  Shorter product life cycles and increasing individualization of production
leads to an increased reconfiguration demand in the domain of industrial
automation systems, which will be dominated by cyber-physical production
systems in the future. In constantly changing systems, however, not all
configuration alternatives of the almost infinite state space are fully
understood. Thus, certain configurations can lead to process instability, a
reduction in quality or machine failures. Therefore, this paper presents an
approach that enhances an intelligent Digital Twin with a self-organized
reconfiguration management based on adaptive process models in order to find
optimized configurations more comprehensively.
