Masterarbeit aus dem Jahr 2018 im Fachbereich Ingenieurwissenschaften - Wirtschaftsingenieurwesen, Note: 1,0, Technische Universität Berlin, Sprache: Deutsch, Abstract: The contemporary car is a highly complex product which results from the concerted cooperation between the automobile manufacturer and its multiple suppliers. The intensive vehicle use often leads to component failures in the field. In order to identify the causes of failure and generate valuable knowledge about the defective parts, the components are analyzed on a random basis as part of the claims process. Instead of a random sample, however, the selection process should take the attributes and the additional information content of the components into account. A large amount of data is created along the entire product lifecycle and can be used to select the components in a more targeted manner. This thesis investigates the opportunities of the intelligent use and analysis of smart data in order to find data patterns, group the components based on their characteristics, and create data-driven samples for the failure analysis. For this purpose, a data processing and analysis concept is developed that can help to lower the analysis costs, reduce the expenditure of time, and improve the product quality. Additionally, this data analysis tool can also be applied to monitor the current condition of the components which are still in the field and preventively detect potential failures. Since the effectiveness of the data analysis and their results highly depend on the provided data, this thesis also describes the requirements and quality of the used data. The thesis concludes with an exemplary application of the selected data analysis method, the k-Means clustering algorithm.