جستجوی مقالات مرتبط با کلیدواژه « network dea » در نشریات گروه « مدیریت »

It is inevitable for a manager to consider the performance effects of each component of a multi-stage financial equity capital. These components serve as inputs in the first stage to raise investments. The investments, as outputs of the first stage, become inputs for the second stage and are used in bank services, such as bank facilities, which are outputs of the second stage. Therefore, when evaluating bank performance, the connectivity between the stages must be considered; otherwise, efficiency may not be calculated correctly. Traditional methods often assess multi-stage systems as black boxes, neglecting the potential connectivity that may exist among the stages. We delve into the system and propose models to improve overall efficiency and the efficiency of each stage. Additionally, the continuity and relationships among stages introduce numerous variables and constraints to linear programming for evaluating the entire system. A centralized approach calculates the efficiency score of units simultaneously by solving only one linear programming problem, significantly reducing computational complexity. This approach, especially in large organizations, is commonly employed by central managers. In this paper, we introduce a centralized method for evaluating units with a multi-stage structure. We apply the proposed models to evaluate the efficiencies of bank branches and insurance companies, demonstrating the superiority of the improved network approach and centralized method in enhancing overall system efficiency. Bank branches typically have a two-stage structure, involving labor, physical capital, and other factors.IntroductionBank branches operate under the supervision of a central management team. The central manager, acting as the decision-maker, allocates resources such as labor and financial equity capital as inputs for these branches. The goal is to optimize the overall efficiency of the branches by minimizing the total consumption of resources while maximizing the desired outputs, such as security investments. A common approach to enhancing the performance of banks involves evaluating each branch separately. However, this method does not guarantee the minimization of total resource consumption and can be time-consuming. Since all bank branches are under the control of central management, the decision-maker can optimize the efficiency scores of branches by allocating resources to them simultaneously. This approach, known as centralized Data Envelopment Analysis (DEA), is particularly relevant when certain variables are controlled by a central authority, such as a Head Office, rather than individual unit managers. DEA is a mathematical programming technique used to assess the performance of homogeneous Decision Making Units (DMUs). However, in cases where DMUs have a network structure, such as banks, where the outputs of one division or sub-process serve as inputs for the next sub-process, traditional DEA models treat two-stage DMUs as black boxes and overlook potential connectivity among the stages. In our approach, we consider the internal activities within the system and propose a non-radial model to optimize multi-stage DMUs by taking into account the connectivity among the stages. Furthermore, in previous network DEA models, constraints related to intermediate activities were treated as inequalities, which, as we will demonstrate in this paper, can lead to contradictions in optimality. We address this issue by carefully considering the connectivity among stages. The presence of connectivity among stages introduces numerous variables and constraints to the corresponding model. This model, when used to measure the overall efficiency scores of all DMUs, would traditionally require solving as many problems as there are DMUs, which can be highly time-consuming. In our paper, we introduce a centralized approach that measures the efficiency scores of multi-stage structure DMUs by solving only one linear programming problem. We have applied these proposed models to evaluate bank branches and insurance companies. This approach provides a more comprehensive and efficient way to assess and improve the performance of multi-stage organizations like banks, taking into account the interconnected nature of their operations.MethodologyWe employ the Data Envelopment Analysis approach to evaluate systems with a multi-stage structure, often referred to as a network structure. Traditional DEA models treat two-stage DMUs as black boxes and overlook the potential for connectivity among these stages. In contrast, we delve into the internal activities of the system and propose a model that optimizes multi-stage DMUs by considering the interconnections among the stages. Moreover, in previous models designed to assess network systems, constraints related to intermediate activities were typically treated as inequalities, which could lead to inconsistencies in optimization. In our approach, we enhance these constraints associated with intermediate activities to ensure more robust optimization. Additionally, we apply a centralized approach to allocate resources to DMUs, allowing for the simultaneous optimization of the efficiency scores of all DMUs through the solution of a single linear programming problem. This centralized method streamlines resource allocation and improves the overall efficiency of the DMUs.ResultsWe evaluated 20 bank branches, treating them as 20 DMUs with a two-stage structure. In the first stage, inputs included paid interest, personnel costs, paid interest related to foreign currency transactions, and personnel costs related to foreign currency transactions. The first stage produced intermediate outputs in the form of raised funds and raised funds related to foreign currency transactions. In the second stage, the outputs consisted of loans and common incomes. Notably, some loans in the second stage might become non-performing, where borrowers are unable to make full or even partial repayments. To address this, we considered non-performing loans as undesirable or bad outputs and transformed them into inverse values to treat them as good outputs. To calculate the efficiency scores of the bank branches, we employed both our improved network model and the traditional DEA approach. Our network-based method revealed that many of the bank branches under evaluation were inefficient, in contrast to the traditional method, which inaccurately identified many of the bank branches as efficient. Subsequently, we extended our network method to a centralized case, significantly reducing computation time. The network-based assessment of bank branches took nearly 5 seconds, whereas solving the centralized model required only 0.1 second. In addition to evaluating bank branches, we applied our methods to assess insurance companies. The results demonstrated that our model provided more accurate efficiency scores compared to previous network-based approaches.ConclusionIn multi-stage production systems, the production process comprises several stages. Banks, for example, operate with a network structure in which labor, physical capital, and financial equity capital serve as inputs in the first stage to generate deposits as intermediate outputs. In the second stage, these banks utilize the deposits obtained from the first stage to create loans and security investments. We have introduced models to assess the efficiency of each stage, whether it's the first, intermediate, or final stage, individually. Additionally, we have developed a non-radial SBM model designed for evaluating DMUs with multi-stage structures. The Centralized DEA approach is a valuable method for central managers, particularly in large organizations like bank branches, to allocate resources effectively. We have extended our network-based method to a centralized approach, allowing us to calculate efficiency scores by solving just one linear programming problem. The results obtained from applying our proposed models to evaluate bank branches and insurance companies, both exhibiting network structures as DMUs, demonstrate the superiority of the network centralized approach over previous models.

