perfomance management
ebrahim golzar; seyyed esmaeil najafi; seyyed ahmad edalatpanah; Amir Azizi
Abstract
Undesirable outputs are an integral part of production in various decision-making units, and to bring analyses closer to the real world, it is necessary to consider, undesirable outputs in performance evaluation research. In this paper, a new hybrid model for evaluating the efficiency of decision-making ...
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Undesirable outputs are an integral part of production in various decision-making units, and to bring analyses closer to the real world, it is necessary to consider, undesirable outputs in performance evaluation research. In this paper, a new hybrid model for evaluating the efficiency of decision-making units in the oil industry is presented, which uses slack-based data envelopment analysis techniques and advanced machine learning algorithms. The proposed model specifically focuses on improving efficiency considering undesirable outputs and conditions of uncertainty. Three machine learning algorithms including artificial neural networks, support vector machines, and XGBoost are used to predict and improve the results of slack-based models. This study involves the evaluation of 37 decision-making units within the National Petroleum Products Distribution Company, and the results show a significant improvement in efficiency using predicted data compared to actual data. This research not only contributes to new perspectives in efficiency evaluation and improvement but also offers innovative hybrid methods to address challenges in operational management.
perfomance management
Esmaeil Keshavarz; abbas shoul; Ali Fallah Tafti
Abstract
Data Envelopment Analysis (DEA) is an approach based on mathematical programming for the relative evaluation of decision-making units treated as similar yet distinct production systems. In this approach, the performance of each unit is characterized by describing the transformation of specific inputs ...
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Data Envelopment Analysis (DEA) is an approach based on mathematical programming for the relative evaluation of decision-making units treated as similar yet distinct production systems. In this approach, the performance of each unit is characterized by describing the transformation of specific inputs into specific outputs. Traditional DEA models assume that the role of each performance factor is clearly defined. However, in some real-world problems, certain factors might be identified as dual-role factors depending on the evaluation nature or the decision-makers' perspective. These factors can play the role of both input and output, or even be considered neutral in assessing the units' performance. In the current paper, to determine the status of dual-role factors and calculate the efficiency of DMUs, two new linear programming models, based on the concept of deviation in the efficiency constraint and a common set of weights, are suggested. The main advantages of the proposed models are significantly reducing the computations and iterations required to solve the model, and involving all DMUs to determine the role of factors. To assess the performance of the proposed models, a data set for the evaluation of eighteen suppliers in the presence of two inputs, three outputs, and two dual-role factors has been employed. The obtained results showed that, compared to other models, the proposed models are computationally more efficient, and the role determination and evaluation of the units, based on the obtained weights from these models, are better aligned with the expectations of decision-makers
perfomance management
Sharmineh Safarpour; Alireza Amirteimoori; Sohrab Kordrostami; Leila Khoshandam
Abstract
Since the healthcare system is one of the most important pillars of community health, and considering that providing healthcare services to the people is one of the elements of individual development in any country, attention and supervision of this sector can lead to development and social welfare. ...
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Since the healthcare system is one of the most important pillars of community health, and considering that providing healthcare services to the people is one of the elements of individual development in any country, attention and supervision of this sector can lead to development and social welfare. To ensure better and higher quality healthcare services, performance evaluation in the health sector plays a crucial role. In order to achieve this, proper and proportional use of existing facilities and assets is inevitable. In this study, by introducing an application in the field of healthcare systems, the educational hospitals of the country have been measured in terms of performance and their managerial ability has been calculated. Additionally, by identifying and introducing the impact of contextual variables on the performance of decision-making units, their efficiency has been assessed. For this purpose, data related to educational hospitals in 31 provinces of the country was collected, and then by identifying contextual variables and with the presence of undesirable factors, the efficiency was evaluated and the managerial ability of each was calculated. To reach this goal, in the first step, technical efficiency with the presence of undesirable factors was calculated using data envelopment analysis technique, and then the logarithm of technical efficiency obtained from the first stage was regressed on a set of contextual variables that affect hospital performance. In the next stage, managerial ability was extracted from the residual of the regression obtained from the previous stage. Finally, a unique ranking based on the managerial ability of each unit was provided. Ultimately, the results obtained were analyzed and examined in order to provide valuable suggestions for managers and more efficient management of