industry
from
europe.
They
started
with
one
plan
and
standardized
the
standardized,
digitized
production
processes
and
they
created
the
template,
and
this
template
is
helping
them
to
roll
out
to
multiple
plants
and
the
left
side.
You
see
it,
it's
helping
them
to
increase
the
efficiency
it's
tightly
integrated
both
to
both
the
walls.
So
I
t
wall,
which
is
the
erp
and
the
ot
world,
which
is
the
machines
and
in
the
shop
floor
level,
one
two
or
three
systems
and
and
standardization.
B
The
value
potential,
what
we
see
we
did
a
survey
with
mpi
group
in
2020
and
I'm
highlighting
four,
but
there
are
many
areas
where
companies
are
seeing
value.
The
first
is
the
definitely
product
quality
and
the
second
is
the
productivity
improvement
profitability
across
that
you
know
optimizing.
The
inventory,
then
reducing
the
cost
in
the
maintenance.
B
Okay,
then
energy,
optimization
and
last
is
that
it
also
helps
customer
satisfaction
because
you
produce
the
right
part
right
quantity
in
the
right.
You
know
location,
okay,
that's
one
and
second,
is
you
take
the
feedback
directly
from
your
end
customer
and
product
in
in
product
design
process
as
well,
and
you
know
by
using
the
predictive
quality
this
ensures.
The
quality
is
always
maintained,
not
once
price
point.
You
know
all
the
time
which
ultimately
translate
to
customer
satisfaction
with
this
initiatives,
so
we
see
a
lot
of
potential.
B
B
So
here
what
we
are
going
to
see
is
that
you
know
the
product
personalization
at
skin,
how
your
customer
can
personalize
the
product.
That's
one,
the
collaborative
robot
that
is
called
cobots,
okay
within
using
coverts.
How
can
we,
like
you,
know,
create
a
very
unique
component
based
on
the
customers
requirement
and
the
visual
inspection
through
cameras
using
ai
and
ml?
So
this
is
something
you
will
see,
then
automated
material
flow
through
agvs,
automated
guided
vehicles
and
kanban,
and
you
will
see
the
it
ot
and
end-to-end
integration
scenario
during
the
course
of
this
video.
C
How
can
you
establish
an
open,
integrated
factory?
How
can
workflows
be
automated
and
simplified
as
soon
as
a
customer
places
an
order
like
a
customized
and
personalized
double
seat
valve
the
intelligently
networked
process
starts.
The
journey
of
the
intelligent
valve
begins
with
component
manufacturing
operators
assemble
the
electronic
valve
head.
According
to
user
specific
requirements,
they
are
supported
by
an
integrated
cobot
that
hands
over
the
order,
specific
components,
an
attached,
camera
uses,
image,
processing
and
machine
learning
to
ensure
consistent
quality.
C
C
C
First,
the
kit
is
lifted
on
a
free,
floating
planar
mover
with
a
cobot
here.
It
moves
smoothly
and
contact
free
over
planar
tiles,
utilizing
electromagnetic
forces,
the
kit
stops
at
the
visual
inspection
station
and
is
photographed
and
verified
by
means
of
optical
quality
inspection,
empowered
by
machine
learning.
C
C
If
all
key
figures
conform
with
the
target
it
circles
until
it
is
picked
up
by
an
agv
through
vertical
and
above
all,
horizontal
integration,
all
areas
of
manufacturing
communicate
with
each
other.
All
processes
are
seamlessly
integrated
and
can
be
monitored,
using
sap
solutions
for
digital
manufacturing
and
sap
extended
warehouse
management.
C
This
way
all
materials
and
components
are
available
at
the
planned
location
in
time
for
final
assembly,
all
materials
are
automatically
staged
to
the
workstations,
with
the
sap
ewm,
using
the
built-in
material
flow
system
to
control
the
automated
warehouse
and
a
direct
communication
to
control
the
agvs
to
pick
up
all
materials
there.
The
kit
arrives
at
the
same
time
as
the
corresponding
raw
valve.
The
agv
system
constantly
evaluates
the
overall
situation
and
possible
routes,
so
the
vehicles
automatically
head
for
a
free
assembly
station
to
supply
all
the
necessary
raw
materials.
C
C
C
C
As
the
valve
is
completed,
the
digital
twin
is
created
as
well,
together
with
all
relevant
production
data.
It
is
the
single
source
of
truth
for
further
processing
and
operation
of
the
double
seat
valve
this
way,
the
open,
integrated
factory
supports
its
operators
and
forms
the
basis
for
a
continuous
improvement
process
and
frictionless
digital
supply.
B
B
So
so
this
is
all
connected
with
sap
through
a
through
a
component
called
connectivity,
which
is
nothing
but
a
opc
client,
and
this
actually
helps
us
to
get
the
data
and
send
the
data
in
both
directions:
okay,
to
achieve
a
real,
real
time,
digital
and
intelligent
manufacturing.
That's
one
and
the
second
is
a
lot
of
data,
get
exchanged
between
erp
and,
and
you
know
to
to
the
various
processes
within
the
plant.
B
D
D
What
you're
about
to
see
and
java-
and
my
team
will
be
showcasing-
is
a
few
examples
about
how
technology
can
be
leveraged
in
a
intelligent
factory
scenario.
In
order
to
help
you
get
the
right,
I
would
say,
benefits
for
your
operation
or
get
you
the
right
or
fix
the
right
business
challenge
so
at
the
expense
center.
We've
really
been
really
focusing
on
this
right.
It's
not
really
about
the
solution.
It
is
a
little
bit
about
the
tech,
but
it's
really
about.
What
do
you
want
to
achieve?
D
Is
this
really
about
you
know
maintaining
or
optimizing
the
connection
between
the
resources,
people
and
machine
together?
Is
it
about
reducing
their
sales
outstanding
into
your
inventory
and
using
robotic
for
robotic
for
this?
Is
it
about
bringing
safety
to
your
worker
as
they
are?
You
know
reducing
the
time
to
learning
thanks
to
augmented
reality.
D
So
all
of
those
topics
that
we
see
a
lot
nowadays
with
a
customer,
we
show
them
in
explain
center,
but
we
also
show
how
this
can
be
integrated
into
your
existing
application
and
which
ecosystem
we've
been
working
on
and
tried
on
a
few
of
those
use
cases
to
help
you
deliver
those
innovation
in
a
faster
time.
So
that's
really
what
the
experience
center
is
all
about,
so
we've
created
for
you.
A
few
demos
is
going
to
take
us
through
that.
Please
ask
your
question
in
the
chat.
D
E
Thanks
marvin,
so
hi
everybody,
my
name
is
gerald
I'll,
be
hosting
you
guys
at
the
experience
center
today.
So
I
just
want
to
say
you
know
hello
to
everybody,
we'll
be
showing
you
four
different
demos.
Across
of
you
know
a
few
different
perspectives,
so
you
know
stay
with
me.
I
think
it'll
be
very
interesting.
So
in
the
interest
of
time
I'm
gonna
get
started
so
right.
So
I'm
gonna
go
straight
into
the
highest
level.
