The world is rapidly evolving towards a data-driven paradigm, with widespread adoption of tools and techniques such as Artificial Intelligence, Machine Learning, Digital Twins, and Cloud Computing. In the automotive sector, vast amounts of data are being generated through both physical and digital test evaluations. Among these, Computer-Aided Engineering (CAE) stands out as one of the largest contributors to data generation, as physical testing is often cost-prohibitive due to the need for prototypes and complex test setups. The field of Automotive Noise, Vibration, and Harshness (NVH) is advancing exponentially, driven by stringent regulatory norms and growing customer demand for comfort. Digitally advanced techniques are playing a pivotal role in revolutionizing NVH processes. Data generation via CAE tools has become an integral part of engineers' daily operations, with the selection of appropriate CAE software and solvers being critical. This choice impacts factors such as user interface experience, accuracy, solution time, hardware requirements, variability analysis proficiency, Design of Experiments (DOE) competency, and integration with other systems. This study aims to evaluate and compare these key parameters across leading CAE software and solvers in the automotive NVH domain, using a vehicle finite element model. A comprehensive matrix is developed to assist engineers in making informed decisions when selecting CAE tools tailored to their specific requirements. In conclusion, leveraging this matrix enables engineers to enhance their efficiency in variability analysis, optimize solution times, and achieve seamless integration with existing systems. Additionally, the matrix provides valuable insights to software vendors, helping them identify areas for improvement and innovation. Keywords: CAE software & solvers, NVH, data generation, digital advanced techniques