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Optimizing business processes by learning from monitoring results

In the current paper we propose analyzing the recorded information about broken rules in business process execution and using it for enhancing business process models defined inside organizations for specific business cases. An alert system is designed as a Web Application for detecting deviations by verifying the event log data of the running cases. We consider it a Business Activity Monitoring solution implementing a custom rule definition language mainly created for ensuring the operational support inside organizations by providing guidance to people as task owners in real time about their actions not compliant with process definition. The current system is able to analyze real-time events with the scope of identifying problems, diagnosing them and generating alerts to recommend managerial action. The main effect should be a better performance of the organization and better business results. We consider that processes are meant to automatize and ease the work and should be commonly understood and accepted by the people contributing inside organizations. A rule constantly broken could indicate that a process difficult to follow in specific areas. Such statistical information about broken rules could represent the input for business process improvements. We also consider as a possibility referencing a process definition instead of a set of rules. The running cases are checked for conformance with the reference process model and if the case is not compliant, a trigger is activated and the information is used further for a possible evolution of business process definitions. The business process model is re-built from monitoring results and statistical information.