Event Log-based Weaknesses Detection in Business Processes

Abstract

The performance of business processes is a critical success factor for companies on highly competitive markets. The improvement of business processes requires a deep understanding of as-is processes and existing weaknesses that hamper their performance. Traditionally, process mapping and weakness detection are conducted manually based on workshops and interviews with employees involved in the process. Consequently, these practices are not merely time-consuming and costly, but at the same time liable to subjective influences of the interviewed participants. For process mapping, these challenges can be overcome with the data based technology of process mining, that can model as-is-processes based on event log data from companies' information systems. For weakness detection in business processes, however, this technology is not applicable yet since this step requires domain knowledge about process weaknesses, which is not available for processing with process mining. This paper presents an approach to model domain knowledge about weaknesses in business processes to enable their automated and databased detection in event logs with process mining. Thereby, the weakness detection within business process improvement can be conducted more objectively at lower efforts.

Authors 5

1. Günther Schuh Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Laboratory for Machine Tools and Production Engineering (WZL),Germany

   Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Germany

2. A. Guetzlaff Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Laboratory for Machine Tools and Production Engineering (WZL),Germany

   Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Germany

3. Seth Schmitz Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Laboratory for Machine Tools and Production Engineering (WZL),Germany

   Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Germany

4. Marco Schopen Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Laboratory for Machine Tools and Production Engineering (WZL),Germany

   Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Germany

5. Florian Broehl Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Laboratory for Machine Tools and Production Engineering (WZL),Germany

   Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Germany