production control

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.

Increasing competition in product costs leads worldwide to the need of process improvement - especially in small and medium-sized enterprises. Projects of production and logistics, with the aim of optimising process organisation and manufacturing control are assumed to have the knowledge of decisive system parameters and adequate possibilities in interference. Against this background the efficient use of the resource information is very important in future. Precisely because the use of RFID in production control has even more rationalisation potentials, when the process monitoring of production process must be fast, safe and efficient real time close controlled.