Principal component analysis of vehicle acceleration gain and translation of voice of the customer

Abstract

Principal component analysis (PCA) is applied to characterize the underlying structures of the acceleration gain surface (AGS) of 15 midsize sedans and to link them with customer preference. The AGS can be characterized by two principal components (PCs), the first PC representing the overall slope of AGS and the second PC representing the linearity of AGS. Findings indicate that customer preference is associated with the AGS linearity. PCA thus proves itself a valuable method for translating the voice of the customer as well as for setting effective and efficient vehicle acceleration gain targets.

Keywords

vehicle acceleration acceleration response surface acceleration gain acceleration gain surface market positioning voice of the customer customer preference principal component analysis eigenvalue decomposition singular-value decomposition

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