The usage of simulation in support of certification holds the promise of reducing costs and enhancing safety significantly. Assuring the credibility of the performed modelling and simulation work is accomplished by following a building block approach, supported by a rigorous verification and validation exercise. An outcome of this process is a substantial amount of data supporting software verification, material characterization, subsystem validation against test data, and full system sensitivity analysis. These processes require engineers to maintain a high level of due diligence in order to keep everything consistent and traceable. Shifting the burden from the engineer to a Simulation Process and Data Management system (SPDM) for managing processes, traceability, and consistency would optimize engineering resources and reduce the overall time and cost of the verification and validation exercise. In this paper, we show the value of an SPDM system that is fully integrated with an enterprise product lifecycle management system, along with some of the key benefits: ability to capture and connect requirements to downstream simulation and test activities, while simultaneously tracking relevant metrics (KPI’s) for both. This creates a consistent digital thread connecting all data and activities in an unambiguous manner. The potential of such a solution will be illustrated by walking through a virtual anthropomorphic test dummy (v-ATD’s) calibration as recommended per ARP5765 focusing on analytical methods for aircraft seat design and evaluation. Starting from the requirements to run four calibration tests for the FAA Hybrid III ATD, simulation and test verification requests will be launched for the forward-facing two-point belt, forward-facing 60 degrees two-point belt, forward-facing three-point belt, and forward-facing four-point belt scenarios. As a primary benefit of directly initiating the verification activities from within the product lifecycle context (PLM) system, traceable links between simulation models, test models, supporting data, and requirements are automatically generated when executing the verification requests. Relevant KPI’s like head injury criterion or lumbar load are extracted from both virtual and physical tests and compared through magnitude or curve shape errors. A rating (pass or fail) for the critical KPI’s under consideration will ultimately result in acceptance or rejection of the v-ATD for use in subsequent analysis. The described digital thread also facilitates communication between various stakeholders and avoids out-of-scope usage of the validated models.