Engineers and scientists often identify robust parameter design (RPD) as one of the most important process and quality improvement methods. Focused on determining the optimum operating conditions that facilitate target attainment with minimum variability, typical approaches to RPD use ordinary least squares methods to obtain response functions for the mean and variance by assuming that process data are normally distributed and exhibit reasonably low variability. Consequently, the sample mean and standard deviation are the most common estimators used in the initial tier of estimation, as they perform best when these assumptions hold. Realistically, however, industrial processes often exhibit high variability, particularly in mass production lines. If ignored, such conditions can cause the quality of the estimates obtained using the sample mean and standard deviation to deteriorate. This paper examines several alternatives to the sample mean and standard deviation, incorporating them into RPD modeling and optimization approaches to ascertain which tend to yield better solutions when highly variable conditions prevail. Monte Carlo simulation and numerical studies are used to compare the performances of the proposed methods with the traditional approach.
