Data in-cladding analysis (DEA) and DATA Envelopment analysis are terms for a technology to the efficiency analysis from the range operations of the Research, which found far spreading in the economic science. It serves the comparative measurement of the efficiency of organization A UNITs or decision units.

General description of the DEA

The DEA decreases/goes back on Charnes, Cooper and Rhodes. It places a technology to the measurement of the relative efficiency of decision units so mentioned (Decision Making unit (DMUs)) . A decision unit can be each object, which can be characterized by inputs (e.g. costs, work expended in hours) and output (e.g. conversion, quality level). Decision units can e.g. be universities, hospitals, branch banks, branches of a commercial company or works of a car manufacturer. It is important that with an application of the DEA to a group of decision units, all decision units have the the same inputs and outputs. So that the application of the DEA supplies a meaningful result, only decision units should be considered with an application, which are similar. Thus no hospitals should be compared with universities. With the help of the DEA the relative efficiency of the decision units is measured, since the decision units serve within the group as comparison yardstick. However one speaks of absolute efficiency, if a comparison yardstick is specified. Such a comparison yardstick, which is evenly not given in the DEA, would be a production function.

The DEA makes possible for a user to consider several inputs and several outputs. Inputs and outputs are comparable often not with one another. For example the conversion is not directly comparable with the quality level. Because the conversion is indicated in euro and the quality level is evaluated e.g. with an efficiency analysis (e.g. 5=hohes quality level, 3=mittleres quality level, 1=niedriges quality level). Therefore the inputs and the outputs are multiplied by meaning meanings. A characteristic of the DEA compared with other efficiency analysis techniques consists of the fact that the meaning meanings of the inputs and the outputs are determined within the model and not by the user to be given to have.

For the evaluation of the efficiency of the decision units for each decision unit an efficiency value is computed. This efficiency and/or Ineffizienzwert measures on the basis of the observed in and outputs of a DMU the distance to the efficient edge (DATA Envelope). This efficient edge is formed from the group of the decision units, which is considered with respective DEA application. From the efficiency value of a decision unit Verbesserungspotenziale can be derived directly for its management.

Mathematical classification

With the application of the DEA to a group of decision units an optimization problem must be solved for each decision unit. In the basic form a DEA model is a problem of quotient programming. Because the efficiency value of a decision unit is a quotient, in whose counter the sum of the weighted outputs is located and into its to denominators the sum of the weighted inputs. The solution of a problem of quotient programming is not simple, since the Zielfunktion is nonlinear. Therefore the problem is converted with the help of the Charnes Cooper transformation in such a way specified into a problem of linear programming.

Alternative techniques to the efficiency analysis

- Technique for order Preference by Similarity ton ideal Solution (TOPSIS)

- Operational Competitiveness Rating (OCRA)

Literature

• And Cantner, H. Hanusch, H. (1998): Efficiency analysis with the help of the DATA Envelopment analysis. Economiceconomics study, 27. Jg., number 5, P. 228-237.
• A. Charnes, W. Cooper, E. Rhodes (1978): Measuring the efficiency OF decision making units. European journal OF operational Research, volume. 2, No. 6, P. 429-444.
• W.W. Cooper, L.M. being Ford, K. clay/tone (2000): DATA Envelopment analysis. Boston/Dordrecht/London 2000.
• H. Dyckhoff, K. all (1999): Theoretical reason of an efficiency analysis by means of DATA Envelopment analysis (DEA). Narrow brook magazine for economical research, 51. Jg., number 5, P. 411-436.
• A. Small one (2002): DEA efficiency, decision and production-theoretical bases of the DATA Envelopment analysis. Wiesbaden 2002.
• H. cross-eyed (2000): Efficiency mass of the DATA Envelopment analysis. Wiesbaden 2000.
• M. Schefczyk (1994): Critical success factors in shrinking industries represented by the example of the foundry industry. Stuttgart 1994.
• Wenk, Thomas (2006): Performance Measurement of systems and their employment as management system, Aachen, 2006. ISBN 3-8322-4901-x (S. 82ff.)

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