Preferred input-output analysis using inverse DEA: an application to retailer performance assessment

Inverse data envelopment analysis (DEA) approach deals with the input or output estimation preserving relative efficiency score. In many real-world applications, there is more prior information like price information or managerial preferences, etc. that should be considered in the efficiency analysis. Corporation of this sort of information is not dealt with in the inverse DEA literature, not in the input estimation process nor in the output estimation. Thus, the current paper develops a new inverse DEA model capable of dealing with prior information via weight restrictions. Proposed models are applied in the efficiency and inverse efficiency analysis of a real-world retailer application.