Ranking and evaluating bank branches using fuzzy data envelopment analysis (case study: Saman bank branches)

The aim of this review was to present research work on the development of the Malmquist index (MI) in various aspects based on the Data Envelopment Analysis (DEA) method. The MI is one of the three methods of dynamic analysis and also the main one, as it is the most commonly used to measure productivity changes. However, over the years, from being a simple productivity index like the models within the DEA method, it has become a more complex and useful index as it has seen many developments and extensions. Modifications and developments of the MI have taken place in many directions. A review of MI studies from 1990 to 2025 shows that the authors considered various aspects of the MI: Alternative ways of decomposing, different ways of measuring, different types of efficiency, different nature of data and variables and network structure or combined dynamic analysis methods into a single framework. The direction of changes and modifications of the MI can be divided into three groups: Adapting DEA solutions within the MI, presenting new MI solutions without being inspired by the DEA method and combining two methods of dynamic analysis into one framework.

Entrepreneurship orientation (EO) approach provides universities with a plan and roadmap for getting over rising uncertainty and complexity. Inspiring universities to embrace transformation and innovation, risk taking and proactive policy for planning and executing development strategies for success in the dynamic competitive environment arise from entrepreneurship orientation spirit. To reach this status, the key factors of success in entrepreneurship should be taken into consideration and assessed in universities. Structural and entrepreneurial policies as key non-financial factors are two concepts that have received considerable attention over the past years. Measuring efficiency of universities by the mentioned factors and using mathematical programming like Data Envelopment Analysis (DEA) method is the aim of this study. Hence, this paper applied DEA method for measuring efficiency of 16 faculties and institutes in Ferdowsi University of Mashhad (FUM) in Iran. Descriptive-survey methodology was used and data were collected through questionnaire survey. The faculties and institutes were ranked based on structural policies and EO through DEA. Finally, the DEA method recommended the development roadmaps template for the 11 faculties and institutes which were inefficient.

This study focuses on the investigation of the influence of human capital factors on the tax collection efficiency. Literature review indicated that although the efficiency evaluation of tax collection has been discussed in previous study, the influence of human capital factors on the tax collection efficiency has seldom been addressed. Hence, the Data Envelopment Analysis (DEA) method has been employed to address this issue in this study. The innovation of this study lays on that the human capital factors have been taken into account in the efficiency evaluation of tax collection. To measure performance of the human capital factors in the tax collection, a case study in Wuhan city, China has been carried out. The DEA analysis results demonstrate that the tax collection efficiency increases with the increase of the number and higher education level of the tax officers and the higher education level of the tax officers still has a large room to improve. Then, relative improvement strategies have been proposed to strengthen tax collection efficiency. Hence, the findings of this study contribute to the body of knowledge in the efficiency evaluation of tax collection and its improvement measurement, which can be referred by other cities over the world.

The main aim of the study was to evaluate the efficiency of Islamic Azad University branches in East Azerbaijan using stochastic frontier analysis (SFA) and to compare the findings with the results of data envelopment analysis (DEA). In this respect, SFA and DEA techniques were applied to measure production efficiency of 28 branches of East Azerbaijan Islamic Azad University considering the following variables as either output or input: 3 variables as output (including the number of graduated students and students accepted for the further pursuit of their studies, as well as the number of articles, books, and research projects) and 6 variables as input (including the number of university faculty members, educational fields, current students, staff working in different sections of the university, and the total university campus in square meters as well as university costs). Estimation of DEA and obtaining of the units' efficiency indicated that the average efficiency of the universities under investigation were 0.66 and 0.80 in SFA and DEA techniques, respectively. In addition, efficiency distributions in SFA and DEA methods were tremendously different from each other so that in SFA method nearly 40% while in DEA method more than 60% of unit efficiency was between 0.8-1 intervals. The minimum level of estimated efficiency (0.14) was observed for Hadishahr using SFA method, and in DEA approach, the minimum level of efficiency belonged to Khoda Afarin branch (0.065).

