mahnaz ahadzadeh namin

Evaluating the performance of organizations can provide managers with useful information about the status of the organization compared to other organizations so that managers can take a step towards the growth and excellence of the organization. Obviously, the number of indicators and their amount affect the performance evaluation of organizations. So, by collecting the exact values of the indicators, an accurate and accurate performance evaluation will be provided to managers of organizations. In this article, we intend to evaluate the companies investing in the stock exchange. Since in the table of indices related to these companies published by the Iran Stock Exchange Organization there are indices whose values have been lost for any reason (not available - heterogeneous index), it is necessary to use envelopment analysis models. We used data (DEA) in the presence of heterogeneous indicators. We have used the model of Cook et al.'s (2013) article to evaluate companies. For the conceptual use of research, we have described and implemented their method step by step. Lastly, we have analyzed the results.

Several studies have used the data envelopment analysis method in the field of portfolio optimization or efficiency evaluation of companies listed on the Tehran Stock Exchange, but less attention has been paid to the network data envelopment analysis method and indicators simultaneously derived from technical and fundamental analysis. Therefore, considering the importance of the issue, the purpose of this article is to evaluate the relative and integrated efficiency of the Tehran Stock Exchange using "production process" and "financial production process" with the approaches of Fundamental Analysis (FA) and Technical Analysis (TA). To achieve this, the network data envelopment analysis model (NDEA) was used for the period 2014-2021. In addition, FAHP and SOCP are developed, respectively, to determine the weights of the first and second stages and to solve the nonlinear problem in the AED model. In addition, the efficiency of the year 2021 is predicted based on the efficiency values of the previous years by using simple regression analysis. The results showed: the production process is more important in defining the overall efficiency of a company; there is a significant difference in the efficiency distribution among different industries; Kh-Sapa and Khodro achieved 100% overall efficiency and the Technical and Engineering Services industry achieved the highest overall efficiency; the predicted efficiency values are very close to the actual efficiency values in 2021. Based on the results, it is suggested that investors pay more attention to the fundamental analysis in order to create an investment portfolio. Additionally, focus more on the companies with higher first and second stage efficiency, since they are investment proposals.

Evaluating the efficiency of low-fee loans in small businesses and comparing them with each other can be a support for strategic planning. Banks and escape from economic inflation. One of the methods of measuring efficiency is data coverage analysis. In this research, the indicators affecting the performance of low-fee loans have been researched and identified, and then the researched samples have been estimated. In three parts, the evaluation of the public institution of Resalat (Qarz Al-Hasna Resalat Bank) which is active in paying microloans has been done and in order to measure the impact of microloans on business creation, it has been compared with 9 other banks. In fact, the purpose of this research is to measure the efficiency of micro-bank facilities and its impact on the creation of micro- and home-based businesses and to identify indicators that affect micro-businesses. At the end, the steps of the research are described with a practical example

The purpose of this article is to evaluate the technical efficiency of listed companies in the pharmaceutical industry in 2021. To achive this, the additive model of two-stage network data envelopment analysis was investigated with semi-positive and negative indicators and simultaneously considering the production process(Fundamental Analysis) in the first stage and the financial production process (Technical Analysis) in the second stage. In addition, FAHP applied to determine the weights of the first and second stages. The results showed: the production process is more important in defining the total efficiency of a company in the Stock Exchange. In the first stage, %33 and in the second stage, %25 of the pharmaceutical industry companies achieved full efficiency of one. According to the efficiency one of %13 companies of the studied industry in both first and second stages, the efficiency of the mentioned companies was also full and found to be one; These companies include Toliddaru, Exir Pharmaceutical and Loghman Pharmaceutical & Hygienic Companies. The lowest total efficiency belongs to Sobhan Pharmaceutical Group. In general, 88% companies of the studied industry have an efficiency higher than 0.5.

