Forecasting consumer satisfaction for vehicle ride using a multivariate measurement system

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Abstract
Consumers perceive quality and performance at the vehicle level. Consumers evaluate vehicle attributes such as ride, handling, roominess, braking and acceleration. Vehicle level attributes are influenced by factors at all levels of the vehicle architecture, and these factors are often correlated. The goal of this research is to efficiently forecast consumer satisfaction measured as a function of vehicle level performance data by developing a multivariate measurement system using the Mahalanobis-Taguchi Gram-Schmidt approach. The Mahalanobis-Taguchi Gram-Schmidt technique is applied to construct the measurement scale and identify a reduced set of useful variables for vehicle ride sufficient to make effective predictions.