The author of the article describes the theoretical assumptions of the DEA method used for measuring the productivity of airports described in the article ‘Research into the methods of analysing the productivity indicators of transport terminals’ (Jaržemskienė 2009). The essential insights presented in the above mentioned paper reveal that Data Envelopment Analysis (DEA) is a relatively new ‘data oriented’ approach to evaluating the performance of the so-called Decision Making Units (DMU) that convert multiple inputs into multiple outputs. The article focuses on the findings of the study carried out by the author in accordance with those assumptions. Research represents the Data Envelopment Analysis (DEA) method testing 15 selected airports situated in the Baltic Region, including Vilnius, Kaunas, Palanga, Riga, Tallinn, St. Petersburg, Helsinki, Turku, Stockholm, Malme, Copenhagen, Hamburg, Gdansk, Warsaw and Minsk. Airport productivity indicators are ranked considering importance and using the method of Delphi expert survey made of two rounds. The author presented the following indicators (expressed as ‘ratio’) as the major ones estimated by PAX/LAND, AIR/LAND, PAX/AIR, PAX/RW, PAX/RWA, GA and INTER experts. The succeeding indicators were introduced by PAX/TERMAREA, PAX/GATES, AIR/RW, AIR/RWA, AIR/TERMAREA, AIR/GATES, FR/LAND and FR/RW. 10 indicators were accepted as the most important and selected from the current set in the following sequence: AIR/LAND, AIR/RW, PAX/RW, PAX/LAND, AIR/RWA, PAX/AIR, PAX/RWA, AIR/TERMAREA, PAX/GATES and PAX/TERMAREA. AIR/LAND and AIR/RW were submitted as two main indicators. The acronyms are explained as follows: LAND – airport area, RWA – runway length, PAX – the number of passengers, AIR – the number of aircraft take-offs and landings, RW – the number of runways, GATES – the number of gates, FR – the amount of freight served, TERMAREA – the area of passenger terminal, GA – a general aviation market share of airport served aircraft by percentage, INTER – the percentage of international passengers considering all passengers served by airports. After two key productivity indicators were chosen conducting the expert survey, airport productivity was compared applying the DEA method.

This study aims to examine the determinants of the level of efficiency of banking performance as measured by the non-parametric Data Envelopment Analysis (DEA) method. The determinants of the level of efficiency in banking performance include bank size, Capital Adequacy Ratio, Loan to Deposit Ratio, Non Performing Loans and capitalization. The data in this study were obtained from the Indonesia Stock Exchange (BEI) from 2013 to 2017. The sampling technique used was purposive sampling. The sample used was 160 data from conventional commercial banks in Indonesia. Input variables for calculations using the DEA method are Third Party Funds and Total Assets. While the output variables used are credit distribution and operating income. The results showed that bank size and capitalization did not affect the level of banking efficiency. Meanwhile, Capital Adequacy Ratio and Non-Performing Loans negatifly affect the level of banking efficiency, while the Loan to Deposit Ratio has a positive effect on the level of banking efficiency.

The article describes the Data Envelopment Analysis (DEA) method and the main features of its application. The main problems of heat and power systems are described, which are addressed by the DEA method of efficiency assessment presented in the article. The approbation of this method is presented at the objects of the centralized municipal heat supply system of the fuel and energy complex: boiler houses and heat and power plants. 9 objects were analyzed according to four input indicators: available heat capacity, installed heat capacity, heat consumption for own needs, fuel consumption. Also, the efficiency of the system was evaluated according to two output indicators: the release of thermal energy to the grid and the mass of the emission. As a result of the analysis and calculations made, it was revealed that 5 objects have the maximum possible efficiency indicator equal to 1, that is, they function as efficiently as possible. 4 objects of the centralized municipal heat supply system have an efficiency indicator less than 1. Accordingly, improvements are required for the operation of the above Decision-Making Units (DMU)s. These objects have deviations in terms of the inputs and outputs of the actual data and those obtained using the DEA method. Based on the calculations obtained for these 4 objects, the article provides recommendations for changing the quantitative values of their input and output indicators. For example, for object number 2, it is recommended to reduce the installed heat capacity in the grid by 72.57%, without changing the available heat capacity and fuel consumption. Reduce the heat consumption for your own needs by 69.383%. In addition, it is recommended to increase the supply of thermal energy to the grid by 6,034%, and reduce the mass of emission by 11.5%. Specific measures have also been developed to modernize the studied objects in order to achieve the recommended indicators of inputs and outputs. The research results presented in the article are of scientific and practical interest and can be used to improve the efficiency of heat and power systems facilities

