On some methods for performance ranking and correspondence analysis in the DEA context

Two novel methods named performance baseline and performance correspondence matrices are proposed to evaluate the performance of decision making units (DMUs) based on the techniques of singular value decomposition (SVD). The performance baseline matrix can be used to rank all the DMUs because it provides a common basis for performance comparison. The performance correspondence matrix can be used to conduct performance cluster analysis, with which to explore the structure of input/output variables that are associated with DMUs. The analysis can reveal the performance difference of the DMUs and the key input/output variables determining the efficiency of a certain DMU, and provides valuable quantitative information for adjusting variables to improve efficiency of the DMU. Three case studies are presented to demonstrate that the proposed methods in this work are effective and easy to use and can provide insights into proper selection of input/output variables for performance comparison to avoid over manipulating DEA models in practice.