Using process mining for workarounds analysis in context: Learning from a small and medium-sized company case

Abstract

Workarounds are deviations in the execution of designed, de jure, work processes. Process mining research has developed methods for unobtrusive workaround analysis using process-aware systems’ datasets. This study applies process mining for workaround analysis in a medium-sized enterprise (SME). SME contexts can be challenging for workaround mining because SMEs often lack de jure process designs and their process-supportive information systems may have ambiguous semantics. The identification of de jure models and the solving of systems data ambiguities are the first steps in workarounds identification. A semantically well-defined information system may enable factual, de facto, process mining. Comparing the de jure and de facto process models may give candidate workarounds. Our study shows that (1) incomplete de jure models hinder the use of process mining for detecting workarounds, and (2) human interpretation of process mining outcomes is needed to realize a useful triple loop organizational learning from workarounds mining.