Abstract
The product development process within the automotive industry is subject to changing demands due to internal and external influences. These influences and adjustments especially affect the car body and its inherent joining technology, as critical stages of variant creation. However, current literature does not offer a suitable analytical method to identify and assess these critical influences. We propose an advanced analytics approach that combines data mining and machine learning techniques within the car body substructure. The evaluation within the Mercedes-Benz AG shows that our approach facilitates a quantitative assessment of unknown interdependencies between car body modules and corresponding joining techniques.
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