in the use of peer group data to assess individual, typical or best practice performance, the effective detection of outliers is critical for achieving useful results. in these ‘‘deterministic’’ frontier models, statistical theory is now mostly available. this paper deals with the statistical pared sample method and its capability of detecting outliers in data envelopment analysis. in the prese...

one of the major problems in data envelopment analysis (dea) is to determine the projection of inefficient decision making units (dmus) into the efficient frontier. in conventional dea models, inputs and outputs of inefficient dmus alter arbitrarily for reaching to the efficient frontier. nevertheless, sometimes the ability of dmus is defined and restricted. moreover, there are situations in th...

This article elaborates on the applicability of basic and extended data envelopment analysis (DEA) models for various information system (IS) decision use-cases including illustrative examples from an enterprise resource planning (ERP) software investment appraisal. The usage of data envelopment analysis models and their extensions for IS decisions remains limited. This omission seems critical ...

over the years we have witnessed the growing importance of supply chain operations and supply chain management (scm), as supply chains play a major role in every step of the product life cycle. therefore, this paper will dea-based performance evaluation approaches for overall supply chain can be categorized. future perspectives and challenges are discussed

This paper adopts two mathematical approaches, data envelopment analysis (DEA) and grey relation analysis (GRA) to measure DEA efficiency using the sample of 69 listed US internet companies. A total of 40 indicators were initially selected for the efficiency evaluation, with 21 related to DEA-input indicators and 19 to DEAoutput indicators. Eight representative indicators selected using GRA are...

Data envelopment analysis (DEA) is an effective method to evaluate the relative efficiency of decision-making units (DMUs). In one hand, the DEA models need accurate inputs and outputs data. On the other hand, in many situations, inputs and outputs are volatile and complex so that they are difficult to measure in an accurate way. The conflict leads to the researches of uncertain DEA models. Thi...

The Trade Offs approach is an advanced tool for the improvement of the discrimination of Data Envelopment Analysis (DEA) models, Meta Malmquist Index was defined by Maria Portella and et. al (2008). In this paper we compute the Meta Malmquist Index in Trade Offs model in DEA and we compare, obtaining results, of Meta Malmquist Index in different models of DEA, Variable Return to Scale (VRS), Co...

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...