Methodical Implementation Of Digital Data Consistency In Assembly Lines Of A Learning Factory

Abstract

The possibility of acquiring data in production and manufacturing processes is almost limitless. But especially small and medium-sized enterprises (SMEs) lack the knowledge to successfully integrate digital tools and use real-time production data for critical decision-making. Numerous initiatives already exist to inform and support SMEs in Germany, funded at various levels by municipal, federal, and state entities. These initiatives offer expertise in digitalisation and provide diverse activities to support SMEs across different industrial sectors. To make abstract concepts such as artificial intelligence (AI) or digitalisation more tangible, demonstrations and practical best practice showcases demonstrate methodological approaches for facilitating independent implementation initiatives within SMEs. However, most of these activities primarily showcase rudimentary and isolated technological implementations, with limited integration into the complex environment of a manufacturing company. This paper focuses on a holistic methodical brownfield implementation of a demonstrator for digital data consistency in an assembly line of a learning factory by applying an extended methodology for implementing demonstrators and its validation by industrial participants. It stresses the complexity of production data acquisition in a practical environment and illustrates a best-practice showcase. Key performance indicators are visualized by acquiring, storing, and cross-linking data points. The demonstrator is implemented and evaluated by SMEs' representatives, to show promising potential for sustainable knowledge transfer into the SMEs.