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Digitalization is one of the major challenges for companies today. Well-established “traditional” companies have to adapt and develop new business models to stay competitive. Digitalization, connected services and alternative drive technologies are identified as key trends in the automotive industry. New competitors from different industry segments, e.g. IT industry, enter the automotive industry. Therefore, the automotive industry faces new challenges due to digitalization. These challenges are identified in the scope of this thesis. One major is a transformation of the classic sales process of a vehicle. Mercedes-Benz Special Trucks developed a new certified partner program. Bodybuilders, who are working with the model series Econic, can apply for the partner status.
Furthermore, Mercedes-Benz Special Trucks will launch a product information platform. Bodybuilders have the opportunity to market themselves and their products on the platform. Therefore, this thesis aims to identify the theoretical factors of the success of a digital platform and to transfer these factors to the automotive industry. Qualitative research methods were conducted to examine the practical relevance of these factors. By analyzing already existing platforms in the automotive industry, it turned out that many industry-specific success factors are already implemented. Thereby a series of recommendations for actions, regarding the design and optimization of the EconicPartner Portal, were derived.
The automotive industry is undergoing substantial structural changes. New forms of powertrains and players are emerging. Manufacturers, due to internal as well as external drivers, are building their cars on platforms. By making use of modular design approaches, they hope to satisfy customer needs best and stay competitive. When designing global platforms, local variations are to be avoided. The presented paper offers an approach to rank components of an electric vehicle according to their likeliness of a local design adaption. To verify the results of the value chain- based analysis, the variant indicator analysis, interviews were carried out. The analysis shows that the battery is most likely subject to design changes. Also, variant drivers in the value chain bear the highest potential for local redesigns.