E
E
Okay,
great,
so
what
you're
actually
seeing
is
actually
a
mini
production
floor
and
what
this
has
actually
is
a
few
different
sensors
across
the
entire
production.
So-
and
you
can
imagine
this
to
be
a
factory
for
you
know,
motorbikes,
you
can
imagine
it
to
be
a
factory
for
whatever
it
is
that
your
company
does.
But
the
main
point
here
is
that
it
leverages
iot
sensors,
in
this
case,
rfid
at
different
stages
within
a
production
lifecycle
in
order
to
gain
information
about
every
single
stage.
E
So
I'm
going
to
turn
that
off
really
quickly
and
show
you
a
bit
of
the
dashboard
as
well.
So
all
this
data
that
you
see
in
the
different
shop
floor,
you
can
actually
see
on
a
unified
dashboard
where
you
can
actually
see
the
production
rate,
the
the
total
units
that
have
been
produced
in
lifetime
and
also
the
individual
id.
So
I'm
gonna
bring
myself
back
up
here
and
explain
a
little
bit
more
about
what
this
is.
E
So
you
can
actually
get
data
from
the
sensors
about
every
single
stage
that
happens
within
that
production
life
cycle.
So
the
main
business
value
here
is
that
this
data
then
empowers
you
to
optimize
business
operations
but,
more
importantly,
it
also
protects
you
and
your
consumer,
so
using
data
from
iot
sensors
that
leverage
technologies
like
rfid
in
this
case
each
individual
product
or
package,
is
tagged
with
a
unique
identity
that
is
registered
at
every
stage.
E
You
cannot
go
through,
for
example,
point
a
without
going
through
point
b,
because
we
will
be
able
to
see
that
so
what
this
means
is
that
it
stops
you
you
know
with
such
great
clarity.
You
know
what
is
happening
is
with
each
individual
on
the
floor.
You
know
with
such
unparalleled
levels
of
specificity.
This
would
protect
you
and
your
consumers
from
say
counterfeits,
which
would
otherwise
not
fit
into
this
ecosystem
at
all.
So
it
would
immediately
be
flagged.
E
So
I'm
going
to
bring
you
over
here
to
the
dashboard
just
one
more
time
and
you
can
actually
see.
For
example,
are
you
guys
able
to
see
the
dashboard
yeah
yeah?
So
what
you'd
be
able
to
see
is
actually
the
over
here
you'd
see
the
individual
ids
of
all
these
different
things.
So
here
here
the
point
is
that
you
can
actually
see
the
life
and
the
live
position
of
every
single
product.
E
So
this
means
that,
if
you
put
an
counter
feed
into
that
system,
you
will
get
noted
immediately,
and
so
what
I
have
here
in
front
of
me
is
actually
this
little
ball
here
which
would
play
the
part
of
a
counterfeit.
So
I'm
going
to
position
you
back
down
over
here
and
what
you'll
see
on
the
screen
when
I
actually
put
this
little
ball
down
through
a
rfid
scanner,
it
would
actually
get
alerted
off
that
error
itself.
So
that's
one
of
the
ways
that
you
can
actually
see
this
and
yeah.
E
So
on
top
of
preventing
counterfeits
as
well.
This
data
will
also
allow
you
allow
for
users
to
get
great.
You
use
this
data
for
greater
analysis
to
optimize
your
operations.
So,
for
example,
you
can
leverage
ai
to
study
this
data
to
quickly
identify
bottlenecks
and
operations
and
even
suggest
solutions
to
optimize
these
processes
as
well.
So
a
great
use
case
here,
for
example,
is
harley
davidson
which
actually
leveraged
this
technology
to
turn
a
four-week
production
process
into
a
six-hour
process.
E
So
this
is
just
one
of
those
sort
of
very
high
level
areas
where
you
can
use
this
data
to
look
at
what's
happening
on
the
shop
floor
in
real
time
and
analyze
that
data,
so
that
you
can
identify
areas
where
there
are
bottlenecks
happening
or
whether
the
quality
control
in
certain
areas
are
not
being
done
very
well,
and
then
you
can
change
business
operations
in
order
to
optimize
that.
So
this
is
a
very
high
level
introduction
into
how
this
might
might
work.
E
D
Thanks
thanks
john,
I
think
maybe
just
something
here
to
to
really
highlight
this.
Those
type
of
I
would
say
right
now,
we're
looking
at
is
sensors
and
traceability
right.
The
idea
is
that
you
can,
you
know,
identify
potential
bottlenecks
and,
if
there's
any
quality
issues,
so
this
demo
that,
obviously
you
could.
You
could
see
a
bit
better
if
you,
if
you
guys,
would
be
coming
to
singapore,
but
it
really
helps
with
product
recall
management.
D
It
helps
as
well
if
the
we
use
rfid
here,
but
it
could
be
any
video
analytics
that
look
at
the,
for
example,
the
way
the
product
look
like
or
the
way
the
product.
You
know
what
is
the
weight
or
the
color,
and
you
can
use
that
information
during
your
production
line
to
actually
help
you
identify
potential
quality
issues.
Go
back
to
your
supplier,
go
back
to
your
maintenance
operator
and
fix
those
before
final
assembly
right.
So
it's
really
about
when
we
talk
about
automation,
is
how
do
you
leverage?
You
know?
D
E
We
also
want
to
look
into
how
we
can
ensure
all
other
supporting
functions
can
be
optimized
as
well,
which
is
why
I'm
introducing
to
you
behind
our
smart
warehouse,
so
this
solution
actually
leverages
the
use
of
drones
in
order
to
gather
and
analyze
data
across
machines
and
business
systems
in
order
to
enable
faster,
more
flexible
and
more
efficient
processes
in
order
to
produce
high
quality
individualized
goods
at
even
lower
costs.
So
what
is
this
process
itself?
E
What
this
process
is
is
a
drone
would
actually
fly
to
a
shelf,
which
is
a
pre
pre-planned
route,
and
it
takes
a
picture
to
store
in
the
back
end
of
the
system,
which
is
then
with,
after
which
it
would
take
that
image
and
then
send
it
back
and
return
to
where
it
actually
took
off.
So
by
scanning
the
inventory.
E
What
is
actually
by
scanning
the
infantry
what's
actually
doing
is
is
counting
the
number
of
packages,
and
then
this
information
is
sent
to
a
database
where
the
numbers
are
actually
being
cross-checked,
so
this
method
is
actually
significantly
quicker
than
manual
staff
checks
as
well.
Moreover,
it's
even
safer.
Imagine
if
your
space
is
very
large,
your
staff
would
have
to
walk
a
very
long
distance
just
to
check
something
and
even
worse,
if
it's
very
high,
they
would
have
to
climb
that
in
introduces
slower
processors
and
even
more
dangerous
processes.
E
So
in
a
short
while,
what
we're
going
to
see
is
actually
the
drone
take
off
in
a
very
short
while,
but
in
for
that
moment,
I
want
to
just
maybe
talk
a
little
bit
more
about
why
this
is
such
a
great
thing
right.
