scholarly journals Dimension-Specific Efficiency Measurement Using Data Envelopment Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Hongjun Zhang ◽  
Youliang Zhang ◽  
Rui Zhang
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
The Other ◽  
Data Envelopment ◽  
Input Output ◽  
Single Dimension ◽  
Measuring Efficiency ◽  
Using Data ◽  
The City ◽  
Decision Making Units

Data envelopment analysis (DEA) is a powerful tool for evaluating and improving the performance of a set of decision-making units (DMUs). Empirically, there are usually many DMUs exhibiting “efficient” status in multi-input multioutput situations. However, it is not appropriate to assert that all efficient DMUs have equivalent performances. Actually, a DMU can be evaluated to be efficient as long as it performs best in a single dimension. This paper argues that an efficient DMU of a particular input-output proportion has its own specialty and may also perform poorly in some dimensions. Two DEA-based approaches are proposed to measure the dimension-specific efficiency of DMUs. One is measuring efficiency in multiplier-form by further processing the original multiplier DEA model. The other is calculating efficiency in envelopment-form by comparing with an ideal DMU. The proposed approaches are applied to 26 supermarkets in the city of Nanjing, China, which have provided new insights on efficiency for the managers.

scholarly journals Selection and Analysis of Input-Output Variables using Data Envelopment Analysis of Decision Making Units - Indian Private Sector Banks

2021 ◽  
Vol 10 (5) ◽  
pp. 119-127
Author(s):  
B. Vittal ◽  
Raju Nellutla ◽  
M. Krishna Reddy
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Banking System ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Input Output ◽  
Key Factor ◽  
And Performance ◽  
Using Data ◽  
Decision Making Units

In banking system the evaluation of productivity and performance is the key factor among the fundamental concepts in management. For identify the potential performance of a bank efficiency is the parameter to evaluate effective banking system. To measure the efficiency of a bank selection of appropriate input-output variables is one of the most vital issues. The suitable identification of input-output variables helps to create and identify model in order to evaluate the efficiency and analysis. The Data Envelopment Analysis (DEA) is a mathematical approach used to measure the efficiency of identified Decision Making Units (DMUs). The DEA is a methodology for evaluating the relative efficiency of peer decision making units of identified input/output variables for the financial year 2018-19. In this study the basic DEA CCR, BCC models used for measure the efficiency of DMUs. In addition to these models for minimize the input excess and output shortfall Slack Based Measure (SBM) efficiency used. The SBM is a scalar measure which directly deals with slacks of input, output variables which help in obtain improved efficiency score compare with previous model. The result from the analysis is

scholarly journals A Slacks-based Measure in the Presence of Ratio Variable with Convexity Consideration

2020 ◽  
pp. 53-62
Author(s):  
Farzaneh Ghaffari ◽  
Morteza Haghiri
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Data Envelopment ◽  
Analysis Method ◽  
Input Output ◽  
Input And Output ◽  
Ratio Data ◽  
Measuring Efficiency ◽  
Decision Making Units

The nature of input-output relationships in general and ratio data in particular has important consequences for practitioners when the data envelopment analysis method is used to  measure technical efficiency of decision making units or production units. Since the data envelopment analysis approach was introduced several studies tried to develop the model from different aspects including when the model deals with ratio data. To date, none of these studies was able to address the aforementioned problem properly and as a result most of them suffered from a lack of clarity in the presence of input-and-output ratios. This study proposes a slacks-based measure of efficiency in the presence of ratio variable. Our approach deals directly with the input excess and the output shortfalls of the decision making units’ concerns, and as a result, improved measuring efficiency scores.

scholarly journals A full investigation of the directional congestion in data envelopment analysis

2019 ◽  
Author(s):  
somayeh khezri ◽  
Akram Dehnokhalaji ◽  
Farhad Hosseinzadeh Lotfi
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Input Output ◽  
Input And Output ◽  
Dea Models ◽  
Systems Of Inequalities ◽  
Full Investigation ◽  
Decision Making Units ◽  
Inputs And Outputs

