scholarly journals Two-stage DEA for Bank Efficiency Evaluation Considering Shared Input and Unexpected Output Factors

2020 ◽  
Vol 214 ◽  
pp. 01036
Author(s):  
Song Aifeng ◽  
Zhang XiaoYang ◽  
Huang Weilai ◽  
Yang xue ◽  
Yang Juan
Keyword(s):  
Market Competition ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Uncertain Information ◽  
Two Stage ◽  
Bank Efficiency ◽  
Dea Model ◽  
Stochastic Dea ◽  
Output Factors ◽  
Fierce Competition

With the increasingly fierce market competition, only by relying on high-quality products and high customer satisfaction can enterprises survive in the fierce competition. Among many evaluation methods, Data Envelopment Analysis (DEA), as a non-parametric statistical method to effectively deal with multi-input and multi-output problems, has received more and more attention in evaluating the relative efficiency of decision-making units. In the process of bank efficiency evaluation based on DEA method, there will be a situation that banks have both dual role factors and unexpected output factors. The Two-stage DEA model provides an effective analysis method to solve the problem of bank efficiency evaluation of complex organizational structure. In order to evaluate the efficiency of unexpected output with uncertain information, a stochastic DEA model of unexpected output is established.

scholarly journals Operational Efficiency of Chinese Provincial Electricity Grid Enterprises: An Evaluation Employing a Three-Stage Data Envelopment Analysis (DEA) Model

2018 ◽  
Vol 10 (9) ◽  
pp. 3168 ◽  
Author(s):  
Haoran Zhao ◽  
Huiru Zhao ◽  
Sen Guo
Keyword(s):  
Data Envelopment Analysis ◽  
Environmental Variables ◽  
Market Competition ◽  
Environmental Values ◽  
Operational Efficiency ◽  
Data Envelopment ◽  
Electricity Grid ◽  
Frontier Analysis ◽  
Dea Model ◽  
Statistical Noise

With the implementation of new round electricity system reform in China, the provincial electricity grid enterprises (EGEs) of China should focus on improving their operational efficiency to adapt to the increasingly fierce market competition and satisfy the requirements of the electricity industry reform. Therefore, it is essential to conduct operational efficiency evaluation on provincial EGEs. While considering the influences of exterior environmental variables on the operational efficiency of provincial EGEs, a three-stage data envelopment analysis (DEA) methodology is first utilized to accurately assess the real operational efficiency of provincial EGEs excluding the exterior environmental values and statistical noise. The three-stage DEA model takes the amount of employees, the fixed assets investment, the 110 kV and below distribution line length, and the 110 kV and below transformer capacity as input variables and the electricity sales amount, the amount of consumers, and the line loss rate as output variables. The regression results of the stochastic frontier analysis model indicate that the operational efficiencies of provincial EGEs are significantly affected by exterior environmental variables. Results of the three-stage DEA model imply that the exterior environmental values and statistical noise result in the overestimation of operational efficiency of provincial EGEs, and the exclusion of exterior environmental values and statistical noise has provincial-EGE-specific influences. Furthermore, 26 provincial EGEs are divided into four categories to better understand the differences of operational efficiencies before and after the exclusion of exterior environmental values and statistical noise.

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.

DEA by sequential exclusion of alternatives in heterogeneous samples

2016 ◽  
Vol 15 (01) ◽  
pp. 5-22 ◽  
Author(s):  
Fuad Aleskerov ◽  
Vsevolod Petrushchenko
Keyword(s):  
Data Envelopment Analysis ◽  
New Method ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Dea Model ◽  
Economic Applications ◽  
Heterogeneous Samples ◽  
Efficiency Estimation ◽  
Dea Approach ◽  
Russian Universities

Data Envelopment Analysis (DEA) is a well-known nonparametric technique of efficiency evaluation which is actively used in many economic applications. However, DEA is not very well applicable when a sample consists of firms operating under drastically different conditions. We offer a new method of efficiency estimation in heterogeneous samples based on a sequential exclusion of alternatives and standard DEA approach. We show a connection between efficiency scores obtained via standard DEA model and the ones obtained via our algorithm. We also illustrate our model by evaluating 28 Russian universities and compare the results obtained by two techniques.

scholarly journals Alternative Secondary Goals in Multiplicative Two-Stage Data Envelopment Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chao Lu ◽  
Haifang Cheng
Keyword(s):  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Data Envelopment ◽  
Nonparametric Method ◽  
Two Stage ◽  
Dea Model ◽  
Second Stage ◽  
China Construction Bank ◽  
Overall Efficiency ◽  
Decision Making Units

Data envelopment analysis (DEA) is a nonparametric method for evaluating the relative efficiency of a set of decision-making units (DMUs) with multiple inputs and outputs. As an extension of the DEA, a multiplicative two-stage DEA model has been widely used to measure the efficiencies of two-stage systems, where the first stage uses inputs to produce the outputs, and the second stage then uses the first-stage outputs as inputs to generate its own outputs. The main deficiency of the multiplicative two-stage DEA model is that the decomposition of the overall efficiency may not be unique because of the presence of alternate optima. To remove the problem of the flexible decomposition, in this paper, we maximize the sum of the two-stage efficiencies and simultaneously maximize the two-stage efficiencies as secondary goals in the multiplicative two-stage DEA model to select the decomposition of the overall efficiency from the flexible decompositions, respectively. The proposed models are applied to evaluate the performance of 10 branches of China Construction Bank, and the results are compared with the results of the existing models.

