Data Envelopment Analyses
Azam Pourhabib Yekta; Mahnaz Maghbouli
Abstract
Purpose: Data Envelopment Analysis (DEA) is a technique used to assess performance and measure the relative efficiency of Decision Making Units (DMUs) through linear programming. In most cases, DEA models evaluate inefficient units on the boundary of the production possibility set using reference points ...
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Purpose: Data Envelopment Analysis (DEA) is a technique used to assess performance and measure the relative efficiency of Decision Making Units (DMUs) through linear programming. In most cases, DEA models evaluate inefficient units on the boundary of the production possibility set using reference points that are not Pareto efficient. Consequently, these models often yield zero weights for multipliers, failing to justify all sources of inefficiency. This paper aims to introduce a model that generates non-zero weights.Methodology: Weight restriction methods have primarily addressed the issue of non-realistic weights. We impose constraints on the weights in the proposed model to achieve our objectives.Findings: This paper presents a one-stage method based on the BCC model, incorporating weight restrictions, to evaluate the relative efficiency of decision-making units. The proposed model ensures non-zero weights and prevents dissimilarity between weights while maintaining feasibility. Notably, the proposed model does not require any prior information on weights or the classification of units, reducing the complexity of the problem.Originality/Value: To highlight the strength of the proposed method, the model is implemented on two case studies and compared with the results obtained from standard BCC models and those of Ramon and colleagues. The results indicate the superior performance of the proposed model.
Data Envelopment Analyses
Mojtaba Karimi Pashaki; Mahnaz Ahadzadeh Namin
Abstract
Purpose: For managers, investors and creditors, it is important to be aware of the continuity of the company. To this end, financial researchers are looking for effective methods to evaluate the company's performance and predict the continuation of its activities in the coming years.Methodology: In previous ...
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Purpose: For managers, investors and creditors, it is important to be aware of the continuity of the company. To this end, financial researchers are looking for effective methods to evaluate the company's performance and predict the continuation of its activities in the coming years.Methodology: In previous research, the standard data envelopment analysis model has been used to predict corporate bankruptcy. The present study aims to provide a model of data envelopment analysis with semi-positive and negative indicators to predict the bankruptcy of companies operating in the Tehran Stock Exchange. The companies listed on the Tehran Stock Exchange constitute the statistical population of the research. To achieve this goal, a sample consisting of 40 non-bankrupt companies and 20 bankrupt companies in the years 1393 to 1397 were selected. The criterion for selecting bankrupt companies is Article 141 of the Commercial Code.Findings: To combine tax ratios that have a more significant correlation with the financial situation of the company, the combined approach of gray relationship analysis and two-level data envelopment analysis has been used.Originality/Value: First, a two-level data envelopment analysis model for semi-positive and negative indices will be developed, then the correct prediction of bankruptcy with its absence will be examined using the results of the proposed model.
Data Envelopment Analyses
Mohammad Izadikhah; Mohadeseh Shamsi; Abbas Sheikhsn; Fariba Ghafouri
Abstract
Purpose: Implementing a credit rating system considering banks' deferred claims is one of the most important means of controlling credit risk in banks and financial institutions. In the case of banking facilities, the possibility of non-repayment of facilities is one of the most important issues. By ...
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Purpose: Implementing a credit rating system considering banks' deferred claims is one of the most important means of controlling credit risk in banks and financial institutions. In the case of banking facilities, the possibility of non-repayment of facilities is one of the most important issues. By identifying various factors that affect the non-repayment of bank facilities, it is possible to provide a framework for reducing and controlling the credit risk of banks and improving the crediting process. The purpose of this paper is to examine the relationship between efficiency and risk in the banking system.Methodology: In this research, a sample of 24 companies from the most important legal customers of 11 branches of the Melli Bank of the city of Arak has been studied. 18 variables affecting credit risk were identified in this paper. Among the variables available, 6 variables were selected using the Factor Analysis Technique and Expert judgment (Delphi method), of which 3 were inputs and 3 were formed the outputsFindings: The efficiency and rank of legal firms were obtained with the help of Data Envelopment Analysis models, and then using the Fitch Institute's data and the efficiency of legal clients, the credit rating of each of the legal firms and their qualitative analysis was expressed.Originality/Value: Accordingly, using the method of data envelopment analysis and data provided by the Fitch Ratings Institute, the credit risk of Arak's Melli Bank's legal customers is assessed and ranked
Data Envelopment Analyses
nasrin bagheri mazraeh; Mohsen Rostami Mal Khalife; Meysam Varzi
Abstract
Purpose: Efficiency is an economic concept which shows the performance of a wide range of economic activities in different areas of an economic sector. Most of studies using frontier technique Data Envelopment Analysis (DEA) do not test for the relationship of efficiency estimation with key performance ...
