Waste Identification from Event Logs

Abstract

Organizations execute a variety of business processes to meet their business objectives. Therefore, they seek to constantly improve such processes. One way to improve the efficiency of processes is to identify and eliminate wastes in a process. Analysts use different process mining software to discover and analyze business processes. Event logs, i.e., data captured from the execution of business processes, are used to discover and analyze processes to identify wastes. To identify wastes from event logs, analysts need to know exactly what to look for. However, wastes are manifested in business processes in different ways. Therefore, manifestations of wastes that the analyst is unfamiliar with, remain hidden. This thesis aims at identifying the manifestations of wastes in business processes and how to detect them from event logs. To this end, 187 relevant papers were identified and subjected to content analysis. From these, manifestations of 8 wastes in business processes were elicited. Following this, a framework for how to detect such wastes from business process event logs was derived. Thus, the contribution of the thesis is a framework for identifying wastes from event logs.