Bayesian Process Networks: An Approach to Systemic Process Risk Analysis by Mapping Process Models onto Bayesian Networks
Abstract
This paper presents an approach to mapping a process model onto a Bayesian network resulting in a Bayesian Process Network, which will be applied to process risk analysis. Exemplified by the model of Event-driven Process Chains, it is demonstrated how a process model can be mapped onto an isomorphic Bayesian network, thus creating a Bayesian Process Network. Process events, functions, objects, and operators are mapped onto random variables, and the causal mechanisms between these are represented by appropriate conditional probabilities. Since process risks can be regarded as deviations of the process from its reference state, all process risks can be mapped onto risk states of the random variables. By example, we show how process risks can be specified, evaluated, and analyzed by means of a Bayesian Process Network. The results reveal that the approach presented herein is a simple technique for enabling systemic process risk analysis because the Bayesian Process Network can be designed solely based on an existing process model.
References
[2] Fenton, N.E. and Neil, M.D. 2012. Risk Assessment and Decision Analysis with Bayesian Networks. CRC Press/Chapman & Hall, ISBN-10: 1439809100, 524 p.
[3] Keller, G., Nüttgens, M., and Scheer, A.-W. 1992. Semantische Prozessmodellierung auf der Grundlage ereignisgesteuerter Prozessketten (EPK). Veröffentlichungen des Instituts für Wirtschaftsinformatik (IWi). Institut für Wirtschaftsinformatik im Institut für Empirische Wirtschaftsforschung. Universität des Saarlandes.
[4] Kjærulff, U.B., and Madsen, A.L. 2013. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis. Springer. Available at: http://dx.doi.org/10.1007/978-1-4614-5104-4
[5] Nadkarni, S., and Shenoy, P.P. 2001. A Bayesian network approach to making inferences in causal maps. European Journal of Operational Research, 12 (3): 479–498. Available at: http://dx.doi.org/10.1016/S0377-2217(99)00368-9
[6] Nadkarni, S., and Shenoy, P.P. 2004. A causal mapping approach to constructing Bayesian networks. Decision Support Systems, 38 (2): 259–281. Available at: http://dx.doi.org/10.1016/S0167-9236(03)00095-2
[7] Pearl, J. 2009. Causality: Models, Reasoning, and Inference. Cambridge: Univ. Press.
[8] Scheer, A.-W. 1995. Wirtschaftsinformatik. Referenzmodelle für industrielle Geschäftsprozesse. Springer-Verlag Berlin Heidelberg, ISBN 978-3-662-10962-5. DOI 10.1007/978-3-662-10962-5
[9] Scheer, A.-W. 1998. ARIS - Modellierungsmethoden, Metamodelle, Anwendungen. Springer-Verlag Berlin Heidelberg, ISBN: 978-3-642-97731-2. DOI 10.1007/978-3-642-97731-2
[10] Scheer, A.-W. 2002. ARIS - vom Geschäftsprozess zum Anwendungssystem. Springer-Verlag Berlin Heidelberg, ISBN: 978-3-642-56300-3. DOI 10.1007/978-3-642-56300-3
[11] Suriadi, S., Weiss, B., Winkelmann, A., Hofstede, A.H.M., Adams, M., Conforti, R., et al. 2014. Current research in risk-aware business process management: Overview, comparison, and gap analysis. Communications of the Association for Information Systems, 34(1): 933–984.
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