2,057,879 research outputs found
Multi-level DEA Approach in Research Evaluation
Full text linkIt is well known that the discrimination power of DEA models will be
diminishing if too many inputs or outputs are used. It is a dilemma if the decision makers
want to select comprehensive indicators to present a relatively holistic evaluation using
DEA. In this work we show that by utilizing hierarchical structures of input-output data
DEA can handle quite large numbers of inputs and outputs. We present two approaches in a
pilot evaluation of 15 institutes for basic research in Chinese Academy of Sciences using
DEA models
Extended Utility and DEA Models without Explicit Input
Full text linkIn this paper, we discuss the relationship between multi-attribute utility theory and data envelopment analysis (DEA) models without explicit inputs (DEA-WEI), including dual models and some theoretical analysis of DEA-WEI models. We then propose generic DEA-WEI models with quadratic utility terms. Finally, we provide illustrative examples to show that DEA-WEI with suitable quadratic utility terms are able to reflect some value judgments that the standard DEA models cannot
Stochastic Nonparametric Envelopment of Data: Combining Virtues of SFA and DEA in a Unified Framework
Full text linkThe literature of productive efficiency analysis is divided into two main branches: the parametric Stochastic Frontier Analysis (SFA) and nonparametric Data Envelopment Analysis (DEA). This paper attempts to combine the virtues of both approaches in a unified framework. We follow the SFA literature and introduce a stochastic component decomposed into idiosyncratic error and technical inefficiency components imposing the standard SFA assumptions. In contrast to the SFA, we do not make any prior assumptions about the functional form of the deterministic production function. In this respect, we follow the nonparametric route of DEA that only imposes free disposability, convexity, and some specification of returns to scale. From the postulated class of production functions, the proposed method identifies the production function with the best empirical fit to the data. The resulting function will always take a piece-wise linear form analogous to the DEA frontiers. We discuss the practical implementation of the method and illustrate its potential by means empirical examples.Productivity Analysis,
Sensitivity analysis of network DEA illustrated in branch banking
Full text linkUsers of data envelopment analysis (DEA) often presume efficiency estimates to be robust. While traditional DEA has been exposed to various sensitivity studies, network DEA (NDEA) has so far escaped similar scrutiny. Thus, there is a need to investigate the sensitivity of NDEA, further compounded by the recent attention it has been receiving in literature. NDEA captures the underlying performance information found in a firm?s interacting divisions or sub-processes that would otherwise remain unknown. Furthermore, network efficiency estimates that account for divisional interactions are more representative of a dynamic business. Following various data perturbations overall findings indicate positive and significant rank correlations when new results are compared against baseline results - suggesting resilience. Key findings show that, (a) as in traditional DEA, greater sample size brings greater discrimination, (b) removing a relevant input improves discrimination, (c) introducing an extraneous input leads to a moderate loss of discrimination, (d) simultaneously adjusting data in opposite directions for inefficient versus efficient branches shows a mostly stable NDEA, (e) swapping divisional weights produces a substantial drop in discrimination, (f) stacking perturbations has the greatest impact on efficiency estimates with substantial loss of discrimination, and (g) layering suggests that the core inefficient cohort is resilient against omission of benchmark branches. Various managerial implications that follow from empirical findings are discussed in conclusions.
Fractional regression models for second stage DEA efficiency analyses
Full text linkData envelopment analysis (DEA) is commonly used to measure the relative efficiency of decision-making units. Often, in a second stage, a regression model is estimated to relate DEA efficiency scores to exogenous factors. In this paper, we argue that the traditional linear or tobit approaches to second-stage DEA analysis do not constitute a reasonable data-generating process for DEA scores. Under the assumption that DEA scores can be treated as descriptive measures of the relative performance of units in the sample, we show that using fractional regression models are the most natural way of modeling bounded, proportional response variables such as DEA scores. We also propose generalizations of these models and, given that DEA scores take frequently the value of unity, examine the use of two-part models in this framework. Several tests suitable for assessing the specification of each alternative model are also discussed.Second-stage DEA; Fractional data; Specification tests; One outcomes; Two-part models.
Calculating the scale elasticity in DEA models.
Full text linkIn economics scale properties of a production function is charcterised by the value of the scale elasticity. In the field of efficiency studies this is also a valid approach for the frontier production function. It has no good meaning to talk about scale properties of inefficient observations. In the DEA literature a qualitative characterisation is most common. The contribution of the paper is to apply the concept of scale elasticity from multi output production theory in economics to the piecewise linear frontier production function, and to develop formulas for calculating values of the scale elasticity for radial projections of inefficient observations. Illustrations also on real data are provided, showing the differences between scale elasticity values for the input- and output oriented projections and the range of values for efficient observations.Scale elasticity; DEA, production theory; Farrell efficiency measures
Extensions of Modified DEA
Full text linkAndersen and Petersen (1993) presented an extension of the basic DEA methodology, called modified DEA, which has the desirable feature of ranking not only the inefficient DMUs, but the e ficient ones as well. However, when their basic approach is extended to the cases of variable returns to scale and non-discretionary inputs, several conceptual problems arise. This paper addresses these problems, and illustrates the proposed extensions to the modified DEA method using data from a major U.S. bank.
Measuring efficiency of a hierarchical organization with fuzzy DEA method
Full text linkThe paper analyses how the data envelopment analysis (DEA) and fuzzy set theory can be used to measure and evaluate the efficiency of a hierarchical system with n decision making units and a coordinating unit. It is presented a model for determining the of activity levels of decision making units so as to achieve both fuzzy objectives of achieving global target levels of coordination unit on the inputs and outputs and individual target levels of decision making units, and then some methods to resolve fuzzy models are proposed.fuzzy DEA, policy making in multi-level organisations, efficiency analysis
The use of supply chain DEA models in operations management: A survey
Full text linkStandard Data Envelopment Analysis (DEA) approach is used to evaluate the efficiency of DMUs and treats its internal structures as a “black box”. The aim of this paper is twofold. The first task is to survey and classify supply chain DEA models which investigate these internal structures. The second aim is to point out the significance of these models for the decision maker of a supply chain. We analyze the simple case of these models which is the two-stage models and a few more general models such as network DEA models. Furthermore, we study some variations of these models such as models with only intermediate measures between first and second stage and models with exogenous inputs in the second stage. We define four categories: typical, relational, network and game theoretic DEA models. We present each category along with its mathematical formulations, main applications and possible connections with other categories. Finally, we present some concluding remarks and opportunities for future research.Supply chain; Data envelopment analysis; Two-stage structures; Network structures
Selecting DEA specifications and ranking units via PCA
No full textDEA model selection is problematic. The estimated efficiency for any DMU depends on the inputs and outputs included in the model. It also depends on the number of outputs plus inputs. It is clearly important to select parsimonious specifications and to avoid as far as possible models that assign full high efficiency ratings to DMUs that operate in unusual ways (mavericks). A new method for model selection is proposed in this paper. Efficiencies are calculated for all possible DEA model specifications. The results are analysed using Principal Components Analysis. It is shown that model equivalence or dissimilarity can be easily assessed using this approach. The reasons why particular DMUs achieve a certain level of efficiency with a given model specification become clear. The methodology has the additional advantage of producing DMU rankings
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