This
information
can
later
also
be
fed
into
inventory
management
systems
when
this
data
would
empower
you
to
be
able
to
run
greater
analysis
and
leverage
analytics
to
study
historical
data
on
consumption
and
even
predict
the
runway
for
current
stock,
and
this
also
builds
the
foundation
for
automated
procurement
in
the
future
as
well.
E
E
D
You
can
put
your
camera
a
bit
lower
so,
and
maybe
I
can
explain
if
you
allow
me
to
step
in
here
java,
right,
yeah,
so
so,
obviously
in
the
center,
what
we've
done
is.
We've
we've
programmed
the
drone
to
be
connected
to
your
warehouse
management
system,
because
the
whole
idea
is
to
make
sure
that
not
only
you
understand
what
is
your
physical
inventory
versus
what
is
in
your
system,
but
also
what
the
drone
can
do
is
analyze.
D
You
know,
with
machine
learning,
recognition,
for
example,
how
your
different
warehouse
you
know
boxes
are
right,
which
states
is
there
gonna,
be
any
water
stain?
Is
there
any
damages
on
the
boxes,
but
eventually
you
want
to
make
sure
you
understand
if
there's
any
shrinkage,
that
should
not
be
here,
for
example,
if
products
are
missing
that
have
not
been
sold
into
your
system,
you'll
be,
you
know,
be
able
to
be
made.
Aware
of
that,
and
just
you
know
automatically
program
the
drone
to
do
those
kind
of
checks
in
into
the
different
spaces
could
be
drawn.
D
It
could
be
a
tv
that
you
implement
as
well
into
the
different
system.
It
really
doesn't
matter
how
you
know
which
hardware
you
use
as
long
as
you
really
connect
what
you've
been
selling
to
inventory,
with
what
you
have
reality
into
the
into
the
stocks
and
what
you're
seeing
here
that
photo.
Maybe
john
I'll,
let
you
continue.
E
D
D
How
many
products
are
left
because
sometimes
you
know
it's
not
only
about
one
box,
but
you
know
some
of
those
delivery
could
be
component
within
that
box
or
within
that
palette.
So
you
can
go
as
complex.
As
you
know,
two
three
layers
of
ai
integrated
into
your
system
to
make
sure
you
can
automate
what's
been
sold
versus
what
you
have
in
the
in
the
in
the
system
and
how
you
know.
How
do
you
send
information
to
the
warehouse
worker.
E
Great
great
so
again,
as
marvin
mentioned
earlier,
if
you
guys
have
any
questions,
please
do
send
them
inside
the
chat
itself
and
you
know
maybe
later
on.
We
can
choose
a
few
to
address,
especially
those
common
questions
that
we
have,
but
that
is
what
we
have
for
smart
warehousing,
and
I
want
to
bring
you
to
our
next
demo.
But
before
you
know
what
it
is
is
basically,
we've
spoken
a
lot
about
optimizing
business
operations
using
data
from
iot
sensors
to
make
your
businesses
better
and
the
experiences
for
the
consumers
more
enjoyable.
E
But
let's
talk
a
little
bit
about
the
experience
for
your
staff
on
the
ground.
How
can
we
leverage
all
this
information
to
ensure
safety?
How
do
we
use
this
information
to
ensure
efficiency?
So
I'm
going
to
bring
you
guys
over
to
our
next
demo,
which
is
actually
called
asset
manager
and,
as
you
can
see,
I'm
walking
to
give
me
a
few
seconds
to
walk
through
the
center
just
really
quickly.
E
And
what
we're
going
to
do
is
we're
going
to
take
the
perspective
of
a
field
technician.
Now
previously
we
looked
at
it
from
a
very
high
level
view
of
what
what
a
manager
would
do,
what
someone
who's
running
a
factory
would
look
like.
But
what
does
it
look
like
for
the
staff
on
the
ground?
What
this?
What
this
does?
Is
it?
So
are
you
guys
able
to
see
the
ipad.
E
So,
first
and
foremost,
you'll
be
able
to
see,
for
example,
all
those
in
red
the
work
orders
in
red,
which
are
more
urgent,
which
are
more
urgent,
work,
orders
and
you'll
be
able
to
see,
for
example,
certain
safety
precautions
like
the
orange
mark
or
range
marker
over
there.
So
I'm
going
to
talk
about
how
these
data
empowers
field
technicians
to
work
efficiently.
So
the
first
thing
I'm
going
to
do
is
take
you
over
to
our
piece
of
technology
here.
E
This
is
this:
is
our
factory
equipment
you
can
think
of
this
in
any
context
right,
it
could
be
any
form
of
equipment,
but
in
this
case
this
is
what
it
is
and
so
what's
what's
going
to
happen,
is
it's
going
to
start
working
and
you'll
see
that
the
light
is
green
here
and
as
we
slowly
turn
it?
It
goes
red,
and
this
means
that
there's
a
low
pressure
in
the
acid.
So
I'm
going
to
stop
it
really
quick
and
bring
it
back
up
to
my
face
to
explain
so
the
information
from
there.
E
The
fact
that,
for
example,
there's
low
pressure
comes
from
iot
sensors
within
this
particular
piece
of
equipment
itself,
so
what
a
field
technician
can
do
is
use
this
information
for
him
to
be
able
to
gain
greater
visibility
into
what's
going
on
within
this
piece
of
equipment
and
then
run
diagnostics
and
later
on,
of
course,
proceed
to
fixing
the
equipment
itself.
So
I'm
going
to
click
on
the
actual
work
order
itself
and
you're
going
to
be
able
to
see
on
the
on
the
platform.
E
You
can
see
that
there's
no
pressure-
and
this
of
course,
is
coming
from
iot
center
you'll,
get
suggestions,
you'll
get
operations,
a
pre-service
checklist
equipment,
error
codes
and,
of
course,
comments-
and
I
particularly
like
this
comment
section
the
most
because
in
you
know
this
day
and
age,
where
people
are
changing
jobs
here
and
there
the
turnover
rates
in
certain
areas
are
higher
than
others.
This
comments
section
will
be
able
to
give
you
history
a
brief
history
into
this
particular
piece
of
equipment.
E
So,
even
if
there's
a
new
technician
on
the
ground,
for
example,
he
would
be
able
to
see
that,
but
beyond
this,
how
else
can
the
iot
sensors
themselves
data
from
that
ensure
efficiency
in
the
diagnostics
of
the
problem?
So
I'm
going
to
actually
analyze
the
source
drop
here,
source
of
the
drop
here
and
you
actually
see
on
the
screen
the
ipad,
it's
a
it's
a
real
ipad.
It's
a
real
screen!
I
didn't
rig
it,
it's
not
a
video,
so
it's
actually
created
a
digital
twin
of
this
piece
of
equipment
itself
and
from
there.
E
What
you're
going
to
be
able
to
see
is
information
being
fed
from
inside
the
inside
this
particular
piece
of
equipment
itself
to
the
data
here.
So,
of
course,
this
is
not
a
live.
