Data-driven production is becoming increasingly important in modern industry. The ever-growing volume of production data is opening up new opportunities to base decisions not only on experience or intuition, but also on objective, data-based analyses. This principle, also known as data-driven decision making, forms a central basis for the smart factory of the future. In the data-driven decision-making process, information that has already been collected from a wide variety of sources, such as sensors, machine controls, and production systems, is analyzed and interpreted. On this basis, decisions can be made in real time to make processes more efficient, stable, and flexible. Whereas in the past, these decisions were primarily made by humans, today intelligent algorithms, supported by artificial intelligence (AI) and machine learning, are increasingly taking over this task. This allows patterns to be identified that would be virtually invisible to humans, such as early indicators of quality deviations or impending machine failures.

A particularly exciting aspect in this context is the so-called back-to-machine principle. It describes the feedback of the knowledge gained and decisions made to the machines themselves. Instead of analysis results simply ending up in reports or dashboards, they are actively used to automatically adjust production parameters. This creates a closed control loop in which data from production is not only collected but also used directly to control production. This leads to self-learning and self-optimizing processes and is a decisive step toward the autonomous factory.
Technologically, this concept requires a close connection between IT and OT systems (information technology and operational technology). Communication standards such as OPC UA or MQTT enable secure and fast data exchange between machines, sensors, and analysis platforms. At the same time, high data quality and low latency are crucial for making reliable real-time decisions. Only in this way can systems react immediately to changes in the production process.