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Automotive manufacturing presents unique challenges for ergonomic analysis. The variety of tasks and frequencies are typically not seen in other industries. Moving these challenges into the realm of digital human modeling poses new challenges and offers the opportunity to create and enhance tools brought over from the traditional reactive approach. Chiang et al. (2006) documented an enhancement to the Siemen's Jack Static Strength Prediction tool. This paper will document further enhancements to the ErgoSolver (formerly known as the Ford Static Strength Prediction Solver).

Digital Human Models are used extensively in virtual manufacturing to evaluate hand clearance and reach. Spatial assessments of accommodation are typically conducted using digital human models representative of the manufacturing population. Unfortunately, these models are often based on anthropometry gathered from sources that are not representative of the actual target worker population. For example, the size and shape might be based on data from the U.S. military, which differs in .fitness, age, and race distributions from the typical automotive manufacturing population. Ford ergonomists traced errors in accommodation predictions to these inaccurate representations. Using a recently developed statistical methodology incorporating principal components analysis, the anthropometry of the target worker population was synthesized. Using these new data, Ford updated the anthropometry of their digital human models to reflect changes due to secular trends in the U.S.