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 ...
Read More
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. ...
Read More
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
supply chain management
Homa Abedi Dehkordi; Ghasem Tohidi; Shabnam Razavyan; Mohammad Ali Keramati
Abstract
Cement production in Iran takes place across various geographical locations, each characterized by distinct weather conditions. The technology employed in cement production varies depending on the availability of raw materials, fuel sources, and essential resources like water. Consequently, diverse inputs ...
Read More
Cement production in Iran takes place across various geographical locations, each characterized by distinct weather conditions. The technology employed in cement production varies depending on the availability of raw materials, fuel sources, and essential resources like water. Consequently, diverse inputs and outputs assume significance in each production technology, resulting in non-homogeneity among cement factories. Despite these differences, all these facilities are engaged in cement production, warranting a comparative analysis of their efficiency. This study examines the operational processes of five different cement production technologies—dry, semi-dry, humid, semi-humid, and wet slurry—across four companies comprising a total of nine factories. The study evaluates their efficiency between 2017 and 2020 using network data envelopment analysis under non-homogeneous conditions across three modeling stages. An important aspect of this study is its focus on the entire supply chain, from raw materials to the final product. Although the raw materials employed vary among different cement production technologies, the end product remains largely consistent.IntroductionIn certain real-world scenarios, even with similar production technologies, the assumption of homogeneous decision-making units may not hold true. Practical applications often involve supply chain structures that differ significantly from others. For instance, some supply chains may, at certain stages, eject intermediate products to meet specific needs, a phenomenon not universal to all supply chains, resulting in non-homogeneous chains. The cement industry, including Iran, constitutes one of the pivotal economic sectors. Therefore, mitigating shortcomings, including resource and material waste reduction, can have a substantial impact on this industry and consequently on the broader economy. Due to varying climatic conditions, cement production employs diverse technologies, primarily categorized as dry or wet processes. This study investigates the operational processes of five different cement production methods—dry, semi-dry, humid, semi-humid, and wet slurry—across four companies with a total of nine factories. Their performance between 2017 and 2020 is evaluated using network DEA under non-homogeneous conditions, encompassing three modeling stages.Materials and MethodsIn novel approaches, DEA is utilized to assess the performance of network decision-making units. The models typically assume homogeneity among decision-making units, which may not always align with real-world conditions. Practical situations often violate assumptions of unit homogeneity and uniformity in input and output parameters. Consequently, it is imperative to present and employ methods and models capable of accommodating non-homogeneous units. This study employs a scientific library research approach and practical purposive data collection to gather relevant information. This information informs specific adjustments to operational processes. Consequently, the development of a robust system for evaluating supply chain performance becomes essential. The study utilizes common models to evaluate efficiency under non-homogeneous conditions. Classification of operational processes and related data, followed by modeling using Lingo software, is employed in this research.Discussion and Result:This article consists of two parts. Initially, it introduces the fundamental performance evaluation model and subsequently delves into the three-stage model of data envelopment analysis (DEA) within the supply chain context. In the second part, the production processes of Portland cement are examined, covering dry, semi-dry, humid, semi-humid, and wet slurry processes. The proposed approach assesses the performance of four cement production companies over a four-year period. Efficiency calculations for nine factories are conducted in three stages:The first stage consists of three steps as follows:First step: Input and output parameters used across the entire production process are categorized based on the different production methods.Second step: Processes utilizing similar production steps, as determined in the first stage, are grouped into four categories.Third step: Efficiency assessments for factories sharing similar production stages from the previous step are conducted, resulting in the identification of nine categories.Second stage: The efficiency of each category, characterized by a common feature from the previous step, is calculated.Third stage: To determine the overall efficiency of each factory, the efficiencies of individual processes are multiplied.ConclusionsThe results indicate that the fourth cement production company exhibits the highest efficiency, while the first company has the lowest efficiency. Notably, the lowest efficiency for the years 2017 to 2020 was recorded by the first company in 2020, while the fourth company achieved the highest efficiency in the same year. Among the factories, the lowest efficiency was observed in 2017 for the first company's five semi-dry factories, the fourth company's four semi-humid factories in 2018, the fourth company's nine wet slurry factories in 2018, the third company's seven semi-humid factories in 2020, and the fourth company's four semi-humid factories in 2020, which recorded the highest efficiency. Further examination and identification of suitable solutions to enhance efficiency in cases with lower efficiency levels can follow this study.
perfomance management
Leila Parhizkar Miyandehi; Alireza Amirteimoori; Sohrab Kordrostami; Mansour Soufi
Abstract
Estimating the revenue efficiency of entities being evaluated is crucial as it provides valuable information about organizations, assuming that the output prices are known. This research introduces a new definition of optimal scale size (OSS) based on maximizing the average revenue efficiency (ARE). ...