One of the important applications of data envelopment analysis is to determine the progress and regress of the units under evaluation at two different times, which has been addressed in many papers. Also, one of the distinctions of data coverage analysis technique with other methods is the introduction of achievable and flexible benchmarks. In the present paper, we intend to study the progress and regress of Iranian regional electricity companies during two consecutive years of 2015 and 2016. Since some of the evaluated indicators are semi-positive and semi-negative indicators, in this study we will develop Emrooznejad et al.  [7] to determine the productivity index of Malmquist for semi-positive and semi-negative indicators. Finally, for further explanation, we have used the proposed models to determine the progress and regression of 16 regional electricity companies in Iran with 3 semi-positive and semi-negative indices in the presence of the limitation on the benchmark, an undesirable index and 11 completely positive indices in the nature of input with constant scale returns as a black box.

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.

Since it plays a significant role in the best choice of industry and science, it is always chosen to find a more accurate job. One of these methods is the TOPSIS method. In this article, after introducing the DEA and TOPSIS methods, we will introduce the TOPSIS-DEA method and then we will expand this method for interval criteria. In this method, there is no need to know the exact weight of the qualities, and this method deals with the best option without changing the qualitative qualities. This method is a suitable method for choosing the best option when the data is an interval. And it is faster than the classic method.

In order to evaluate the companies of the cement industry active in the Tehran Stock Exchange (Iran), it is first necessary to identify the indicators of the industry. Note that some of the indicator values provided by the Exchange Organization to the users may have been lost for any reason. All of the existing models for calculating the efficiency of such units calculate the weight of the indicators related to each unit under evaluation independent of the weight of other units. So, in this study, researchers decided to develop a common set of weight models and propose a model that evaluates efficiency in the presence of heterogeneity decision-making units (DMUs) based on the common set of weight models. Finally, the proposed model evaluates 25 cement industry companies in the presence of heterogeneity DMUs of indicators and the results are being analyzed.

In this paper, we investigate the production planning and warehouse layout for an authentic case, in which, a factory usually faces a challenge in the quest for sufficient space for produce and the management of warehouse items. We consider a network comprising of a production area, a warehouse area and a delivery point area and to solve this problem an integrated model for the produce and warehouse management has been rendered. We contemplate on a mixed integer, nonlinear model programming, targeting at minimizing total production costs, set-up costs, warehouse reservation and storage costs, transportation and delay penalty costs for this problem; besides which, the  issue of perishable goods is also under consideration. Morever, we utilized the data envelopment analysis (DEA) to measure the efficiency of the model results. This factory is taken into consideration as a dynamic network and a multiplicative DEA approaches are utilized to measure efficiency. Given the non-linearity of the models, a heuristic method is used to linearize the models. The ranking results of the dynamic network under study, demonstrated that, the time periods namely, (24) and (1) were the best and the poorest periods, respectively, in terms of efficiency.

The efficiency evaluation of a network opens the “black box” and deliberates on the internal structures and innermost interactions of the system. In this paper, we have made efforts to consider a three-stage system, comprising of six sub-DMUs, in combination with additional inputs and undesirable outputs. The proposed models are simulations of a factory, with a production area and three warehouses for goods and two delivery points. Hence, an authentic example, in concern with production planning and inventory control in a factory for a year and within duration of 24 periods, was taken under consideration, as a dynamic structure. In this simulation, all costs are considered, including, production costs, setup cost, maintenance costs of the products, warehouse reservation costs, transportation costs, delay penalty costs and the profit obtained from the sale of products. We utilized the multiplicative DEA with a double-frontier approach to measure the efficiency of a general system and improve the accuracy of efficiencies. Moreover, a heuristic technique was used to convert non-linear models into linear models. The ranking results amongst the 24 time periods, which, indicate that the time periods 24 and 1, are the best and poorest periods in terms of efficiency, respectively. Finally, we suggest to use a k-means method to cluster DMUs into several groups with similar characteristics based on double-frontier Standpoint.

This paper presents a method for performance evaluation, ranking and clustering based on the double-frontier view to analyze the complex networks. The model allows us to open the structure of the “black box” and can help to obtain important information about efficient and inefficient points of the system. In this paper, we consider a three-stage network, in respect to the additional desirable and undesirable inputs and outputs and utilize the cooperative approach to measure the efficiency of the overall system. Due to the fact that, a conclusion implying only one of these two, optimistic or pessimistic views is one-sided and incomplete, so, in this paper we used the double-frontier to analyze the network. Moreover, a heuristic technique was used to convert non-linear models into linear models. After obtaining the effective and inefficient points of the network, the DMUs are classified into several clusters by the k-means algorithm.Finally, in this article, in order to apply the proposed model a factory producing dairy products with a production area, warehouse premises and a delivery point are simulated. This factory has been regarded as a dynamic network with a time period of 24 intervals. The results of the ranking showed that, the time periods, (10) and (1) were the best and poorest respectively, in context to the efficiency within 24 phases of time.