Each steel company buys the raw materials it needs from the manufacturers in question, thus forming supply chains with network properties. Therefore, there is a need to conduct performance research to improve the performance of these chains. In studies, the study of specific structures in the supply chain has received less attention. In this research, we have tried to examine the network and how to evaluate its performance in the presence of uncontrollable, undesirable factors. For this purpose, network DEA models are proposed that evaluate the performance of the chain in the presence of uncontrollable inputs and outputs, undesirable output even in its internal structure. Then, 11 present steel companies, the corresponding chain of each of which has two stages, were evaluated by the presented models in 1398. The designed model was based on the network Data Envelopment Analysis of tailored the companies under study and their efficiency was calculated. Based on the results, one company under the first stage and one company under the second stage were recognized as efficient, and all companies had a total efficiency of less than one.

In this paper, data envelopment analysis (DEA) was discussed for non-homogenous mixed network structure where the outputs of each stage can enter the next stage in the same or another layer as inputs or come out from the system as final products. In this model, in addition to intermediate inputs, each stage can also have independent inputs. For this purpose, a mathematical model has been developed in which the independent inputs and final outputs for the components of the decision making units (DMUs) are examined. To show the efficiency of the model, real data for 20 DMUs were used and the results were compared with those of traditional models. The results obtained from the proposed method eliminate the shortcomings of traditional methods for proper detection of units that are on the efficient border (with an efficiency of one). The developed method can provide the managers and decision makers with a more accurate understanding of the performance of components of DMUs.

Energy saving regarding its high share in energy consumption of industries has a significant impact on the growth and development of countries. This study aims to evaluate the performance of Iran's electricity generation, transmission, and distribution processes.

By using a network data envelopment analysis (DEA) model, the overall efficiency scores, and efficiency scores of production, transmission, and distribution processes are calculated. The network structure considers the main and surplus inputs (fuel consumption costs, internal consumption, transmission substation capacity, power transmission lines length, transformers capacity, low and medium voltage network length), intermediate sizes (net power generation, gross power generation, and delivered energy), desirable (nominal power, actual power, and delivered energy) and undesirable outputs (environmental pollutants, and energy losses).

An algorithm based on a multi-objective programming model is presented to evaluate network performance and simultaneously to evaluate processes efficiency. The proposed algorithm is used to evaluate 16 electricity areas in Iran.

The results showed that Tehran, Khorasan, Khuzestan, and Zanjan are the most efficient areas.

