Ana Patricia García Amado, Manuela Gonzalez Hurtado · Universidad Industrial de Santander · 2011
Análisis Envolvente de Datos Eficiencia Supereficiencia RESUMEN Con el fin de hacer un seguimiento en el desempeño de las Instituciones de Educación Superior (IES), entidades como el Ministerio de Educación Nacional (MEN) y el Sistema de Universidades Estatales (SUE) han desarrollado los Indicadores de Gestión que permiten monitorear el funcionamiento de las universidades y el manejo de sus recursos. Para cuantificar tal desempeño, en términos de eficiencia, este trabajo propone como herramienta de medición el Análisis Envolvente de Datos (AED), una eficaz técnica de optimización que mide y compara la eficiencia de unidades organizacionales, en este caso universidades, con respecto a otras de la misma línea de producción. Este trabajo tuvo como fin evaluar la eficiencia de las universidades públicas de Colombia en el año 2009, así mismo, evaluar la eficiencia de cada una de ellas respecto a sí misma y respecto al grupo al cual pertenecen, clasificadas de acuerdo a su capacidad, en el período 2003-2009. Para lo anterior se tomó como insumos los indicadores del índice de capacidad y como productos los de investigación y formación y se aplicó los modelos CCR y Supereficiencia orientado a los productos, es decir, se evaluó la eficiencia en términos de producción. Si bien la eficiencia en formación se puede considerar que ha sido alta y estable durante los años considerados, presenta una ligera tendencia de disminución en los dos últimos años. Contrariamente, la eficiencia en investigación muestra una tendencia creciente en los últimos años, fruto del empeño que las universidades han puesto en este aspecto en estos años bajo la influencia, entre otros aspectos, del proceso de evaluación de la gestión de las universidades que el SUE y el MEN han venido realizando desde 2003.
Cited by 0Open access
Jean‐Marc Huguenin · 2013
This contribution introduces Data Envelopment Analysis (DEA), a performance measurement technique. DEA helps decision makers for the following reasons: (1) By calculating an efficiency score, it indicates if a firm is efficient or has capacity for improvement; (2) By setting target values for input and output, it calculates how much input must be decreased or output increased in order to become efficient; (3) By identifying the nature of returns to scale, it indicates if a firm has to decrease or increase its scale (or size) in order to minimise the average total cost; (4) By identifying a set of benchmarks, it specifies which other firms' processes need to be analysed in order to improve its own practices. This contribution presents the essentials about DEA, alongside a case study to intuitively understand its application. It also introduces Win4DEAP, a software package that conducts efficiency analysis based on DEA methodology. The methodical background of DEA is presented for more demanding readers. Finally, four advanced topics of DEA are treated: adjustment to the environment, preferences, sensitivity analysis and time series data.
Cited by 8
Cecilio Mar Molinero, David Woracker · OR Insight · 1996
Data Envelopment Analysis (DEA) is a Linear Programming based technique for the analysis of efficiency of organisations with multiple inputs and outputs. DEA may not achieve its full potential due to the mathematical language in which most papers and books have been written. In this article the authors explain in a non-mathematical way the principles on which DEA is based. Concepts, definitions and methodology issues are explored by means of a hypothetical example involving prison establishments.
Cited by 15
Pereira, José Veiga · Instituto Politécnico do Porto · 2017
A avaliação das organizações e a deterntinação da performance obtida pelo exercício da gestão, tem sido uma preocupação constante de gestores e accionistas, embora com objectivos diversos. Nos dias de hoje, a questão coloca-se com maior acuidade quer pela competitividade acrescida quer pela dimensão e complexidade actual das empresas. Pretendemos com este trabalho fazer uma descrição da metodologia DEA - Data Envelopment Analysis - nas suas formulações iniciais mais simples. A metodologia do DEA, pretende obter uma medida única e simples de avaliação da eficiência, combinando um conjunto de outputs e de inputs relativos às diferentes unidades homogéneas que se pretendem avaliar. O método DEA é um método não paramétrico que pelas suas características é particularmente adequado à avaliação de unidades homogéneas não necessariamente lucrativas. Concluímos, em geral, que são úteis e constituem um avanço importante, as informações obtidas através do DEA mas que outros métodos, designadamente rácios e análises de regressão, podem dar um contributo importante para complementar aquela análise.
Cited by 0Open access
Farhad Hosseinzadeh Lotfı, Tofigh Allahviranloo, Morteza Shafiee et al. · Studies in big data · 2023
Cited by 8
Quanling Wei · Chinese Science Bulletin · 2001
Cited by 208
Rowena Jacobs, Peter Smith, Andrew Street · Cambridge University Press eBooks · 2006
Data envelopment analysis (DEA) has become the dominant approach to efficiency measurement in health care and in many other sectors of the economy (Hollingsworth 2003). While the parametric approach is guided by economic theory, DEA is a data-driven approach. The location (and the shape) of the efficiency frontier is determined by the data, using the simple notion that an organisation that employs less input than another to produce the same amount of output can be considered more efficient. Those observations with the highest ratios of output to input are considered efficient, and the efficiency frontier is constructed by joining these observations up in the input-output space. The frontier thus comprises a series of linear segments connecting one efficient observation to another. The construction of the frontier is based on ‘best observed practice’ and is therefore only an approximation to the true, unobserved efficiency frontier.
