In conventional vehicle design approaches, there is typically little understanding of the consequences of early stage design choices. This may be attributed to the conventional approach’s limitation in capturing complex interactions, further leading to increased design iterations. To overcome this, holistic multidisciplinary models were developed. However, they introduce the burden of complexity and costs because of their intricate nature. Furthermore, it is challenging to gain meaningful insights without a deeper understanding of the model’s nature and structure. Therefore, in this article, an alternative form of model representation was proposed to address these shortcomings. This was achieved by integrating two concepts: network theory and sensitivity analysis. A detailed and robust framework that represents complex multidisciplinary models as network models, reduce their complexity, and navigate insights from them, was provided. This is further demonstrated by a case study of a rail vehicle traction system including a traction motor and an inverter coupled with operational drive cycle. Among the identified 246 factors in the traction system network model, the three most influential inputs were identified for the chosen output factor of interest. Subsequently, the knock-on effects of these inputs were determined. The results indicate a significant reduction in the network graph size compared with the complete network graph of the traction system model. This indicated a significant reduction in the number of factors to consider in the analysis. This demonstrates the capability of the proposed framework to reduce the complexity of the analysis while retaining the ability to analyze intricate interaction effects.