The objective of this study is to analyze and assess multi criteria quality of products by an integrated multivariate approach. The integrated multivariate method is based on data envelopment analysis (DEA), principle component analysis (PCA) and numerical taxonomy (NT). To achieve the objective of this study 51-product quality indicators were identified. These indicators (inputs and outputs) were classified as direct and indirect product quality indices and they were classified according to balanced score card (BSC) arrangements. To show the applicability of the proposed approach, five random indicators were considered for seventeen workshops within a large machinery manufacturer. Moreover, PCA, DEA and NT were applied to the set of data. Furthermore, PCA and NT are used to verify and validate the findings of DEA. The results (ranking) of the three approaches were then compared by non-parametric Spearman and Kendall Tau correlation techniques. The results of the non-parametric analysis show should high level of correlation between the three approaches. Previous studies evaluate quality characteristics based on a set of selected criteria that does not reflect total quality characteristics, whereas this study proposes a total multi criteria quality approach to overcome these shortcomings. Moreover, this is the first study to utilize and apply an integrated multivariate approach based on DEA, PCA and Numerical Taxonomy for assessment, ranking and verification and validation of industrial units based on multi criteria quality characteristics. This means that DEA is used for ranking, PCA is used for evaluation of the importance of each indicator and NT is used for validation and verification purpose. The approach of this study may be applied to other manufacturers for total quality assessment of quality characteristics.
Research on Reliability Modelling Method of Machining Center Based on Monte Carlo Simulation