OriginTrail Decentralized Knowledge Graph for trusted cross-organization real-time data integration in EU-funded DMaaST

General

Trace
Labs,
the
core
developers
of OriginTrail,
has
joined
the
European
Union’s
initiative
to
foster
a
resilient
and
adaptive
manufacturing
ecosystem
through
the
DMaaST
project.
Collaborating
with
partners
from
Slovenia,
Spain,
Germany,
Portugal,
Turkey,
Serbia,
Belgium,
Lithuania,
France,
Denmark,
and
Switzerland,
the
initiative
will
leverage
the
OriginTrail
Decentralized
Knowledge
Graph
(DKG)
and
Knowledge
Assets
(KA)
to
encapsulate
all
pertinent
information
regarding
products,
processes,
facilities,
and
human
expertise.
This
comprehensive
approach
will
facilitate
the
precise
mapping
of
data
flows
and
knowledge
interconnections,
laying
the
groundwork
for
comprehensive
information
mapping
within
the
manufacturing
ecosystem
using
OriginTrail
DKG.
Consequently,
this
will
ensure
trustworthy
cross-organizational
real-time
data
integration.

Once
more,
attention
has
been
drawn
to
challenges
within
the
aeronautic
and
manufacturing
industries
following
a
January
incident
in
which
a Boeing
737
MAX
9
door
plug
blew
out in
the
middle
of
an
Alaska
Airlines
flight.
If
the
company
had
established
reliable
cross-organizational
communication,
it
could
have
prevented
this
incident.
Such
communication
would
enhance
the
value
chain’s
responsiveness
to
external
and
unforeseen
events,
as
well
as
improve
operability
and
production
planning
capacity.

Effective,
transparent,
and
reliable
data
exchange
are
the
most
important
points
for
fostering
sustainability,
resilience,
and
energy
efficiency
in
the
manufacturing
industry.
However,
over
the
past
years,
various
challenges
have
come
to
the
forefront
within
this
sector.

Supply
Chain
Disruptions: The
COVID-19
pandemic
highlighted
existing
vulnerabilities
in
global
supply
chains,
leading
to
disruptions
in
the
flow
of
materials
and
components.
Issues
such
as
raw
material
shortages,
transportation
bottlenecks,
and
labor
shortages
have
persisted,
impacting
manufacturing
operations
worldwide.
Cybersecurity
Risks: With
the
increasing
digitization
of
manufacturing
processes
through
technologies
like
the
Internet
of
Things
(IoT)
and
Industry
4.0,
cybersecurity
threats
have
become
a
significant
concern.
Manufacturing
facilities
are
increasingly
vulnerable
to
cyberattacks
that
can
disrupt
operations,
steal
sensitive
data,
or
compromise
product
quality
and
safety.
Data
Silos: Manufacturing
organizations
often
operate
with
fragmented
data
systems,
leading
to
isolated
data
silos
across
departments
or
functions.
This
fragmentation
inhibits
seamless
data
interoperability
and
hampers
comprehensive
insights
that
could
drive
operational
efficiency
and
innovation.
Lack
of
Standards: The
absence
of
standardized
data
formats
and
protocols
complicates
data
exchange
and
integration
efforts
within
and
across
manufacturing
enterprises.
Without
universally
accepted
standards,
interoperability
becomes
a
significant
challenge,
impeding
the
flow
of
data
between
different
systems
and
stakeholders.
Data
Privacy
Concerns: With
the
proliferation
of
data
collection
and
sharing
practices
in
manufacturing,
ensuring
data
privacy
and
protection
is
paramount.
Manufacturers
must
navigate
complex
regulatory
landscapes,
safeguarding
sensitive
information
from
unauthorized
access
or
misuse
while
balancing
the
need
for
data-driven
decision-making.
Ownership
and
Control: Determining
ownership
rights
and
control
over
manufacturing
data
can
be
contentious,
especially
in
collaborative
environments
or
supply
chain
networks.
Disputes
may
arise
regarding
data
ownership,
usage
rights,
and
intellectual
property,
complicating
data
sharing
agreements
and
hindering
collaborative
initiatives.
Legacy
Systems
Integration: Many
manufacturing
facilities
still
rely
on
legacy
systems
that
were
not
designed
with
interoperability
in
mind.
Integrating
these
outdated
systems
with
modern
data
platforms
and
technologies
poses
significant
challenges,
requiring
extensive
customization,
retrofitting,
and
investments
in
interoperability
solutions.

DMaaST
aims
to
enhance
manufacturing
ecosystem
resilience
and
adaptability
by
employing
a
Smart
Manufacturing
Platform
comprising
four
layers.
The
data
layer
establishes
a
foundation
for
real-time
data
integration
across
organizations
using
ontologies
and
OriginTrail
Decentralized
Knowledge
Graph.
Following
this,
a
two-level
cognitive
digital
twin
is
deployed
to
model
both
manufacturing
services
production
lines
and
value
chain
stages.
It
incorporates
human
expertise,
data-driven
algorithms,
and
physical
modeling.
An
algorithm
for
multi-objective
distributed
decision
support
systems
leverages
this
data
to
facilitate
optimal
production
decisions.
Outcomes
will
be
communicated
via
user-friendly
interfaces
and
timely
scoreboards,
assessing
circularity,
sustainability,
and
product
traceability.
Over
the
four-year
period,
DMaaST
ensures
scalability
and
innovation
by
providing
insights
for
replicating
and
improving
manufacturing
processes,
advancing
technologies
in
aerospace
and
electronics
sectors.

Trace
Labs
will
lead
the
data
working
group
to
develop
and
validate
technologies
aimed
at
facilitating
data
understanding,
interoperability,
and
secure
cross-organization
integration.
With
integration
of
OriginTrail
DKG
for
the
electronic
and
aeronautical
sector,
creating
a
new
powerful
knowledge
base
with
artificial
intelligence
capabilities.
The
DKG
will
establish
a
decentralized
database
accessible
to
all
participants
in
a
manufacturing
value
chain,
including
manufacturers,
suppliers,
distributors,
retailers,
regulatory
bodies,
research
institutes,
and
others.
This
will
enhance
the
manufacturing
ecosystem’s
ability
to
autonomously
withstand
and
adapt
to
external
events.

OriginTrail
DKG
has
been
widely
utilized
to
foster
trust
and
transparency
in
enterprise
knowledge
exchange
across
various
industries.
Now,
it
is
evolving
to
facilitate
global
knowledge
connectivity,
powering
the Decentralized
Retrieval
Augmented
Generation
(dRAG)
framework for
more
precise
and
inclusive
AI.
Given
the
challenges
of
verifying
AI-generated
results,
OriginTrail
DKG,
with
Knowledge
Assets
as
its
primary
resource,
represents
a
pivotal
innovation
in
this
context.
It
offers
a
robust
framework
for
ensuring
the
ownership,
discoverability,
and
verifiability
of
information
utilized
by
AI
systems
for
the
manufacturing
industry.

Project
information
available
here:

DMaaST
Project