In an increasingly turbulent environment, convincing methods of production planning and control are needed. Many of the necessary decisions are made at shop-floor-level. They depend on the knowledge and the abilities of the workers to react on unpredictable impact and hence are not explicitly described. For a realistic, concomitant plant simulation, however, it is important, to model the control strategies as exactly as possible. This paper presents a method to identify applied control strategies by adopting artificial neural networks to data from the operating and machine data logging.