A
live
piece
of
equipment,
but
if
it
were
you'd
be
able
to
see
the
inflow
rate,
the
outflow
rate,
the
temperature
of
this
equipment,
the
distance,
the
rpm-
and
this
is
not
limited
to
those
specific
categories.
It's
just
in
this
case.
E
That's
what
the
iot
sensors
are
built
to
be
able
to
do,
but
imagine
in
any
context
of
yours,
depending
on
what
kind
of
data
you're
trying
to
capture.
You
can,
of
course,
use
different
iot
sensors
in
order
to
do
that.
So,
let's
start
with
the
first
basics.
I'm
david
wants
to
fix
this
particular
piece
of
equipment.
E
Sorry
so,
for
example,
if
an
ai
is
trained
to
recognize
that
you
know
a
particular
sound,
a
particular
clinking
sound,
then
it
would
be
able
to
diagnose
that
of
that
particular
problem.
The
next
thing
that
it
could
do
is
through
temperature.
So,
through
you
know,
sensors
that
measure
temperature
within
the
equipment.
We
could
actually
create
a
heat
map
for
the
field,
technician
to
be
able
to
visualize
this
and
maybe
see,
for
example,
in
this
corner.
E
Here,
it's
particularly
red
this
might
mean,
for
example,
it's
very
hot
there
that
might
be
excessive
friction
due
to
something
being
loose
or
two
pieces
being
too
tight
together
along
that
area
as
well,
and,
of
course,
next
something
that's
really
cool
as
well
is
through
the
components
say.
Those
two
are
those
two
things:
don't
really
work.
How
else
can
we
use
this
information
to
digitally
disassemble,
so
you
can
further
that
diagnostic
process
by
digitally
disassembling,
this
piece
and
you'll
be
able
to
see
here,
for
example,
each
individual
piece
here.
E
So
this
is
particularly
useful
for
complex
pieces
of
machinery,
where
technicians,
for
example,
would
not
be
able
to
memorize
every
single
thing
and
you'll
be
able
to
see
within
each
piece
what
the
problem,
what
the
problem
probability
of
it
being
the
problem
might
be
in
this
case,
you'll
be
able
to
see
the
component
issue
predictions
so
by
looking
at
historical
data
on
consumption
and,
of
course,
data-
that's
been
logged
by
field
technicians
you'd
be
able
to
see
that
most
likely.
The
problem
is
the
o-ring
pressure
seal.
E
So
this
is
one
of
those
ways
that,
where
that
we're
able
to
leverage
the
iot
sensors
in
order
data
from
iot
sensors
in
order
to
make
sure
that
workers
are
able
to
work
as
efficiently
as
possible
and
make
that
cut
that
experience
a
lot
more
seamless
and
less
technical
in
nature,
so
I've
covered
a
lot
on
the
efficiency
part.
But
I
haven't
spoken
enough
on
safety
earlier
I
mentioned
that
iot
sensors
also
help
us
to
ensure
safety
beyond
just
efficiency.
So
what
you're
actually
able
to
see
here?
E
The
first
point
I
mean-
and
I
briefly
mentioned
this
earlier-
is
the
orange
demarcated
area.
Here
you'll
see
headphones
over
there
on
the
ipad
itself
and
what
this
is
actually
meant
to,
what
this
is
actually
meant
to
describe
is
a
area
in
which
is
very
loud,
and
this
is
information
coming
from
an
iot
sensor
placed
inside
the
room
itself,
and
so.
E
All
right
great,
so
this
is
actually
information
coming
from
iot
sensor
within
that
room
itself,
not
someone
actually
going
there
to
listen,
but
this
isn't
all
this
isn't
the
extent
to
how
impressive
this
can
actually
get.
We
can
actually
use
iot
sensors
to
measure
the
actual
physical
state
of
our
staff
itself.
So
what
you're,
seeing
here,
I'm
going
to
show
you
on
the
camera
is.
E
So
in
this
helmet
you
see
the
black
the
very
light
blue
light.
You
can
actually
see
on
the
light
blue
light
itself.
You
can
actually
see
that
it's
actually
an
iot
sensor
embedded
within
the
helmet,
so
we
go
over
to
the
asset
manager.
Here
we
check
for
the
worker
safety.
We
can
actually
look
at
information
coming
from
the
sensor,
for
example
the
temperature
of
the
head.
E
Of
course,
I'm
not
wearing
it
right
now,
which
is
why
it's
26
degrees,
but
you
can
also
measure
things
like
the
ambient
sound,
the
heart
rate
and,
of
course,
just
to
prove
that
I
didn't
rig
this.
I'm
going
to
artificially
increase
the
temperature
over
here
off
the
this
of
the
helmet
itself
and
you'll
be
able
to
see
the
temperature
actually
rising.
E
You
guys
able
to
see
that
yeah
30
degrees
and
it
can
even
get
a
fairly
dangerous
level
if
it
goes
up
for
more.
So
this
is
one
of
those
ways
that
we're
able
to
make
sure
that
workers,
in
particularly
warm
environments,
don't
suffer
from
things
like
heat
stroke
or
they
faint.
So
we
make
sure
that
and
lastly,
of
course,
one
of
the
most
common
one
of
the
most
common
issues
knocking
right.
If
someone
takes
a
fall,
you
would
get
a
notification.
E
Sorry
give
me
a
second
in
order
for
us
to
be
able
to
ensure
worker
safety
on
the
factory
floors
and
even
more
importantly,
sorry
also
just
as
importantly
efficiency,
so
we're
able
to
make
sure
that
work
is
being
done
very
fast,
but
not
at
the
expense
of
the
safety
of
your
workers.
So
I'm
gonna
stop
here
for
a
quick
second
to
see.
If
you
know,
if
there's
anything,
you
would
like
to
add
anyone
on
the
team
or
marvin
anything
to
add
on
this
front.
E
Otherwise
I
will
move
on
to
the
next
part
of
the
demo.
So
I
mentioned
here
how
we
leverage
technology
in
iot
technology
in
order
to
in
order
to
make
sure
that
diagnostics
is
done.
But
now
I
mentioned
also
the
importance
of
making
sure
that
the
experience
of
each
field
technician
is
comfortable,
is
seamless
and,
of
course,
is
efficient.
So
what
I'm
going
to
show
you
next
is
the
follow-up.
So,
let's
say
in
this
case
I've
already
diagnosed
that
this
particular
piece
of
gear
is
the
problem
with
the
equipment.
E
So
behind
me,
what
I'm
going
to
be
showing
you
is
actually
an
app
here.
So
what
I'm
going
to
be
showing
you
in
my
next
demo
is
actually
called
a
reborn
and
a
reboot
is
a
demo
that
combines
it's
a
chatbot
that
combines
ariba
and
chatbot
together,
and
this
is
to
help
the
procurement
process
of
each
field
technician
much
more
seamless
and
less
administrative
in
nature.
So
how
is
it
that
we're
actually
able
to
do
this?
E
Finally,
you're,
actually
seeing
the
app
so
after
a
field
technician
has
diagnosed
the
problem,
for
example,
with
that
piece
of
equipment
earlier
he's
decided
that
this
piece
of
equipment
is
the
issue.
