###### Abstract  
Digitalization and networking are taking on an increasingly important role in manufacturing. Fifth Generation mobile networks (5G) allow us to wirelessly connect multiple assets in factories with guaranteed quality of service (QoS). A 5G non-public network (5G-NPN) realizes a dedicated network with secure communication within the factory. Time-sensitive networking (TSN) provides deterministic connectivity and reliability in local networks. Edge computing moves computing power near factory locations, reducing the latency of edge applications. Making production processes more flexible, more robust, and resilient induces a great challenge for integrating these technologies. This paper presents the benefits of the joint use of 5G-NPN, TSN, and edge computing in manufacturing. To that end, first, the characteristics of the technologies are analyzed. Then, the integration of different 5G-NPN deployment options with edge (and cloud) computing is presented to provide end-to-end services. For enhanced reliability, ways of interworking between TSN and edge computing domains are proposed. Afterward, as an example realization of edge computing, the investigation on the capabilities of the Kubernetes container orchestration platform is presented together with the gap analysis for smart manufacturing requirements. Finally, the different integration options, interworking models, and Kubernetes-based edge computing are evaluated to assist smart factories to use these new technologies in combination in the future. edge computing; industrial LAN; 5G-NPN; 5G-TSN; Kubernetes +
Footnote †: journal: Accepted: 7 December 2021  
technologies or non-standardized Ethernet. TSN technology is centrally managed and provides guarantees of delivery and minimized jitter using time scheduling for applications that require determinism. There is an increasing need for wireless connectivity and real-time data transmission with mobile robots and automatic guided vehicles (AGVs) that move in the factory. 5G, the fifth generation of mobile networks, is foreseen as the key to enhancing and enabling these advances in manufacturing, as it offers low latency and high-speed connections. The integration of 5G technologies in the manufacturing ecosystem has great potential to accelerate the ongoing digital transformation of the manufacturing industry. Since different uses of the terms edge and cloud computing have been adopted in industrial applications, the terminology used in this article for the computing domains is defined in the following. Cloud computing is a very centralized paradigm where processing and data storage are realized on centralized servers located in a remote data center. Edge computing is a distributed computing paradigm that has processing and data storage capability right at \"the edge\" of a given application's network, closer to the source of data. As an advantage, collecting and processing data closer to the source reduces latency and brings better performance to high-bandwidth applications. From the technical point of view, the main differentiating factors between cloud and edge computing are the location of the computing power and the centralized vs. distributed nature. Cloud or edge computing can be used independently or combined, whatever best fits the specific use case. Industrial edge computing is a system of micro data centers installed at the edge of the network, close to or within the premises of the factories. In edge computing, the computing tasks are executed closer to the end users or devices in terms of geographical and network proximity. This allows for delivering levels of latency and throughput that are not possible with cloud computing. On the one hand, the tasks executed in edge computing are data processing and analytics to be close to where the data is generated and captured. On the other hand, the evolving edge computing and networking technologies (TSN, 5G) make it possible to offload industrial control applications to edge computing to provide control loops with tight delay requirements, too.