Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/87244
Type of publication: Tezės kituose recenzuojamuose leidiniuose / Theses in other peer-reviewed publications (T1e)
Field of Science: Matematika / Mathematics (N001)
Author(s): Laurinavičius, Eligijus;Laurinavičienė, Nijolė
Title: Measuring Efficiency of Agriculture Industry Using Multi-Directional Efficiency Analysis Method
Is part of: IX nordic – baltic agrometrics conference: abstracts of papers, Kaunas, June 11–13, 2014 / Aleksandras Stulginskis University Kaunas, 2014
Extent: p. 12–13
Date: 2014
Keywords: Data Envelopment Analysis;Multi-directional Efficiency Analysis;Agriculture
Abstract: Multi-directional Efficiency Analysis (MEA), like Data Envelopment Analysis (DEA), is a non-parametric method, differing from DEA in the way in which efficiency is measured. In other words, MEA makes use of an entirely different efficiency index. DEA has become one of the more popular tools for productivity analysis. DEA is especially useful when there are multiple inputs and outputs with different units of measure. There are several ways of applying the DEA methodology that all stem from the seminal papers of Farrell (1957) and of Charnes, Cooper and Rhodes (1978). The input-oriented method as its efficiency is determined by holding outputs constant and inputs to be improved (decreased) in order for a DMU to be considered efficient, thus the output-oriented method by holding inputs constant and outputs be improved (increased). An efficient DMU has no potential improvement, whereas inefficient DMUs have efficiency scores reflecting the potential improvement based on the performance of other DMUs. When utilizing DEA to evaluate a set of (DMUs), an efficient frontier is created that determines which DMUs are performing well (efficient) and which are not (inefficient). The selection of the decision making units (DMUs) to benchmark against is a critical part of this analysis. These different methodologies typically give the same efficient set, although by moving solely along the input space or solely along the output space. The efficiency score, which will be less than one if a DMU is inefficient, is the proportion by which all inputs must be reduced in order to become efficient. This is an important point: for a particular inefficient DMU, the projection onto the frontier is, in essence, calculated by reducing the input dimension until the DMU reaches the frontier. If the purpose of the benchmarking set is to determine those efficient DMUs most like an inefficient DMU, it is better to have the ability to move along
Internet: https://hdl.handle.net/20.500.12259/87244
Affiliation(s): Vytauto Didžiojo universitetas
Žemės ūkio akademija
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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