So
what
I'm
going
to
do
is
I'm
going
to
place
it
down
here
very
quickly
and
what
I'm
going
to
do
is
try
to
procure
this
through
the
app
itself.
So
behind
immediately
I'm
I'm
showing
the
interface
and
it's
asking
me
what
item
I
would
like
to
purchase
and
of
course
I
want
to
purchase
this
piece
of
equipment.
E
D
E
Right,
so
what
you're
going
to
be
able
to
see
is
that
I've
taken
a
picture
of
this
equipment
and,
what's
going
to
happen,
is
an
ai,
that's
trained
to
recognize
these
pieces
of
equipment
will
start
to
recognize
this
and
are
you.
It
looks
a
little
bit
blur
on
my
end,
but
what
it's
showing
here
is
that
it's
recognized
this
piece
of
equipment
and
it's
shown
an
89
match
so
from
there.
What
I
can
do
is
I
can
actually
order
this
piece
of
equipment
now
and
from
there.
E
What
would
happen
is
that
it
would
immediately
ask
the
follow-up
questions,
the
kind
of
questions
you
would
get
in
that
in
a
form
that
can
be
very
slow
and
very
annoying.
But
when
you
take
it
step-by-step
process,
it's
a
lot
more
seamless
and
more
comfortable.
So,
for
example,
this
is
asking
how
many
pieces
I
would
like.
E
Let's
say
I
want
three
and
it's
an
expensive
piece
of
equipment,
so
it
requires
a
certain
layer
of
approval,
and
so
it's
prompting
me
to
send
an
approval
request
from
my
manager,
and
I
click
click
yes
here
and
the
requisition
would
have
been
sent
over.
The
requisition
request
would
have
been
sent
over
to
my
manager,
who
will
then
be
able
to
approve
and
create
that
process.
E
So
this
would
mean
that
field
technicians
no
longer
have
to
fill
out
very
technical
names
or
have
to
be
very
specific
with
what
they
need,
because
they
are
able
to
take
pictures
and
the
ai
would
do
all
that
heavy
lifting
for
them,
and
this
would
also,
of
course,
take
off
administrative
work
off
the
main
office
as
well.
So
this
is
one
of
those
ways
that
we're
able
to
have
that
end-to-end
experience
from
the
diagnosis
to
the
procurement
and
later
to
the
fixing
as
well.
E
So
that
is
actually
the
last
demo
I
had
shown
for
today,
so
yeah.
So,
just
to
summarize,
today,
we've
shown
you
our
smart
manufacturing,
our
smart
warehousing.
Those
are
demos
that
leverage
iot
sensors
to
show
us
how
we
can
really
make
sure
from
a
very
high
perspective.
We
can
optimize
business
operations,
we
can
make
sure
things
are
faster,
smoother
and,
of
course,
safer
and
with
the
asset
manager
and
a
rebot.
We
show
that
this
technology
isn't
just
limited
to
making
sure
that
the
consumers
and
the
managers
perspectives.
E
You
know
the
experience
is
well
and
smooth
and
comfortable,
but
we
can
also
make
sure
that,
from
the
field
technician's
perspective
for
the
people
on
the
ground,
this
technology
can
be
leveraged
to
make
sure
that
they're,
safe
and,
of
course,
just
as
efficient
and
if
not
even
more
efficient
yeah.
So
that
concludes
my
presentation
for
today.
Are
there
any?
Maybe
is
there
any
time,
maybe
for
us
to
address
some
questions
or
marvin
anything
to
add.
B
D
No,
no,
no,
I
say
no
thank
you
very
much.
Welcome
and
yes,
any
follow-up.
You
know
I've
already
answered
to
a
few
of
you
in
the
chat
around
what
we
do
for
process
industry,
where
you
know,
I
obviously
send
you
something,
but
what
you've
seen
today
guys
just
obviously
just
a
snapshot
of
of
what
we
could
be
doing.
D
B
B
Yes,
so
so
let
me
take
it
from
here
so
so
we
have
got
a
website
called
industry.
Four
dot
now
experience,
so
this
is
the
link
which
we
will
be
sharing
as
a
part
of
the
deck.
So
if
you
go
here,
you
will
find
a
lot
of
details
about
some
of
the
statistics,
some
of
the
survey
as
a
part
of
industry,
400
and
now
and
dispute
and
process
industry.
B
We
know
that
you
know
the
industry
for
the
way
it's
implemented
varies
from
industry
to
industry,
so
hence
what
we
have
done
is
we
have.
We
have
actually
designed
this
scenario
for
per
industry,
and
that
is
something
covered
in
this
two
links.
What
is
shown
here
so
the
moment
you
go
into
the
details.
This
actually,
you
know
takes
you
through
that.
You
know
how
sap
can
help
in
you
know,
making
your
design
more
smart,
making
your
plant
more
smart
and
you
know
manufacturing
so
on
and
so
forth.
Okay,
so
this.
B
B
Okay,
so
next
is
sap's
point
of
view
on
industry
photo,
so
sap
is
point
to
view
for
industry.
Four
o
is
to
move
the
digitization
from
indus
from
a
factory
focused
initiative
to
a
company-wide,
and
you
have
seen
when
I
was
doing
the
white
boarding.
It's
not
only
the
factory
focused
initiative
to
make
the
factory
smart.
It
has
to
be
well
connected
in
the
entire
value
chain.
So
that's
that's.
Why
sap's
point
of
view
is
to
move
the
digitization
and
industry
photo
from
factory
focused
initiative
to
company-wide
and
competition.
B
Fitting
business
style
strategy
combine
the
manufacturing
automation
with
enterprise
business
processes.
This
has
to
be
combined
to
achieve
you,
know
the
subjective,
and
this
has
got
three
key
focus
areas.
One
is
the
center
and
the
customer,
so
their
input
and
preferences
at
the
source
of
all
you
do.
That's
one
the
moment
you
think
from
customers
point
of
view.
B
You
will
see
that
your
customers
requirement
is
very
unique
and
how
you
can
make
the
manufacturing
more
and
more
smart
so
that
it
can
produce
the
customization
at
a
scale
and
to
produce
the
customization
at
a
scale
which
you
say,
reinvent
the
production.
So
the
plant
has
to
be
connected
with
your
entire
company
from
sales
service
and
logistics
with
you
know,
production
to
transform
how
you
work
and
to
achieve
this,
what
you
have
done
is
we
came
up
with
four
themes
and
themes
are
intelligent
product.
B
This
actually
helps
the
companies
to
accelerate
your
product
design
by
getting
the
accurate
customer
experience,
data
and
product
experience
data
from
the
embedded
sensors.
It
also
do
take
care
of
seamlessly
handing
it
over
to
the
manufacturing
team
to
speed
up
the
mass
production
of
the
new
product,
and
this
actually
helps
the
companies
to
improve
the
kpis
around
new
product
and
introduction
to
the
market.
So
that's
the
one
and
second,
is
the
intelligent
factory.
