Model based computer aided processes offer an economical and accelerated alternative to traditional build-and-test Edisonian approaches to engineering design. Typically a CAE based design problem is formulated in two parts, viz. (1) the inverse problem which involves identification of the appropriate product geometry given desired property requirements, and (2) the forward problem which is the computation or prediction of product performance measures from the product geometry. Solution to the forward problem requires development of an accurate model that is correlated to physical data. This validated model could then be used for virtual verification and design of engineering systems efficiently. This paper demonstrates the rigorous process of model development, model calibration, model validation, and use of the calibrated model in the design process with practical examples from automotive suspension, brakes and powertrain systems. The nature of incremental improvement in model towards the goal of elimination of physical tests with virtual verification techniques is showcased with a body top-mount example. Further a design problem of a lower control arm is highlighted to show as to how a model correlated to vehicle data can be used to improve an existing design. Systematic model based topology optimization to reduce weight is illustrated using a rear twist beam example. A dust shield design problem is presented to show how multiple objectives from durability, Noise Vibration and Harshness (NVH), and manufacturing perspectives that may often be conflicting can be effectively handled within the framework of topography optimization. Further a systems approach of using multiple solution techniques (implicit static analysis, explicit dynamic analysis and multi-body dynamic analysis) to solve a practical problem is explained with the design of a protection rib for an engine oil pan sensor. Results from the analysis of a battery model is used to design limiting pins in a battery tray to effectively arrest shocks encountered by batteries during transport. Finally the paper offers general conclusions about how selection of appropriate model complexity can further improve the efficiency of computer aided engineering approaches.