The relative efficiency of decision-making units: a case study of TFT-LCD industry in Taiwan

2007 ◽  
Vol 1 (2) ◽  
pp. 182
Author(s):  
Ming Chun Tsai ◽  
Shu Ping Lin ◽  
Ssu Ying Liu ◽  
Ming (Michael) Chang ◽  
Jason C.H. Chen
Keyword(s):  
Decision Making ◽  
Relative Efficiency ◽  
Decision Making Units

Evaluation of Supplier Performance and Efficiency

2017 ◽  
pp. 184-216
Author(s):  
Chandra Sekhar Patro
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Efficient Frontier ◽  
Business Environment ◽  
Vital Role ◽  
Data Envelopment ◽  
Supplier Performance ◽  
Decision Making Units

In the present competitive business environment, it is essential for the management of any organisation to take wise decisions regarding supplier evaluation. It plays a vital role in establishing an effective supply chain for any organisation. Most of the experts agreed that there is no one best way to evaluate the suppliers and different organizations use different approaches for evaluating supplier efficiency. The overall objective of any approach is to reduce purchase risk and maximize overall value to the purchaser. In this paper Data Envelopment Analysis (DEA) technique is developed to evaluate the supplier efficiency for an organisation. DEA is a multifactor productivity technique to measure the relative efficiency of the decision making units. The super efficiency method of DEA provides a way, which indicates the extent to which the efficient suppliers exceed the efficient frontier formed by other efficient suppliers. A case study is undertaken to evaluate the supplier performance and efficiency using DEA approach.

Relative Efficiency of Decision Making Units Producing Both Desirable and Undesirable Outputs: A Case of Textile Processing Units in Pakistan.

2011 ◽  
Vol 50 (4II) ◽  
pp. 685-698
Author(s):  
Samina Khalil
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Value Added ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Textile Processing ◽  
Decision Making Unit ◽  
Desirable And Undesirable Outputs ◽  
Decision Making Units

This paper aims at measuring the relative efficiency of the most polluting industry in terms of water pollution in Pakistan. The textile processing is country‘s leading sub sector in textile manufacturing with regard to value added production, export, employment, and foreign exchange earnings. The data envelopment analysis technique is employed to estimate the relative efficiency of decision making units that uses several inputs to produce desirable and undesirable outputs. The efficiency scores of all manufacturing units exhibit the environmental consciousness of few producers is which may be due to state regulations to control pollution but overall the situation is far from satisfactory. Effective measures and instruments are still needed to check the rising pollution levels in water resources discharged by textile processing industry of the country. JEL classification: L67, Q53 Keywords: Data Envelopment Analysis (DEA), Decision Making Unit (DMU), Relative Efficiency, Undesirable Output

A Hybrid Method for Prediction and Assessment Efficiency of Decision Making Units

2013 ◽  
Vol 5 (1) ◽  
pp. 66-83 ◽  
Author(s):  
Iman Rahimi ◽  
Reza Behmanesh ◽  
Rosnah Mohd. Yusuff
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Rules ◽  
Large Data ◽  
Poultry Meat ◽  
Small Data ◽  
Data Set ◽  
Data Mining Techniques ◽  
Decision Making Units

The objective of this article is an evaluation and assessment efficiency of the poultry meat farm as a case study with the new method. As it is clear poultry farm industry is one of the most important sub- sectors in comparison to other ones. The purpose of this study is the prediction and assessment efficiency of poultry farms as decision making units (DMUs). Although, several methods have been proposed for solving this problem, the authors strongly need a methodology to discriminate performance powerfully. Their methodology is comprised of data envelopment analysis and some data mining techniques same as artificial neural network (ANN), decision tree (DT), and cluster analysis (CA). As a case study, data for the analysis were collected from 22 poultry companies in Iran. Moreover, due to a small data set and because of the fact that the authors must use large data set for applying data mining techniques, they employed k-fold cross validation method to validate the authors’ model. After assessing efficiency for each DMU and clustering them, followed by applied model and after presenting decision rules, results in precise and accurate optimizing technique.

