Abstract
The aim of this study is to define a robust estimation model of the most dominant labour productivity measurement, Hours per Vehicle (HPV), in the auto industry. Data utilised in this study were from 10 different multinational North American carmakers from 1999 to 2007. Through a comprehensive literature review and practical consideration, 13 important variables that affect HPV were defined and developed. Several robust and advanced statistical methods were utilised to determine the best possible HPV regression equations. The MM estimator, multiple M-estimator, was defined as the best method to perform the data analysis and to derive the robust regression model to estimate HPV. Depending on the car class, the vehicle variety, model types, annual working days, car assembly utilisation and launching a new model penalise HPV; however, annual production volume, flexible manufacturing and year of production improve HPV. Moreover, Japanese plants are the benchmark regarding HPV followed by joint ventures, and American plants.
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