Selection of design alternatives for smart product service system: A rough-fuzzy data envelopment analysis approach

In order to make better assessment of intermodal container transportation, the authors have analyzed DEA (Data Envelopment Analysis) method in deciding the most favorable container line from Serbian ports to the near East ports. To allow for applications to a wide variety of activities, term organization unit (Decision Making Unit – DMU) refers to container line. DEA Method has been used to give the estimation of efficiency of operations in organization units. Organization units are container lines in which containers are loaded with mineral water in bottles. The main points in transportation net are factories of mineral water (Knjaz Milos Arandjelovac and Minaqua Novi Sad), Serbian ports ( the Port of Belgrade, the Port of Danube Pancevo and the Port of Prahovo) and nine Mediterranean ports in the near East area. Amount of mineral water that is dispatching from factories to Serbian ports is equal to amount that can be loaded to one container. Having containerized in Serbian ports mineral water is reloaded to the river–sea ships with unified dimensions, so the ships are continuing to travel to the near East ports. The near East ports are the ending points. Input variables in DEA problem are transportation costs and strategy resistance factor in function of container line. Time traveling of one container is in a function of container line and transportation capabilities of container lines are output variables. Transportation costs are counted on the base of integer programming method and Dijkstra algorithm.

The article presents the results of assessing the development efficiency of the tourism and recreation complex of Russia’s regions for the period from 2017 to 2021. This period includes both years of stability, characterized by predominance of positive trends in tourism development, and crisis and post-crisis years associated with the effects of the COVID-19 pandemic. Taking into account the complexity of the structure of the tourism and recreation system and the need to improve territorial planning and management, an urgent task is to introduce a comprehensive, aggregated efficiency indicator to be used for assessment of regional tourism. The efficiency coefficient (efficiency score) calculated by means of the DEA (Data Envelope Analysis) method is proposed as such an indicator. The study is novel in that it develops a methodology for assessing the efficiency of regional tourism based on DEA. The essence of this method is as follows: basing on the theory of duality, having data of input and output parameters, it is possible to calculate the value of the efficiency indicator for a set of N-objects (in our study – subjects, i.e., constituent entities of the Russian Federation). For the calculations, we used a model focused on the ‘input’ and taking into account the variable scale effect. In models of this type, each inefficient object is compared with efficient objects that have the structure (ratios) of indicator values closest to the structure of this inefficient object. This allows one to determine how much to change the parameters of the tourism and recreation complex in order to achieve efficiency equal to 1 (which is the maximum score). The model used seven ‘input’ indicators and six ‘output’ indicators. The analysis of the results was carried out in the context of the tourist macro-territories designated in the strategic documents for the development of tourism in the Russian Federation. The data obtained indicate a decrease in the efficiency of tourism development in the Russian Federation, with the exception of a number of regions and two tourist macro-territories. The results of the study can be used to adjust regional tourism development programs.

