A knowledge graph-based decision support system for resilient supply chain networks
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Author (Corporation)
Publication date
2024
Typ of student thesis
Course of study
Collections
Type
04B - Conference paper
Editor (Corporation)
Supervisor
Parent work
Research challenges in information science
Special issue
DOI of the original publication
Link
Series
Lecture Notes in Business Information Processing
Series number
513
Volume
1
Issue / Number
Pages / Duration
66-81
Patent number
Publisher / Publishing institution
Springer
Place of publication / Event location
Guimarães
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
Events in recent years such as the Russo-Ukrainian war of 2022 and the covid-19 pandemic have once again shown the importance of relying on resilient supply chain networks. The creation and maintenance of such networks is, however, a rather knowledge intensive task, which is still challenging. To tackle this, we introduce a first version of a knowledge graph-based decision support system aiming to help supply chain risk managers to make sourcing decisions. The system was designed by following the design science research methodology, which is supplemented with the Ontology Development 101 [25] for rigor in creation of the knowledge graph schema. Competency questions elicited with domain experts were used to evaluate the proposed system.
Keywords
Subject (DDC)
Event
18th International Conference, RCIS 2024
Exhibition start date
Exhibition end date
Conference start date
14.05.2024
Conference end date
17.05.2024
Date of the last check
ISBN
978-3-031-59464-9
978-3-031-59465-6
978-3-031-59465-6
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Closed
License
Citation
Düggelin, W., & Laurenzi, E. (2024). A knowledge graph-based decision support system for resilient supply chain networks. In J. Araújo, J. L. de la Vara, M. Y. Santos, & S. Assar (Eds.), Research challenges in information science (Vol. 1, pp. 66–81). Springer. https://doi.org/10.1007/978-3-031-59465-6_5