ABSTRACT. In this paper we present a brief overview of 10 years of research on Parametric Data Envelopment Analysis (DEA). We begin with a brief introduction to DEA. Then, we present the central problem of how to distribute a new and total fixed variable (input or output) across all DMUs (Decision Making Units) present in the analysis. We then tell the history of the developments in Parametric DEA, beginning with its first conception in 2002, moving onward to the current research. We conclude with a brief discussion of future perspectives.

This work aims at complementing the development of the EFM (Ellipsoidal Frontier Model) proposed by Milioni et al. (2011a). EFM is a parametric input allocation model of constant sum that uses DEA (Data Envelopment Analysis) concepts and ensures a solution such that all DMUs (Decision Making Units) are strongly CCR (Constant Returns to Scale) efficient. The degrees of freedom obtained with the possibility of assigning different values to the ellipsoidal eccentricities bring flexibility to the model and raises the interest in evaluating the best distribution among the many that can be generated. We propose two analyses named as local and global. In the first one, we aim at finding a solution that assigns the smallest possible input value to a specified DMU. In the second, we look for a solution that assures the lowest data variability.