EFFICIENCY EVALUATION BY MEANS OF DATA ENVELOPMENT ANALYSIS: STRENGHTS AND WEAKNESSES

During the last years great attention has been given to the assessment and improvement of the performance of productive systems. In this context, there are two types of modeling methods of comparative performance measurement: a non-parametric one, represented by Data Envelopment Analysis (DEA) and a parametric one, represented by Stochastic Frontier Analysis (SFA). Both DEA and SFA approaches are derived from the methods of measuring efficiency introduced by Farrell (1957), who suggested measuring the productive efficiency of an organisation relative to an empirical production frontier, called “best practice frontier”. This best practice frontier specifies for a unit the maximum quantities of outputs it can produce given any level of inputs and, for any levels of outputs, the minimum quantities of inputs needed for producing the outputs. According to Farrell, a technical efficient organisation would be one that produces the maximum possible outputs from a given set of inputs or one that produces a certain level of outputs with the minimum amount of inputs. The aim of this article is to give an overall view of the non parametric approach to efficiency measurement and to analyse its advantages and disadvantages with respect to SFA. We also present a modified DEA model and we show its usage in some applications with respect to the variables that represent the strategies and objectives in place in the different units analysed. The paper is organized as follows. Section 2 describes some of the theoretical background of DEA method and reviews its strengths and its weaknesses, Section 3 provides an introduction to a modified DEA model which includes undesirable outputs, Section 4 presents three case studies which include the presence of bad outputs and Section 5 introduces a general discussion.