An integrated asset management data platform

Part
2
of
this
four-part
series
discusses
the
complex
tasks
energy
utility
companies
face
as
they
shift
to
holistic
grid
asset
management
to
manage
through
the

energy
transition.
The

first
post
of
this
series
addressed
the
challenges
of
the
energy
transition
with
holistic
grid
asset
management.
In
this
part,
we
discuss
the
integrated
asset
management
platform
and
data
exchange
that
unite
business
disciplines
in
different
domains
in
one
network.

The
asset
management
ecosystem

The
asset
management
network
is
complex.
No
single
system
can
manage
all
the
required
information
views
to
enable
end-to-end
optimization.
The
following
figure
demonstrates
how
a
platform
approach
can
integrate
data
flows.

Asset
data
is
the
basis
for
the
network.
Enterprise
asset
management
(EAM)
systems,
geographic
information
systems
and
enterprise
resource
planning
systems
share
technical,
geographic
and
financial
asset
data,
each
with
their
respective
primary
data
responsibility.
The
EAM
system
is
the
center
for
maintenance
planning
and
execution
via
work
orders.
The
maintenance,
repair
and
overhaul
(MRO)
system
provides
necessary
spare
parts
to
carry
out
work
and
maintains
an
optimum
stock
level
with
a
balance
of
stock
out
risk
and
part
holding
costs.

The
health,
safety
and
environment
(HSE)
system
manages
work
permits
for
safe
work
execution
and
tracks
and
investigates
incidents.
The
process
safety
management
(PSM)
system
controls
hazardous
operations
through
safety
practices,
uses
bow-tie
analysis
to
define
and
monitor
risk
barriers,
and
manages
safety
and
environmental
critical
elements
(SECE)
to
prevent
primary
containment
loss.
Monitoring
energy
efficiency
and
greenhouse
gas
or
fugitive
emissions
can
directly
contribute
to
environmental,
social
and
governance
(ESG)
reporting,
helping
to
manage
and
reduce
the
carbon
footprint.

Asset
performance
management
(APM)
strategy
defines
the
balance
between
proactive
and
reactive
maintenance
tasks.
Asset
criticality
defines
whether
a
preventive
or
predictive
task
is
justified
in
terms
of
cost
and
risk.
The
process
of
defining
the
optimum
maintenance
strategy
is
called
reliability-centered
maintenance.
The
mechanical
integrity
of
hazardous
process
assets,
such
as
vessels,
reactors
or
pipelines,
requires
a
deeper
approach
to
define
the
optimum
risk-based
inspection
intervals.
For
process
safety
devices,
a
safety
instrumented
system
approach
determines
the
test
frequency
and
safety
integrity
level
for
alarm
functions.

Asset
data
APM
collects
real-time
process
data.
Asset
health
monitoring 
and
predictive
maintenance 
functions
receive
data
via
distributed
control
systems
or
supervisory
control
and
data
acquisition
systems
(SCADA).
Asset
health
monitoring
defines
asset
health
indexes
to
rank
the
asset
conditions
based
on
degradation
models,
failures,
overdue
preventive
work
and
any
other
relevant
parameters
that
reflect
the
health
of
the
assets.
Predict
functionality
builds
predictive
models
to
predict
imminent
failures
and
calculate
assets’
remaining
useful
life.
These
models
often
incorporate
machine
learning
and
AI
algorithms
to
detect
the
onset
of
degradation
mechanisms
in
an
early
stage.

In
the
asset
performance
management
and
optimization
(APMO)
domain,
the
team
collects
and
prioritizes
asset
needs
resulting
from
asset
strategies
based
on
asset
criticality.
They
optimize
maintenance
and
replacement
planning
against
the
constraints
of
available
budget
and
resource
capacity.
This
method
is
useful
for
regulated
industries
such
as
energy
transmission
and
distribution,
as
it
allows
companies
to
remain
within
the
assigned
budget
for
an
arbitrage
period
of
several
years.
The
asset
replacement
requirements
enter
the
asset
investment
planning
(AIP)
process,
combining
with
new
asset
requests
and
expansion
or
upgrade
projects.
Market
drivers,
regulatory
requirements,
sustainability
goals
and
resource
constraints
define
the
project
portfolio
and
priorities
for
execution.
The
project
portfolio
management
function
manages
the
project
management
aspects
of
new
build
and
replacement
projects
to
stay
within
budget
and
on
time.
Product
lifecycle
management
covers
the
stage-gated
engineering
process
to
optimize
the
design
of
the
assets
against
the
lowest
total
cost
of
ownership
within
the
boundaries
of
all
other
stakeholders.

An
industry-standard
data
model

A
uniform
data
model
is
necessary
to
get
a
full
view
of
combined
systems
with
information
flowing
across
the
ecosystem.
Technical,
financial,
geographical,
operational
and
transactional
data
attributes
are
all
parts
of
a
data
structure.
In
the
utilities
industry,
the
common
information
model
offers
a
useful
framework
to
integrate
and
orchestrate
the
ecosystem
to
generate
optimum
business
value.

The
integration
of
diverse
asset
management
disciplines
in
one
provides
a
full
360°
view
of
assets.
This
integration
allows
companies
to
target
the
full
range
of
business
objectives
and
track
performance
across
the
lifecycle
and
against
each
stakeholder
goal.

Read
more
about
IBM
Data
Model
for
Energy
and
Utilities