Data envelopment analysis (DEA) is a non-parametric approach to evaluate a set of decision making units (DMUs) consuming multiple inputs to produce multiple outputs. Formally, DEA use to estimate the efficiency score into the empirical efficient frontier. Also, DEA can be used to allocate resources and set targets for future forecast. The data are continuous in the standard DEA model whereas th...

This study examines the efficiency of 12 Taiwanese laptop manufacturers in 2002. This work demonstrates the feasibility of adopting Data Envelopment Analysis (DEA) to identify individual manufacturing firms that are less efficient than other ones. Efficiency is measured in terms of output variables relative to input variables. For those efficient laptop manufacturers lacking of productivity, th...

This paper examines three essential components which comprise efficiency evaluation in data envelopment analysis. The three components are present in each DEA model and determine the implicit evaluation scheme associated with the model. These components provide a framework for classifying the various DEA models with respect to (i) the form of envelopment surface, (ii) the orientation, and (iii)...

Data envelopment analysis (DEA) mainly utilizes envelopment technology to replace production function in microeconomics. The input and output of decision making units (DMUs) are projected into the attributes to evaluate or measure their performance. However, if the inputs and outputs are linguistically termed or are fuzzy-numbered, conventional DEA can not easily measure the performance. Theref...

for efficiency evaluation of some of the decision making units that have uncertain information, rough data envelopment analysis technique is used, which is derived from rough set theorem and data envelopment analysis (dea). in some situations rough data alter nonradially. to this end, this paper proposes additive rough–dea model and illustrates the proposed model by a numerical example.

Discretionary models of data envelopment analysis (DEA) assume that all inputs and outputs can be varied at the discretion of management or other users. In any realistic situation, however, there may exist ”exogenously fixed” or non-discretionary factors that are beyond the control of a DMU’s management, which also need to be considered. Also DEA requires that the data for all discretionary inp...

Utilizing recent developments in data envelopment analysis (DEA), this paper examines the performance of the top 55 U.S. commercial banks via a two-stage production process that separates profitability and marketability. Substantial performance inefficiency is uncovered in both dimensions. Relatively large banks exhibit better performance on profitability, whereas smaller banks tend to perform ...

Supply chain management (SCM) has developed more than 30 years. However, few studies explore intellectual structure of SCM. The purpose of this study is to examine the intellectual structure of SCM through co-citation method. Data was collected from Web of Science online database. A total of 4,033 articles and 117,984 references were identified. Authors’ productivity and 41 highly cited/value a...

this paper reports a survey and case study research outcomes on the application of data envelopment analysis (dea) to the ranking method of european foundation for quality management (efqm) business excellence model in iran’s automotive industry and improving benchmarking process after assessment. following the global trend, the iranian industry leaders have introduced the efqm practice to thei...