Forecasting Warranty Cost for Vehicle Handling Using the Mahalanobis-Taguchi System

Consumers judge quality and performance at the system level, but important cost-effective decisions at the sub-system or component level must be made by the producer in order to economically satisfy consumer's needs by providing affordable and high quality products. Mahalanobis distance (MD) is a distance measure that is based on correlations between variables and the different patterns that can be identified and analyzed with respect to a reference population. MD is a discriminant analysis tool, which will be used to predict warranty cost using multiple characteristics at all levels of a hardware set. The Mahalanobis-Taguchi System is a diagnosis and forecasting method for multivariate data. 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. This paper presents the application of the Mahalanobis-Taguchi System in multidimensional systems to forecast warranty cost. The proposed methodology uses a pattern recognition scheme known as the Mahalanobis-Taguchi System to translate the performance of lower level elements into an estimate of warranty cost at the system level. An automotive case study involving vehicle handling is provided to illustrate the proposed methodology.