With the ever-increasing need to reduce the use of fossil fuels, Tesla is accelerating the world's transition to sustainable energy. This means replacing all internal combustion vehicles with electric ones over time. The growing number of Tesla vehicles on the road poses interesting scaling challenges for all departments especially for the Service Engineering team. To help prioritize issues and reduce service costs, frequencies, and duration, a way to generate an overview of all the costs separated by types of repair is needed. This thesis aims to automatically generate such an overview by borrowing techniques used in Natural Language Processing. In particular, the LDA and GSDMM algorithms for topic generation are tested. Additionally, a novel method based on the cross frequencies of items is presented. Methods are also presented to compute the novelty of every topic and a new metric, here called the growing score, is introduced as a mean to monitor the frequencies of the topics over time. The methods are applied to service records data from Tesla and the results are analyzed in detail. The results are also enhanced by computing additional information regarding costs for all parts of the service visit and also the configurations of the cars. It is found that the novel approach for the topic generation and the novelty and growing scores produce useful information that can be used to optimize and avoid the need for service procedures to reducing total cost of ownership.