IMECE2006-13161 APPLYING THE MAHALANOBIS-TAGUCHI SYSTEM TO VEHICLE RIDE

ABSTRACT The Mahalanobis Taguchi System is a diagnosis and forecasting method for multivariate data. Mahalanobis distance is a measure based on correlations between the variables and different patterns that can be identified and analyzed with respect to a base or reference group. The Mahalanobis-Taguchi System is of interest because of its reported accuracy in forecasting small, correlated data sets. This is the type of data that is encountered with consumer vehicle ratings. MTS enables a reduction in dimensionality and the ability to develop a scale based on MD values. MTS identifies a set of useful variables from the complete data set with equivalent correlation and considerably less time and data. This paper presents the application of the Mahalanobis-Taguchi System and its application to identify a reduced set of useful variables in multidimensional systems. INTRODUCTION The primary objective of this research is to develop a methodology, which demonstrates the relationship between actions of a producer and its suppliers (for instance, target setting of sub-systems or components' performance attributes) and consumer satisfaction ratings. The goal is to efficiently forecast consumer satisfaction ratings (CSRs) as a function of available vehicle level performance data for vehicle ride