A Fuzzy Rule-Based Decision Support in Process Mining: Turning Diagnostics into Prescriptions

In this study, a fuzzy rule-based framework has been developed that expands from the diagnostic analyses traditionally offered by process mining to a decision-support structure that provides recommendations to managers. While traditional process mining methods are widely used to identify bottlenecks and inefficiencies, they have often produced results that merely describe the current situation and have failed to provide managers with applicable solutions. Therefore, this paper designs a hybrid method combining statistical data preprocessing, process mining, and fuzzy inference mechanisms. First, statistical analysis was carried out to determine which activities are most influential in terms of process lead time. Subsequently, a procedure mining approach was used to locate structural bottlenecks and delay patterns, which the Bottleneck Severity Index rated. To translate diagnostic insights into managerially actionable recommendations, the study constructed a fuzzy decision tree-based inference model. While the model is easily understood and implemented, it presents its results in explicit IF-THEN rules. The approach was applied to a real IT service process with 1500 cases and 46,618 events. The fuzzy rule-based system generated tangible improvements: the cycle time was reduced by 26.4%, the bottleneck events decreased by 55.1% and the operational cost savings were calculated to be of 17.7%.