the country's hospitals to maintain public health. According to the study, without considering contextual variables, 25 effective units were evaluated, but by applying the effect of contextual variables on the efficiency index, no unit becomes effective, proving the high impact of such indices on the performance of units. Additionally, in the ranking of units based on managerial ability, Lorestan province ranked first and Golestan province ranked last.IntroductionThe issue of increasing productivity and efficiency in healthcare costs is important for all countries. The health sector, by identifying the factors that affect community health precisely, influences national macroeconomic planning and minimizes their adverse effects on health. By utilizing the best practices in healthcare, significant improvements in the health of individuals and communities can be achieved. Therefore, proper investment in healthcare facilities and health centers, as well as improving the quality and efficiency of their services, is essential for sustainable development. In order to increase efficiency and productivity, understanding the current status and measuring the performance of hospitals in the healthcare system is of paramount importance. Ensuring the provision of better and higher quality health services requires evaluating the performance of the healthcare system. Therefore, it seems that employing efficiency measurement techniques and improving performance and productivity in this sector can improve processes and optimize the use of resources and the fair distribution of resources for the provision of desirable services. In recent years, various studies and methods have been proposed by researchers to measure the efficiency of decision-making units, which can be divided into two categories: parametric and non-parametric methods. Farrell (1957) first introduced the non-parametric method, and then Charnes et al. (1978) extended the initial analysis by Farrell from multi-input and single-output to multi-input and multi-output. The model developed by them was named the Charnes-Cooper-Rhodes model. Then, Banker et al. (1984) introduced the model. The non-parametric method is a linear programming-based method in which a linear programming problem is solved for each decision unit. This branch of operations research has rapidly advanced and is called data envelopment analysis. Data envelopment analysis is a mathematical programming technique for evaluating decision-making units and plays a fundamental role in identifying efficient boundaries and measuring the relative efficiency of units under scrutiny. Data envelopment analysis allows for the comparison of units with each other. Considering the importance of the health sector in improving the quality of life for individuals in society, we felt it necessary to examine the performance level and calculate the managerial capacity of hospitals in all 31 provinces of the country to ensure the proper functioning of this sector and take even small steps towards improving the quality of this sector. The aim of this research is to analyze and evaluate the performance of health sector hospitals in Iran in the presence of contextual variables and provide a ranking method based on managerial capacity. For this purpose, data related to educational hospitals in all 31 provinces of the country were collected, and then, by identifying contextual variables and the presence of undesirable factors, an attempt was made to evaluate the efficiency and calculate the managerial capacity of each hospital unit. To achieve this goal, in the first step, technical efficiency with the presence of undesirable factors was calculated using data envelopment analysis technique, and then the logarithm of technical efficiency resulting from the first step was regressed on a set of contextual variables that affect hospital performance. In the next step, managerial ability was extracted from the residual of the regression from the previous step. Finally, a unique ranking based on the managerial ability of each hospital was presented.MethodologyIn this article, based on studies conducted by Demerjian et al. (2020) and Banker et al. (2020), we examine the performance analysis and managerial abilities of 31 hospitals in the country through a three-stage process. Firstly, considering the presence of undesirable outputs, the efficiency analysis of the units of interest is obtained using the efficiency model proposed by Kuosmanen (2005) with the (3) technology. Then, using the least squares method, the impact of each of the contextual variables in this study, including "asset base", "density", and "number of physicians", on the efficiency scores obtained from the first stage is regressed. Subsequently, managerial ability is obtained from the residuals of the previous least squares method. Finally, a unique ranking based on the managerial ability of each hospital is presented.ResultsIn this study, which was conducted on the performance of the health care in Iran, a new ranking based on managerial ability was provided for comparing units. Based on calculations performed on a number of hospitals in 31 provinces of the country without considering contextual variables, 25 efficient units were evaluated. However, by applying the effect of contextual variables on the efficiency index, no unit appears to be efficient, proving the significant impact of contextual variables on the performance of units. Furthermore, the relationship between contextual variables and efficiency index was determined. For example, an increase in the amount of the contextual variable "number of physicians" will lead to an increase in managerial ability. This means that an increase in the number of physicians will benefit the improvement of the system's efficiency and managerial ability.ConclusionWithout a doubt, studying and investing in the healthcare industry is one of the most profitable and best areas for investment. In this regard, government