## 5 Discussion
It is expected that by automating the process of responding to supply network events in manufacturing control, the ACSN will enable a more efficient and a more effective overall process. On the one hand, this is due to the prevention of media discontinuities and, therefore, less sources of errors regarding data transfer. On the other hand, less media discontinuities also massively accelerate communication. In addition, the process also becomes more efficient because fewer people are involved, thus eliminating time-consuming coordination processes. Consequently, people in supply network and manufacturing departments can focus on their daily business processes rather than time-consuming and unpredictable escalation management. Additionally, the ACSN allows for an optimization of PPC-which has not been in scope so far-by deriving measures for mitigating the consequences of supply network events on manufacturing control level. Furthermore, by knowing effects and dependencies of countermeasures from the training phase RL will make the decision on countermeasures in manufacturing control more robust by avoiding errors that occur when manual interventions are made-under- and over-steering could be avoided. Even complex combinations of countermeasures and their associated control values can be designed automatically. Due to their combinatorial complexity and dynamic complexity mathematical optimization reaches its limits in complex job shops very fast. Since the use of RL in the controlling element allows for generalization to unknown situations it is, therefore, ideally suited here. Additionally, since a simulation-based training phase is used, RL in particular has the decisive advantage that no elaborately labeled data need to be available. Furthermore, with its solely data-driven decisions the ACSN avoids biases usually brought in by human decision-makers and reduces the selection of wrongfully chosen countermeasures due to its direct feedback-loop with manufacturing. Moreover, the ACSN increases its performance over time through continuous training in parallel with its operation. In addition, to increase acceptance, it is possible to override the ACSN's decisions at any time-production planner and line manager always have the ability to intervene. Furthermore, the scalability of the ACSN must be highlighted. Besides scalability regarding data variety-as described by the generalization to unknown situations-scalability is also necessary regarding data volume, since the processing of large amounts of data has been standard for many years in the semiconductor industry. However, large amounts of data must be processed especially during the pre-deployment training phase. In this phase, however, processing can be scaled to multiple resources through distributed learning. After deployment, decisions can be executed with comparatively little data and very quickly. Furthermore, the system architecture of the ACSN enables a seamless integration by using standardized interfaces of an integration layer to connect to common manufacturing IT landscapes as well as to IoT and CPS. This allows for the implementation of even complex combinations of countermeasures since their associated control values can be orchestrated with the MES automatically. Just as importantly, the ACSN also improves the database for other systems of the manufacturing IT landscape. Most importantly, the manufacturing site as well as the whole supply network benefit from the ACSN. With the ACSN's decision-making process it is possible to improve delivery reliability despite the occurrence of numerous supply network events. Consequently, customer satisfaction increases with the improved delivery reliability, which is of great importance regarding the challenging environment of manufacturing companies. In addition, significant cost reductions can be achieved, since the deeper integration of supply network and manufacturing alongside with the effective mitigation of supply network events allows safety stocks to be reduced.