B
B
Third,
one
is
the
asset,
if
you
see
so
far,
the
data
generated
by
the
asset
or
the
equipments
through
the
plant
automation
systems
has
been
used
in
silos
and
then
now
combining
the
ot
data
and
iit
data,
achieving
a
predictive
maintenance.
So
now
only
we
are
saying
that's
possible
at
scale.
I
would
say,
because
there
are
some
few
pocs
in
the
past,
but
now
we
are
seeing.
Many
companies
are
actually
you
know
best
leveraging.
B
This
tit
and
ot
data
to
accurately
predict
the
maintenance
and
last
is
the
empowering
your
people
and
no
matter
how
much
automation
you
do
in
the
factory
floor.
People
take
action,
okay
and
they
need
to
have
all
the
information
to
take
the
informed,
informed
action
and
decision
by
getting
the
data,
and
it
should
be
a
prescriptive
finance
so
that
they
can
take
the
decision
quickly.
B
Okay,
so
that's
fun,
and
last
one
is
the
business
network,
as
we
see
that
you
know
no
company
runs
in
and
then
silos
so
companies
collaborate
with
the
partners
across
design,
manufacturing,
asset
maintenance
and
logistics
and
operations
as
well.
So
we
provide
a
cloud
platform
for
the
solution
using
which
companies
can
better
collaborate,
or
you
know,
collaborate
in
real
time
for
any
any
need
across
all
the
value
across
all
the
departments.
B
Okay.
So
this
is
the
four
pillars
and
business
network
cross
across
all
these
four
four
pillars.
So
that's
that's.
Four
things
from
industry
photo
from
sap,
and
today
we
are
focusing
on
diligent
factors
others
covered
in
the
past
business
and
from
intelligent
factory
point
of
view.
We
have
for
the
solutions.
One
is
the
digital
manufacturing
cloud
solution.
This
is
a
public
cloud
offering
from
a
sap.
This
helps
the
companies
to
get
the
insight
how
the
plant
is
or
the
production
is
running
and
helps
to
digitize
the
factory.
Okay,
that
is
with
the
manufacturing
execution.
B
This
is
nothing
but
a
mes
on
the
cloud
and
also
helps
the
companies
in
solving
the
quality
issues.
So
digital
manufacturing
cloud
is
the
public
cloud.
A
manufacturing
suite
is
the
private
cloud
version
of
sap,
and
in
addition
to
this,
we
have
got
a
number
of
capability
within
s4hana
manufacturing
which
actually
a
great
foundation
for
many
companies
to
start
their
industries
overall
journey.
B
So
this
supports
the
production
planning
and
the
supports
the
planning
and
scheduling
that
is
which
is
actually
you
know
the
constraint
based
planning
based
basis
on
which
you
can
maximize
the
capacity
utilization,
and
we
have
got
a
new
product
called
product
production
engineering
operation.
This
is
targeted
to
complex
manufacturing
type
of
scenario
like
aerospace,
defense,
marine
and
a
few
more
for
the,
where
the
design
keeps
changing
during
the
production,
and
it
has
got
quality
management
planning,
and
you
know,
of
quality
planning,
quality,
recording
and
a
few
more
things.
B
3D,
quality
and
last
is
that
you
know
environmental,
health
and
safety.
This
covers
the
three
aspects
like
people's
health.
That
is
one
product
compliance,
that's
the
second
and
third,
is
the
environmental
compliance
which
is
part
of
ehs,
which
is
embedded
into
the
is
for
hana
manufacturing.
So
this
is
the
complete
suite
of
products
for
to
digitize
and
to
ensure
your
manufacturing
is
not
only
intelligent,
but
you
know
highly
flexible,
which
is
the
need
of
the
hours
in
today's
environment,
and
this
is
a
like.
B
You
know
blueprint
architecture,
intelligent
manufacturing
decides
here
which
actually
combines
connects
with
you
know
both
software
automation
and
enterprise,
to
make
you
know
the
manufacturing
flow
more
and
more
intelligent,
and,
lastly,
that
you
know
from
sustainability
point
of
view
across
designed
to
operate
value
chain.
We
have
got
solutions,
for
example,
design
products
that
are
recyclable
and
environmentally
sustainable
plan
to
reduce
the
emission
and
ethically
source
the
materials.
B
These
are
the
capabilities
available
in
our
ibp
solution
and
manufacturing
solutions
helps
you
to
minimize
the
waste
and
environmental
impact
and
and
solutions
for
delivery
that
is
transportation
and
warehouse.
Okay.
This
helps
with
logistics
processes
that
optimize
loads
to
reduce
the
mileage
and
carbon
footprint
and,
finally,
the
operate
assets
and
equipments
in
an
energy
efficient
manner
that
is
safe
for
environment
and
in
the
workforce.
And,
lastly,
you
can
leverage
the
network,
which
is
the
business
network,
to
collaborate,
your
partners
to
support
the
software
economy
and
overall
sustainability
business
models.
B
Let's
spend
10
seconds
so
you
saw
four
things
intelligent
product
purely
from
design
factory
from,
like
you
know,
making
the
factory
more
agile,
more
smart
and
digitized
and
intelligent
asset
empowering
people
so
so
want
to
know
which
themes
are
the
most
relevant
for
your
for
your
company's
industry.
40
initiative.
B
Okay,
we
see
asset
also
is
one
of
the
key
initiatives,
because
it's
the
capex
investment
and
ai
and
ml
can
do
a
lot
in
improving
the
asset
life
cycle.
Empowering
people
definitely
one
of
the
key
with
a
lot
of
data
which
is
generated
in
the
system
which
can
be
best
leveraged
by
deriving.
What
is
that
you
know?
People
should
do
on
that
data
and,
if
you
could
automate
the
workflows,
that
adds
more
value.
B
F
So,
while
brings
up
the
slide,
what
we
are
going
to
be
doing
is
take
a
step
back
a
little
bit
and
find
out
why
we
need
predictive
maintenance.
What
are
those
things
that
we
can
do
on
the
shop
floor?
What
are
the
things
that
we
have
done
on
the
shop
floor
and
got
some
roi
out
of
it
and
ultimately
also
discuss?
Where
do
we
start,
and
where
do
we
end?
What
is
it
that
is
going
to
give
me
the
right
path
to
implementing
all
of
these
pieces?
F
So
what
happens
if
I
start
halfway
through
or
wherever
I
feel
like
starting
and
what
are
the
consequences
or
repercussions
of
doing
that,
so
we'll
be
discussing
all
of
that
piece
right.
So
if
you
see
here,
there
is
a
graph
that
has
been
put
in
and
we'll
be
talking
about
the
the
predictive
maintenance
piece
first
and
then
we'll
talk
about
the
other
other
pieces
that
we
can
bring
into
play
as
far
as
the
hardcore
shop
floor
activities
are
concerned.
F
So
if
you
see
here,
there
is
a
let's
say:
an
audible
noise
starts
coming
in
and
you
you,
then
you
know
between
the
noise
starting
to
come
in
and
the
failure.
There
is
very
little
time
left
and
therefore
you
know
by
the
time
you
react.
The
the
part
has
already
broken
down
and
therefore
your
machine
is
just
at
a
standstill
and
you
are
losing
time
and
production
out
of
it.
