## 2 State of the Art

Table 1: Clusters as Constructed by VOSviewer Software. Figure 2: Density Visualization of the Scientific Literacy Topic Areas. which the data flow is semi-automated (near real-time communication), and (iii) Digital Twin, in which the data flow is fully automated (real-time/synchronous communication), thus minimizing human intervention (Figure 3). The industrial case study presented in this research work involves a process industry, specifically within the food industry, in which there is a continuous flow of materials/resources and data. Considering the delicacy of the final products (food products) and the strict regulations to comply with, it becomes obvious that a digital twin modeling approach is more suitable, in order to act proactively, reduce waste and ensure compliance \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Criteria** & **Digital Model** & **Digital Shadow** & **Digital Twin** \\ \hline \multirow{3}{*}{Efficiency} & Identification of inefficiencies and bottlenecks virtually for process optimization & Near real-time data monitoring and analysis to monitoring inefficiencies and bottlenecks & Uses sensors and data analytics to create a real-time, virtual replica of a physical production system, enabling bi-directional communication \\ \hline \multirow{3}{*}{Quality Control} & Identification of potential quality issues before implementation & Near real-time monitoring and analysis of product quality parameters & Bi-Directional near real-time monitoring of product quality, enabling process adjustments for improving quality \\ \hline \multirow{3}{*}{Safety} & Offline identification of safety hazards before implementation & Near real-time monitoring of safety protocols \& alerts for protocols \& provision of safety countermeasures \\ \hline \multirow{3}{*}{Real-time Data Analysis} & Offline support for decision-making & Near real-time data analysis for decision-making support & Uses real-time data to provide continuous insights for decision-making and enable predictive maintenance to reduce downtime \\ \hline \multirow{3}{*}{Cost Savings} & Simulation models can help identify cost-saving opportunities in a virtual environment before implementation & Optimized production processes and improved quality control, quality control costs & Enables optimized production processes, improved quality control, and predictive maintenance to reduce costs and increase efficiency \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of Digital Model, Digital Shadow, and Digital Twin. Figure 3: From left to right: Digital Model; Digital Shadow; Digital Twin, adapted from [24]. to these regulations and high-quality standards. Consequently, in this research work, a digital shadow model has been modeled and developed prior to the setup of the required infrastructure for a fully connected industry, which will facilitate the implementation of the digital twin model. With the integration of the developed digital shadow, engineers are capable of monitoring offline the production processes and proceeding easily with changes and/or modifications to production processes. Furthermore, the developed digital shadow model can be used as an analytical tool, in order to provide feedback and insights for decision-making, enabling adjustments to the production process parameters. Digital Shadow is precisely designed to support decision-making in near real-time in specific modules and has huge potential to reduce time and cost in manufacturing. According to [33], a conceptual model for Digital Shadows is discussed and a holistic data view on production planning and control-specific tasks based on ontology is presented. A Digital Shadow model was implemented in a real case study for an automotive supplier, in which raw data were collected from different machines, processed, analyzed, and reused to make changes in the physical model [35].