Working capital indicators can be one of the most influential indicators in companies' financial decision-making. Therefore, research in this field can be useful. The main purpose of this study is to measure efficiency by considering working capital management indicators with the help of super-efficient data envelopment analysis models. Many researchers, including Goal et al. (2014), have used current measurements of the efficiency of working capital management indicators, despite the shortcomings of the cash conversion cycle, specifically in the cash conversion cycle. In this study, according to their idea, 21 companies active in the Iran Insurance Industry Exchange have been evaluated over a 5-year period. Periodic review can reveal information about firms' performance fluctuations as well as the relationship between changes in their rankings and changes in working capital indicators. Since this assessment is based on working capital indicators, the results will provide better opportunities for business managers, shareholders and investors to make large and partial decisions. Finally, the above method of data envelopment analysis will be compared with traditional methods.

The indicators used in economics to calculate the productivity scale are not always positive, therefore, existing models need to be developed to calculate the productivity scale for the semi-negative and semi-positive output index. However, companies with a lack of capacity need to achieve economies of scale and demand for realization simultaneously in the presence of a negative outflow index, especially when demand of companies varies, but always the amount of customer demand is equal to the amount Output, the demand level may be less than the maximum output level of the most productivity scale (MPSS), between the highest output level of the MPSS and the maximum output level, or greater than the maximum output level. When the output indicator is evaluated, the return on capital is in circulation, since this index can have negative values. One of the questions is, what models can be used to maximize the efficiency of the ROI in comparison with the realization of the demand? In this paper, we intend to find, in the three scenarios, the demand level, the highest productivity efficiency indicator of the return on capital employed in comparison with the demand for 24 active insurance companies in 1396. In fact, we will provide a development of Lee's (2016) paper for the positive and negative outflow index, and eventually we will launch on 24 active insurance companies in 1396.

Data Envelopment Analysis (DEA) is one of the methods most widely used for measuring the relative efficiency of DMUs in the world today.  The efficiency evaluation of the network structure opens the “black box” and considers the internal structure of systems. In this paper, a three-stage network model is considered with additional inputs and undesirable outputs and obtains the efficiency of the network, as interval efficiency in presence of the imprecise datum. The proposed model of this paper simulates a factory in the factual world with a production area, three warehouses and two delivery points. This factory is taken into consideration as a dynamic network and a multiplicative DEA approach is utilized to measure efficiency. Given the non-linearity of the models, a heuristic method is used to linearize the models. Ultimately, this paper concentrates on the interval efficiency to rank the units. The results of this ranking demonstrated that the time periods namely, (24) and (1) were the best and the poorest periods, respectively, in context to the interval efficiency within 24 phases of time.

By using the Double Frontiers Criteria, Hurwicz succeeded to achieve The Most Productive Scale Size of decision making units. This Double frontiers criteria was achieved by using two models of “CCR” in optimistic viewpoint in input form and the “CCR” model in pessimistic viewpoint in input form. In this paper, we intend to find a criteria for Double frontiers of super efficiency by using two models of super efficiency in input form and in both viewpoint of optimistic and pessimistic in such a way that conclude in Hurwicz Double frontiers of MPSS.

In this paper, we consider a three-stage network comprised of a leader and two followers in respect to the additional desirable and undesirable inputs and outputs. We utilize the non-cooperative approach multiplicative model to measure the efficiency of the overall system and the performances of decision-making units (DMUs) from both, the optimistic and pessimistic views. Moreover, we utilize the concept of a goal programming and define a kind of cooperation between the leader and followers, so that the objectives of the managers are capable of being inserted in the models. In actual fact, a kind of collaboration is considered in a non-cooperative game. The non-cooperative models from these view cannot be converted into linear models. Therefore, a heuristic method is proposed to convert the nonlinear models into linear models. After obtaining the efficiencies based on the double-frontier view, the DMUs are ranked and classified into three clusters by the k-means algorithm. Finally, this paper considers a genuine world example, in relevance to production planning and inventory control, for model application and analyzes it from the double-frontier view. The proposed models are simulations of a factory in a real world, with a production area as leader and a warehouse and a delivery point as two followers. This factory has been regarded as a dynamic network with a time period of 24 intervals.

