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A Knowledge Graph-Based Decision Support System for Resilient Supply Chain Networks

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Research Challenges in Information Science (RCIS 2024)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 513))

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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.

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Acknowledgements

This work has been supported by metaphacts GmbH. We would like to thank Jesse Lambert and Sebastian Schmidt. Further we thank all our interviewees for their willingness to share their knowledge and experiences with us. Especially J.M. for acting as a constant sparring partner throughout our research.

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Authors and Affiliations

  1. FHNW University of Applied Sciences and Arts Northwestern Switzerland, Riggenbachstrasse 16, 4600, Olten, Switzerland

    Wilhelm Düggelin & Emanuele Laurenzi

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  1. Wilhelm Düggelin
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  2. Emanuele Laurenzi
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Correspondence to Emanuele Laurenzi .

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Editors and Affiliations

  1. NOVA University Lisbon, Caparica, Portugal

    João Araújo

  2. University of Castilla La Mancha, Albacete, Albacete, Spain

    Jose Luis de la Vara

  3. University of Minho, Guimarães, Portugal

    Maribel Yasmina Santos

  4. Institut Mines-Télécom Business School, Evry, France

    Saïd Assar

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Düggelin, W., Laurenzi, E. (2024). A Knowledge Graph-Based Decision Support System for Resilient Supply Chain Networks. In: Araújo, J., de la Vara, J.L., Santos, M.Y., Assar, S. (eds) Research Challenges in Information Science. RCIS 2024. Lecture Notes in Business Information Processing, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-031-59465-6_5

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  • DOI: https://doi.org/10.1007/978-3-031-59465-6_5

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  • Online ISBN: 978-3-031-59465-6

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