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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).

The Snook tables (Liberty Mutual Tables) are a collection of data sets compiled from studies based on a psychophysical approach to material-handling tasks. These tables are used to determine safe loads for lifting, lowering, carrying pulling, and pushing. The tables take into account different population percentiles, gender, and frequency of activity. However, while using these tables to analyze a work place, Ergonomists often have to select from discrete data points closest to the actual work place parameters thereby reducing accuracy of results. To compound the problem further, multiple interrelated variables are involved, making it difficult to analyze parameters intuitively. For example, it can be difficult to answer questions such as, does reducing the lifting height lower the recommended lifting weight, if the lifting distance is increased? To resolve such issues, this paper presents a new methodology for implementing the Snook tables using multi variable regression.