Empirical analysis of productivity enhancement strategies in the North American automotive industry

Abstract

Even though there is a pressing need for continuous productivity improvement, studies that employ robust empirical analysis of strategies and factors to enhance productivity in the North American Automotive Industry are very scarce. In this study, robust and hybrid models of the most popular productivity measurement in the automotive industry, Hours per Vehicle (HPV), is developed. Data examined in this research was compiled for a 9-year period, 1999–2007, for North American automotive manufacturers. Through practical considerations and a comprehensive literature review, 14 important variables that influence HPV were defined and developed. A hybrid method, the combination of multiple M-estimators and a lasso, was developed and was shown to be the best method to determine a robust regression model to estimate HPV. The vehicle variety, number of available working days in a year, car model types, new model launch, and car assembly and capacity utilization penalize HPV. However, annual production volume, flexible and lean manufacturing, platform sharing strategy, and year of production improve HPV. Using lean and flexible manufacturing, platform strategy, and reducing the percentage of hourly employees help improve productivity and reduce HPV while launching a new vehicle. Additionally, Japanese plants were appeared to be the benchmark with respect to HPV, followed by joint ventures and American plants.