Rules-based process mining to discover PLM system processes

Abstract

Process Mining (PM) is defined by a set of techniques used in Business Process Management that combines computational intelligence and data mining with process analysis and process modeling. The growing interest in PM is based on the ability to discover, monitoring, and improve the processes model. To reach this objective knowledge is extracted from the Event Logs generated by Information Systems, and quality metrics are used to evince the quality of the matching obtained when replaying a process model against the event log. The application of PM to logs extracted from PLM systems is an almost unexplored topic in this research area. Our study enhances the application in the field of PLM with the use of business rules to filter the log, verifying the BRs impact on PM metrics in order to minimize the divergences between modeled processes and executed one and to increase the resulting quality metrics. This helps the business user to identify a line of investigation for explaining occurring misbehavior and propose alleviation/improvement measures. Our approach is finally validated on data provided by an industrial company, by confirming the impact that controlling the business process characterizations via BR can decrease the gap between the expected modeled process and the executed one.