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...

The existence of alternate optima for the DEA weights may reduce the usefulness of the cross-efficiency evaluation, since the ranking provided depends on the choice of weights that the different DMUs make. In this paper, we develop a procedure to carry out the cross-efficiency evaluation without the need to make any specific choice of DEA weights. The proposed procedure takes into consideration...

Within data envelopment analysis is a subgroup of papers in which many researchers have sought to improve the differential capabilities of DEA and to fully rank the decision making units (DMU). However, whilst each technique is useful in a specialist area, no one methodology can be prescribed as the complete solution to the question of ranking. Different results which are reached in applying th...

Abstract: The radial measures of classical DEA models (CCR, BCC) are incomplete, they are only separate measures of input and output efficiency and their efficiency index omit the non-zero input and output slacks. Enhanced Russell graph measure (ERM) eliminates these deficiencies. All of the existing fuzzy DEA models are extension of CCR or BCC model, efficiencies of DMUs, ultimately, are solut...

As regards of the necessity of ranking efficient units different Data Envelopment Analysis (DEA) models are introduced. Each of the existing models has advantages and deals with ranking efficient units from special aspects. But, there exist no model has all these benefits in a unified manner. The aim of this paper is to present a new ranking method which can incorporate, to a great extent, adva...

One of the weak points of DEA (Data Envelopment Analysis) models indicated in literature [1,2] is their sensitivity to variable measurement errors. The occurrence of data interference, which is the basis of the productivity analysis, may distort the classification of the units and may cause misjudgement of their effectiveness. In the article the results of simulation concerning the DEA models s...

data envelopment analysis (dea) with considering the best condition for each decision making unit (dmu) assesses the relative efficiency for it and divides a homogenous group of dmus in to two categories: efficient and inefficient, but traditional dea models can not rank efficient dmus. although some models were introduced for ranking efficient dmus, franklin lio & hsuan peng (2008), proposed a...

DEA Classic models cannot be used for inaccurate and indeterminate data, and it is supposed that the data for all inputs and outputs are accurate and determinate. However, in real life situations uncertainty is more common. This article attempts to get the common weights for Decision-Making Units by developing DEA multi-objective models in the grey environment. First, we compute the privilege o...

A fundamental weakness of the Data Envelopment Analysis (DEA) is its weak discrimination in cases when a small number of decision making units are compared. Therefore, in such cases the basic DEA model (optimistic and pessimistic) is used in combination with other methods or additional constraints are added to the model. In this paper, the cross-efficiency method was combined with a self-rankin...