Since vehicles are comprised of thousands of components, it is essential to reduce the Life Cycle Inventory (LCI) modelling workload. This study aims to compare different LCI modeling workload-reducing scenarios to provide a trade-off between the workload efforts and result accuracy. To achieve the optimal balance between computational effort and data specification requirements, the driver seat is used as a case study, instead of the entire vehicle. When all the components of a conventional light-duty commercial vehicle are sorted by mass descending order, seats are among the first five. In addition, unlike the other components, seats are comprised of metals as well as a wide range of plastics and textiles, making them a representative test case for a general problem formulation. In this way, methodology and outcomes can be reasonably extended to the entire vehicle. Regarding the methodology, this study investigates the use of the International Material Data System (IMDS), thus primary data are used. First, the Life Cycle Assessment (LCA) of the reference scenario is evaluated, in which the LCI model is developed using the full list of substances at element level. The reference scenario is characterized both by the highest degree of details and major workload efforts. Second, the authors consider three workload-reducing scenarios, which they refer to as: the cut-off, the Verband Der Automobilindustrie (VDA) and the one-substance-one-material scenarios. Then, granularity is added, and different levels of disaggregation are considered for all scenarios. Results indicate that when the reference scenario is compared to the cut-off scenarios, environmental impacts are significantly different in certain impact categories (e.g., Abiotic Depletion) even with the smallest cut-off (1%). In contrast, when Global Warming Potential (GWP) is considered, the difference is negligible for any value of cut-off ranging from 1 to 5%. As a result, if the focus is solely on the GWP, the cut-off is a viable workload-reducing strategy. Finally, the VDA and the One-substance-one-material scenarios appear to be the best compromises in terms of workload and accuracy. The One-substance-one-material scenario achieves the highest accuracy compared to the other workload-reducing scenarios.