Manufacturers are continuing to drive forward their digital transformation and are equipping their machines and systems with modern sensor and communication technology. Systematic analysis of operating data can provide information about how customers actually use products and thus optimize companies' product planning.

Until now, manufacturers of machines and systems have often known too little about how their products are actually used. Many of the systems they deliver are a black box. Strategic product planning or a retrofit is therefore based on market research, feedback from customer service or personal discussions. This time-consuming approach is usually not representative enough and slows down the potential of digitization. A systematic data analysis delivers valid results more quickly about which functions are used frequently or why some components fail more often. In this way, manufacturers can better decide whether to retrofit certain features or plan them for the next product generation.

This is where the joint project DizRuPt, coordinated by the Chair of Advanced Systems Engineering at the University of Paderborn, comes in. The goal is to create the organizational and technical prerequisites for user data analyses and their interpretation. Based on the Elements for IoT platform and the Digital Twin, analytics applications are implemented and tested at well-known industrial partners. In the process, data-based methods for product management in CIM Database PLM are created that systematically support feature planning.

The consortium

Chair of Advanced Systems Engineering at the Heinz Nixdorf Institute of the University of Paderborn (coordination), CONTACT Software, Diebold Nixdorf, Lasco, Weidmueller, Westaflex, TU Berlin, University of Applied Sciences Südwestfalen