Digital Platforms for Industrial Metaverse Applications: A Framework to Identify Data Quality Insufficiencies

Abstract

The metaverse is one of the most disruptive technologies to evolve from the digital transformation. While the potential use cases of creating an immersive virtual world are numerous, the vision of an industrial metaverse is only recently emerging as a concept from the technology. In the automotive sector, manufacturers are starting to use simulation, digital twin technology and Building Information Modelling (BIM) to build virtual factories in an industrial metaverse. The benefits of this innovation are believed to significantly boost production flexibility and efficiency, which is why manufacturers set up data-driven digital platforms to enable an industrial metaverse that interconnects multiple actors. How-ever, technical barriers still hamper the implementation of such platforms whose dependence on flawless data grows with the number of use cases for an industrial metaverse. Accordingly, quality insufficiencies of spatial data and the absence of automatic quality assessments to identify these insufficiencies are one of the most decisive barriers to a widespread adoption of industrial metaverse applications. This thesis examines this problem and investigates how data quality insufficiencies in an industrial metaverse en-vironment can be identified and overcome at the example of an automotive manufacturer that uses the Nvidia Omniverse digital platform to create virtual factory models. A design science approach is pursued to create an extension to the Omniverse software that identifies the most critical data quality insufficien-cies, derives key performance indicators (KPIs) and proposes preventive measures to induce a sustained data quality improvement. Thereby, this thesis lays the groundwork for future research emerging around the concept of an industrial metaverse and the remaining obstacles of digital platforms to enable its applications. The pursued DSRM approach to overcome such barriers is capable to serve as guidance for future research projects that pave the way for a gradual enablement of further industrial metaverse use cases in other industries.