جستجوی مقالات مرتبط با کلیدواژه « کارایی » در نشریات گروه « صنایع »

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.

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.

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.

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.

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.

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.

A mathematical programming based non-parametric technique for the performance assessment of decision-makingunits (DMUs) with multiple inputs and outputs is considered in a general framework called data envelopment analysis (DEA). DEA models have been utilized for estimating the eciency scores of the DMUs with certain input-output levels. However, in the last two decades, various studies have been concentrated on the inverse DEA as an analytical framework of DEA to nd the required inputs and outputs levels for achieving a predetermined eciency target. The main aim in the inverse DEA is to estimate the inputs and/or outputs for a special DMU to attain a given eciency target while evaluating the performance of a speci c DMU is the main objective in the DEA analysis. Inverse DEA has been studied from both theoretical and practical aspects including sensitivity analysis, resource allocation, preserving or improving eciency scores, and merging DMUs to achieve the predetermined eciency target. Also, inverse DEA has been employed for modeling generalized restructuring DMUs. In a generalized restructuring a set of pre-restructuring DMUs through consolidation/split to create synergy/reverse synergy, proceed with a restructuring to produce a new set of post-restructuring DMUs to achieve prede ned eciency targets.
This paper deals with the problem of units' restructuring using inverse data envelopment analysis (DEA). A generalized restructuring refers that a set of decision-making units based on synergies through mergers/acquisitions or reverse synergies through split, proceed with a restructuring to produce a new set of post-restructuring entities to improve eciency. The problem of units' restructuring is investigated in this paper, and to achieve a pre-speci ed eciency level for each post-restructuring decision-making units, models for estimating inputs (outputs) have been proposed. The most important advantages of the proposed models, compared to other the provided models, as follows: i) Due to the use of multiobjective programming (MOP) tools, allows the decision maker to pursue multiple goals in the problem of restructuring units. ii) The proposed method has less computational complexity because the number of variables is greatly reduced. In addition, a numerical example with real data is employed to evaluate the performance of the proposed models.

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.

 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.

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.

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.

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.

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 outputs

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.

 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 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.

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.

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 Analysis (DEA) is a mathematical model that evaluates the relative efficiency of Decision Making Units (DMUs). The purpose of this paper was systematic study of applying this technique in Iran Banking System. In this systematic review that was conducted in September 2017, initially by searching keywords in scientific databases attempted to search the research has been conducted a bout 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 percent 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 percent of researches; input oriented model is used also in 36.7 percent 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 Analysis (DEA) is a method based on linear programming to measure 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’ modeling 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.

In recent decades, as a result of the global economic growth and the development of investment, extensive researches have been carried out in the field of ranking companies, Data Envelopment analysis (DEA) and Analytical Hierarchy Process (AHP ) are the most important methods in this field, However, perhaps the most notable method is DEA/AHP which is, in fact, the combination of the mentioned ones. This method utilizes the strengths of DEA and AHP to rank the decision-making units (DMUs) in a company.
The aim of this study is to provide a rating of cement companies operating in the stock using improved methods DEA / AHP is. The results of the research show that in 1391, Isfahan, Bojnoord and Qaen cement companies took the first to third places, but in 1392 these places belonged to Abyek, Isfahan and Cazroon companies. Therefore, it can be concluded that while using the improved integrated DEA and AHP method, not only the companies with higher efficiency are placed in higher ranks, but also, the inefficient companies are ranked effectively

In recent years, many economic and social institutions allocate their budget in a traditional manner or in some cases with the use of indicators. Although traditional methods may seem easier when the data are limited and the bargaining method is a solution however, in today's complex and changing environment, they are not ecient. Universities are among public institutions that need planning and budgeting at the university level to allocate their funds in dierent departments to achieve their objectives. In this study, we tried to provide a model at the university level to solve this problem. The model consists of three parts. In the rst part, the coecients of eciency were calculated using Data Envelopment Analysis (DEA). The number of graduates and researchoutcomesbasedontheamountoffacultygrants as the outputs, and the number of faculty members and staastheinputsweretakenintoaccount. Inthesecond part, the coecients of eectiveness were calculated using a multi-objective mixed integer linear programming model to achieve multiple and con icting goals of the university. The goals were ratios of faculty to students, postgraduate students, faculty members with the ranking of associate professor or higher, and research grants. Then, in the third section, the coecient of each department's budget was calculated based on the current budget demand of department and eciency and eectivenesscoecients. Finally,theperformanceoftheproposed model was checked using data from Yazd University. The data for the 12 departments at the university were gathered on which basis the analysis was carried out. The results of the model and its sensitivity analysis showed that the proposed model was consistent with what is happening in the reality. The model can be applied for those organizations whose management aims at distributing the budget based on the eciency and eectiveness of the departments.

To develop an organization, its units must be continuously evaluated and according to the results of evaluations awarded. Without these policies, the inefficient units are located on the margin of safety and the efficient units are discouraged. In this study, a fair way is presented to distribute a common reward among the units of organization. Using data envelopment analysis, this method determines the share of each unit relative to its performance. To this end, the minimum and maximum possible shares of each DMU are determined, then, a combination of them is used for determining its final share. In this method, the shares are objectively determined.

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 researcher's 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.

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.

Always allocate man - machine is considered as one of the main concerns of operation planners. Utilize an appropriate using of workforce in situations that the operation is dependent on the capabilities of the workforce, can lead to improve the systems efficiency and productivity indicators. Since these issues, Categories in np-hard problems, so optimization and solving the mathematical model of these problems accurately is complexIn this study, with computer simulation approach, it has been noticed to the issue and based on the collected data and carefully studying the operation flow, with utilizes statistical hypothesis tests, models of operation and processes have been identified. It also with using appropriate non-parametric test, validity of simulation model verified and daily production of various possible options of man - machine allocation via computer simulation model has been compared, so the most suitable option in production and assembly of gas regulators has been introduced. In this approach, the simulation results showed that with choosing appropriate methods of using workforce with 95% confidence level, we can increase daily production to 25%.

One of the most important decisions in supply chain network (SCN) design is choosing the optimal location for the facilities. The facilities in SCN have different efficiency according to their locations. In this paper, efficiency of facilities is added to the supply chain network design by using data envelopment analysis model, and a multi-objective model is presented for the design of efficient supply chain network. The proposed model chooses the most appropriate place for manufacturers and distributors thereby decreases the total cost of the supply chain and simultaneously increases the efficiency. The desired supply chain has several raw materials and products, with four layers of suppliers, manufacturers, distributors and customers. The proposed model is locating manufacturers and distributors, and planning the purchase of any suppliers. The results of numerical example show that adding efficiency promotes the supply chain network model. Namely, with regard to the tradeoff between cost and efficiency objectives, SCN design with efficient facilities is better than networks based only on cost objective function.