# Analysis and Visualization of Production Bottlenecks as Part of a Digital Twin in Industrial IoT
## 5 Integration of Analysis Results into a Digital Twin  
The bottleneck analysis, presented in the last section, analyzes the criticality of each machine within an overall production system and enables the optimization of the production system layout and performance. On a wider scale, such a bottleneck analysis can now be used as part of a dynamic simulation of a production system in the form of a digital twin \begin{table}
\begin{tabular}{c c} \hline \hline
**Percentage** & **Color** \\ \hline
0–20 & blue \\
20–40 & green \\
40–60 & yellow \\
60–80 & orange \\
80–00 & red \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Color bars for percentage ranges of bottleneck likelihoods. Figure 9: Scheme of a possible visualization of the process heatmap. (DT). Hereby, the bottleneck analysis can be improved by using the DT as a much more precise system model. This way, the cycle times of machines can be considered depending on different process parameters. Compared to the bottleneck analysis approach described in the last section, the behavior of a concrete machine is now not simply defined based on pre-assumptions on its performance under certain conditions, but is deduced from real system behavior based on applying machine learning algorithms on past system execution data. As a consequence, the analysis in Figure 9 only relies on a static model. Instead, the DT can be used to perform a sensitivity analysis analogously to Figure 8, yet being more precise due to the adaptivity of the underlying AI models. In this section, we will present the concept of an AI-based digital twin, using machine learning techniques to automatically learn and adapt to the observed behavior of the real cyber-physical production system and using the bottleneck analysis method for overall system optimization. Figure 10 shows the technical architecture of the proposed DT. A central element of the DT architecture is a semantically rich knowledge base, represented as a knowledge graph, storing all information necessary for or generated by the AI-based DT. The starting point for this knowledge base is the Design Compiler 43 [21,22], which is the core element of the Design Cockpit 43% by IILS mpH, Trochtelfingen, Germany. This Design Cockpit enables the coding of design knowledge in design languages and the transformation of these design languages into a central model--the design graph. In the given context, this software can be understood as a specialized editor to design, amongst others, a semantic model of a product and its production system, including, e.g., the production layout and geometry. The Design Cockpit 43 produces outputs in the form of an ontology definition and product geometry, structure and behavior as well as production structure, behavior and layout. The execution and performance data represent past production runs and thus instances of the production system and product models designed in the Design Cockpit 43. The performance and execution data include information about the effectiveness of the process and process data that are collected during production. Performance data comprise, for example, cycle times, downtimes, throughput and quality metrics for the products. The execution data contain information regarding the (running) process (inputs, outputs and status information). They include, for example, sensor signals from production machines and tools, information about (raw) materials in use and workers involved. After an appropriate preprocessing and data preparation, the execution and performance data are loaded into the central knowledge graph. These data, together with the semantically rich production system and product models, serve as input to automatically  
Figure 10: Digital twin architecture. learn the behavior of the real production system by machine learning approaches. Additionally to traditional machine learning techniques, explainable AI (XAI) approaches are used to describe and explain machine learning models and increase their understandability [23].