Suche
Mein Konto
Data analysis for SMEs: The future of quality assurance starts now!
The “Predictive Quality” project will start in 2025 at Chemnitz University of Technology to optimize quality assurance in SMEs.
Data analysis for SMEs: The future of quality assurance starts now!
On March 20, 2025, a workshop on requirements analysis in production technology took place, which marked the starting signal for an important project. Under the title “Predictive Quality through development-guiding data analysis for varied production in SMEs” (GeoPreQ), the project aims to significantly improve quality assurance in small and medium-sized companies (SMEs), especially in special machine construction. The project, which is supported by the Professorship of Manufacturing Metrology at Chemnitz University of Technology, N+P Informationssysteme GmbH and SITEC Industrietechnologie GmbH, runs until June 30, 2027.
The overall goal of this project is to optimize process quality through the comprehensive use of machine data. Against the background of economical production of workpieces in small batch sizes, the companies involved face challenges such as high complexity and individuality as well as limited prior knowledge and a lack of databases. The project is co-financed by the European Union and receives additional support from tax funds from the Saxon state parliament.
Optimization through data analysis
The GeoPreQ initiative aims to strengthen the competitiveness of regional SMEs. The central approaches are to link measurement data with production metadata, including machine data, process parameters and tool data. The definition of data-based quality assurance processes is intended to support decision-making in test planning. According to current developments in data analytics, as mentioned in the German Society for Quality (DGQ) blog, forecasts from data analyzes are increasingly crucial for product and process optimization.
The aspects of data selection, data preparation and data integration, which require a seamless connection of data points, are particularly challenging. Predictive Quality enables companies to systematically optimize their product and process-related quality. This form of quality assurance is based on data-driven forecasts, which serve as a basis for decision-making for requirements in the production process.
The role of the Internet of Production
A key component in the implementation of predictive quality is the Internet of Production (IoP), which has received funding from the German Research Foundation (DFG) since its presentation at the Aachen Machine Tool Colloquium in 2017. The IoP aims to provide real-time information and support context-specific decisions. The infrastructure of the IoP is divided into four levels: the raw data level, middleware for managing data access, the smart data level for knowledge generation and the smart expert level for using the aggregated knowledge.
The implementation of data-based quality management offers challenges, but also holds company-wide potential. Going forward, there will be an increased focus on moving from predictive analytics to prescriptive action. The goal here is to accelerate the continuous optimization of quality and make it more resilient.
The contact person for more information about the project is Prof. Dr. Sophie Gröger from Chemnitz University of Technology. Anyone interested can call 0371 531-32212 or send an email sophie.groeger@mb.tu-chemnitz.de to reach. Overall, GeoPreQ offers a promising approach to improving quality assurance in production that promises concrete economic benefits for regional SMEs.