Using Fuzzy-DEA to Measure the Efficiency of Korean Cattle Farms

• Published in: Korean Journal of Agricultural Management and Policy Vol. 38; no. 4
• Main Authors: Kim, Y.H., National Institute of Animal Science, RDA, Suwon, Republic of Korea, Chun, D.W., National Institute of Animal Science, RDA, Suwon, Republic of Korea, Park, S.Y., National Institute of Crop Science, RDA, Suwon, Republic of Korea, Lee, J.V., Chungbuk National University, Cheongju, Republic of Korea
• Format: Journal Article
• Language: Korean
• Published: 01.12.2011
• Subjects: Data Envelopment Analysis; EXPLOITATION AGRICOLE; EXPLOTACIONES AGRARIAS; FARMS; Fuzzy-DEA
• ISSN: 1229-9154

DEA(Data Envelopment Analysis) model is a methodology for performance analysis. It is a set of LP technique used to construct empirical production frontiers and evaluates the relative efficiency of DMU(Decision Making Units) with multiple inputs and outputs by given input-output data. The DEA model is particularly very useful when a form of the production function of a DMU like an cattle farm is not known. It has also advantages that can evaluate the relative efficiency by using survey real data without trying to unify or modify units for measured data, and can provide the extend of inefficiency factors of DMUs, which is especially useful information required to improve efficiency. The assumptions underlying DEA are that all the data assume the form of specific numerical value and require a consistent and/or homogeneous operating environment. However, in real world problem as cattle farms system, the input-output data may be imprecise. Another problem in the classical DEA is its low discriminating power when evaluated DMUs are insufficient or inputs-outputs are too many relative to the number of DMUs. To resolve these problems, this study take to incorporate fuzzy set theory with classical DEA. The fuzzy set theory has been proposed as a way to quantify imprecise and vague data in DEA model. Fuzzy-DEA model enables the result to have a higher discrimination, a meaningful interpretation and comparison of the efficiency among DMUs. Simultaneously, the result of DEA offer decision makers bench-marking informations that can help to transform inefficient farms into efficient farms.