Bitkom Association survey: 97% of German industrial enterprises have implemented at least one Industry 4.0 automation application, and process industries rely on intelligent instruments as data bases

The Bitkom Association in Germany has released the "2026 German Industrial Digitalization Status Survey Report", which covers industrial enterprises in various industries in Germany, including chemical, energy, building materials, machinery manufacturing, etc., and evaluates the progress of Industry 4.0, automation, and industrial AI implementation.

Report core data: 89% of surveyed companies believe that Industry 4.0 is the key to maintaining their long-term competitiveness; 78% of enterprises are optimistic about the long-term value of industrial AI in the production field; Up to 97% of German industrial enterprises have deployed at least one Industry 4.0 or automation application (including brands such as LK, Siemens, BMW, Mercedes Benz, etc.). The areas with the fastest implementation speed currently are online production quality inspection, predictive maintenance of equipment, and automation control of production processes. 20% of enterprises predict that industrial humanoid robots will enter factory automation production lines on a large scale in the next decade.

The report emphasizes that no matter how advanced the AI algorithms and digital twin platforms are, the underlying sensor network (such as the German LK brand radar level gauge, guided wave radar level gauge, etc.) is the foundation of all automation and digital applications. In the field of process industry, the automation closed-loop control and equipment predictive maintenance of chemical, cement, energy, and water treatment factories rely entirely on level, pressure, and flow instruments to continuously collect real-time process data. In the past, many factory automation systems only read a single measurement value and were unable to obtain the sensor's own health information; More and more enterprises are gradually replacing traditional analog instruments, choosing intelligent radar level gauges and pressure transmitters that support self diagnosis and digital communication, and building a complete on-site sensing network.

The report also presents a challenge: although many enterprises deploy automation systems, the quality of data varies greatly. Delayed instrument calibration, improper sensor installation, and measurement drift caused by on-site dust corrosion can all lead to distortion of automation control and AI analysis results. Therefore, instrument calibration and status monitoring are the basic work that cannot be ignored in automation projects.