The concept of sustainabilty requires the balanced consideration of economic, ecological and social aspects. Environment-friendly product design, hence, is a necessary element of any company's sustainability strategy. In turn, one of the key prerequisities is a continuous, quantitative assessment of the environmental profile of the respective product design options. In this context, Life Cycle assessment (LCA), as described in the ISO 14040 series, is one important tool to consider environmental aspects. As far as environmental assessments of complete vehicles are concerned, one may distinguish two areas of application: firstly, the descriptive LCA, which focuses on the identification of the 'hot spots' of the product, and, secondly, the comparative LCA, which compares the environmental profile of different product alternatives that fulfil the same function. The focus of this paper is Life Cycle Inventory (LCI) studies on complete vehicles. Based on this, we will describe a procedure developed by Volkswagen, which reduces the workload significantly in a way that not only reduces the overall effort of conducting LCA studies but also offers advantages in terms of LCA quality. Nevertheless, the procedure as well applies to goods such as vehicle parts (e.g., the fuel tank or a driver's seat). Despite important benefits concerning manpower and overall quality, the presented procedure nevertheless faces various challenges that have to be taken into consideration. First of all, just like in any other LCA study, the LCI results of a specific product model heavily rely on the completeness and quality of the respective data source. Another challenge to be mentioned is the dependence on the availabilty of representative data sets. Especially in highly innovative industries such as the automotive industry, it will not always be feasible to provide LCI data for all of the identified materials and manufacturing processes. Hence, the use of surrogate data, based on physical, chemical or other resemblances, remains an important strategy. In essence, the Volkswagen slimLCI does not liberate the LCA practicioner of general problems concerning data gaps, but, in turn, provides a strong basis for the iterative evaluation of these shortcomings as a prerequisite for a continuous learning and improvement process. Lastly, one has to adress the demand for further research in the field of the evaluation of LCA quality. While the displayed benefits are undeniable, it is not possible to specify the degree of improvement. It can be seen that the amount of time and manpower necessary for compiling a vehicles LCI can be significantly decreased, compared to the former proceeding. The maximum time span, which used to be roughly seven months, is reduced to about six weeks, representing an efficiency gain of more than 80%. In addition, it has been pointed out that Volkswagen slimLCI generates numerous advantages concerning LCA quality. Therefore, the efficiency gain is actually larger because a superior results achieved with less effort. In the near future, the procedure has to be validated for different vehicles, in order to confirm that the underlying specifications about materials, manufacturing processes and their respective representations through available data sets are indeed appropriate to properly describe the significant environmental aspects of the vehicle under study. This fine-tuning is inevitable to establish an ample level of confidence in the procedure's results.