Read More
Estimating the revenue efficiency of entities being evaluated is crucial as it provides valuable information about organizations, assuming that the output prices are known. This research introduces a new definition of optimal scale size (OSS) based on maximizing the average revenue efficiency (ARE). Additionally, the ARE is defined for both convex and non-convex sets, independent of returns to scale and the assumption that the vector of input-output prices of units is uniform. Moreover, to address the presence of uncertain data in real-world applications, the introduced ARE model is extended to evaluate systems with random inputs and outputs, along with approaches for its calculation. Finally, the proposed method is applied in an experimental example, calculating the ARE for a dataset of postal areas in Iran.IntroductionThe concept of optimal scale size has been extensively studied in the field of data envelopment analysis. Cesaroni and Giovannola's research on non-convex FDH technology reveals that the optimal scale size is a point in the production possibility set that minimizes average cost efficiency. Average cost efficiency, a new measure combining scale and allocation efficiencies, provides a more accurate performance assessment compared to cost and scale efficiencies. When evaluating units with known output prices instead of input prices, assessing revenue efficiency can offer more valuable insights. This paper extends the research on cost evaluation to revenue evaluation. It introduces the concepts of average revenue efficiency and optimal scale size based on revenue maximization. The optimal scale size based on revenue maximization is defined as the point in the production possibility set that maximizes the average radial income for the unit under investigation. Average revenue efficiency serves as an evaluation measure of unit revenue, surpassing revenue and scale efficiencies in accuracy. The paper examines methods for calculating average revenue efficiency in both convex and non-convex technologies. It demonstrates that the average revenue efficiency model in convex technology with variable returns to scale is equivalent to the revenue model with constant returns to scale. Furthermore, the relationship between optimal scale size points based on revenue maximization and the most productive scale size is determined. Next, the paper presents the average revenue efficiency model for stochastic sets with the presence of stochastic data. An experimental example is used to calculate the average revenue efficiency and obtain the optimal scale size for a set of postal areas in Iran.Materials and MethodsThe study builds upon Cesaroni and Giovannola's method for calculating average cost efficiency and optimal scale size to develop models for average revenue efficiency and optimal scale size based on revenue. It also utilizes chance-constrained probabilistic models with a deterministic objective function in DEA literature to present average revenue efficiency for stochastic sets. The model is transformed from stochastic to deterministic and then converted into a linear model using the error structure method.Discussion and ResultsThis paper introduces average revenue efficiency and optimal revenue scale size, demonstrating the equivalence between the average revenue efficiency models in convex technology with variable returns to scale and those with constant returns to scale. It also presents the average revenue efficiency model for stochastic sets, enabling the calculation of average revenue efficiency and optimal revenue scale size for units with random inputs and outputs.ConclusionIn many real-world scenarios, particularly when output prices are known, evaluating revenue efficiency holds greater significance than cost efficiency. This study develops the concepts of average cost efficiency and optimal scale size for revenue evaluation, expanding upon the existing literature on data envelopment analysis. The paper demonstrates how average revenue efficiency can be calculated as a valuable and accurate measure of efficiency in convex and non-convex technologies, without making assumptions about returns to scale. By assuming the randomness of input and output variables and employing chance-constrained models, a quadratic deterministic model is presented to calculate average revenue efficiency. It is then transformed into a linear model assuming uncorrelated variables, enabling the determination of average revenue efficiency and optimal scale size based on revenue maximization for random units. The proposed models are applied to a real-world sample, evaluating the average revenue efficiency of twelve postal units. The results highlight the models' ability to provide a more accurate evaluation of revenue efficiency and identify the best revenue scale size as the reference for inefficient units.
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 ...
Read More
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 ...
Read More
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. Ansari; J. Salehi Sadaghiani
Volume 2, Issue 5 , June 2004, , Pages 71-89
Abstract
Modern and new technologies have changed the management view and the methods of problem solving and management of organizations. For effectively managing the organizations at todays changing environment managers should view different global economic, social, political and legal conditions and must pay ...
Read More
Modern and new technologies have changed the management view and the methods of problem solving and management of organizations. For effectively managing the organizations at todays changing environment managers should view different global economic, social, political and legal conditions and must pay attention to factors such as developing deformation and communication technologies and changing customers expectations. For with respect to each condition and factor they needs appropriate information to increase their knowledge and decrease their uncertainty to have effective performance. In this way it seems necessary to consider information technologies and total quality management and understand their impact on efficiency and effectiveness.
In this article through citing the relationships between information technology and total quality management we attempt to examine their impact on organizations efficiency and effectiveness. Therefore we study the concepts of effectiveness efficiency and productivity and then point to the relationships between information technology and total quality management and to the impact of these relationships on organizations efficiency and effectiveness. Because, today managers' attitudes and views to quality in firms is new. Production with desired quality requires not only clear definition of goals, specific policies and procedures for each part of work and each stage of process but also real time inspection, measurement and documentation systems.