One of interesting subjects in Data Envelopment Analysis (DEA) is estimation of congestion of Decision Making Units (DMUs). Congestion is evidenced when decreases (increases) in some inputs re- sult in increases (decreases) in some outputs without worsening (im- proving) any other input/output. Most of the existing methods for measuring the congestion of DMUs utilize the traditional de nition of congestion and assume that inputs and outputs change with the same proportion. Therefore, the important question that arises is whether congestion will occur or not if the decision maker (DM) increases or de- creases the inputs dis-proportionally. This means that, the traditional de nition of congestion in DEA may be unable to measure the con- gestion of units with multiple inputs and outputs. This paper focuses on the directional congestion and proposes methods for recognizing the directional congestion using DEA models. To do this, we consider two di erent scenarios: (i) just the input direction is available. (ii) none of the input and output directions are available. For each scenario, we propose a method consists in systems of inequalities or linear pro- gramming problems for estimation of the directional congestion. The validity of the proposed methods are demonstrated utilizing two nu- merical examples.

scholarly journals New Approach in Fixed Resource Allocation and Target Setting Using Data Envelopment Analysis with Common Set of Weights

Complexity ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Marzieh Ghasemi ◽  
Mohammad Reza Mozaffari ◽  
Farhad Hosseinzadeh Lotfi ◽  
Mohsen Rostamy malkhalifeh ◽  
Mohammad Hasan Behzadi
Keyword(s):  
Decision Making ◽  
Resource Allocation ◽  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Target Setting ◽  
New Approach ◽  
Common Set Of Weights ◽  
Programming Techniques ◽  
Using Data ◽  
Decision Making Units

One of the mathematical programming techniques is data envelopment analysis (DEA), which is used for evaluating the efficiency of a set of similar decision-making units (DMUs). Fixed resource allocation and target setting with the help of DEA is a subject that has gained much attention from researchers. A new model was proposed by determining a common set of weights (CSW). All DMUs were involved with the aim of achieving higher efficiency in every DMU after the procedure. The minimum resources and targets allocated to each DMU were commensurate to the efficiency of that DMU and the share of DMU in the input resources and the output productions. To examine the proposed method, other methods in the DEA literature were examined as well, and then, the efficiency of the method was demonstrated through a numerical example.

scholarly journals Fuzzy clustering of homogeneous decision making units with common weights in data envelopment analysis

2021 ◽  
Vol 40 (1) ◽  
pp. 813-832
Author(s):  
Sajad Kazemi ◽  
Reza Kiani Mavi ◽  
Ali Emrouznejad ◽  
Neda Kiani Mavi
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Ideal Point ◽  
Weight Vector ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Complex Organizations ◽  
Measuring Efficiency ◽  
Common Set Of Weights ◽  
Decision Making Units

Data Envelopment Analysis (DEA) is the most popular mathematical approach to assess efficiency of decision-making units (DMUs). In complex organizations, DMUs face a heterogeneous condition regarding environmental factors which affect their efficiencies. When there are a large number of objects, non-homogeneity of DMUs significantly influences their efficiency scores that leads to unfair ranking of DMUs. The aim of this study is to deal with non-homogeneous DMUs by implementing a clustering technique for further efficiency analysis. This paper proposes a common set of weights (CSW) model with ideal point method to develop an identical weight vector for all DMUs. This study proposes a framework to measuring efficiency of complex organizations, such as banks, that have several operational styles or various objectives. The proposed framework helps managers and decision makers (1) to identify environmental components influencing the efficiency of DMUs, (2) to use a fuzzy equivalence relation approach proposed here to cluster the DMUs to homogenized groups, (3) to produce a common set of weights (CSWs) for all DMUs with the model developed here that considers fuzzy data within each cluster, and finally (4) to calculate the efficiency score and overall ranking of DMUs within each cluster.

