# Proactive Approach to Production Control Utilizing Heterogeneous Shop-Level Production Data
## 4 Pilot Solution
### Use Case #1: Agro-Food and Beverages Training Model  
For the first model, the extraction of topological data was performed using two different approaches. Initial system parameters, such as the connections between production machinery, their spatial coordinates or the sequence of performed operations, were directly integrated into the simulation environment without requiring extensive pre-processing. However, the more intricate aspects of the fluid system, including tank levels and flow rates, along with precise positional data related to individual products within the PrS, necessitated a more detailed extraction process. This involved generating CSV files containing comprehensive product-specific data and utilizing ECFs to capture the real-time operational states of the machinery within the PrS. The final implementation of the simulation model, including an indication of individual data type utilization, is represented in Figure 7. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Transfer** & **Production** & **Supervisory** & **PLC-Level** & **Detailed** \\
**Method** & **Orders** & **Data** & **Data** & **Process Data** \\ \hline CSV & (+) & (*) & & (*) \\ Database & (*) & (+) & & \\ UDP/TCP/OPC & & (*) & (+) & (*) \\ ECF & & & & (+) \\ \hline \hline \end{tabular} (+)—chosen method for the proof of concept. (*)—method only partially viable for the specified type of data.  
\end{table} 
Table 1: Application of proposed methods for the first use case. The section of our simulation model dedicated to PO data integrates raw input from the MES. This data is then separated into singular values, which are subsequently allocated to individual products as they enter the PrS, or, in our case, the simulation model. Production level data, which provides a real-time snapshot of the PrS at the precise moment a simulation request is initiated, represents a critical component of each specific production scenario analyzed. This data was seamlessly integrated into the simulation model via the transport communication layer of the shop-floor PLCs. The database interface within our model facilitates a real-time comparison between the current state of the simulated scenario and the anticipated outcomes. This interface presents comprehensive information to the operator, including details regarding the types and volumes of both planned and completed products. Finally, the analytical and monitoring functionalities embedded within our model enable expeditious generation of reports and data files. These files can be subjected to further in-depth analysis after the simulation process is concluded. It is important to note that this analytical data is generated internally by the simulator itself and is not extracted from the physical PrS, serving instead as a direct reflection of the ongoing simulation's performance. Exploration of possibilities for data utilization and identification of the scope of data, which can be seamlessly integrated directly into the simulation model without the necessity of data pre-processing algorithms, could be considered as the main takeaway from this implementation. Within the context of a production system which consists of discrete (individual products) and continuous (filling material) elements, it is important to distinguish between the static and dynamic parts of the production process. The processing machines, transport stations or robotic manipulators could be considered static, as the likelihood of changes in the shop-floor topology during an ongoing production is minimal. On the other hand, movement of individual products or the flow of resources used during the production process is not pre-defined, as these are dependent on the production plan at the moment of the production snapshot in the PCS. Static elements fall into the data category, which is often modeled in the simulator, and serves as a base structure for the movement of aforementioned dynamic elements.