OriginTrail Decentralized Knowledge Graph for trusted cross-organization real-time data integration in EU-funded DMaaST
03 Mayo 2024 - 3:00PM
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
Martina Poberaj
Communications Manager
Trace Labs - OriginTrail core developers
office at origin-trail.com