Performance-based Decision Support for Business Process Analysis and Design

Abstract

Performant business processes constitute decisive advantages for companies on highly competitive markets. However, conventional approaches to business process improvement are prone to subjectivity and high manual efforts. Latest approaches address these challenges by semi-automating the inherent phases of process analysis and process design with data-based weakness detection and measure derivation. What is still missing is their integration into a holistic and data-based decision support that helps users to design a to-be process based on performance information about existing process weaknesses and potential improvement measures. This aim is pursued with this paper’s approach. First, a business process performance indicator is developed that serves as the central optimization variable for all decisions in process analysis and design. Using event logs, it can indicate performance losses caused by process weaknesses and performance potentials of improvement measures. Next, a calculation model for calculating the business process performance indicator is derived. By prioritizing process weaknesses and potential improvement measures according to their impacts magnitude, a decision support for process design is provided that can enhance the effectiveness and efficiency of business process improvement.

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. Alexander Obladen 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