## 2 State of the Art
The authors of [51] present the design, development and validation of an automated DOE toolset that is part of a larger Cyber-Physical System (CPS); this research aims to demonstrate the capability to automate, characterize, and predict the power of phases during an industrial machining process and to choose the machining toolpath that improves energy consumption. The authors of [52] describe how an autonomous mobile robot in production systems in the process industry can improve manufacturing performance in terms of productivity, flexibility, and costs. This is a study where the cutting-edge technologies of Industry 4.0 are used to improve the operation and design of the system. With the use of advanced simulation tools, the authors of [53] have developed a new manufacturing system for the production of railcar subassemblies. Several examples have also been discussed in the literature regarding the food industry, investigating issues such as (i) thermal inhibition kinetics of enzymes in fruits and vegetables, (ii) modeling \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & & **Key Contributions** & **Challenges—Limitations** \\ \hline
[26] & \(\bullet\) & real-time monitoring data analysis & Significant investment in: data infrastructure analytics tools \\ \hline
[28] & \(\bullet\) & real-time monitoring analysis for decision-making for production optimization & \(\bullet\) & Lack of standardized data formats Lack of protocols \\ \hline
[29] & \(\bullet\) & real-time monitoring, and analysis for predictive maintenance and quality control in injection molding & \(\bullet\) & Requires specialized expertise for development maintenance of the Digital Shadow model \\ \hline
[34] & \(\bullet\) & real-time monitoring analysis & \(\bullet\) & Interoperability and data governance issues \\ \hline
[35] & \(\bullet\) & real-time monitoring data analysis optimization of manufacturing processes & \(\bullet\) & Availability \& data quality \\ \hline
[36] & \(\bullet\) & predictive maintenance & \(\bullet\) & Lack of standardized data formats Lack of protocols \\ \hline
[37] & \(\bullet\) & Integration of diverse data sources real-time monitoring data analysis & Requires specialized expertise for development maintenance of the Digital Shadow model \\ \hline
[38] & \(\bullet\) & real-time monitoring data analysis & \(\bullet\) & data privacy \& security requires reliable internet connectivity \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Achievements and Limitations Using Digital Shadow Technology. of the heat transfer of tomatoes and other fruit and vegetables during the peeling process, (iii) modeling of the heating process, and (iv) moisture diffusion in rice kernels during the drying process [54; 55]. The authors of [56] focus on the design and development of a simulation tool, enabling the analysis of food processing systems. A methodology according to DES is implemented to explore the dynamics and behavior of the processing systems based on an event-driven approach. According to [57], ANOVA was used to investigate the key control parameters and identify the factor with the biggest contribution to the performance of the system. Thus, ANOVA is used to achieve higher quality in the final product and for predictive maintenance. ANOVA was also used to discover the water jet machining parameters and the interactions between them and revealed that pressure and flow rate influence the ker width, whereas stand-off distance and pressure influence material removal [58]. According to [59], ANOVA analysis is performed to show the contribution of each factor during the joining process. The three factors show an influence on the electromagnetically crimped threaded surfaced tube-to-tube joint (EMCT3), but the pitch of the thread contributes more to the strength of the EMCT3 joint than the other two factors. The author of [60] uses ANOVA to optimize the turning parameters and, among these, the considered parameters are cutting speed, feed rate and depth of cut.