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The selection of proper automation components for a given task is a complex, challenging, and time-consuming task. As customer requirements tend to change more frequently, it becomes necessary to pursue flexible and variable automation approaches. Recent research has introduced approaches based on capabilities and skills using holistic data models, like ontologies, DSLs, or variability models. While capabilities are seen as abstract descriptions of the (manufacturing) processes that systems perform, skills are often described as their executable counterparts (i.e.,modelinginvocationinterfaceslikeOPCUA). To automatically find solutions for customer requirements, required tasks and domain-specific constraints have to be matched with capabilities provided by automation components. This matching can be done with various techniques like AI planning or knowledge graph exploration and reasoning. Skill-based process plans can then be orchestrated by combining the skills related to the previous step's capabilities. Finally, simulation andoptimization of such process plans can be performed before their deployment.