Data envelopment analysis (DEA) is a nonparametric approach to estimate relative efficiency of Decision Making Units (DMUs). DEA is one of the best quantitative approaches and balanced scorecard (BSC) is one of the best qualitative methods to measure efficiency of an organization. Since simultaneous evaluation of network performance of the quad areas of BSC model is considered as a necessity and separate use of DEA and BSC is not effective and leads to miscalculation of performance, integrated DEA-BSC model is applied. Regarding to multi-objective nature of the proposed model, two techniques including goal programming and weighted average method are used to solve such problems. At the end of the study, based on data relating to indexes of quad areas of BSC model, the results of the mentioned methods are compared. Besides assessing validation of the proposed model, the overall efficiency and each of the different stages of BSC are obtained. So that, finding a model for decision making units in various stages of BSC is the innovation of this research study.

The traditional data envelopment analysis model allows the decision maker unit (DMU) to use the most favorable weights to evaluate the efficiency. This type of evaluation with the most flexibility can prevent the full ranking of DMUs and the evaluation results for DMUs are unacceptable. To solve this problem in data envelopment analysis, a common weight approach based on the concept of the degree of satisfaction of a DMU has been introduced. Also, to solve a common weight model, which is a nonlinear model, an algorithm is presented that can be modeled step by step. In this paper, we will evaluate the performance of companies active in Tehran Stock Exchange using a common weight model for 24 companies active in Tehran Stock Exchange. The weighted average performance of Tehran Stock Exchange companies for the years 1394 and 1395. The results show that this method will rank companies without any interference during these two years.

The insurance industry in Iran is growing, despite its long history of its presence in the Iranian economic market, has not yet been able to find a suitable penetration factor in the market.On the other hand, the increase of private companies and the increase of competition along with the unfavorable economic conditions of private sector actors in the industry and services sectors and the reduction of the share of insurance from the Iranian family of portfolios, due to other existing costs, made it harder for the insurance industry to work.In this paper, we will use the cross-over efficiency method in DEA based on the Shannon entropy, recently proposed by Sung and Liu (2016), to evaluate insurance companies in Iran. First, through interviews with experts in this field, the most important indicators of financial efficiency assessment were identified, which included 6 indicators. In the next step, in order to evaluate performance and ranking in the statistical society, 20 private and public insurance companies use cross-efficiency integration model and Shannon entropy. The ranking of insurance companies in Iran for the three consecutive years of 1393 to 1395. The results of the research indicate a complete ranking of insurance companies in Iran using the model presented in this paper

Data envelope analysis (DEA) is an approach to estimate the relative efficiency of decision making units (DMUs). Several studies were conducted in order to prioritize efficient units and some useful models such as cross-efficiency matrix (CEM) were presented. Besides, a number of DEA models with interval data have been developed and ranking DMUs with such data was solved. However, presenting an obtained crisp data derived interval data is a critical problem, so that many researches were implemented so as to compute weights and averaging the interval data. In this paper we propose the new algorithm to find more suitable weight applying a data mining approach of DMU’s data. For this purpose, we employed clustering and pair-wise comparison matrix on given relative efficiency from CEM. Results indicate there is meaningful different between efficiency of DMUs with lower bound and that of DMUs with upper bound.

