Analyzing business process changes using influence analysis

Real world business operations are continuously changing. Periodical business performance review sessions typically focus on monitoring changes in key performance indicator (KPI) measures. However, the detection and review of activity level changes in actual business processes is often based on subjective manual observations. This means that many changes are not detected in timely manner making the organization slower to adapt to changes. In this paper we present a systematic method for detecting business process changes for business review purposes based on transaction level data. Our method uses process mining principles and is based on our previously published influence analysis methodology. Unlike most process mining change detection algorithms which operate on case level our method analyzes changes in the individual event level. We show how case level data can be used to construct features to the event level. Our method detects changes in timely manner since there is no need to wait for the cases to be completed. We present two alternative ways, binary approach and continuous event-age approach, for dividing events into recent and old for business review purpose. We also demonstrate the method with data from a real-life case.