Authors:
Norbert Gronau
1
and
Marcus Grum
2
Affiliations:
1
Business Informatics, esp. Processes and Systems, Potsdam University, Germany
;
2
Department of Business Informatics, esp. Processes and Systems, University of Potsdam, Germany
Keyword(s):
Process Modeling, Artificial Intelligence, Machine Learning, Neuronal Networks, Knowledge Modeling Description Language (KMDL), Process Simulation, Simulation Process Building, Process Optimization.
Abstract:
Modern process optimization approaches do build on various qualitative and quantitative tools, but are mainly
limited to simple relations in different process perspectives like cost, time or stock. In this paper, a new
approach is presented, which focuses on techniques of the area of Artificial Intelligence to capture complex
relations within processes. Hence, a fundamental value increase is intended to be gained. Existing modeling
techniques and languages serve as basic concepts and try to realize the junction of apparently contradictory
approaches. This paper therefore draws a vision of promising future process optimization techniques and
presents an innovative contribution.