perfomance management
Sharmineh Safarpour; Alireza Amirteimoori; Sohrab Kordrostami; Leila Khoshandam
Abstract
Due to the importance of the health sector in the people’s lives, attention and monitoring of this sector can lead to development and social welfare. In this paper, data on 31 educational and therapeutic hospitals are considered to analyze their performance and managerial ability. A two-step procedure ...
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Due to the importance of the health sector in the people’s lives, attention and monitoring of this sector can lead to development and social welfare. In this paper, data on 31 educational and therapeutic hospitals are considered to analyze their performance and managerial ability. A two-step procedure is used to evaluate the relative efficiency and to measure the impact of contextual variables. Toward this end, in the first step, data envelopment analysis has been used to calculate the technical efficiencies of the hospitals, and then the logarithm of the technical efficiency obtained from the first step was regressed on a set of contextual variables that affect the performance of hospitals. Then, the residual is interpreted as managerial ability of the hospitals. At the end, a unique ranking was presented based on the criteria of managerial ability of each unit. Finally, the results were analyzed and analyzed in order to provide valuable suggestions for managers and more efficient management of the country's hospitals and to maintain the health of the society. According to the study carried out without considering contextual variables, 25 efficient units were evaluated, but by applying the effect of contextual variables on the efficiency, no unit becomes efficient, this proves the high impact of such indicators on the performance of units he does. Also, in the ranking of the units with the approach of managerial ability, Lorestan province is in the first place and Golestan province is in the last place.
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 ...
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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). ...
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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 ...
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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. 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 ...
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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.