hospitals in each country are one of the main and most important components of the healthcare sector. The hospitals studied in this research are considered as 3 government hospitals per province. Based on past efficiency studies, we find that each decision-making unit had its own specific inputs and outputs. The aim of this study is to analyze and examine the managerial ability of public hospitals in Iran. In this study, the performance of selected hospital units is analyzed in terms of managerial efficiency, considering the impact of other variables known as contextual variables on the performance of a decision-making unit. In this study, the performance of government hospitals in Iran is analyzed from a managerial perspective. The first step involves calculating the efficiency of units using basic models and considering undesirable outputs. Then, in the second step, the logarithm of technical efficiency obtained from the first step is regressed on a set of contextual variables that affect hospital performance. Furthermore, the impact of contextual variables, including total assets, physician density, and number of physicians, on the size of unit efficiency is measured in this study. Based on the results, 25 efficient units were evaluated, but with the application of contextual variables on efficiency indicators, no unit becomes efficient, proving the high impact of such indicators on unit performance. Additionally, based on the calculations performed, in the ranking of units with a managerial approach, Lorestan province ranks first and Golestan province, which has the weakest performance among the units under study, ranks last. The impact of contextual variables on efficiency indicators has been examined. For example, the impact of the "number of physicians" indicator on efficiency is direct, and a one-unit increase in it will lead to an increase in managerial efficiency. This means that an increase in the number of physicians will benefit the system's efficiency and managerial ability. However, the impact of the density variable, unlike the number of physicians, has an inverse effect on managerial ability. To provide suggestions for future studies, one can refer to generalizing the problem to the uncertainty space and studying different applications by bringing the problem into random spaces, providing more predictive predictions. Furthermore, this study can be implemented in analyzing performance and calculating managerial ability in various industries such as power plants, insurance industry, banks, etc., and based on the applications and the type of technology used, different approaches can be provided for calculating managerial
Maryeh Nematizadeh; Alireza Amirteimoori; Sohrab Kordrostami; Leila Khoshandam
Abstract
The electricity industry plays a pivotal role in a country's economic growth and development. Therefore, it is imperative to assess its performance and identify the strengths and weaknesses of its different sectors, such as production, transmission, and distribution, to enhance economic growth in diverse ...
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The electricity industry plays a pivotal role in a country's economic growth and development. Therefore, it is imperative to assess its performance and identify the strengths and weaknesses of its different sectors, such as production, transmission, and distribution, to enhance economic growth in diverse areas. Given the significance of the transmission sector, this research focuses on analyzing and evaluating the performance of 16 regional electricity companies in Iran from 1390 to 1398, with the aim of comprehending the impact of contextual variables on efficiency. To achieve this, the study will utilize two techniques - Data Envelopment Analysis (DEA) and Ordinary Least Squares (OLS) - to determine the efficiency score and estimate the effect of contextual variables on efficiency, respectively. In the first stage, the DEA technique is employed to calculate the technical efficiency of each company, considering their specific inputs and outputs. In the second stage, the logarithm of the efficiency scores obtained is regressed on contextual variables to establish their effect on efficiency. The residual derived from the regression is referred to as managerial ability. Finally, the companies are ranked based on their modified efficiency after removing the impact of contextual variables.
Introduction
The electricity industry comprises three key sectors: production, transmission, and distribution. It stands as one of the most crucial economic infrastructures in the country, exerting significant influence on industrial, agricultural, service, and other sectors. Undoubtedly, the growth of the electricity industry drives the nation's economic development and progress, contributing to the prosperity and comfort of its citizens (Tavassoli et al., 2020). Consequently, analyzing and examining the growth trajectory of each sector across different years becomes pivotal in mitigating adverse effects and fostering progress within this domain.
In recent years, numerous researchers have conducted studies in this field. Some have independently evaluated each production, transmission, and distribution sector, while others have adopted a comprehensive approach by considering the integrated three-stage network structure. The research background highlights that the transmission sector has received less attention from researchers than other sectors. This is noteworthy because, following electricity production, the transmission process and energy accessibility to consumers are paramount. The absence of proper energy transfer can result in consumer dissatisfaction, financial losses, and stagnation within the competitive economic market. Therefore, identifying the strengths and weaknesses of the transmission sector's performance and comparing regional electricity transmission companies can effectively help enhance the performance level of each.
