In modern markets, increasing quality requirements necessitate high performance quality assurance processes to guarantee the fulfillment of these requirements sustainably. Thus, the quality assurance must be able to take targeted countermeasures which efficiently correct process deviations. In this context, quality control loops play a major role for quality monitoring and control. Currently, however, a great deal of manual effort goes into the design and implementation of mostly knowledge-based control logics. A heterogeneous data landscape and the resulting data preparation processes cause high effort. Autonomous quality control loops represent a new development and are intended to provide an efficient and data-based approach to setting up quality control loops. As an enabling step for autonomous quality control loops, this paper discusses the development of an Asset Administration Shell based, standardized quality data model.