scholarly journals An investigation of the undesirable outputs congestion in data envelopment analysis

2020 ◽  
Vol 33 (02) ◽  
pp. 468-475
Author(s):  
Soodabeh Nazari ◽  
Mohsen Rostamy-Malkhalifeh ◽  
Ali Hamzehee
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Decision Maker ◽  
The Other ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Total Cost ◽  
Know How ◽  
Total Profit ◽  
Decision Making Units

Congestion is one of the most important subjects in Data Envelopment Analysis (DEA) which helps the Decision Maker (DM) to decide about changing the size of units. The estimation of congestion has attractive advantages from different perspectives. For example, the total cost of a partiular DMU, in which the congestion occurs, can be reduced by the decreases in inputs. On the other hand, the output of units can be increased by the recognizing and eliminating the congestion of DMUs and so, the total profit of decision making units can be increased. Hence, the management is eager to know how to recognize and eliminate the congestion of units. Most of the existing methods to estimation of the congestion in the literature consider only the desirable outputs. This study focuses on the evaluation of congestion in the presence of undesirable outputs and proposes an approach to recognize the congestion of units. The method is demonstrated on a numerical example to illustrate the validity of the proposed method.

Evaluating the Efficiencies of Academic Research Groups: A Problem of Shared Outputs

2018 ◽  
Vol 35 (06) ◽  
pp. 1850042
Author(s):  
Sonia Valeria Avilés-Sacoto ◽  
Wade D. Cook ◽  
David Güemes-Castorena ◽  
Francisco Benita ◽  
Hector Ceballos ◽  
...  
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Academic Research ◽  
Original Model ◽  
Current Paper ◽  
Data Envelopment ◽  
Journal Articles ◽  
Research Groups ◽  
Measuring Efficiency ◽  
Decision Making Units

Data envelopment analysis (DEA) is a methodology for evaluating the relative efficiencies of a set of decision-making units (DMUs), based on their multiple inputs and outputs. The original model is based on the assumption that DMUs operate independently of one another. However, this assumption may not apply in some situations, as in the case we present in this paper, in which DMUs can work together to produce joint outputs. What makes it more interesting is the situation in which this characteristic of sharing outputs among some DMUs differs from one DMU to another; this makes it more challenging to determine independent efficiency scores that cater for this phenomenon. To address this, the current paper presents a methodology for measuring efficiency in situations in which DMUs share outputs with other units. We examine the case of a set of research groups in a Mexican university. For this study, the inputs used are professors belonging to various groups, and outputs are the published journal articles, some of which are produced completely within a group, whereas others arise from collaboration with professors from other research groups. Jointly published articles form a link connecting the groups.

A ranking system based on inverse data envelopment analysis

2019 ◽  
Vol 31 (3) ◽  
pp. 367-385 ◽  
Author(s):  
Khosro Soleimani-Chamkhorami ◽  
Farhad Hosseinzadeh Lotfi ◽  
Gholamreza Jahanshahloo ◽  
Mohsen Rostamy-Malkhalifeh
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Commercial Banks ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Input Output ◽  
Data Set ◽  
Ranking System ◽  
Numerical Example ◽  
Decision Making Units

Abstract Inverse (DEA) is an approach to estimate the expected input/output variation levels when the efficiency score reminds unchanged. Essentially, finding most efficient decision-making units (DMUs) or ranking units is an important problem in DEA. A new ranking system for ordering extreme efficient units based on inverse DEA is introduced in this article. In the adopted method, here the amount of required increment of inputs by increasing the outputs of the unit under evaluation is obtained through the proposed models. By obtaining these variations, this proposed methodology enables the researcher to rank the efficient DMUs in an appropriate manner. Through the analytical theorem, it is proved that suggested models here are feasible. These newly introduced models are validated through a data set of commercial banks and a numerical example.

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