Historically and up to some extend currently, Product validation focusing on NVH heavily relies on physical testing which put NVH focused validation on right hand side of the Product Validation Process. Due to this, lot many times design changes are not possible or expensive or time consuming which reduces overall product quality. It is important to push NVH workflows towards left hand side of product validation cycle to drive designs focusing on NVH. Industries like Automotive, ground transportation, Aero & Defense, High Tech, Industrial products making huge investments on NVH validation to win over customer perception battle. Automotive industry in particular highly sensitive. Whether it is related to improving driver & passenger comfort and riding experience, reducing driver fatigue, ensuring desired structural behavior to adhere to reliability & fatigue life requirements, creating desired sound and even design sound to alert driver, passenger & pedestrian or adhering to noise regulations. Electric vehicles making automotive industry more sensitive to NVH performance. Without an internal combustion engine vibrating and generating noise, sources like transmission, electric motor, active sounds to alert driver/passenger/pedestrian, doors, normal squeaks and rattle of vehicle structure, braking, cooling fans, road noise, wind, doors are key in NVH performance. A common mistake engineers make when simulating NVH is that they start right away with highly complex and computationally expensive simulations. This mistake is primarily due to the fact that some engineers forget to make sure that the basic dynamic behavior of a simulation model is well correlated before they start performing complex and highly expensive simulations & drive designs based on those findings. It’s important that basic quantities of simulation models like overall mass, stiffness and damping matrices are well correlated with physical testing using parameters like modal assurance criteria (MAC), coordinate modal assurance criteria (CoMAC), frequency response assurance criteria (FRAC) before running highly complex analysis. During the session, I will share different best practices like this.