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Purpose: Efficiency is an economic concept which shows the performance of a wide range of economic activities in different areas of an economic sector. Most of studies using frontier technique Data Envelopment Analysis (DEA) do not test for the relationship of efficiency estimation with key performance indicators. This is despite the fact that DEA is one of the most effective tools for measuring and evaluating efficiency. Nevertheless, identifying the relationship between efficiency estimates and commonly accepted financial measures of performance could guide benchmarking activities, pricing decisions, and regulatory monitoring.Methodology: In this paper, the DEA super-efficiency formula is tested in two profitability models. Four ratios of net interest income to total assets, post-tax profit to total assets, owner’s equity returns and impaired loans to total assets, were calculated with a developed profitability model; besides, the growth rate of assets was calculated with main profitability model and all the aforementioned ratios addressed a significant association with efficiency estimates.Findings: In this study, the DEA super-efficiency formula is tested in two profitability models for 15 banks for two years. The correlation obtained is generally low. However, the four ratios of net interest income to total assets, post-tax profit to total assets, owner’s equity returns and impaired loans to total assets, in the EPM model and asset growth rate in the CPM model have a significant relationship with performance estimates. Finally, the results indicate poor credit quality in Iranian banks in 1397-1397.Originality/Value: In this study, for the first time, the nature of the relationship between performance and key performance indicators has been estimated. DEA technique has been used to purposefully identify criteria for analyzing financial ratios.
stochastic/Probabilistic/fuzzy/dynamic modeling
Alireza Hajihosseini; Ali Payan; Ali Salari
Abstract
One of the most important tasks of an organization manager is to evaluate the performance of the units within the organization because only the performance evaluation can inform the manager about the extent to which the organization has stepped toward the goals established to achieve it so that the manager ...
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One of the most important tasks of an organization manager is to evaluate the performance of the units within the organization because only the performance evaluation can inform the manager about the extent to which the organization has stepped toward the goals established to achieve it so that the manager can identify weaknesses, and poor performance of his organization take corrective action and improve the performance of his organization. In this regard, this study has evaluated accurately and inaccurately the relative performance of 11th-grade nongovernmental schools in Sistan and Baluchestan province. There are various methods for assessing the performance of organizations. One of the most important of these methods is data envelopment analysis technique which by comparing the efficiency of organization units with each other separates efficient units from inefficient units. Selection of inputs and outputs in this study was done according to similar research and interviews with experts. In this study, the relative efficiency of 55 of 11th grade nongovernmental Schools in Sistan and Baluchestan province was calculated by using data envelopment analysis for the academic year 1395-1396 and the efficient and inefficient units were identified, then the performance of these schools is presented by a fuzzy interval based on the technical efficiency and the subjective efficiency and finally ranked.
Data Envelopment Analyses
Reza Maddahi; Hamidreza Yazdani
Abstract
Data Envelopment Analysis (DEA) is a technique for evaluating homogeneous decision-making units. In this method, the efficiency score for each unit is obtained by comparing the performance of each Decision-Making Unit (DMU) with the performance of the other units. This performance score can be used as ...
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Data Envelopment Analysis (DEA) is a technique for evaluating homogeneous decision-making units. In this method, the efficiency score for each unit is obtained by comparing the performance of each Decision-Making Unit (DMU) with the performance of the other units. This performance score can be used as a criterion for ranking units. In many cases, a significant number of units are efficient, and therefore, the efficiency of the classic DEA models cannot be a good criterion for accurate ranking of DMUs. In this study, a method for ranking DMUs using their efficiency is presented in several time periods, so that three types of production possibility sets are introduced. In the first type, for each time period, an independent production possibility set is defined, in the second type, a combination production possibility set is used for all time periods, and in the third type, a community production possibility set is created, which considers all time periods. Then, corresponding to each type, one efficiency number is obtained for each DMU. Therefore, the three values of efficiency resulting from the three methods are combined using the Shannon entropy method and define a general performance criterion for each unit. This criterion is ultimately considered as the main indicator for ranking units.
Data Envelopment Analyses
Azizollah Nosrat; Gholamreza Roozbehi
Abstract
Determining the efficiency of each system for scheduling is one of the requirements of that system. Data envelopment analysis models are often used to determine the performance of systems. In this paper, the simplest and most practical two-stage systems are introduced as the basic two-stage system. Then ...
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Determining the efficiency of each system for scheduling is one of the requirements of that system. Data envelopment analysis models are often used to determine the performance of systems. In this paper, the simplest and most practical two-stage systems are introduced as the basic two-stage system. Then the proposed TSDEA models based on the distance function measures are investigated. Subsequently, Russell's idea of non-radial TSDEA models was developed.
Data Envelopment Analyses
Abbas Jahangiri
Abstract
Data Envelopment Analysis (DEA) is a mathematical model that evaluates the relative efficiency of Decision Making Units (DMUs). The purpose of this paper was the systematic study of applying this technique in Iran Banking System. In this systematic review that was conducted on September 2017, initially ...
