## 2 State of the Art
According to [36], Digital Shadow was implemented for the optimization of production systems, and DOE analysis was proposed to find the number of experimental runs. Based on [37], Digital Shadow is characterized as one of the most important concepts in Industry 4.0 and is used for data collection for the entire maintenance, repair, and overhaul (MRO) service, regardless of the source data. The authors in [38] present a conceptual framework of an application for the aeronautic machining industry. Regarding [39], a Digital Shadow model was used to improve the design and geometry of a turbine blade. According to the literature review and the network visualization of the literacy topic area, as depicted in Figure 1, there is no variety within publications on Digital Shadow models with implementation in PI, food industries, and food manufacturing. However, there are many publications on Digital Twins technology based on food manufacturing [40; 41; 42; 43]. Edge computing technology enables near real-time data processing at the edge of a network before sending this data to the cloud. The authors of [44] present an architecture based on edge computing as an enabling technology for adaptive production, together with the Digital Shadow. Digital Shadow is not only used for the aggregation of data but also for pushing the data back to the knowledge space to analyze it and to control the process. According to [45], a Digital Twin framework with the assistance of mobile edge computing architecture was developed for industrial automation. The result was that multiple IoT devices offload computing tasks to multiple mobile edge computing servers to reduce end-to-end latency in near real-time intelligent analysis. The authors of [46] develop a platform and test it in a real case study on a dairy farm. The contribution of the integration of edge computing technology in this case study was a reduction in data traffic and an improvement in the reliability of the communication between the edge layer and the cloud layer. An edge computing framework based on the utilization of microcomputers has been presented [47]. The contribution of this work is focused on the offloading of the cloud layer, thus enabling the minimization of simulation times and, by extension, the response time of the Digital Twin paradigm. Digital Twin is the most prevalent modeling approach. Consequently, the Digital Shadow model presented in this research can be considered as a preparatory stage towards the complete digitalization of the investigated industry. Furthermore, Digital Shadows have been previously researched, as concluded from the online literature. In Table 3, the authors have compiled a list of key research works in the area, in order to highlight key contributions to the body of knowledge, as well as to present ongoing challenges. Robust engineering is used for designing high-quality systems at reduced costs, increasing the performance of the manufacturing system, and providing inherent strength in a system to cope with external disturbances. In the context of robust engineering, statistical DOE is utilized for solving and optimizing alternative system configurations. According to [48], the authors combined the discrete event simulation to select data from a real case study and implemented DOE to analyze the experimental data and optimize the operation of the current system. The authors of [49] describe how different geometrical designs and machining parameters can affect the energy consumption in milling processes, using DOE analysis. As stated in [50], robust engineering is conducted to provide the quantitative and visual interactive effects of independent parameters on the mechanical performance of different cable bolts.