• Data Envelopment Analysis Online Software


    DEAOS is a Data Envelopment Analysis Online Software designed for DEA models. DEA models are used to assess a set of similar DMUs (Decision-Making Units) and obtain their relative efficiency. DEAOS is an appropriate tool for obtaining results easily and quickly. The DEAOS package is designed to be extremely user-friendly, particularly in terms of access, presentation of results, various report options, and other features.

    Data envelopment analysis (DEA) is a nonparametric method in operations research and economics for the estimation of production frontiers. It is used to empirically measure productive efficiency of decision making units (or DMUs). Data Envelopment Analysis (DEA) was accorded this name because of the way it "envelops" observations in order to identify a "frontier" that is used to evaluate observations representing the performances of all of the entities that are to be evaluated. DEA has wide applications in agriculturehealth caretransportationeducationenergy and environment, as well as banking and finance. The term "Decision Making Unit" (DMU) was therefore introduced to cover, in a flexible manner, any such entity, with each such entity to be evaluated as part of a collection that utilizes similar inputs to produce similar outputs. These evaluations result in a performance score that ranges between zero and unity and represents the "degree of efficiency" obtained by the thus evaluated entity. In arriving at these scores, DEA also identifies the sources and amounts of inefficiency in each input and output for every DMU. It also identifies the DMUs (located on the "efficiency frontier") that entered actively in arriving at these results. These evaluating entities are all efficient DMUs and hence can serve as benchmarks en route to effecting improvements in future performances of the thus evaluated DMUs. The different types of efficiency covered in this text range from "allocative," or "price," efficiency, and extend through "scale" and "technical" efficiency, as well as "mix" and other kinds of efficiencies. Technical inefficiency, which represents "waste," is the one we focus on in this Preface because it requires the least information, makes the fewest assumptions, and is the one most likely to be agreed upon as to what is meant by the term "inefficiency." Uses of DEA to effect these evaluations are almost entirely "data dependent" and do not require explicit characterizations of relations like "linearity," "nonlinearity," etc., which are customarily used in statistical regressions and related approaches where they are assumed to connect inputs to outputs, etc.


     The story of data envelopment analysis (DEA) begins with Edwardo Rhodes's Ph.D. dissertation research at Carnegie Mellon University's School of Urban and Public Affairs (now the H. J. Heinz III School of Public Policy and Management). Under the supervision of W. W. Cooper, Edwardo Rhodes was evaluating Program Follow Through the educational program for disadvantaged students (mainly black or Hispanic) undertaken in U.S. public schools with support from the Federal Government. The analysis involved comparing the performance of a matched set of school districts that were participating and not participating in Program Follow Through. Program Follow Through recorded the performance of schools in terms of outputs such as "increased self-esteem in a disadvantaged child" (as measured by psychological tests) and inputs such as "time spent by mother in reading with her child." It was the challenge of estimating the relative "technical efficiency" of the schools involving multiple outputs and inputs, without the usual information on prices, that resulted in the formulation of the CCR (Charnes, Cooper, and Rhodes) ratio form of DEA and the publication of the first paper introducing DEA in the European Journal of Operations Research in 1978 (Charnes, Cooper, and Rhodes, 1978). CCR used the optimization method of mathematical programming to generalize the Farrell (1957) single-output/input technical-efficiency measure to the multiple-output/multiple-input case by constructing a single "virtual" output to a single "virtual" input relative efficiency measure. Thus DEA began as a new Management Science tool for technical-efficiency analyses of public-sector decision-making units (DMUs). In this regard, the emergence of DEA was an extension of the historical focus of OR/MS methodologies on the development and application of heuristics and optimization techniques to resource allocation problems.




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