But
this
is
the
human
driven
approach
which
has
been
there
for
quite
a
long
time.
F
These
days
it
is
it
is,
you
know
it
is
reduced
to
a
minimal,
because
not
many
people
now
still
are
working
on
human
driven
approach
to
maintenance.
So
now
what
the
next
step
was
that
you
know,
then
we
get
into
the
equipment
driven
initiative,
which
means
that
we
will
try
and
figure
out
yeah.
There
is
a
noise
coming
in.
Do
some
vibration
analysis
figure
out
if
it
is
hard
to
touch
figure
out
if
the
rpm
etc?
F
Has
you
know,
sort
of
increased,
reduce
whatever
and
then
find
out
that
there
is
something
that
is
wrong
now,
in
an
earlier
video,
we
saw
how
you
know
that
noise
can
tell
us
whether
whether
there
is
or
vibration
can
tell
us
whether
there
is
a
possibility
of
a
failure
or
or
not.
Now
this
is
giving
us
a
little
bit
more
time
for
us
to
react
and
ensure
that
we
don't
get
into
a
breakdown
situation.
F
We
can
probably
shut
down
the
machine
repair
it
and
then
you
know
restart
again,
but
you
don't
get
into
a
breakdown
piece
normally
in
a
breakdown,
because
it
is
a
surprise
stoppage
of
the
machine.
Now
it
takes
a
longer
time
and
duration
to
you
know,
get
back
into
the
production
mode
and
therefore
this
is
a
mode
where
we
should
be
avoiding.
F
F
You
move
from
your
preventive
maintenance,
to
a
condition
based
monitoring
and
maintenance
piece
which
in
itself
reduces
the
cost
of
maintenance
on
a
regular
basis,
and
there
are,
there
are
multiple.
You
know,
advantages
of
moving
from
preventive
maintenance
to
the
to
the
condition
based
maintenance
piece
is:
how
can
we
go
next?
Please
thank
you.
F
So
what
are
we
trying
to
do
here?
We
are
trying
to
say
that
there
are
twins
digital
twins
that
we
will
create,
which
you
saw
in
detail
earlier
in
the
session,
and
then
on
top
of
that,
we
will
put
in
some
machine
learning
models
and
therefore
we
should
be
able
to
detect.
An
anomaly
normally
means
that
something
is
not
right
and
therefore
you
will
see
a
peak
somewhere
in
the
graphs
that
that
you
are
seeing
on
the
screen,
which
means
that
it
is
time
for
you
to.
F
You
know,
act
and
ensure
that
things
are
under
control
and
there
is
nothing
that
is
that
that
can
go
wrong.
It
will
give
you
probability
of
failure,
which
means
that
the
the
system
will
tell
you
that
there
is
a
60
likelihood
or
there
is
a
20
likelihood
of
this
equipment
failing
or
this
component
failing,
but
once
it
has
failed
or
whenever
it
fails.
There
will
be
a
analysis
of
why
it
failed.
What
was
the
root
cause?
What
was
the
action
taken?
F
The
copper
piece
of
it
right
and
therefore
you
have-
and
my
colleague
also
showed
this
to
you-
you
have
a
very,
very
clear
picture
of
why
certain
things
failed
and
next
time,
when
you,
you
know,
encounter
a
similar
conditions
you're
in
a
position
to
know
that
you
know
this
is
going
to
fail.
This
is
these:
are
the
part
replacement
that
I
need
to
do,
or
this
is
the
action
that
I
need
to
take
on?
You
also
get
the
remaining
useful
life
analysis
done
here.
What
does
that
mean
so
I'll?
F
F
But
if
you
have
been
maintaining
this
bearing
very
very
nicely-
and
you
have
been
adhering
to
these
condition
based
monitoring
alerts,
you
can
possibly
extend
this
life
to
let's
say
60
000
60
lakh
cycles.
Now
in
a
normal
course,
you
would
have
removed
that
bearing
at
50
lakh
cycles
and
replace
it
with
the
new
one.
But
here
because
of
the
ml
and
because
of
the
insight
that
you
have,
you
might
as
well
increase
that
life
by
15
20,
which
is
again
savings
of
money,
and
you
are
extracting
the
full
value
out
of
the
whole
thing.
F
So
let
us
talk
about
some
of
the
use
cases
that
we
have
deployed
some
of
the
things
that
can
actually
give
you
the
roi.
Now,
how
is
this
roi
calculated
so
normally
and
there
are
formats
available?
We
can
share
those
with
you.
We
can,
you
know
collaborate
with
you
on
this
roi
part,
but
here
what
we
did
in
in
this
specific
case
was
that
we
found
out
what
is
the
opportunity
cost,
how
much
how
much
time
would
have?
F
For
you
know,
which
was
visible
for
anybody
who's
operating
on
that
particular
machine
or
that
particular
line.
Now
what
happened
was
that
because
of
these
probabilities
of
failure
because
of
these
early
indicators,
because
of
these
anomaly
detection
pieces,
they
were
able
to
convert
most
of
their
breakdowns
into
normal
shutdowns,
which
means
that
they
were
ultimately
able
to
reduce
or
the
time
taken
to
either
repair
or
replace
a
certain
equipment
or
part.
Their
estimation
was
if,
if
this
goes-
and
this
is,
this
was
a
pilot
that
we
had
done
with
mahindra
the
heavy
engines
limited.
F
If
their
estimation
was
per
year,
they
will
be
saving
about
2
million
dollars
if
they
were
able
to.
You
know
put
this
expand
this
across
their
engine
manufacturing
line
additional
savings
because
of
low
warranty
cost,
because
now
the
product
quality
also
is
now
good.
Therefore,
their
their
estimate
was
upwards
of
one
million
dollars.
They
will
save
from
the
warranty
cost
and
about
350.
F
Labor
days
were
saved,
which
in
itself
has
a
certain
cost
that
you
can
easily
calculate,
based
on
the
the
salaries
etc
right
so
clearly,
this
is
where
the
roi
comes
in,
and
we
need
to
at
the
start
of
the
project
we
need
to.
You
know,
fix
up
to
say
this
is
where
we
are
starting
when
we
are
starting.
This
is
the
situation
that
we
have
and
when
we
end
up,
we
should
do
another.
F
Now,
oee
or
overall
equipment
efficiency.
We
are
all
used
to
this
thing.
Some
of
us
are
monitoring
this
religiously.
Some
of
us
are
planning
to
get
into
this
thing.
This
gives
us
the
overall
efficiency
of
the
plant
or
the
line
that
we
are
running
now.
This
is
a
very,
very
good
indicator
of
whether
we
are
on
the
right
track
or
are
we
missing
something,
or
are
we
doing
something
which
can
still
be
improved,
but
it
is
a
lag
indicator,
meaning
that
it
is.
F
F
This
is,
this
is
one
very
important
piece
that
I
believe
should
be
used
by
most
of
the
manufacturing
organization.
This
is
about
artificial
intelligence
being
used
in
order
to
find
out
what
is
wrong
with
the
part
right.
