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.

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

Assessing most productive scale size with fuzzy stochastic data envelopment analysis

2022 ◽  
pp. 1-11
Author(s):  
Hooshang Kheirollahi ◽  
Mahfouz Rostamzadeh ◽  
Soran Marzang
Keyword(s):  
Relative Efficiency ◽  
Data Envelopment ◽  
Random Inputs ◽  
Dea Model ◽  
Scale Size ◽  
Stochastic Dea ◽  
Probability Constraints ◽  
Most Productive Scale Size ◽  
Decision Making Units ◽  
Fuzzy Random

Classic data envelopment analysis (DEA) is a linear programming method for evaluating the relative efficiency of decision making units (DMUs) that uses multiple inputs to produce multiple outputs. In the classic DEA model inputs and outputs of DMUs are deterministic, while in the real world, are often fuzzy, random, or fuzzy-random. Many researchers have proposed different approaches to evaluate the relative efficiency with fuzzy and random data in DEA. In many studies, the most productive scale size (mpss) of decision making units has been estimated with fuzzy and random inputs and outputs. Also, the concept of fuzzy random variable is used in the DEA literature to describe events or occurrences in which fuzzy and random changes occur simultaneously. This paper has proposed the fuzzy stochastic DEA model to assess the most productive scale size of DMUs that produce multiple fuzzy random outputs using multiple fuzzy random inputs with respect to the possibility-probability constraints. For solving the fuzzy stochastic DEA model, we obtained a nonlinear deterministic equivalent for the probability constraints using chance constrained programming approaches (CCP). Then, using the possibility theory the possibilities of fuzzy events transformed to the deterministic equivalents with definite data. In the final section, the fuzzy stochastic DEA model, proposed model, has been used to evaluate the most productive scale size of sixteen Iranian hospitals with four fuzzy random inputs and two fuzzy random outputs with symmetrical triangular membership functions.

Measuring the relative performance of forest management units: a chance-constrained DEA model in the presence of the nondiscretionary factor

2019 ◽  
Vol 49 (7) ◽  
pp. 788-801 ◽  
Author(s):  
Majid Zadmirzaei ◽  
Soleiman Mohammadi Limaei ◽  
Alireza Amirteimoori ◽  
Leif Olsson
Keyword(s):  
Decision Making ◽  
Forest Management ◽  
Efficiency Score ◽  
Management Problem ◽  
Strong Positive Correlation ◽  
Deterministic Models ◽  
Management Units ◽  
Dea Model ◽  
Management Plans ◽  
Decision Making Units

In this study, we develop a marginal chance-constrained data envelopment analysis (DEA) model in the presence of nondiscretionary inputs and hybrid outputs for the first time. We call it a stochastic nondiscretionary DEA model (SND-DEA), and it is developed to measure and compare the relative efficiency of forest management units under different environmental management systems. Furthermore, we apply an output-oriented DEA technology to both deterministic and stochastic scenarios. The required data are collected from 24 forest management plans (as decision-making units) and included four inputs and an equal amount of outputs. The findings of this practical research show that the modified SND-DEA model in different probability levels gives us apparently different results compared with the output from pure deterministic models. However, when we calculate the correlation measures, the probability levels give us a strong positive correlation between stochastic and deterministic models. Therefore, approximately 40% of the forest management plans based on the applied SND-DEA model should substantially increase their average efficiency score. As the major conclusion, our developed SND-DEA model is a suitable improvement over previous developed models to discriminate the efficiency and (or) inefficiency of decision-making units to hedge against risk and uncertainty in this type of forest management problem.

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
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