One technique that has captured researchers' attention for evaluating the electricity industry's performance is the data envelopment analysis (DEA) technique. DEA is a non-parametric method used to assess the performance of homogeneous units, considering multiple inputs and outputs. It was initially introduced in 1978 by Charnes et al. The initial model was built upon the assumption of constant returns to scale. Subsequently, Banker et al. (1984) extended it by presenting a model under the assumption of returns to a variable scale.
Importantly, traditional DEA models evaluate a system's performance based on specific inputs and outputs consumed and produced by the unit. However, various factors, such as contextual variables, managerial ability, and skill, can significantly influence performance and productivity. A crucial point to consider is that managerial abilities are not always overtly visible. This lack of direct visibility can impede accurate measurement. Hence, recognizing these variables among the existing indicators and assessing their influence on the performance and efficiency of each unit holds particular significance. This procedure enhances the precision of evaluation and opens avenues for delivering enhanced solutions aimed at improving the system's overall performance.
Methodology
The objective of this study is to analyze and evaluate the performance of Iran's regional electricity transmission sector while considering contextual variables and establishing a ranking methodology based on managerial ability. This perspective enables the identification of strengths and weaknesses in the system's structure from various angles and offers appropriate solutions for enhancement. To accomplish this, the first step involves identifying all variables within the transmission section, encompassing inputs, outputs, and contextual factors. Subsequently, we determine the technical efficiency of each regional power transmission company, taking into account specific inputs and outputs, using meta-frontier technology. The concept of meta-frontier in DEA measures the gap or distance between decision-making units (DMUs) across different boundaries. This approach assumes a unified boundary for all subgroups, enabling efficiency estimation based on a single boundary (Battese, 2004; O'Donnell, 2008). Its primary advantage lies in resolving the challenge of evaluating efficiency at varying levels. As a result, meta-frontier technology enhances the precision of evaluating regional power companies over multiple periods. After assessing the efficiency of each regional electricity transmission company, we employ the linear regression method to estimate the impact of contextual variables on efficiency, subsequently yielding a measure of managerial ability. Ultimately, we introduce a method for ranking each company based on managerial ability. The advantage of the proposed method is that, in addition to reviewing and analyzing the technical efficiency of each of the companies in the regional electricity transmission sector during different periods, it will be possible to evaluate the managerial ability of each of the companies. Such a perspective allows for companies to be compared from different dimensions. Moreover, providing a new ranking criterion based on managerial ability also facilitates a better and more accurate comparison.
Results
In this study, the performance of Iran's regional power companies was analyzed and evaluated from two systems and management perspectives during the years 1390-1398. Additionally, a new rating criterion based on managerial ability was presented to compare the performance of companies during 9 time periods. In this regard, firstly, the technical efficiency of 16 regional electricity companies during 9 time periods was calculated based on the inputs of the number of employees and receiving energy from neighboring companies and the outputs of sending energy to neighboring companies and delivering energy to distribution companies, using meta-frontier technology and the DEA approach. Then, the effect of contextual variables, such as line length, transformer capacity, and loss magnitude, on the efficiency score of each company was estimated using the ordinary least squares method (OLS). Furthermore, the managerial ability of each company was determined during different periods. Ultimately, a ranking criterion was established based on the results of technical efficiency after removing the effect of contextual variables.
Conclusion
The results of efficiency measurements over 9 time periods indicate that the highest and lowest average efficiencies were observed in the years 1390 and 1398, respectively. Furthermore, it's evident that, in general, the performance of Iran's 16 regional electricity companies exhibited a consistent upward trend from 1390 to 1398. Among the 16 evaluated companies, the Guilan regional electricity company consistently achieved the highest level of efficiency across all 9 time periods, reflecting its strong performance. Conversely, the Fars regional electricity company consistently had the lowest efficiency, indicating its weaker performance compared to other companies. When analyzing the companies' performance by year, it's noteworthy that the Tehran regional electricity company secured the highest rank in 1390, 1391, and 1394, while the Fars regional electricity company held the top spot in the remaining years. In contrast, the Sistan regional electricity company consistently displayed the lowest performance throughout all periods. The assessment of management performance over the 9 time periods indicates that the Kerman regional electricity company demonstrated superior performance from 1390 to 1393, whereas the Guilan regional electricity company excelled from 1394 to 1398, outperforming other companies. Conversely, the Gharb regional electricity company exhibited weaker performance compared to its counterparts. Additionally, the results of the regression analysis highlight a positive relationship between the efficiency score and two variables: line length and transformer capacity. Conversely, the relationship with loss magnitude is observed to be inversely correlated.
