Discovery of Improvement Opportunities in Knock-Out Checks of Business Processes.

Abstract Overprocessing is a source of waste that occurs when unnecessary work is performed in a process. Overprocessing is often found in application-to-approval processes since a rejected application does not add value, and thus, work that leads to the rejection constitutes overprocessing. Analyzing how the knock-out checks are executed can help analysts to identify opportunities to reduce overprocessing waste and time. This paper proposes an interpretable process mining approach for discovering improvement opportunities in the knock-out checks and recommending redesigns to address them. Experiments on synthetic and real-life event logs show that the approach successfully identifies improvement opportunities while attaining a performance comparable to black-box approaches. Moreover, by leveraging interpretable machine learning techniques, our approach provides further insights on knock-out check executions, explaining to analysts the logic behind the suggested redesigns. The approach is implemented as a software tool and its applicability is demonstrated on a real-life process.