Cited by 19
K. W. Wöber · CABI Publishing eBooks · 2002
This chapter assesses the suitability of data envelopment analysis (DEA) for the selection of comparison partners in benchmarking studies within the hotel industry. The analysis is based on the Austrian Hotel and Restaurant Panel (AHRP) database. Several extensions that have been proposed to address severe shortcomings of the basic DEA model are discussed in detail. The chapter concludes by highlighting the strengths and weaknesses of the DEA approach.
Cited by 13
Renato De Leone · Encyclopedia of Optimization · 2001
Cited by 1
Andreas Behr · 2015
Cited by 32
Timothy R. Anderson · 2022
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José Veiga Pereira · Review of Business and Legal Sciences · 2017
A avaliação das organizações e a deterntinação da performance obtida pelo exercício da gestão, tem sido uma preocupação constante de gestores e accionistas, embora com objectivos diversos. Nos dias de hoje, a questão coloca-se com maior acuidade quer pela competitividade acrescida quer pela dimensão e complexidade actual das empresas. Pretendemos com este trabalho fazer uma descrição da metodologia DEA - Data Envelopment Analysis - nas suas formulações iniciais mais simples. A metodologia do DEA, pretende obter uma medida única e simples de avaliação da eficiência, combinando um conjunto de outputs e de inputs relativos às diferentes unidades homogéneas que se pretendem avaliar.O método DEA é um método não paramétrico que pelas suas características é particularmente adequado à avaliação de unidades homogéneas não necessariamente lucrativas.Concluímos, em geral, que são úteis e constituem um avanço importante, as informações obtidas através do DEA mas que outros métodos, designadamente rácios e análises de regressão, podem dar um contributo importante para complementar aquela análise.
Cited by 7Open access
Kankana Mukherjee · Wiley Encyclopedia of Management · 2015
Abstract The method of Data Envelopment Analysis (DEA) was introduced in the Management Science/Operations Research literature by Charnes, Cooper, and Rhodes. The method can be utilized to measure the efficiency of a decision making unit (DMU), such as a firm, relative to other firms that produce the same outputs. The intellectual roots of DEA can be traced back to the nonparametric analysis of production economics due to Debreu and Farrell, and has its foundations in neoclassical production theory. DEA uses linear programming techniques to construct a piecewise linear frontier that envelopes all the data points.
Cited by 6
Subhash C. Ray · Cambridge University Press eBooks · 2004
Using the neo-classical theory of production economics as the analytical framework, this book, first published in 2004, provides a unified and easily comprehensible, yet fairly rigorous, exposition of the core literature on data envelopment analysis (DEA) for readers based in different disciplines. The various DEA models are developed as nonparametric alternatives to the econometric models. Apart from the standard fare consisting of the basic input- and output-oriented DEA models formulated by Charnes, Cooper, and Rhodes, and Banker, Charnes, and Cooper, the book covers developments such as the directional distance function, free disposal hull (FDH) analysis, non-radial measures of efficiency, multiplier bounds, mergers and break-up of firms, and measurement of productivity change through the Malmquist total factor productivity index. The chapter on efficiency measurement using market prices provides the critical link between DEA and the neo-classical theory of a competitive firm. The book also covers several forms of stochastic DEA in detail.
Cited by 14
Paul Gemmel · Ghent University Academic Bibliography (Ghent University) · 2003
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Joe Zhu · International series in management science/operations research/International series in operations research & management science · 2015
Cited by 55Open access
Davide Scotti, Nicola Volta · 2020
This chapter presents an application of data envelopment analysis (DEA) on European NUTS2 regions. It introduces the basic notions of the DEA methodology and its main assumptions. The chapter describes the production process under investigation together with the dataset and the research hypothesis to test. It presents the methodology adopted and the main results obtained. The most important assumptions in basic DEA models are those related to the free disposability of inputs and outputs, the convexity, and the typology of the returns of scale. Convexity implies the practicability of any weighted average of feasible production plans. Returns to scale refers to the fact that some rescaling of production is possible: the two extreme assumptions are the so-called constant returns to scale and variable returns to scale. The frontier is the boundary of the technology set and the relative efficiency can be defined as the distance of a given decision-making units to the frontier.
Cited by 0
Peng Zhou · 2023
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SpringerReference · 2011
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Vincent Blackburn, Shae Brennan, John Ruggiero · International series in management science/operations research/International series in operations research & management science · 2014
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