Measuring efficiency of the Greek flourmill firms using data envelopment analysis

2015 ◽  
Vol 1 (1) ◽  
pp. 61
Author(s):  
George Vlontzos
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Measuring Efficiency ◽  
Using Data

scholarly journals Measuring Efficiency of Knowledge Production in Health Research Centers Using Data Envelopment Analysis (DEA): A Case Study in Iran

10.19082/3266 ◽  
2016 ◽  
Vol 8 (11) ◽  
pp. 3266-3271
Author(s):  
Mohammad Meskarpour Amiri ◽  
Taha Nasiri ◽  
Seyed Hassan Saadat ◽  
Hosein Amini Anabad ◽  
Payman Mahboobi Ardakan
Keyword(s):  
Data Envelopment Analysis ◽  
Knowledge Production ◽  
Health Research ◽  
Data Envelopment ◽  
Research Centers ◽  
Measuring Efficiency ◽  
Using Data

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 Measuring efficiency of rice growing farmers using data envelopment analysis

2015 ◽  
Author(s):  
Nerda Zura Zaibidi ◽  
Maznah Mat Kasim ◽  
Razamin Ramli ◽  
Md. Azizul Baten ◽  
Sahubar Ali Nadhar Khan
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Measuring Efficiency ◽  
Using Data

Measuring Efficiency at the Regional Level: A Data Envelopment Analysis Approach

2019 ◽  
Vol 17 (3) ◽  
pp. 679-696
Author(s):  
Suncana Slijepcevic
Keyword(s):  
Data Envelopment Analysis ◽  
Local Government ◽  
Local Level ◽  
Regional Level ◽  
Public Expenditures ◽  
Data Envelopment ◽  
Average Decrease ◽  
Measuring Efficiency ◽  
State And Local ◽  
Using Data

European countries have been continuously under the pressure to improve public balances and efficiency of public spending. Economic crisis which started during 2007 weakened public finances at the state and local level in countries all over the world. In Croatia local government budgets are still below the pre-crisis level in many local government units. This paper empirically examines efficiency of public expenditures at the regional level. Performance has been investigated by developing a composite indicator of output. Spending efficiency at the regional level was analysed using Data Envelopment Analysis methodology. Results suggest that there are large differences at the regional level in using resources to provide public services. The results show that the local government units in the least efficient county should on average decrease their expenses by 55 percent, while achieving the same performance to become efficient.

An Improved Method for Predicting and Ranking Suppliers Efficiency Using Data Envelopment Analysis

2014 ◽  
Vol 73 (2) ◽  
Author(s):  
Mohammadreza Farahmand ◽  
Mohammad Ishak Desa ◽  
Mehrbakhsh Nilashi ◽  
Antoni Wibowo
Keyword(s):  
Data Envelopment Analysis ◽  
Supplier Selection ◽  
Large Data ◽  
Computational Time ◽  
Support Vector ◽  
Data Sets ◽  
Data Envelopment ◽  
Process Data ◽  
Measuring Efficiency ◽  
Using Data

Supplier selection problem (SSP) is a problem to select the best among suppliers based on input and output data of the suppliers. Since different uncontrollable and unpredictable parameters are affecting selection, choosing the best supplier is a complicated process. Data Envelopment Analysis (DEA) is a method for measuring efficiency and inefficiencies of Decision Making Units (DMUs). DEA has been employed by many researchers for supplier selection and widely used in SSP with inputs for supplier evaluation. However, the DEA still has some disadvantages when it is solely used for SSP. Hence, in this paper, a combination of DEA and Neural Network (NN), DEA-NN, is proposed for SSP. We also develop a model for SSP based on Support Vector Regression (SVR) to improve the stability of DEA-NN. The proposed method was evaluated using small and large data sets. The experimental results showed that, the proposed method solve the problems connected to the previous methods. The results also showed that stability of proposed method is significantly better than DEA-NN method. In addition, CCR-SVR model overcome shortcomings such as instability and improves computational time and accuracy for predicting efficiency of new small and large DMUs.

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