Business intelligence for improving supply chain risk management

L. Liu, H.A.M. Daniels, W. Hofman

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Abstract

The risk management over a supply chain has to be founded on the management controls in each of the partner companies in the chain. Inevitably, the business relationship and operations dependence bind the control efforts of partner companies together. This proposes challenges for supply chain risk management and at the same time for the BI application. In this paper we analyse the management control situations where business intelligence technology can be applied and describe the concepts of systematic risk analysis to improve the management controls, based on causal analysis of business exceptions. The analysis process is driven by diagnostic drill-down operations following the equations of the information structure in which the data are organised. Using business intelligence, the analysis method can generate explanations supported by the data. A “risk template” is provided to assist analysts to fully comprehend the risk scenario in the practical business setting, so as to evaluate and re-design the existing controls, and to apply BI for management improvement.
Original languageEnglish
Title of host publicationEnterprise Information Systems
Subtitle of host publicationRevised Selected Papers, ICEIS 2013
EditorsS. Hammoudi, J. Cordeiro, L.A. Maciaszek, J. Filipe
Place of PublicationHeidelberg
PublisherSpringer Verlag
Pages190-205
Volume190
ISBN (Print)9783319094915
DOIs
Publication statusPublished - 2014
Event15th International Conference on Enterprise Information Systems (ECEIS 2013) - Angers, France
Duration: 4 Jul 20137 Jul 2013

Publication series

NameLecture Notes in Business Information Processing
Volume190

Conference

Conference15th International Conference on Enterprise Information Systems (ECEIS 2013)
CountryFrance
CityAngers
Period4/07/137/07/13

Fingerprint

Business intelligence
Management control
Supply risk management
Diagnostics
Analysts
Scenarios
Risk management
Process analysis
Redesign
Systematic risk
Template
Risk analysis
Information structure
Business relationships
Supply chain

Keywords

  • risk management
  • supply chain exceptions
  • business intelligence
  • collaboration

Cite this

Liu, L., Daniels, H. A. M., & Hofman, W. (2014). Business intelligence for improving supply chain risk management. In S. Hammoudi, J. Cordeiro, L. A. Maciaszek, & J. Filipe (Eds.), Enterprise Information Systems: Revised Selected Papers, ICEIS 2013 (Vol. 190, pp. 190-205). (Lecture Notes in Business Information Processing; Vol. 190). Heidelberg: Springer Verlag. https://doi.org/10.1007/978-3-319-09492-2_12
Liu, L. ; Daniels, H.A.M. ; Hofman, W. / Business intelligence for improving supply chain risk management. Enterprise Information Systems: Revised Selected Papers, ICEIS 2013. editor / S. Hammoudi ; J. Cordeiro ; L.A. Maciaszek ; J. Filipe. Vol. 190 Heidelberg : Springer Verlag, 2014. pp. 190-205 (Lecture Notes in Business Information Processing).
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Liu, L, Daniels, HAM & Hofman, W 2014, Business intelligence for improving supply chain risk management. in S Hammoudi, J Cordeiro, LA Maciaszek & J Filipe (eds), Enterprise Information Systems: Revised Selected Papers, ICEIS 2013. vol. 190, Lecture Notes in Business Information Processing, vol. 190, Springer Verlag, Heidelberg, pp. 190-205, 15th International Conference on Enterprise Information Systems (ECEIS 2013), Angers, France, 4/07/13. https://doi.org/10.1007/978-3-319-09492-2_12

Business intelligence for improving supply chain risk management. / Liu, L.; Daniels, H.A.M.; Hofman, W.

Enterprise Information Systems: Revised Selected Papers, ICEIS 2013. ed. / S. Hammoudi; J. Cordeiro; L.A. Maciaszek; J. Filipe. Vol. 190 Heidelberg : Springer Verlag, 2014. p. 190-205 (Lecture Notes in Business Information Processing; Vol. 190).

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

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Liu L, Daniels HAM, Hofman W. Business intelligence for improving supply chain risk management. In Hammoudi S, Cordeiro J, Maciaszek LA, Filipe J, editors, Enterprise Information Systems: Revised Selected Papers, ICEIS 2013. Vol. 190. Heidelberg: Springer Verlag. 2014. p. 190-205. (Lecture Notes in Business Information Processing). https://doi.org/10.1007/978-3-319-09492-2_12