## 2 Methodology--Related Works
### Smart Production Control and Service-Oriented Architectures  
Due to the deficits of the existing IT solutions cited above, the rapid increase in the affordability and availability of computing power (especially on the Edge), and the rise of the IIoT, more agile and decentralized approaches have seen a tremendous increase over the last decade [28; 49]. In particular, the rise and ubiquity of CPSs in the wake of the IIoT is a crucial component. The trend towards transparent production and logistical systems based on the availability of real-time information would not be possible without these CPSs. The combination of computing power with sensors and network connectivity allows for the gapless tracking and tracing of almost all material and production/customer orders throughout the entire production system. This counts across company borders and the supply chain [28]. Based on these technological trends and improvements, various concepts for enhanced planning and control of production systems were developed [50; 51; 52]. Another approach that has received a lot of interest and attention in Industry 4.0, in general, is concepts tied to utilizing artificial intelligence (AI). The idea of applying AI to the domain of manufacturing (e.g., for scheduling problems) is not new. The first concepts go back to the year 1999 [53]. Following the initial attempts, various concepts in different application areas were developed. They ranged from the prediction of production costs and the evaluation of different layout/organizational concepts to the domain of predictive maintenance to real-time scheduling on the shop floor [54; 55; 56; 57; 58; 59]. The approaches, depending on the application and the area of consideration, encompassed methodologies ranging from supervised and unsupervised learning to algorithms from the domain of reinforced learning. For a more extensive and detailed review and analysis of these concepts and studies, please see [28] and the literature cited there. Even though the algorithms and concepts often produced tangible results in many of the abovementioned cases, almost all these developments suffer from the same fundamental problem. This problem is described by [28; 60] as the solution linearity problem. Almost all studies analyzed (that the authors are aware of) used specifically designed or pre-processed data. These data had been cleaned, explored, and further processed in a linear process until the results were generated. The problem is that real-life manufacturing is dynamic. Relevant parameters like processing times, setup times, available machines, maintenance intervals, and many more constantly change. Often, these changes are not documented in the company's IT systems. This leads to a phenomenon called conceptual drift [60; 61]. Since inconsistent data undercut all results generated, a linear approach would be only sustainable with massive and constant human support, if ever. Following these lines of argument and considering the presented challenges, various authors suggest the concept or approach of service-oriented or microservice architecture. This suggestion is a counter design to the monolithic conventional production planning systems. The concept is easily scalable and highly adaptable. Also, past research has indicated that these self-adapting and self-optimizing multi-agent distributed control systems perform better and are more flexible compared to conventional systems [28; 62]. The origins of the SOA concept emerged in the IT world as a model for infrastructure architecture in the early 2000s [63]. The general idea was to provide a solution to a typical problem with the automation pyramid (cf. Figure 1): various functionalities existing in multiple applications at the same time, increasing (among other things) maintenance costs, adding unnecessary complexity to the infrastructure, and, due to missing interfaces and consistency checks, posing the risk of different data sets for the same indicator in different systems (e.g., a credit check).