Detecting temporal workarounds in business processes – A deep-learning-based method for analysing event log data

Abstract

Business process management distinguishes the actual “as-is” and a prescribed “to-be” state of a process. In practice, many different causes trigger a process’s drifting away from its to-be state. For instance, employees may “workaround” the proposed systems to increase their effectiveness or efficiency in day-to-day work. So far, ethnography or critical incident techniques are used to identify how and why workarounds emerge. We design a deep-learning-based method that helps detect different workaround types in event logs. Our method tracks indications of potential workarounds in the early stages of their emergence among deviating behaviour. Our evaluation based on four real-life event logs reveals that our method performs well and works best for business processes with fewer variations and a higher number of different activities. The proposed method is one of the first information technology artefacts to bridge the boundaries between the complementing research disciplines of organisational routines and business processes management.