Davood Gharakhani; Abbas Toloie Eshlaghy; Kiamars Fathi Hafshejani; Farhad Hosseinzadeh Lotfi; Reza Kiani Mavi
Abstract
Data Envelopment Analysis (DEA) is a powerful analytical technique for measuring the relative efficiency for a set of Decision Making Units (DMUs) based on their inputs and outputs. There are weaknesses in conventional models DEA. Most important of which is the weight shift input and output which makes ...
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Data Envelopment Analysis (DEA) is a powerful analytical technique for measuring the relative efficiency for a set of Decision Making Units (DMUs) based on their inputs and outputs. There are weaknesses in conventional models DEA. Most important of which is the weight shift input and output which makes the efficiency of Decision Making Units with different weights measured. A characteristic of Traditional DEA models is that it allows DMUs to measure their maximum efficiency score with the most favorable weights. As well as the conventional DEA models are not focused network of evaluation units. In this paper we propose to correct the weaknesses the common set of weights (CSW) in network DEA model based on the Goal programming approach. To test the effectiveness of the proposed model and solve real data is used by insurance companies active in Qazvin province. The model presented in this paper units decide on a similar scale with a set of weights for neutral evaluation is common. Proposed approach helps policy makers to better understand the strengths and weaknesses of DMUs and try to promote the strengths and remove weaknesses to improve the efficiency and ranking of given DMUs.
Akram Oveysiomran
Abstract
Input and output selection in Data Envelopment Analysis (DEA) has many important. In this research, inputs and outputs of reginal power companies are selected with artifitial neural network. The application of neural network in the selection of inputs and outputs of reginal power companies is not a precedent ...
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Input and output selection in Data Envelopment Analysis (DEA) has many important. In this research, inputs and outputs of reginal power companies are selected with artifitial neural network. The application of neural network in the selection of inputs and outputs of reginal power companies is not a precedent in the literature and it is considered the main advantage of the proposed method. In order to train two layers MLP neural network, after presenting of error resilience, learning method was used. After neural network training, neural network performance is examined by using the test set. RMSE value for 15 test set equals 0/0269 which reflects the high accuracy of training network. The Sensitivity Analysis of the studied parameters which are the same inputs and outputs of Data Envelopment Analysis, with ten percent increase of parameter, compared to the prior one was carried out and output relative error average for neural network parameters was calculated. Based on the output relative error average, inputs and outputs were determined. By comparing the efficiency scores of regional electricity companies before and after reducing the number of variables, it is noticed that the number of efficient companies during the above four periods decreased from 50 percent to 11 percent. Finally, the neural network application in inputs and outputs selection of the regional electricity companies was unprecedented in the literature and this is the main advantage of this method.
Hossein Safari; Aliyeh Kazemi; Ahmad Mehrpoor Layeghi
Abstract
One of the most known subjects in management literature is performance assessment. In this paper, we have tried to provide a hybrid model in order to evaluate the performance of Iranian gas Transmission Company's operational zones. After an initial screening, due to the low number of Decision making ...
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One of the most known subjects in management literature is performance assessment. In this paper, we have tried to provide a hybrid model in order to evaluate the performance of Iranian gas Transmission Company's operational zones. After an initial screening, due to the low number of Decision making units, different combination of two input and one output are defined. 6 inputs and 4 output criteria have been chosen in initial screening. Based on the experts' judgments, weights or importance of different combinations of input and output extracted using SWARA technique. Then, based on the 22 different combinations, we calculated operational zones efficiency using DEA in the form of CCR and BCC models. In fact for each combination of two input and one output, efficiency for each operational zone has been calculated. Finally, there has been a decision matrix which its’ criteria were equal to different combinations of input and output criteria and their weights gained by SWARA and scores within the matrix were equal to taken efficiency from DEA. In order to calculate final efficiency and prioritizing each operational zone WASPAS technique is used. We use Cohen's kappa coefficient in order to prioritizing validation. The results show that zones 10, 7 and 6 with the highest efficiencies can be appropriate pattern for other zones in resource saving
Moslem Nilchi,; Mohammadesmaeil Fadaeinejad; Seyyed Hossein Razavi-Hajiagha; Ahmad Badri
Abstract
Looking at the economic definition of efficiency as optimal use of resources to produce possible maximum output, it can be understood the importance of this concept in management systems. Basically, Managers are trying to meet the satisfaction of all their stakeholders by optimally utilize of their resources ...
