## 5 Discussion  
When applying the approach of the ACSN to an application as the one described in Section 4, some obstacles must be overcome before achieving its benefits. First, the problem formulation as MDP as well as the simulation model necessary for the agent's pre-deployment training phase require several assumptions and simplifications compared to reality. Furthermore, the design of the agent's reward function may be biased by human experience. The deployment of the ACSN to real applications is therefore associated with the challenge of ensuring the agent's performance despite changed conditions. As a preventive measure, the use of a digital twin (cf. Section 3.4) is suitable in the training phase, which simulates the system behavior as close as possible to the real environment. As a protective measure, intervals can be set for the control values of the ACSN controller, which may not be exceeded. In addition, after deployment, the ACSN can be used in a transition period as a control system instead of a closed loop, with decisions reviewed by the production planner prior to implementation. Second, the concept of the ACSN is highly dependent on data from supply network and manufacturing including data availability and data quality. Consequently, even if the architecture allows for a modular integration into the existing manufacturing IT landscape, several interfaces to existing IT systems need to be designed and implemented. This is especially true for the typically complex manufacturing IT landscape of semiconductor manufacturers [68,75]. It is, therefore, highly recommended to use platforms and standardized interfaces (e.g., [67]) instead of developing individual interfaces for each IT system risking vendor lock-in effects. Third, the link between supply network and PPC is presently essentially established by planning objects only. To make the ACSN's approach work to its full extent, besides the need for effective event identification and communication within the supply network (e.g., [76]) data exchange across companies and manufacturing sites has to be enabled. Again, platforms and standardized interfaces are a promising technical solution. Events then continue to occur, but they are detected and processed at the earliest possible time, allowing for a longer period for countermeasures to take effect. Fourth, data exchange is not only a technical obstacle. To enable data exchange across companies and manufacturing sites, the so-called silo thinking in supply networks must be broken down. A relationship of trust must therefore be established between the partners  
Figure 10: Countermeasures and control values for the exemplary event of a change in delivery date [10]. in a supply network, allowing extensive live data to be exchanged between them. How challenging this could be even within organizations as well as approaches to do so are discussed in the literature [77]. Fifth, there must also be a relationship of trust between employees and systems for automated decision-making. Among others, the reason for dispatching rules being so popular in semiconductor manufacturing is that their decisions are easy to understand [78]. To build up trust such as this and thus increase acceptance among employees, it is useful to give the decisive employees, especially in manufacturing control, the opportunity to monitor and override the ACSN's automated decisions at any time. The emerging field of explainable artificial intelligence may offer additional strategies for transparent decisions and thus acceptance in the future [79]. However, after overcoming these obstacles, multiple benefits could be expected by the ACSN as they have been elaborated in workshops with experts from the areas of supply network planning, PPC and manufacturing of a semiconductor manufacturer. These benefits can be categorized by their relation to the event responding process itself, to decision-making, to manufacturing IT landscape and to the supply network.