Improving Supply-Chain-Management based on Semantically Enriched Risk Descriptions

Sandro Emmenegger, Emanuele Laurenzi, Barbara Thönssen


To discover risk as early as possible is a major demand of today’s supply-chain- risk-management. This includes analysis of internal resources (e.g. ERP and CRM data) but also of external sources (e.g. entries in the Commercial Register and newspaper reports). It is not so much the problem of getting the information as to analyze and evaluate it near-term, cross-linked and forward-looking. In the APPRIS project an Early-Warning-System (EWS) is developed applying semantic technologies, namely an enterprise ontology and an inference engine, for the assessment of procurement risks. The approach allows for integrating data from various information sources, of various information types (structured and unstructured), and information quality (assured facts, news); automatic identification, validation and quantification of risks and aggregation of assessment results on several granularity levels. For representation the graphical user interface of a project partner’s commercial supply-management-system is used. Motivating scenario is derived from three business project partners’ real requirements for an EWS with special reference to the downstream side of supply chain models, to suppliers’ company structures and single sourcing.


  1. Bertolazzi, P. et al., 2001. An Approach to the Definition of a Core Enterprise Ontology?: CEO. In International Workshop on Open Enterprise Solutions: Systems, Experiences, and Organizations - OES-SEO 2001. Rome, pp. 104-115.
  2. Chi, Y.-L., 2010. Rule-based ontological knowledge base for monitoring partners across supply networks. Expert Systems with Applications, 37(2), pp.1400- 1407. Available at: retrieve/pii/S0957417409006514 [Accessed March 6, 2012].
  3. Chung, W. W. C., Yam, A. Y. K. & Chan, M. F. S., 2004. Networked enterprise: A new business model for global sourcing. International Journal of Production Economics, 87(3), pp.267-280. Available at: http:// 226 [Accessed July 12, 2011].
  4. Cyganiak, R., 2012. The D2RQ Platform - Accessing Relational Databases as Virtual RDF Graphs. Available at:
  5. Dietz, J. L. G., 2006. Enterprise Ontology. Theory and Methodology, Berlin Heidelberg: Springer-Verlag.
  6. Emmerson, C., 2011. Global Risks 2011 Sixth Edition,
  7. Fox, M. S. & Grüninger, M., 1998. Enterprise Modeling. AI Magazine, 19(3), pp.109-121.
  8. Fox, M. S. et al., 1996. An Organization Ontology for Enterprise Modelling. Simulating Organizations: Computational Models of Institutions and Groups, (AAAI/MIT Press), pp.131-152.
  9. Friedman-Hill, E., 2008. Jess, the Rule Engine for the Java Platform V7.1., pp.1-198. Available at: http://
  10. Geerts, G. L. & McCarthy, W. E., 2000. The Ontological Foundation of REA Enterprise Information Systems,
  11. Geissbauer, R. & D'heur, M., 2011. 2010-2012. Global Supply Chain Trends,
  12. Grosse-Ruyken, P. T. & Wagner, S. M., 2011. APPRIS Project Report, Zürich.
  13. Grubic, T. & Fan, I.-shing, 2010. Computers in Industry Supply chain ontology?: Review , analysis and synthesis. Computers in Industry, 61(8), pp.776-786. Available at: j.compind.2010.05.006.
  14. Hinkelmann, K., Merelli, E. & Thönssen, B., 2010. The Role of Content and Context in Enterprise Repositories. In Proceedings of the 2nd International Workshop on Advanced Enterprise Architecture and Repositories - AER 2010.
  15. Horrocks, I. et al., 2004. SWRL?: A Semantic Web Rule Language Combining OWL and RuleML. Syntax, (May), pp.1-31.
  16. Leppänen, M., 2005. A Context-Based Enterprise Ontology. In G. Guizzardi & G. Wagner, eds. Proceedings of the EDOC International Workshop on Vocabularies, Ontologies and Rules for the Enterprise (VORTE'05). Enschede, Netherlands: Springer Berlin, pp. 17-24.
  17. O'Connor, M. & Das, A., 2011. SQWRL: a Query Language for OWL.
  18. Park, K. H. & Favrel, J., 1999. Virtual Enterprise - Information System and Networking Solution. Computers & Industrial Engineering, 37, pp.441-444.
  19. Pergler, E. & Lamarre, M., 2009. Risk: Seeing around the corners,
  20. Priddat, B. P., 2002. E-Government als Virtualisierungsstrategie des States. Demokratisierung der Wissensgesellschaft und professioneller Staat. TECHNIKFOLGENABSCHÄ TZUNG Theorie und Praxis, 11(3/4).
  21. Prud'hommeaux, E. & Seaborne, A., 2008. SPARQL Query Language for RDF. Available at: http://
  22. Tah, J. H .M. & Carr, V., 2001. Towards a framework for project risk knowledge management in the construction supply chain. Advances in Engineering Software, 32(10-11), pp.835-846. Available at: http:// 357.
  23. The Institute of Operational Risk, 2010. Key Risk Indicators,
  24. The Open Group, 2009. ArchiMate 1.0 Specification, Available at: 20087110.
  25. Thönssen, B., 2010. An Enterprise Ontology Building the Bases for Automatic Metadata Generation. In Proceedings of the 4th International Conference on Metadata and Semantics, MTSR1200. Madrid, pp. 195-210.
  26. Thönssen, B., 2011. Forrmalizing low - level governance instruments for a more holistic approach to automatic metadata generation. In Proceedings of the 5th International Conference on Methodologies, Technologies and Tools enabling e-Government. Camerino, Italy, pp. 1-12.
  27. Thönssen, B., 2012. Turning Risks Into Opportunities. Electronic Government, tbp. Available at: http:// 72.
  28. Thönssen, B. & Wolff, D., 2010. A broader view on Context Models to support Business Process Agility. In S. Smolnik, F. Teuteberg, & O. Thomas, eds. Semantic Technologies for Business and Information Systems Engineering: Concepts and Applications.
  29. Uschold, M. et al., 1997. The Enterprise Ontology,
  30. Volatier, L., Cordon, C. & Gallery, C., 2009. Suppliers and vendors first. International Association For Contract & Commerical Management (IACCM). Available at:
  31. Xiwei, W., Stößlein, M. & Kan, W., 2010. Designing knowledge chain networks in China - A proposal for a risk management system using linguistic decision making. Technological Forecasting and Social Change, 77(6), pp.902-915. Available at: http:// 03X [Accessed November 16, 2011].

Paper Citation

in Harvard Style

Emmenegger S., Laurenzini E. and Thönssen B. (2012). Improving Supply-Chain-Management based on Semantically Enriched Risk Descriptions . In Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2012) ISBN 978-989-8565-31-0, pages 70-80. DOI: 10.5220/0004139800700080

in Bibtex Style

author={Sandro Emmenegger and Emanuele Laurenzini and Barbara Thönssen},
title={Improving Supply-Chain-Management based on Semantically Enriched Risk Descriptions},
booktitle={Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2012)},

in EndNote Style

JO - Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2012)
TI - Improving Supply-Chain-Management based on Semantically Enriched Risk Descriptions
SN - 978-989-8565-31-0
AU - Emmenegger S.
AU - Laurenzini E.
AU - Thönssen B.
PY - 2012
SP - 70
EP - 80
DO - 10.5220/0004139800700080