Digital Library

of the European Council for Modelling and Simulation



Use Of Fuzzy Reasoning In The Simulation Of Risk Events In

Business Processes


Paul Taylor, Jesús Jimenez Godino, Basim Majeed

Published in:


ECMS 2008 Proceedings

Edited by: Loucas S. Louca, Yiorgos Chrysanthou, Zuzana Oplatkova, Khalid Al-Begain


ISBN: 978-0-9553018-6-5

Doi: 10.7148/2008


22nd European Conference on Modelling and Simulation,

Nicosia, June 3-6, 2008


Citation format:

Taylor, P., Godino, J. J., & Majeed, B. (2008). Use of Fuzzy Reasoning in the Simulation of Risk Events in Business Processes. ECMS 2008 Proceedings edited by: L. S. Louca, Y. Chrysanthou, Z. Oplatkova, K. Al-Begain (pp. 25-30). European Council for Modeling and Simulation. doi:10.7148/2008-0025.



The current drive towards Service Oriented Architecture (SOA) and Business Process Execution Language (BPEL) in enterprises will increase dependency on efficient businesses processes. In the current competitive environment, process efficiency gains are seen as a crucial factor for business success. However it is not sufficient to design a process that works well under normal conditions. Risk analysis and mitigation is an important activity that should be tackled systematically during process design and improvement. The process designer’s job has thus become particularly complex, requiring tools that combine traditional business process management with operational risk analysis.

In this paper we introduce a simulation environment that has been developed within British Telecommunications plc to simulate business process performance. The simulator incorporates a facility to simulate arbitrary risk effects on the performance of the process. Since risk analysis typically deals with qualitative values such as “high probability risk” or “low impact risk”, measuring key risk indicators (KRIs) can be difficult. The simulator allows the process designer to formulate a fuzzy system of rules to define how risk is measured; these allow the user to produce KRIs that utilise the qualitative risk knowledge in addition to the ability to derive quantitative risk measures should they be needed.

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