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Data Envelopment Analysis (DEA) is a mathematical model that evaluates the relative efficiency of Decision Making Units (DMUs). The purpose of this paper was the systematic study of applying this technique in Iran Banking System. In this systematic review that was conducted on September 2017, initially by searching keywords in scientific databases attempted to search the research has been conducted about DEA in Iranian banks regardless of the time of publication that a total of 409 research was found. Then 349 researches were removed from the study because of the irrelevant, repetitive and inaccessibility to the full text. Then attempted to statistical analysis of 60 remaining researches via Excel 2010 software. 80 % of the researches were published in 2009 onwards. Most research has been conducted in Tehran Province and Melli and Saderat banks. The variety of choices outputs and inputs indexes was very high. In 36.7 % of researches; the input-oriented model has used also in 36.7% of researches constant returns to scale is assumed. The results showed that although the interest of Iranian researchers in this field has increased, each with a particular view has analyzed the efficiency of the banks and have achieved different results.
Data Envelopment Analyses
Naser Amani; Hadi Bagherzadeh valami
Abstract
Data Envelopment Analysis (DEA) is a method based on linear programming to measure the efficiency of Decision-Making Units (DMU). In classic models of DEA, the whole system had been usually considered as a Decision-Making Units to evaluate respective efficiency and it is also ignored the separate processes ...
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Data Envelopment Analysis (DEA) is a method based on linear programming to measure the efficiency of Decision-Making Units (DMU). In classic models of DEA, the whole system had been usually considered as a Decision-Making Units to evaluate respective efficiency and it is also ignored the separate processes inside the system. Whereas, the internal relations of various sectors of a Decision-Making Unit can have had diverse structures which cause complexity in evaluating its efficiency, because, the type of structures and the performance of these components would have different effects on efficiency of the system. Network standpoint is one of the appropriate ways for the internal relations of units’ modelling and the relation among sub-units in a DMU may be communicated in series, parallel or mixed way. In this paper, a new convert called Star Structure was introduced as a comprehensive one. The one that every structure existing between a Decision-Making Units’ sub-units can easily be converted to such structure so that can accurately evaluate a Decision-Making Units’ efficiency and also using star structure, we evaluated the performance of regional electronic companies in Iran.
supply chain management analyzing/modelling
Amir Rahimi; Faranak Hosseinzadeh Saljooghi
Abstract
In recent years, a new approach which has been dominant over operation management is the “supply chain management approach”. Supply chain management has attracted most researchers' attentions in recent years. This is the way to improve the economic, social and environmental performance. Therefore, ...
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In recent years, a new approach which has been dominant over operation management is the “supply chain management approach”. Supply chain management has attracted most researchers' attentions in recent years. This is the way to improve the economic, social and environmental performance. Therefore, the evaluation of SSCM is an important task for all types of organizations. Among the methods of evaluation SSCM, it seems that data envelopment analysis (DEA) is an appropriate approach. Some of decision-making units composed of several sections or stages that make a network of sub-processes. In order to evaluate such units, data envelopment analysis (DEA) is applied. This paper presents two approaches to calculate supply chain management as a network process. In the first approach, we consider the generalization of weighted sum model to calculate efficiency and returns to scale (RTS) of supply chain with two-stage process as single-objective approach. In the second one, we introduce the weighted sum model to calculate efficiency and returns to scale (RTS) of supply chain with two-stage process as multi-minded approach, so we try to make it to be single-objective approach and then calculate the efficiency of its total production process, according to the decision-maker's ideas and interests. In the next section of this paper according to the two above approaches, we determine the percent of returns to scale (RTS) of supply chain. Suggested ideas are used to evaluate the efficiency and returns to scale (RTS) of supply chain in resin production companies.
Data Envelopment Analyses
Mohammad Javad Goleij
Abstract
One of the best tools to evaluate the performance is DEA technique. Data envelopment analysis technique is a tool for ranking and identifying efficient and inefficient units. Since, in many cases, the decision-making units in organization have an intermediate (middle values) and also in some cases the ...
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One of the best tools to evaluate the performance is DEA technique. Data envelopment analysis technique is a tool for ranking and identifying efficient and inefficient units. Since, in many cases, the decision-making units in organization have an intermediate (middle values) and also in some cases the real world, available values for inputs and outputs are obscure (ambiguous) and uncertain, and using classic data envelopment model does not reach us to a certain result. Also, return on the scale is a very important argument in data envelopment analysis and economics and can provide us with useful information about the status of the decision-maker units.In this paper, we present a new model for fuzzy two-stage data envelopment analysis and to evaluate this efficiency, we study the efficiency of industrial workshops between 10 and 49 employees. The efficiency of industrial workshops is evaluated by province. The results are the importance of the proposed model.