The digital representation of physical assets and process steps by digital twins is key to address pressuring challenges like adaptive manufacturing or customised production. Recent breakthroughs in the field of digital twins and Edge-based AI already enable digital optimization of individual process steps. However, high-value goods typically include multiple step process chains including a broad range from generative and additive processes over several steps of material removal up to assembly. Therefore, a digital twin over the holistic process chain is necessary. While even the set-up of representative twins for a single step is already challenging, a concept for monitoring of the interaction and overall quality control of holistic process chains does not exist yet. The paper introduces a machine-learning method based on probabilistic Bayesian networks to monitor the »digital twin quality« of coupled digital twins which includes several sub-instances of digital twins. The approach identifies the contribution of each instance to the overall prediction quality. Furthermore, it is possible to give a range-estimation for the prediction accuracy of the individual sub-instances. It is therefore possible to identify the most influential sub-instances of digital twins as well as their individual prediction quality. With the help of this information, the quality of the digital twin can be improved by considering individual sub-instances in a targeted manner. Finally, a preview emphasises the potential benefits of the quantum computing technology when dealing with parallel computation of large-scale inference models.