# Proactive Approach to Production Control Utilizing Heterogeneous Shop-Level Production Data
## 4 Pilot Solution
### Use Case #2: Sub-Assembly Line of a Just-in-Time Production Process  
Building upon the methodological framework described in this paper, a similar approach was then used with a functionally and topologically dissimilar PrS. The variability between not only the tested systems but also any PrS in general excludes the possibility of universal applicability for our proposed approach. While not being able to create a plug-and-play solution, the data acquisition process itself demonstrated remarkable adaptability, requiring only minor adjustments when transitioning to the new PrS. Due to the differences between the two investigated systems, a new DTw as well as new data processing interfaces had to be created.  
The simulation model for this case study, even though simpler in terms of representation, fulfilled a unique requirement for abstraction and design of background calculations. Notably, this system was a sub-assembly line within a larger production environment, focused on a Just-In-Time (JIT) production strategy. The time-sensitive nature of JIT processes amplified the impact of any adjustments or interventions, regardless of their scope. Due to this fact, exploration and simulation using different scenarios, facilitated through adjustments to various attributes using ECFs, were limited to a notable extent; however, the enabled utility for rapid experimentation and validation still positively contributed to the overall agility of the DTw. Data utilization in our second system is showcased in Figure 8.  
Figure 8: Simulation model with data utilization for the Sub-Assembly Line.  
A key simplifying factor when creating a model for the second production system was the absence of fluid dynamics. Unlike the first PrS, this implementation allowed for a higher degree of abstraction in certain sections of the DTw, which further lowered the computational requirements for background processes without compromising its accuracy, while achieving the primary goal of the DTw: proactivity in production simulation.  
A significant advancement in the data acquisition strategy for this system involved the direct utilization of an OPC UA server for real-time tracking of automated guided vehicles (AGVs). This approach exploited the inherent functionality of the AGVs' internal control systems and/or their associated fleet managers, which were designed to continuously publish their positional data [51]. This continuous stream of positional data (e.g., coordinates, velocity, status) was then leveraged by the manufacturing execution system (MES). The MES acted as an OPC UA client, subscribing to and collecting this dynamic data from the AGV's OPC UA server. This enabled the MES to generate snapshots of the current system status, which were then transmitted to the simulator [52], providing the DTw with up-to-date data on AGVs.  
A similar approach was used by [PERSON] et al. (2019) [53], where they employed an OPC UA technology to gather data related to mechanical arms located within their respective manufacturing system.  
The two presented iterations of our proposed methods adhere to the majority of recommendations and standards discussed in the ISO 23247 standard. Figure 9 showcases areas which our solution managed to incorporate (marked green). The remaining functional entities (FEs) are subject to future implementation.