=Paper=
{{Paper
|id=Vol-2180/paper-90
|storemode=property
|title=Semantic Material Master Data Management at Aibel
|pdfUrl=https://ceur-ws.org/Vol-2180/paper-90.pdf
|volume=Vol-2180
|authors=Martin G. Skjæveland,Anders Gjerver,Christian M. Hansen,Johan Wilhelm Klüwer,Morten R. Strand,Arild Waaler,Per Øyvind Øverli
|dblpUrl=https://dblp.org/rec/conf/semweb/SkjaevelandGHKS18
}}
==Semantic Material Master Data Management at Aibel==
https://ceur-ws.org/Vol-2180/paper-90.pdf
Semantic Material Master Data Management at Aibel
Martin G. Skjæveland1 , Anders Gjerver2 , Christian M. Hansen3 , Johan W. Klüwer4 ,
Morten R. Strand3 , Arild Waaler1 , and Per Øyvind Øverli2
1 Department of Informatics, University of Oslo; 2 Aibel; 3 Acando; 4 DNV GL
Aibel is a global engineering, procurement and construction (EPC) service company
based in Norway, best known for its major capital contracts for building and maintaining
large offshore platforms for the oil and gas industry.
Building an oil and gas platform is a complex task. It involves multiple engineer-
ing disciplines, handling a range of suppliers and vendors, large-scale logistics and
warehouse management—all of which is managed by specialised IT infrastructure and
tools. The design of the platform must be in accordance with the customer’s build order,
conform to multiple engineering standards and governmental requirements, and be mate-
rialised by products that match the design. All these different specifications are typically
available only as semi-structured PDF documents that require manual assessment by
experienced discipline specific engineers.
Aibel has taken significant steps to move away from the manual document-driven
process to a digitalised process of selecting appropriate design artefacts and finding
matching products. This is done by representing requirements and specifications in
a custom-built large-scale ontology of ∼80.000 classes called the Material Master
Data (MMD) ontology, and using automated reasoning and queries over the ontology to
perform matching and selection. The effect is that these tasks are performed with greater
precision and less effort than with Aibel’s legacy system, ultimately resulting in a design
of higher quality, which again reduces the total time and cost of construction. Using the
open standard language OWL to represent the information in an application-independent
manner allows the MMD ontology to be applicable throughout Aibel’s tool-chain and
avoids locking in to specific vendor’s tools and work-processes.
The project of constructing the MMD ontology started in 2012 by a team of seasoned
engineers and ontology experts, and is ongoing. The system is in production use by all
of Aibel’s EPC projects. As the ontology is considered a competitive advantage for Aibel,
it is not publicly available.
In comparison to its previous material master system, which was based on a relational
database, Aibel can, as an example, document a reduction of errors in the specification
of bolt lengths from 15 % of all design drawings to a low 0.5 %. (A large project can
have more than 50.000 design drawings.) Such errors can be costly; each incorrect
design drawing requires manual revision by affected stakeholders, and new bolts must
be ordered, causing delay for depending processes.
Aibel now also experiences better storage management and reduced erroneous bulk
orders. The added expressivity and flexibility of the MMD ontology support a more
efficient and precise description of design artefacts. This allows all permissive variations
in the design to be clearly represented, removing practically all duplicate design artefacts
recorded in the system. (It is estimated that more than 30 % of the legacy system’s data
was duplicate data.) The lack of duplicates and added detail in design descriptions make
it easier to manage the material storage and order a better selection of materials for a
given project. The effect is an estimated cost reduction of ∼5 % for bulk material orders,
which in large projects amounts to more than e 100 million.
The MMD ontology comprises ∼200 ontology documents arranged in a strict import
hierarchy. On top are generic ontologies, like PAV and SKOS, and an upper level ontology
based on ISO 15926 (cf. [3]). The next level of ontologies describe generic concepts
in the engineering domain. These top-most ontologies are either directly imported or
hand-crafted by ontology experts in cooperation with domain experts. The lower level
ontologies, which contain more then 90 % of the classes in the MMD ontology, represent
different industry standards and requirements, and, at the very bottom, assembled design
artefacts and product descriptions. These ontology documents are generated from ∼700
structurally simple spreadsheets and from simple web application wizards, both designed
to produce specific types of product descriptions or design artefacts. The spreadsheet
formats and wizards are prepared by ontology experts and experienced domain experts
to let regular engineers actively participate in developing the ontology by populating
the spreadsheets and using the wizards. The ontologies generated from spreadsheets
are constructed via a work-flow of relational databases, R2RML mappings, ISO 15926-7
templates [3], and SPARQL queries. The wizards make continuous use of DL queries over
the ontology to present the user with permissible selections in each step.
The MMD ontology is deployed in an Oracle database which serves variants of the
ontology through different endpoints to support different reasoning profiles. Aibel’s
existing tools, such as their enterprise resource planning (ERP) system and 3D computer-
aided design (CAD) tools, import data from these endpoints. The ontology may also be
browsed by end-users in an easy-to-use custom-made web interface that presents the
ontology in a similar fashion as linked-data front-ends. Aibel’s data managers find that
the expressivity and declarative nature of OWL, paired with the capabilities of ontology
reasoners, make it easy to express and check complex relationships between concepts.
This was practically impossible to implement and maintain in their legacy system.
Aibel will continue to develop and extend the MMD ontology and welcomes advances
in tool-supported methods for constructing and maintaining large-scale ontologies. In
particular tools for: high-performance OWL DL reasoning with better customisation
possibilities and user feedback; lifting and lowering between tabular formats and the
ontology (cf. [2]); query library management; ontology packaging, versioning and
dependency management; improved ontology navigation; and hybrid systems that exploit
the best of the open and closed world paradigms.
Lastly, an appeal: in order for the industry to become truly digital, industry standard
organisations need to stop publishing their data only in formats that are unintelligible to
computers and instead use formats equivalent to at least 4-star linked data [1] quality!
References
1. T. Berners-Lee. Linked data, 2006. https://www.w3.org/DesignIssues/LinkedData.html.
2. J. Farrell and H. Lausen. Semantic Annotations for WSDL and XML Schema, 2007. W3C
Recommendation.
3. J. W. Klüwer, M. G. Skjæveland, and M. Valen-Sendstad. ISO 15926 templates and the
Semantic Web. W3C Workshop on Semantic Web in Oil & Gas Industry, 2008.