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Looking at the economic definition of efficiency as optimal use of resources to produce possible maximum output, it can be understood the importance of this concept in management systems. Basically, Managers are trying to meet the satisfaction of all their stakeholders by optimally utilize of their resources to produce outputs. Due to high cost of holding money this point has more importance to the banking industry in Iran. In this paper, by looking at the structure of bank activities in Iran, a model with five different parts is provided that depicts the flow of affairs in banks. A mathematical model based on data envelopment analysis is presented to evaluate the efficiency of proposed structure and by using fuzzy approach, a method has been proposed to solve it. The results of applying the proposed model to 210 branches of one bank show that despite relative acceptable efficiency in resource attracting, and management, the efficiency of service, resource allocation and profitability parts are facing with important problem
Kaveh Khalili Damghani; Mohammad TaghaviFard; Kiaras Karbaschi
Abstract
The main goal of this paper is to evaluate the relative efficiency of each level of customer services in MELLI bank branches. A three stage process is defined as consecutive results of service provision to the customers. This process consists of sub-process such as customer expectations, customer satisfaction, ...
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The main goal of this paper is to evaluate the relative efficiency of each level of customer services in MELLI bank branches. A three stage process is defined as consecutive results of service provision to the customers. This process consists of sub-process such as customer expectations, customer satisfaction, and customer loyalty. A hybrid method based on Multi-criteria Satisfaction Analysis (MUSA) and network Data Envelopment Analysis (DEA) is proposed to evaluate the relative efficiency of 30 branches. In this way, first the customer satisfaction was measured through a direct questionnaire based on customers perceptions analysis and quantified using MUSA method. Then, the customer satisfaction scores and the other important evaluating criteria such as number of employees, average evaluation scores of staff, operating costs, the amount of deposits, total credit facilities, the number of new checking accounts, expectations and customer loyalty were considered in DEA model as inputs and outputs. A three-stage DEA model was used to evaluate the efficiency of bank branches. The proposed DEA model was based on multipliers perspective, output-oriented with constant return to scale. The proposed three-stage DEA model quantified and assessed the efficiency of customer expectations, customer satisfactions, and customer loyalties in branches. The results showed that the mean relative efficiency of selected branches in three sub-processes namely customer satisfaction, operational results and customer loyalty were 83%, 94%, and 90%, respectively. The mean efficiency of the overall process is 89%.And four branches (about 13% of sample) were placed on efficient frontier for all sub-processes. Based on research findings, the branches which have been efficient in customer expectations were also efficient in other sub-processes and the main process.
Alireza Alinezhad; Niki Jalili Taghavian
Volume 13, Issue 39 , January 2016, , Pages 115-144
Abstract
Improving products quality and services is the best and most important factor to win competitors and get majority of the market share. In this regard, Failue mode and Effect analysis is an efficient tool to improve the quality products. Considering many criticisms to taraditional method, the risk priority ...
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Improving products quality and services is the best and most important factor to win competitors and get majority of the market share. In this regard, Failue mode and Effect analysis is an efficient tool to improve the quality products. Considering many criticisms to taraditional method, the risk priority number in FMEA is formed by multiplying of three factors (seveirity, Occurrence and Detect). In order to existing defects, a new method to calculate the risk priority number in FMEA based on data envelopment analysis method is introduced. The aim of this study is to provide a new kind of risk priority number by assigning different weights to each of the risk factors. Also according to severity, Occurance and detection numbers that are achieved by a team of experts and are not a constant and certain factor, in this research has been used Robust optimization because of covering the result of DEA and less complexity. The results of example indicate that, proposed model is more effective than traditional RPN and provide a full ranking.
seyyed heydar mirfakhradini; fatemeh azizi
Volume 13, Issue 36 , April 2015, , Pages 5-26
Abstract
Performance evaluation is a systematic review that helps organizations to achieve their goals. This study evaluates and ranks Yazd Science and Technology Park’s high-tech firms, using integration Data Envelopment Analysis into Six Sigma methodology. In this study a combination of both methods is ...