Traditional DEA models cannot determine the source of inefficiency for structures with more than one stage (network structures). Continuous two-stage structures are one of the most applicable and basic network structures, and one of their main challenges is determining the relationship between the total efficiency and the efficiency of the stage and also determining the optimum amount of intermediate variables. The available models in solving the challenges and calculating the efficiency have orib or aren’t applicable for all two-stage structures. The purpose of this study is developing a multi-objective network DEA model that doesn’t have the weaknesses of the previous model.

In this study, it is attempted to develop a multi-objective model with a composition approach that considers the efficiency of the stages simultaneously, and also to interpret the results geometrically and compare it with the available models. The presented model was developed to multi-optimal and VRS conditions.

In all the models, efficiencies are between zero to one and a unit is network efficient only and only when it is efficient in both stages.

The presented model was used in an applicable example to evaluate the sustainability of 17 supply chains and the results showed that the model does a realistic evaluation in comparison to the traditional models. In the end, the model priority over the literature review models was mentioned with examples.

Data envelopment analysis has been in the center of attention due to independency of the production function. But the initial models of data envelopment analysis are incapable of examining the internal structure of the units and have a black-box view. One of the most common network structures is consecutive two-staged structure. Available models for evaluating this structure are mainly based on the decomposition approach, in other words, their priority is overall efficiency, and the efficiency of the stages is obtained by decomposing the total efficiency. In this paper, an attempt is made to develop a multivariate model that simultaneously considers the overall efficiency and efficiency of the stages. In addition, for multi-response mode, the models were developed to calculate the efficiencies and it was proved that in all models, efficiency scores range from zero to one, and a unit is efficient if only it is efficient in both stages. The presented models were used in an applied example and the results showed that the existing model performed more realistic evaluation than traditional models.

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.

Customer relationship management (CRM) is one of the fastest growing management approaches which can lead to stronger competitive position, resulting in larger market share and profitability. In this study, CRM efficiency among the customers of the Iranian banks is analyzed using a network data envelopment analysis (NDEA) approach. To implement CRM in the NDEA model, input, intermediate and output variables are service quality, customer satisfaction and customer loyalty, respectively. This research is a descriptive survey in which the total customers of different Iranian banks in Isfahan comprise the statistical population. The sample included 420 people that were selected by cluster sampling. After distributing questionnaires, only 245 questionnaires were completed. The model is tested via PLS path modeling and confirmed. To rank banks performance, NDEA model is used. Results show the power of NDEA model in the differentiation of the banks since there are no two banks with the same rank. The efficiency of sub-process is also presented to extract the reason of inefficiency in the total process. Because of the adopted research approach, the research results may lack generalization. Therefore, researchers are encouraged to test the proposed propositions further. The paper includes a model for assessing CRM with NDEA model and helps managers rank their companies in the customers' point of view.

The results of dynamic efficiency evaluation not only help managers to realize their business' position in competitive market, but also enable them to compare current company’s performance with previous periods and do strategic planning properly. For doing so, network data envelopment analysis is a logical approach. Hence, the main objective of this illustration is to measure dynamic efficiency by means of network data envelopment analysis technique. Although different approaches in network DEA are introduced recently, the need for a comprehensive methodology in this area is remained because of the defects of previous methodologies. Consequently, a novel approach based on multi-objective optimization is introduced in this paper in order to measure the efficiency of a network structure. Finally, the case of Machine Made Carpet Industry (MMCI) is used and the dynamic performance of MMCI's companies in the period of four years is measured. Efficiency results of case data showed that the methodology proposed in this paper is able to eliminate defects of previous approaches and evaluate both total and annual efficiency simultaneously

Efficiency calculations for units belonging to one group according to data envelopment analysis widely have received attention; while experiences related to efficiency comparison of several groups consisted of decision making units is restricted. Kao's parallel system network DEA model that has been designated aimed at such a comparison, practically leads to accomplish comparison among units instead of groups. This paper presents a new version of kao's model to consider group's generality, accordance with aim of comparison, while possibility of calculating inefficiency of internal units is still provided. This version due to calculating bigger amounts of efficiency is more eligible to allocate efficiency of one to several groups. For this reason a ranking method of efficient groups is presented to support proposed model. Proposed models included in new version of kao's model and ranking efficient groups model were applied on data provided about supervisory sections of a bank.