Adapted by: Dony saputra
Data Envelopment Analysis (DEA) is a “data oriented” approach that Convert multiple input to multiple output for evaluating the performance of a set of peer entities (Decision Making Units/DMU). It’s requires very few assumptions that also opened up possibilities for use in cases which have been resistant to other approaches for unknown nature of the relations between the multiple inputs and multiple outputs involved in DMUs. DEA is also commonly used as method that has previously been evaluated by other methods as new insights into activities (and entities). DEA is an excellent and easily used methodology for modeling operational processes for performance evaluations. For instance, Zhu (2002) provides a number of DEA spreadsheet models that can be used in performance evaluation and benchmarking. for instance, the use of DEA to guide removal of the Diet and other government agencies from Tokyo to locate a new capital in Japan, as described in Takamura and Tone (2003).
In an article by Farrell (1957), DEA was developed by the need for developing better methods and models for evaluating productivity. A method that was used have a conflict where attempts to solve the problem need to produced careful measurements but it was very restrictive because they failed to combine the measurements of multiple inputs into any satisfactory overall measure of efficiency. By these inadequacies of separate indices of labor productivity, capital productivity, etc., Farrell proposed an activity analysis approach that is DEA where he extended the concept of “productivity” to the more general concept of “efficiency”.
This method differs from the Ordinary Least Squares (OLS) statistical technique that bases comparisons relative to an average producer.DEA identifies a “frontier” which are characterized as an extreme point method that assumes that if a firm can produce a certain level of output utilizing specific input levels, another firm of equal scale should be capable of doing the same. DEA is a nonparametric method that handle fraction that cannot be done by linier programming, it transform the formulation, such that we limit the denominator of the objective function and only allow the linear programming to maximize the numerator.
- Cooper, W.W., H. Deng, Z. M. Huang and S. X. Li, 2002, A one-model approach to congestion in data envelopment analysis, Socio-Economic Planning Sciences 36, 231-238.
- Seiford, L.M. and Thrall, R.M.(1990), Recent developments in DEA DEA: The mathematical programming approach to frontier analysis,Journal of Econometrics, Vol.46;7-38,
- Takamura, T. and K. Tone, 2003, “A Comparative Site Evaluation Study for Relocating Japanese Government Agencies Out of Tokyo,” Socio-Economic Planning Sciences 37, 85-102.
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