Analyzing the Research Grant Process in Iran’s National Elites Foundation: An Approach Based on Process Mining and Machine Learning

Analyzing the event logs extracted from the process-aware information systems provide critical insights into improving the organizational processes. This case study reports the essential findings and lessons from a process mining project run in analyzing the postdoctoral research grant process in Iran’s National Elites Foundation (INEF). Different deductions are reached by exploring the process participants’ activities in the INEF web portal, including (1) the organizational inefficiencies exposed through the process mining techniques, where the most time-consuming activities are detected and suggested to the domain experts, and (2) the decision tree technique applied in determining how the successful applicants are scored. The extracted rules indicate an 18% application admission with a final score of more than 403. This article contributes to interpreting the behavioral patterns in INEF and determining who among the applicants has a higher chance of receiving the grant, supporting the policymakers and managers to assign rational budgeting and adopt appropriate human resource strategies.