Fuzzy stochastic Data Envelopment Analysis with application to NATO enlargement problem

2019 ◽  
Vol 53 (2) ◽  
pp. 705-721 ◽  
Author(s):  
Ali Ebrahimnejad ◽  
Seyed Hadi Nasseri ◽  
Omid Gholami
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Threshold Values ◽  
Discrimination Power ◽  
Dea Model ◽  
Nato Enlargement ◽  
Proposed Model ◽  
Stochastic Dea ◽  
Dea Models ◽  
Stochastic Data

Data Envelopment Analysis (DEA) is a widely used technique for measuring the relative efficiencies of Decision Making Units (DMUs) with multiple deterministic inputs and multiple outputs. However, in real-world problems, the observed values of the input and output data are often vague or random. Indeed, Decision Makers (DMs) may encounter a hybrid uncertain environment where fuzziness and randomness coexist in a problem. Hence, we formulate a new DEA model to deal with fuzzy stochastic DEA models. The contributions of the present study are fivefold: (1) We formulate a deterministic linear model according to the probability–possibility approach for solving input-oriented fuzzy stochastic DEA model, (2) In contrast to the existing approach, which is infeasible for some threshold values; the proposed approach is feasible for all threshold values, (3) We apply the cross-efficiency technique to increase the discrimination power of the proposed fuzzy stochastic DEA model and to rank the efficient DMUs, (4) We solve two numerical examples to illustrate the proposed approach and to describe the effects of threshold values on the efficiency results, and (5) We present a pilot study for the NATO enlargement problem to demonstrate the applicability of the proposed model.

Evaluating Taiwanese Bank Efficiency Using the Two-Stage Range DEA Model

2016 ◽  
Vol 16 (04) ◽  
pp. 1043-1068 ◽  
Author(s):  
Wei-Hsin Kong ◽  
Tsu-Tan Fu ◽  
Ming-Miin Yu
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Analysis Model ◽  
Negative Data ◽  
Two Stage ◽  
Data Set ◽  
Dea Model ◽  
Domestic Banks

This paper develops a range directional distance data envelopment analysis (DEA) model to simultaneously deal with the problems of negative data and undesirable outputs in the study of performance measurement with two-stage DEA. We report on the development of this model to handle both positive and negative data in a DEA framework and accommodate the problem of undesirable intermediate outputs in the first stage of operational processes. Unlike previous two-stage DEA models we allow for a nonuniform abatement factor imposing on stage 1’ production technology. Such a model is then applied to evaluate Taiwanese bank efficiencies both at the operational stage and profitability stage in banking activities based on a data set consisting of 35 domestic banks in Taiwan in the period 2007. The results indicate that, by the range directional two-stage data envelopment analysis model, the operational efficiency was smaller than the profitability efficiency. Many banks generated too many performing loans in which independent banks should reduce more performing loans than financial holding company subsidiary banks. Both the ratio of investments to loans and the ratio of nonperforming loans to performing loans did not have significant contributions to the efficiency. This paper is able to provide information for bank operators and researchers on the managerial and strategic implications of how negative data and undesirable outputs affect efficiency and how to measure efficiency appropriately.

scholarly journals Efficiency Evaluation of China’s Public Sports Services: A Three-Stage DEA Model

2021 ◽  
Vol 18 (20) ◽  
pp. 10597
Author(s):  
Pengyu Ren ◽  
Zhaoxia Liu
Keyword(s):  
Technical Efficiency ◽  
Environmental Variables ◽  
Positive Impact ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Fitness Program ◽  
Dea Model ◽  
Service Efficiency ◽  
The Impact

Improving the level of public sports services enhances citizens’ physical fitness by implementing the national fitness program. A systematic and scientific efficiency evaluation is a prerequisite for optimizing and improving the level of public sports services in China. Based on data of the Chinese Statistic Yearbook, this study adopted the three-stage data envelopment analysis (DEA) model to measure and analyze the efficiency of public sports services in 31 provinces in China in 2016. To analyze the efficiency of public sports services, technical efficiency was decomposed into pure technical efficiency and scale efficiency. Simultaneously, environmental variables were added to improve accuracy. The results showed that scale efficiency was overestimated, and external technical efficiency was underestimated, before the elimination of external factors and environmental variables. Environmental factors significantly impacted the efficiency of public sports services. Regional gross domestic product (GDP) had a potentially positive impact, while population size partially restricted public sports service efficiency. After eliminating the impact of environmental and random factors, the comprehensive efficiency, pure technical efficiency, and scale efficiency of public sports services all showed improvement in varying degrees. The results provide beneficial insights for the formulation of rational improvement policies for public sports services.

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