So
there
are
two
things
that
are
happening
now.
One
artificial
intelligence
is
picking
up
the
sensor
data
and
analyzing
and
telling
you
what
might
be
wrong
or
what
might
fail
or
what
might
go
wrong
here.
F
So,
while
you
know
like
preventive
maintenance,
there
are
prescribed
times
when
you
need
to
take
this
inspection,
but
with
the
ai
we
can
figure
out
when
at
what
what
pace
or
what
time
duration
or
what
component
duration
that
we
need
to.
You
know
sort
of
start
inspecting
again.
It
brings
in
efficiency
and
it
tightens
the
the
the
quality
of
the
product
and
therefore,
you
know
again
improves
the
uptime,
improves
the
production
and
throughput
etc.
F
Perspective,
although
this
is
this
is
an
automotive
manufacturing
use
case,
but
from
any
perspective,
if
you
look
at
how
to
find
out
what
went
wrong
root
cause
and
then
therefore
the
kappa
it
is,
it
is
imperative
that
we
understand
what
these
you
know.
Root
causes
titles
are,
or
categories
are
so
this
this
application
will
help
you
identify
the
right
root
cause
and
therefore
log
in
the
right
details,
log
in
the
right
defects,
log
in
the
right
solution
to
it
and
and
therefore
you
know
next
time
it
happens.
F
F
And
these
all
have
been,
you
know,
put
in
into
dfs,
which
is
an
air
traffic
control
infrastructure
we
put
in
this
infrastructure
there,
predictive
intelligence
and-
and
we
are
trying
to
figure
out-
we
are
trying
to
ascertain
that
you
are
getting
the
right
kind
of
you
know,
services
from
the
this
air
traffic
controller.
Similarly,
sbb
is
keeping
their
trains
running
constantly
regularly
without
any
fail
in
the
swiss
in
the
swiss
country.
Because
of
this
predictive
maintenance
piece
being
in
being
in
place.
B
B
B
F
F
So
what
we
prescribe
from
sap
side
is
that
you
start
at
the
core,
which
is
the
manufacturing
piece.
You
start
with
predictive
maintenance
product
as
a
service.
Then
you
get
into
the
asset
intelligence
network.
Then
you
start
moving
outwards
to
say.
Logistics
will
start
coming
in
move
further
outwards
to
get
your
vendors
brought
on
on
on
industry
4.0.
F
And
then
you
look
at
the
customers.
Normally,
I
have
seen
that
people
start
worrying
about
the
customers
and
vendors
before
they.
They
have
internally
done
some
homework
and
got
the
data
in
place
most
of
the
times.
People
think
that
they
have
the
right
data,
they
have
the
right
quantity
of
data
and
they
have
sufficient
data
for
them
to
be
able
to
do
a
good
machine,
learning
algorithm
or
a
good
predictive
kind
of
a
implementation.
F
You
are
okay
and
I'll.
Take
another
30
seconds
to
finish
this
off.
First
of
all,
get
a
maturity
assessment
done.
We
can
help
you
do
that
or
you
can.
You
know
just
do
a
self-service
online
get
a
maturity
assessment
done
too,
as
to
where
you
are
right
now
and
find
out
what
are
the
opportunities
available?
F
What
is
where
is
the
business
potential
that
will
help
you
put
all
of
these
realize
certain
you
know
roi
and
then
like
like,
I
spoke
earlier,
do
an
assessment
of
where
you
are
today
and
if
this
this
is
implemented,
what
is
the
you
know,
impact
that
you're
going
to
create
think
about
what
is
going
to
happen
in
the
future
once
you
have
already
deployed
these
technologies
based
on
the
industry,
four
dot,
now
principle
on
industry,
4.0
principles.
Once
you
have
done
this
assessment,
then
you
start
small.
F
If,
if
you
need
to
fail,
you
fail
fast
and
then
you
know
start
moving
from
one
to
the
other
piece.
We
usually
recommend
a
design
thinking
human
centered
innovation
workshop,
which
will
you
know,
sort
of
give
you
very
clear
picture
of
where
you
are
where
you
need
to
go.
What
are
the
actions
that
you
need
to
take
right?
There
are
if
there
are
partners
who
are
you
know,
sort
of
working
with
you
on
these?
We
are
happy
to
support
you.
F
We
are
happy
to
safeguard
your
investments
and
we
are
happy
to
support
the
partner
in
getting
you
on
this
journey
and
moving
forward.
We
also
have
services
where
we
can,
you
know,
do
the
implementation
or
do
in
our
enterprise
architecture,
or
so
that
you
know
you
have
a
very
clear
picture
of
how
you're
going
to
move
forward
and
what
is.
F
Yes,
you
have
you
have
all
of
these
information.
I
think
mukesh
is
going
to
share
this
deck
with
you.
You
will
be
able
to
access
all
of
these
yeah
and
if
there
are
any
other
challenges
that
you
are
looking
at,
you
want
answers
to.
We
can
do
those
answers.
Question
answer
session
now
or
we
can.
You
know,
connect
later
as
well.
B
Thank
you
so
much
for
staying
late,
five
minutes
and
we
are
sorry
to
keep
you
like.
You
know,
wait
for
another
five
minutes.
So
what
you
can
do
is
that
you
can
take
some
questions
that
we
will
be
here
for
another.
You
know
10
minutes,
please
let
us
know.
If
you
have
got
any
questions,
we
will
be
happy
to
address
any
questions
you
may
be
having.
B
B
Okay,
so
if
no
more
questions,
thank
you
so
much
from
all
of
us
for
staying
late
and
attending
the
session
and
participating
in
the
survey
and
asking
the
question
we'll
be
sharing
the
recording
and
the
tech
with
all
of
you.
And
if
there
is
any
questions
like
you
know,
mukesh
who
works
with
you
all
the
time,
so
you
can
either
reach
out
to
him
myself
or
within
nandi.
Anything
last
word
from
your
side:
mukesh.
A
Thanks
a
lot
to
the
entire
recipe
team,
to
satya,
to
vpn,
to
marvin,
to
general
for
taking
us
through
the
entire
session
and
thanks
everyone
for
joining
and
staying
till
the
end
of
the
session.
At
any
point
in
time
you
have
any
questions
please
do
reach
out
to
me.
I
have
uploaded
all
the
previous
four
recordings
already
on
to
the
indus
youtube
channel.
I
will
be
adding
this
to
the
channel
as
well.
So
you'll
have
one
place
all
the
five
sessions
available.
A
If
you
want
to
share
it
with
your
colleagues
and
teams,
if
you
want
to
do
a
one-on-one
conversation
with
with
any
of
the
experts
that
we've
had
so
far
in
this
entire
series,
please
drop
me
an
email
you
can
write
to
mukesh.sap.com
or
you
can
talk
to
administrator
desk
community.com,
and
I
can
connect
you
to
the
respective
speakers
so
with
that.
Thank
you
so
much
for
your
time
and
I
hope
that
the
series
was
useful,
helpful
and
helps
you
in
your
transformation
journey
as
well.
So
thank
you
so
much
take
care
bye.