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Nowadays, organizations have to adjust their business processes along with the changing environment in order to maintain a competitive advantage. Changing a part of the system to support the business process implies changing the entire system, which leads to complex redesign activities. In this paper, a bottom-up process mining and simulation-based methodology is proposed to be employed in redesign activities. The methodology starts with identifying relevant performance issues, which are used as basis for redesign. A process model is "mined" and simulated as a representation of the existing situation, followed by the simulation of the redesigned process model as prediction of the future scenario. Finally, the performance criteria of the current business process model and the redesigned business process model are compared such that the potential performance gains of the redesign can be predicted. We illustrate the methodology with three case studies from three different domains: gas industry, government institution and agriculture.

Abstract. Effective information systems require the existence of explicit process models. A completely specified process design needs to be developed in order to enact a given business process. This development is time consuming and often subjective and incomplete. We propose a method that constructs the process model from process log data, by determining the relations between process tasks. To predict these relations, we employ machine learning technique to induce rule sets. These rule sets are induced from simulated process log data generated by varying process characteristics such as noise and log size. Tests reveal that the induced rule sets have a high predictive accuracy on new data. The effects of noise and imbalance of execution priorities during the discovery of the relations between process tasks are also discussed. Knowing the causal, exclusive, and parallel relations, a process model expressed in the Petri net formalism can be built. We illustrate our approach with real world data in a case study.

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