## 1 Introduction  
The manufacturing industry faces enormous challenges and changes worldwide. Mass personalization leads to smaller lot sizes with a simultaneously increasing number of variants [1]. Furthermore, the market is changing from a seller's market to a buyer's market. To meet the growing importance of customer satisfaction, a high delivery reliability must be achieved [1, 2]. How challenging this can be was particularly evident during the COVID-19 pandemic: quarantine and other restrictions were making deliveries unreliable. Entire supply networks were thus not able to fulfill their logistic goals [3]. Therefore, the market environment in which manufacturing companies operate is becoming increasingly volatile, uncertain, complex, and ambiguous [4]. Manufacturing companies interact with this environment by organizing in supply networks, establishing a deeper integration in their supply network as well as by using enabling technologies for advanced automation in their smart factories to face this complexity [5; 6]. Hence, competition has shifted from single companies to supply networks [7; 8].  
However, in a volatile, uncertain, complex, and ambiguous environment such as this, planning accuracy decreases significantly with an increasing time horizon [9]. This leads to a rising number of events in the supply network companies need to react to [6]. Events can be classified by their origin, cause, life cycle stage of the supply network, occurrence, subject and decision-making horizon and they must therefore be counteracted individually [10]. Since events can result in substantial financial implications (positive and negative), it is essential for manufacturing companies to react effectively and efficiently to events in the supply network [8; 11]. This increases the need for coordination across all units of the supply network to achieve a high robustness and adaptivity, and thus high resilience [12; 13]. Manufacturing control, which has the task of implementing the production plan despite unforeseen events, is, therefore, of high and further increasing importance [2].  
Consequently, this leads to the research question of how manufacturing control can be effectively linked to supply network events and incorporate them into its decisions on countermeasures to prevent losses in delivery reliability. Therefore, this article describes the development of a concept transferring control engineering principles to establish an event-driven interface between supply network and manufacturing. Furthermore, a possible application with its resulting obstacles and benefits is discussed. The work contributes to the knowledge base in supply chain management, Production Planning and Control (PPC) as well as organizational theory. First, by recognizing the value of supply network events and designing an appropriate systematic reaction in manufacturing. Second, by deriving requirements for such a deeper integration between supply network and manufacturing. Third, by developing an intelligent concept establishing a deeper and event-driven integration between the two. Fourth, by complementing existing strategies for planning and control using approaches of areas such as control engineering and Machine Learning (ML). Fifth, by describing and appropriate application example in semiconductor manufacturing as well as its expected obstacles and benefits.  
The article is organized as follows. Section 2 presents relevant related work. Section 3 then describes the concept to link supply network events and manufacturing effectively and efficiently. A possible application is detailed in Section 4. Section 5 discusses obstacles and benefits of the approach. The conclusion and contributions of the article are outlined in Section 6, whereas future work is described in Section 7.