Improving the discriminating power of DEA: focus on globally efficient units

Despotis, Dimitris K.

doi:10.1057/palgrave/jors/2601253

Cited by 116 publications

(53 citation statements)

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“…The results of our models show that only one set of solutions are obtained. Secondly, Despotis [16] pointed out that using the simple weighted method to aggregate efficiencies is not good enough since it is not a Pareto solution. However, this problem does not appear in the model in our study.…”

Section: The Results and Compared With Other Modelsmentioning

confidence: 99%

“…Usually, the optimal weights obtained by traditional DEA models are non-unique. If a set of weights are selected arbitrarily, then cross-efficiency scores will be arbitrarily generated [16]. To solve the problem of weight non-uniqueness, Sexton et al [9] improved the cross-efficiency evaluation method by incorporating a secondary goal model.…”

Section: Introductionmentioning

confidence: 99%

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Entropy Cross-Efficiency Model for Decision Making Units with Interval Data

Wang

Hong

2016

Entropy

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Abstract:The cross-efficiency method, as a Data Envelopment Analysis (DEA) extension, calculates the cross efficiency of each decision making unit (DMU) using the weights of all decision making units (DMUs). The major advantage of the cross-efficiency method is that it can provide a complete ranking for all DMUs. In addition, the cross-efficiency method could eliminate unrealistic weight results. However, the existing cross-efficiency methods only evaluate the relative efficiencies of a set of DMUs with exact values of inputs and outputs. If the input or output data of DMUs are imprecise, such as the interval data, the existing methods fail to assess the efficiencies of these DMUs. To address this issue, we propose the introduction of Shannon entropy into the cross-efficiency method. In the proposed model, intervals of all cross-efficiency values are firstly obtained by the interval cross-efficiency method. Then, a distance entropy model is proposed to obtain the weights of interval efficiency. Finally, all alternatives are ranked by their relative Euclidean distance from the positive solution.

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Section: The Results and Compared With Other Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Entropy Cross-Efficiency Model for Decision Making Units with Interval Data

Wang

Hong

2016

Entropy

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“…Following Despotis (2002), the linear programming problems to be solved in this study and corresponding to each season in the sample have sufficient degrees of freedom because they comply with the rule established by the author, according to which:…”

Section: Resultsmentioning

confidence: 99%

Team improvement in the UEFA Champions League: an application of data envelopment analysis

Escuer

Cebrián

2016

IJSMM

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Football teams, when considered economic organizations, are expected to achieve their objectives without consuming excess resources. Put another way, they must produce their products and services efficiently.In this study, we calculate efficiency on the playing field for teams in the UEFA Champions League between 2003 and 2012. We do this not only with an aim of separating the efficient and inefficient teams, but also to propose a way for inefficient teams to become more efficient. To this end, we use Data Envelopment Analysis to calculate efficiency ratios. From those results, we calculate improvement percentages that we then use as a basis for team-management recommendations.We find that no single input is to blame for inefficiency. The general recommendation for inefficient teams is to use fewer attacking plays.JEL Codes: M10, M21

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“…The non-negative polychoric PCA reduces the number of variables, removes correlation, transforms discrete variables to continuous principal components, and ensures that all principal component elements are non-negative. The resulting principal components are suitable for the common-weight DEA, which further increases the discrimination power of DEA (Despotis, 2002).…”

Section: Methodology For Data Analysismentioning

confidence: 99%

Assessing sustainability and improvements in US Midwestern soybean production systems using a PCA–DEA approach

Dong

Mitchell²,

Knuteson

et al. 2015

Renew. Agric. Food Syst.

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Documentation of on-farm sustainability in agricultural sectors is becoming an essential element to ensure market access. An assessment process was developed to help soybean farmers document practices and verifiable advances in community, environmental and economic sustainability. Technical difficulties in analyzing and summarizing such assessment data include a large number of practices, correlation in variables, and use of discrete measures. By combining nonnegative principal components analysis and common-weight data envelopment analysis, we overcame these difficulties to calculate a composite sustainability index for each individual farm and for the farm group as a whole. Applying this method to assessment data from 410 US Midwestern soybean farmers gave average sustainability scores of 0.846 and 0.842 for the soybean-specific and whole-farm assessments, respectively. Scenario analysis examined the impact if the bottom 10% of growers adopted the top ten sustainability drivers identified by the analysis. The average sustainability score only increased by 2%, but the minimum score increased from 0.515 to 0.647 for the soybean-specific assessment, and from 0.624 to 0.685 for the whole-farm assessment, while the lowest 10th percentile increased from 0.635 to 0.819 for the soybean-specific assessment, and from 0.634 to 0.920 for the whole-farm assessment. These results suggest that significant advancements could be made through focused efforts to improve adoption of sustainable practices by soybean farmers at the lower end of the spectrum.

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Improving the discriminating power of DEA: focus on globally efficient units

Cited by 116 publications

References 0 publications

Entropy Cross-Efficiency Model for Decision Making Units with Interval Data

Entropy Cross-Efficiency Model for Decision Making Units with Interval Data

Team improvement in the UEFA Champions League: an application of data envelopment analysis

Assessing sustainability and improvements in US Midwestern soybean production systems using a PCA–DEA approach

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