Companies that perform virtual prototyping of their products strive to improve product quality and performance while reducing time to market. Virtual prototyping helps companies deliver innovation and desirable product features faster than traditional development. In fact, many companies have initiatives that trend towards reducing physical prototypes in order to reduce development costs and get products into the hands of consumers much faster than ever before. The demand for speed to marketplaces applies significant pressure on product development to get the design right with fewer iterations. That can only happen if the virtual product that is being simulated accurately represents the physical product. Inaccurate virtual prototypes reduce confidence, quality and risk failures in the market such as recalls and product performance that leads to a poor consumer experience. As long as physical products are delivered, physical prototyping will always exist. It is important to keep in mind that physical prototyping still has a place in product development by guiding the development, confirming the accuracy, and quality of virtual models. Accurate virtual models no doubt will meet company objectives with respect to being faster to market with reduced development costs. Three examples are presented in which correlation assesses the accuracy of a virtual model, and eventually improves the performance of what will become the physical product. Additionally, the use of design space exploration will be shown as an effective way an analysis model can be fine-tuned to better match test results. These examples are used to support the use of correlation and design space exploration in virtual prototype development processes. The first example concerns the analysis of an electric motor and gear box assembly. It will show how correlation between test and analysis modes identifies deficiencies in the analysis model. Then with design space exploration, the analysis model is updated to better represent the physical behavior of the product. The correlation and design space exploration demonstrate the importance of accurate analysis component and assembly level attributes of a system. Once an accurate analysis model is realized, one can be confident in the downstream results it produces in other performance evaluations. The second example is an automotive body in white. A key performance attribute of automotive bodies is the frequency of their first torsion mode. NVH analysts will study system attributes to improve stiffness and reduce mass, increasing the torsional mode frequency. This example uses mode shape correlation in which a well-qualified analysis model acts as the reference to the model currently under development. Then a design space exploration is setup that exercises a set of parameters that influence overall system stiffness and mass. These include connections, material, and panel thickness assignments, as well as the inclusion/exclusion of certain components. A material cost function is applied to the exploration too. Given different materials and their cost, the exploration targeted a low-cost material solution for mass market appeal. This set up competing objectives (low cost, high performance). The last example demonstrates thermal correlation concepts. Here the objective is to validate analysis methods that would lead to a better virtual prototype in the turbomachinery industry. Given test measurements of temperatures at key locations across an engine mission profile, the objective is to fine tune loads applied to a 2D whole engine model that best matches the test measurements across the entire mission profile. The objective is met by minimizing temperature differences between the test data and the analysis data, not at discrete time points, but by considering the entire time history.