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Performance evaluation is a systematic review that helps organizations to achieve their goals. This study evaluates and ranks Yazd Science and Technology Park’s high-tech firms, using integration Data Envelopment Analysis into Six Sigma methodology. In this study a combination of both methods is used to propose more effective and efficient performance evaluation framework. By studying research literature in the field of Science and Technology Park’s firms’ performance evaluation and using Fuzzy Delphi, Effective criteria for measuring the performance of Yazd Science and Technology Park’s firms are identified, then by reviewing research literature and experts’ opinions, inputs and outputs of DEA model are identified. Then, by using combining DMAIC circle and DEA, an inclusive approach for In the analysis phase of Six Sigma evaluating performance is suggested. process ranking of firms is identified using DEA scores. This research helps firms’ managers to find the effective criteria on their performance and how they can improve these factors and control its impacts.
Mohamad Hosein Tahari Mehrjardi; Dariush Farid; Hamid Babaei Meybodi
Volume 8, Issue 21 , June 2011, , Pages 21-37
Abstract
Data Envelopment Analysis (DEA) has been a very popular method for measuring and benchmarking relative efficiency of peer Decision Making Units (DMUs) with multiple input and outputs. However, some problems have also appeared as the applications of DEA advance. One of inter-related problems that has ...
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Data Envelopment Analysis (DEA) has been a very popular method for measuring and benchmarking relative efficiency of peer Decision Making Units (DMUs) with multiple input and outputs. However, some problems have also appeared as the applications of DEA advance. One of inter-related problems that has long been known is the lack of discrimination power. The lack of discriminating power problem occurs when the number of DMUs under evaluation is not large enough compared to the total number of inputs-outputs. In this situation, classical DEA models often yield solutions that identify too many DMUs as efficient. In this study the base of the modeling is technique Data Envelopment Analysis But in order to increase accuracy in assessing banks performance and identify the inefficiency and efficiency units, designing a model that combines data envelopment analysis and Goal Programming and also performance of the banks are measured in this perspective. The results of this study showed the higher ability of the presented model toward the basic models to separate the banking units.
Maghsoud Amiri; Amir Alimi; Seyed Hossein Abtahi
Volume 6, Issue 17 , September 2007, , Pages 135-151
Abstract
Data envelopment analysis model is a model for calculating the efficiency of decision making units (DMUs). In previous models there are some weaknesses that the most important one is changing weights of inputs and outputs in model that lead to evaluate efficiencies of DMUs with different weights. The ...
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Data envelopment analysis model is a model for calculating the efficiency of decision making units (DMUs). In previous models there are some weaknesses that the most important one is changing weights of inputs and outputs in model that lead to evaluate efficiencies of DMUs with different weights. The important subject is that How we should evaluate all of decision making units with one set of weights and optimize their efficiencies simultaneously. This paper aims to present a new model that eliminates the weaknesses of previous models. Odeveloped model is designed based on multi objective decision making models and this model is solved with fuzzy solution method of multi objective decision making models and leads to creating common weights. The main object of research that was better ranking of DMUs rather than basic models have been done by using this model and this is showed with solving the model on an example.
M.R. Mehregan; M. Rahmani
Volume 1, Issue 3 , January 2003, , Pages 1-30
Abstract
The paper investigates excesses and deficits in Iranian industrial productivity for the years by combining data envelopment analysis with other management science approaches such as analytical hierarchy process. The application also identifies factors that affect the productivity of Iranian industry ...
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The paper investigates excesses and deficits in Iranian industrial productivity for the years by combining data envelopment analysis with other management science approaches such as analytical hierarchy process. The application also identifies factors that affect the productivity of Iranian industry in a positive or negative way i.e., simultaneously identifies both excesses and deficits. The current study demonstrates that DEA can be combined with other methods to yield more valid results insights and recommendations such as : 1- the trend of Iranian industrial productivity in this 21 year period demonstrates some periods of fall and rise that is due to the conditions of the time 2- in the main factors of productivity we see some continuous trends of weakness; this means that it is necessary to think about this point in the strategic plans for industrial development plans can be set and corrections and revisions can be made.
