## 6 Conclusions and Contributions  
The market environment in which manufacturing companies operate is becoming increasingly volatile, uncertain, complex, and ambiguous. This increases the need for coordination across all units of their supply network to achieve high resilience. Therefore, this article describes our concept developed to effectively and efficiently link supply network and manufacturing control by incorporating supply network events into manufacturing control's decisions on adapted schedules. This aims at improving delivery reliability and resilience. Control engineering principles are transferred to establish an event-driven interface between supply network and manufacturing. The ACSN as the resulting control loop becomes adaptive and learning fusing RL in the controlling element. Additionally, by its modular approach the ACSN can be seamlessly integrated into a common manufacturing IT landscape.  
Our work contributes to the knowledge base in supply chain management, PPC and organizational theory. First, by recognizing the value of supply network events and designing an appropriate systematic reaction in manufacturing. Manufacturing control then is no longer limited to internal disturbances considered to be events, but instead systematically complemented to include external events from the supply network. Second, by deriving requirements for such a deeper integration between supply network and manufacturing. Besides the suitability for this application considering supply network events in manufacturing control for complex job shops, these requirements are adaptivity and learning capability of the controlling element, a certain degree of automation, the capability to determine multi-dimensional parameters as control values and a seamless integration with existing manufacturing IT landscape. Third, by developing an intelligent concept establishing a deeper and event-driven integration between supply network and manufacturing. This is achieved by the concept of the ACSN as a discrete, event-driven, adaptive and learning control loop. Fourth, by complementing existing strategies for planning and control using approaches of areas such as control engineering and ML. On the one hand, the ACSN is designed as cascade control to complement existing internal control loops in manufacturing. On the other hand, its RL-based controlling element allows for decisions on complex combinations of countermeasures and their associated control values. Fifth, by describing and appropriate application example in semiconductor manufacturing as well as its expected obstacles and benefits. Obstacles include transferring the decision model from a simplified training environment to a real manufacturing environment, the concept's strong dependence on data, sharing data between partners in a supply network-technically and organizationally-and building employee trust in automated decision-making processes. However, after overcoming these obstacles, multiple benefits could be achieved by the ACSN. It accelerates the process for responding to supply network events by automating communication, involving less people and reducing media discontinuities. Furthermore, the data-driven decision-making not only allows for robust decisions without human biases, but also enables complex combinations of countermeasures to mitigate the consequences of supply network events. Most importantly, the whole supply network benefits from the ACSN by an ensured delivery reliability. Manufacturing sites are getting more resilient despite large numbers of supply network events.