Aggregating the results of benevolent and aggressive models by the CRITIC method for ranking of decision-making units: A case study on seven biomass fuel briquettes generated from agricultural waste

2021 ◽  
pp. 79-92
Author(s):  
Narong Wichapa ◽  
Porntep Khokhajaikiat ◽  
Kumpanat Chaiphet
Keyword(s):  
Decision Making ◽  
Agricultural Waste ◽  
Ranking Method ◽  
Data Envelopment ◽  
Decision Matrix ◽  
Ranking Models ◽  
Biomass Materials ◽  
Ranking Problems ◽  
Decision Making Units

The ranking of decision-making units (DMUs) is one of the main issues in data envelopment analysis (DEA). Hence, many different ranking models have been proposed. However, each of these ranking models may produce different ranking results for similar problems. Therefore, it is wise to try different ranking models and aggregate the results of each ranking model that provides more reliable results in solving the ranking problems. In this paper, a novel ranking method (Aggregating the results of aggressive and benevolent models) based on the CRITIC method is proposed. To prove the applicability of the proposed ranking method, it is examined in three numerical examples, six nursing homes, fourteen international passenger airlines and seven biomass materials for processing into fuel briquettes. First, benevolent and aggressive models were used to calculate the efficiency rating for each DMU. As a result, the decision matrix was generated. In the decision matrix, the results of benevolent and aggressive models were viewed as criteria and DMUs were viewed as alternatives. Then, the weights of each criterion were generated by the CRITIC method. Finally, each DMU was ranked. In a comparative analysis, the proposed method can lead to achieving a more reliable decision than the method which is based on a stand-alone method.

AGGREGATED RATIO ANALYSIS IN DEA

2005 ◽  
Vol 04 (03) ◽  
pp. 369-384 ◽  
Author(s):  
DESHENG WU ◽  
LIANG LIANG ◽  
ZHIMIN HUANG ◽  
SUSAN X. LI
Keyword(s):  
Decision Making ◽  
Relative Efficiency ◽  
Efficient Frontier ◽  
Ratio Analysis ◽  
Ratio Model ◽  
Analysis Model ◽  
Frontier Analysis ◽  
Dea Model ◽  
Insight Into ◽  
Decision Making Units

This paper proposes an aggregated ratio analysis model which can be utilized to evaluate relative efficiency of decision making units (DMUs). We show that our proposed ratio model is equivalent to the CCR DEA model. This equivalence property offers a great deal of opportunities for DEA to be interpreted and applied in different ways. Our model also offers an insight into the frontier analysis. Whether a DMU is on the frontier or efficient frontier can be informed by using our aggregated ratio analysis. Several results developed in the paper are coincident with that in the literature.

A Robust Data Envelopment Analysis Method with Bounded Data for Ranking Operations Strategies

2015 ◽  
Vol 6 (3) ◽  
pp. 49-64 ◽  
Author(s):  
Mohamad Amin Kaviani ◽  
Mehdi Abbasi
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Cement Industry ◽  
Data Envelopment ◽  
Analysis Method ◽  
Interval Numbers ◽  
Complete Ranking ◽  
Bounded Data ◽  
Decision Making Units

This paper introduces a new robust data envelopment analysis (RDEA) approach for analyzing and ranking the organizations' operations strategies. In the proposed RDEA method, pessimistic and optimistic efficiencies of decision making units (DMUs) obtained from the robust counterpart of the envelopment form and the optimistic counterpart of the multiplier form of DEA are introduced. The inputs and outputs data are assumed to be bounded data (interval numbers) in the proposed models. A case study in the cement industry is presented to demonstrate the applicability of the proposed RDEA approach. The results obtained from the authors' proposed RDEA approach is more robust and their method provides a more complete ranking of the DMUs compared to conventional Likert-based DEA model.

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