Management of manufacturing operations requires a detailed insight into industrial processes and their understanding, followed by a meaningful optimization of their order and parameters. This technological experiment is focused on supporting this kind of business intelligence. The goal is to provide a generic environment for data logging and processing aiming at improving and optimizing production processes from the user perspective. Detailed understanding/capabilities on data science is not required/expected, as positions of data scientists are frequently not present in current small- and medium-size industrial production companies. Implementing and deploying this experiment in practice can bring significant improvements in production line efficiency, which may be mainly caused by reducing costs compared to introducing and purchasing new hardware solutions (e.g., sensors, PCs onsite, etc.). The effort needed is then reduced compared to traditional approaches as the effort/workload for its implementation is systematic, it eliminates ad-hoc engineering work, which is not as efficient as a systematic work. Among the expected impacts is thus a reduction of time-consuming effort/work that is currently made in industrial production facilities for implementing traditional approaches. It reduces the costs needed for purchasing licenses for instances of simulation software by joining the simulations in an industrial facility under one umbrella.