Designing the most efficient roadway illumination is a strategic and complex activity, as it requires consideration of both environmental and economic factors. The aim of this study is to propose a novel structured multi-methodological framework based on the systematic application of data envelopment analysis (DEA), qualityfunction deployment (QFD), and simple additive weighting (SAW) methods to support a critical decision-making process on roadway illumination design. First, the DEA is exercised to find efficient alternatives for roadway illumination. Then customer requirements are included and satisfied by the QFD. Lastly, using the DEA method, the SAW is used to rank the alternatives which are found to be efficient. As a case study, illumination alternatives for a typical urban roadway are analysed to select the most suitable choice in terms of energy and cost effectiveness, which are of course high priorities. The results show that the proposed multi-methodological approach takes advantages of DEA, QFD, and SAW to select an efficient roadway illumination alternative. The proposed approach also supplies a simple and very well-suited decision-making tool for this type of strategic decision-making issue.

ABSTRACTThis study presents an integrated approach, based on data envelopment analysis (DEA) and principal component analysis (PCA) methods, to evaluate the influence of Six Sigma deployment on key job characteristics in an automotive industry. The job characteristics are defined as satisfaction, stress, and security. A standard questionnaire is designed and distributed among the employees at the company's production site, who were affected by the implementation of Six Sigma. DEA and PCA methods are applied to measure the performance of the sub-groups of employees in the company. Consequently, the most efficient and inefficient sub-groups are determined. According to the findings of this investigation, it was perceived that the implementation of Six Sigma has had the greatest impact on job satisfaction. Additionally, a design of experiment was carried out to recognize the most effective job factor, which was identified to be the overall working conditions for the related case study. This is the first study that integrates DEA and PCA toward identifying and optimizing job characteristics in terms of Six Sigma implementation. The approach, employed in this study, can be easily used in the other manufacturing systems, in order to assist them to identify and improve their key job characteristics.

The fuzzy data envelopment analysis (FDEA) method lacks sufficient discrimination power to rank efficient decision making units (DMUs) with fuzzy data; moreover, it evaluates DMUs using only self-evaluation. This paper develops the fuzzy cross efficiency (FCE) DEA model that combines self-evaluation with peer-evaluation to eliminate the weaknesses of traditional FDEA. This method solves the efficiency evaluation problem in fuzzy environments from a new perspective. An effective method is provided to solve this model, and the ranking results of DMUs at different -levels are obtained. Finally, an example is given to illustrate the proposed method in greater detail.

This paper uses data envelopment(DEA) analysis method to analyze the total factors productivity (TFP) and efficiency of 16 cities in the River Delta Region from the year 2000 to 2004 and draw the following conclusions: (1) the TFP of Shanghai, Nanjing city is increasing while the TFP of the other cities is decreasing from the year 2000 to 2004 year, (2) From the point of view in technical efficiency, Shanghai, Wuxi, Shaoxing city are in the frontier of technical efficiency, while the other cities are far from the frontier of technical efficiency. (3) From the perspective of scale efficiency, Shanghai, Wuxi, Shaoxing and Taizhou city have been in constant returns in scale from the year 2000 to 2004, but the other cities some times are in increasing returns to scale, and some times are in decreasing returns to scale.

Pineapple chips is a processed product made of pineapple produced in Kampar Regency. Efficient pineapple chips processing will produce both an added value and high profit. The purpose of this research was to determine the level of relative efficiency of pineapple chips business in Kampar Regency in Riau Province. The level of efficiency achieved is a reflection of the quality of good performance. This research used Data Envelopment Analysis (DEA) method to measure the level of efficiency. An analysis using Data Envelopment Analysis (DEA) method with Constant Return to Scale (CRS) assumption through input oriented approach was done to understand the levels of the pineapple chips producers relative efficiency. The research result showed that Most of the pineapple chips producers in Kampar Regency had not been efficient in relative terms, in which from the total of 21 pineapple chips producers, 8 producers were efficient (38,10%) and 13 producers had not been efficient (61,90%). The efficient producers should be a reference for inefficient producer in using inputs. By referring to the efficient producers, it is expected that the inefficient producers could use the input optimally so that the processing pineapple chips business could reach an efficient condition.