In this study, the multi-objective programming (MOP) method was used to solve network DEA (NDEA) models with assumption that, negative data is considered for the proposed NDEA model which consists of semi-negative and semi-positive input and output. At first, two stage and then k stage production models were formulated with consideration of negative data. In the multi-objective programming, two separate objective functions including the divisional efficiencies and the overall efficiency of the organization are modeled. In comparison to conventional DEA with negative data, the advantage of the proposed NDEA models is consideration of intermediate processes and products, in order to calculate the organization's overall efficiency. However, in conventional DEA, sub-stages of the organizations are neglected. To measure the efficiencies of an organization regarding interactive internal process, two case studies were investigated by application of the NDEA-MOP method with negative data. Case study 1 is focused on units with two stages having semi-negative and semi-positive indexes. In case study 2, units with three stages are evaluated. These units also have semi-negative and semi-positive indexes. The overall efficiency of each unit is calculated using the proposed models. Fuzzy approach as a solution procedure is applied.

Data envelopment analysis (DEA) is a nonparametric approach to estimate relative efficiency of Decision Making Units (DMUs). DEA is one of the best quantitative approaches and balanced scorecard (BSC) is one of the best qualitative methods to measure efficiency of an organization. Since simultaneous evaluation of network performance of the quad areas of BSC model is considered as a necessity and separate use of DEA and BSC is not effective and leads to miscalculation of performance, integrated DEA-BSC model is applied. Regarding to multi-objective nature of the proposed model, two techniques including goal programming and weighted average method are used to solve such problems. At the end of the study, based on data relating to indexes of quad areas of BSC model, the results of the mentioned methods are compared. Besides assessing validation of the proposed model, the overall efficiency and each of the different stages of BSC are obtained. So that, finding a model for decision making units in various stages of BSC is the innovation of this research study.

Performance Evaluation is a crucial issue in organizational decision making. In order to gain competitive advantage, managers need information about organization’s performance to know the state of the organization better and be able to make decisions for improving it. Therefore, the importance and necessity of a model for providing feedback on the performance of the organization and acquiring a tool for fulfilling this need is clarified. According to the position of management of technology and innovation and its importance in business and service firms, the purpose of this research is to evaluate the performance of management of technology and innovation among 49 firms. This evaluation is considered in two stages of enablers and results using DEA. It will to identify Effective and inefficient firms, Detect source and size of inefficiency and identify reference firms. The results show that despite the fact that most firms are operational at the enablers stage, they are inefficient in the results stage. The majority of firms have the most surplus in the "management of information and knowledge resources" index, while they are in the most lack of "Knowledge and Technological results".

As strategic companies with a competitive nature, pharmaceutical companies have a significant contribution to the country's competitive advantage. Thus, competitiveness has become a challenging and important issue in such companies today. In complex competitive environments, just organizations who strengthen their technological innovation capabilities can continue to compete. Technological innovation capabilities utilize the new data and information in the field of technology and needs technology intelligence itself. So, in this research we want to assess the effects of technology intelligence on the efficiency of technological innovation capabilities and competitiveness of pharmaceutical companies.
In terms of objectives, this research is practical and descriptive-analytical, and field and library method is used for data gathering. This research's data is extracted from 55 active companies in the pharmaceutical industry and with the help of network data envelopment analysis (DEA) analyzed and efficiency to companies, with and without technology intelligence assessment.
In this research it was found that utilizing technology intelligence has a positive effect on the efficiency of technological innovation capabilities and competitiveness of these companies and has often increased their efficiency. This study showed that the use of technology intelligence, only 54/34 percent of the companies under investigation received full performance score.
The approach of this study was to investigate the role of technology intelligence that was studied and compared. The results show that this role was significant and effective role of technology intelligence on the efficiency of technological innovation capabilities and competitiveness to prove.

Data envelopment analysis (DEA) is a nonparametric approach to estimate relative efficiency of Decision Making Units (DMUs). DEA and is one of the best quantitative approach and balanced scorecard (BSC) is one of the best qualitative method to measure efficiency of an organization. Since simultaneous evaluation of network performance of the quad areas of BSC model is considered as a necessity and separate use of DEA and BSC is not effective and leads to miscalculation of performance, integrated DEA-BSC model is applied. Regarding to multi-objective nature of the proposed model, two techniques including goal programming and weighted average method are used to solve such problems. At the end of the study, based on data relating to indexes of quad areas of BSC model, the results of the mentioned methods is compared. Besides assessing validation of the proposed model, the overall efficiency and each of the different stages of BSC is obtained. So that, finding a model for decision making units in various stages of BSC is the innovation of this research study.