Cyber-physical systems in manufacturing have the ability to deeply integrate physical manufacturing processes with virtual management processes and foster adaptive and exible manufacturing. CPS development requires knowledge in many diu000berent disciplines which increases complexity. To reduce the complexity, model-based approaches are employed to gain a better understanding of the reality. While humans are able to interpret incomplete or unstructured models, machines demand models that represent formal knowledge such as ontologies. Ontologies provide formal semantics to any sort of information, introducing new prospects for information retrieval. Some research has proven the usability of ontologies in cyber-physical systems and manufacturing. However, human-interpretable and machine-interpretable models are mostly separated, which causes consistency issues. Also, existing modeling languages might not be expressive enough to address specifc domains, such as manufacturing. An approach to solve both issues is the Agile and Ontology-Aided Modeling Environment (AOAME), which is based on ontology-based metamodeling. Hence, the contribution of this thesis is to build on AOAME's approach to create an ontology-based domain-specifc model which can be exploited to support decision-making for domain experts in manufacturing.