Digital Human Modeling (DHM) is a computational design tool that allows designers to use virtual prototypes of design for exploring ergonomics requirements. DHM brings in the advantage of reducing the time and finances committed to building the physical prototypes as well as minimizing the efforts put on performing human subject data collection. As a result, it is extensively used today in the automotive, aircraft, and military industry to evaluate designs for better ergonomics. However, DHM is still being used during evaluation stages when major aspects of the design are already finalized. Using DHM needs expert knowledge as the engineer has to perform manual manipulations which require significant user effort and time. Additionally, engineers often rely on intuition and expertise when performing DHM manipulations which has the possibility of injecting bias into the design, thus resulting in erroneous analyses. As a result, engineers generally follow a conservative approach focusing on a limited set of ergonomics simulations rather than performing an exhaustive search. These ergonomic simulations often consist of repetitive task simulations performed on the designs by considering only the standardized human-machine interactions in normal work conditions. Also, very limited research exists that focuses on using DHM for the design and analysis of high-risk tasks in emergency/non-nominal conditions. These pressing limitations make it necessary to simplify the use of DHM tools by automating the repetitive task simulations and extend analysis capabilities to encourage the use of DHM in early design for generating design alternatives. The first manuscript in this study introduces an early design framework to automate task simulation and ergonomic evaluations within DHM to provide quick-and-dirty design exploration capabilities and compares the framework with traditional manual DHM simulations. This research study explores a cockpit packaging design problem as proof of concept for the DHM automation framework. An exhaustive design study was performed to generate cockpit packaging ergonomics for accessibility, specifically for measuring reach gap and eye view distance. Results suggest that the automation methodology has the potential to reduce a significant amount of time required to perform DHM simulations and minimize user bias or error. The second manuscript implements the DHM-based automation framework for performing a computational design exploration study to measure the effects of fire/smoke emergency occurring in a civilian aircraft cockpit. Results suggested that the automation framework has the potential to enable emergency studies (such as smoke in the cockpit analysis) as a part of the early design ergonomics which has not been explored in the traditional ergonomics studies.