Relative Efficiency Of Decision Making Units Research Articles

Evaluation of Operating Performance of Listed Companies in Taiwan’s Tourism Industry - Application of Data Envelopment Analysis

Data envelopment analysis is an efficiency measurement method proposed by Charnes, Cooper and Rhodes in 1978, including CCR model efficiency analysis and BCC model efficiency analysis, which is used to evaluate the relative efficiency of decision-making units. The measured efficiency value is the most favorable result of the evaluated unit under objective circumstances. This article applies data envelopment analysis method to evaluate the performance of listed companies in Taiwan's tourism industry. After analysis, it was found that the total efficiency of the six tourism industries including Huayuan, No. 1 Store, Gourmet KY, Wangpin, Xiongshi, and Holaday is effective, while the total efficiency of the three tourism industries including Farglory, Phoenix, and Xintiandi is not Effective, but technical efficiency is still effective. Except for the tourism industry whose total efficiency is effective, its returns to scale remain unchanged. Two tourism industries, such as Guobin and Jinghua, have diminishing returns to scale because their return to scale value is >1 and should reduce their business scale. The returns to scale of other tourism industries are all Increasingly, it is recommended to expand the scale of operations.

Comprehensive intuitionistic fuzzy network data envelopment analysis incorporating undesirable outputs and shared resources

The economic growth rate is intricately linked to the efficiency and effectiveness of the banking industry. A widely applicable mathematical technique for such assessments is Data Envelopment Analysis (DEA), which evaluates the relative efficiency of Decision-Making Units (DMUs) by comparing their inputs and outputs. Traditional DEA treats DMUs as black boxes, neglecting internal processes that contribute to inefficiencies in individual DMUs. Additionally, it assumes precise values for inputs and outputs that do not apply to real-world problems. This study introduces a comprehensive network series of two-stage DEA, incorporating shared inputs and intermediate measures, undesirable outputs, external inputs and outputs, initial inputs, and terminal outputs. The network two-stage DEA is extended to intuitionistic fuzzy circumstances to address uncertainty. In this extension, a non-linear intuitionistic fuzzy number, namely a parabolic intuitionistic fuzzy number, represents higher-order imprecise datasets. An illustrative example validates the proposed methodology, and comparisons with existing methods are conducted. Moreover, the methodology is applied to assess the efficiency of Indian public sector banks, demonstrating its applicability and showcasing the efficacy of the procedures and algorithms used. Decision-makers can make better choices using optimal efficiency values to gain insights into inputs, intermediate measures, and outputs.•The research study focused on a network two-stage DEA model, incorporating undesirable outputs and shared resources in the presence of uncertainty.•The methodology involves solving the network two-stage DEA model using parabolic intuitionistic fuzzy numbers.•The experimental analysis involves assessing the efficiency of Indian public sector banks.

Malmquist index evaluation of countries: 2000–2019

One of the sophisticated mathematical techniques for evaluating the relative efficiency of decision-making units (DMUs) in a multi-inputs-output setting is data envelopment analysis. A non-parametric productivity index called the Malmquist index (MI) tracks changes in a DMU’s overall factor output over time. This study seeks to present a broad overview of the changes in the MI for nearly all of the world’s nations over 19 years beginning in 3061. This research evaluates a time series data set made up of 16 economic indexes. Each nation’s MI, which compares 2000 and 2019, shows how each nation has changed over that period. One of the most important purposes of this study comparing the countries by their MI is that it allows for a fair comparison of productivity across different time periods and regions. It also provides insights into the sources of productivity growth, such as changes in technology or improvements in efficiency. As a result, the countries can allocate resources more wisely and develop more effective investment plans by understanding the elements that lead to productivity increase. By implementing some statistical techniques, nations are further divided into four categories based on their MI. Furthermore, a yearly distribution of the MI has been included to illustrate its trends between the years 2000 to 2019. Finally, the changing flow for some countries of each category is shown in three-year tracks.

Fully fuzzy DEA: a novel additive slacks-based measure model

AbstractThe subject of assessing the relative efficiency of Decision-Making Units (DMUs) has been explored extensively in the literature on Data Envelopment Analysis (DEA). While typical DEA models require exact and conclusive data, the observed values of the inputs and outputs in real-world problems may be inaccurate or ambiguous. This paper presents a novel fully fuzzy DEA (FFDEA) model based on the additive slacks-based measure (ASBM). FFDEA models imply that all inputs, outputs, and variables, are fuzzy. It is demonstrated how fuzzy ranking and fuzzy arithmetic approaches can be used to handle the trapezoidal fuzzy number assumptions. To solve the fuzzy ASBM model under variable returns-to-scale assumptions, we proposed two different methods, first method use of lexicographic method for solving multi-objective linear programming (MOLP) approach, which provides an efficiency measure and a fuzzy goal operating point for each DMU under assessment. In alternative method, we convert the fuzzy linear fractional programming problems to an equivalent MOLP problem. A case study is provided to illustrate the applicability of the proposed approach for selecting sustainable suppliers, we compare the results with other fuzzy DEA approaches, while shedding light on future research venues.

Development of intuitionistic fuzzy cost efficiency model in data envelopment analysis

Data envelopment analysis (DEA) is a non-parametric linear programming (LP) based technique to measure the relative efficiencies of decision-making units (DMUs). The conventional DEA models assume that input-output data is crisp, which may not always be feasible in practical situations due to the presence of ambiguity and imprecision. Therefore, to handle uncertain and imprecise data, the concept of intuitionistic fuzzy sets (IFS) has been introduced. In this study, the relative efficiencies of DMUs with uncertain data will be determined. For this reason, the conventional cost efficiency (CE) model of DEA is extended to IF environment. Also, the lower and upper-cost efficiency models are developed using α-cut and β-cut approach. The data for inputs, outputs and input prices are considered intuitionistic fuzzy numbers (IFNs), in particular triangular intuitionistic fuzzy numbers (TIFNs). To demonstrate the practical application of the proposed intuitionistic fuzzy cost efficiency models (IFCEMs), a numerical example is presented.

A credibility approach on fuzzy slacks based measure (SBM) DEA model

Data Envelopment Analysis (DEA) is a multi-criteria technique based on linear programming to deal with many real-life problems, mostly in nonprofit organizations. The slacks-based measure (SBM) model is one of the DEA model used to assess the relative efficiencies of decision-making units (DMUs). The SBM DEA model directly used input slacks and output slacks to determine the relative efficiency of DMUs. In order to deal with qualitative or uncertain data, a fuzzy SBM DEA model is used to assess the performance of DMUs in this study. The credibility measure approach, transform the fuzzy SBM DEA model into a crisp linear programming model at different credibility levels is used. The results came from the fuzzy DEA model are more rational to the real-world situation than the conventional DEA model. In the end, the data of Indian oil refineries is collected, and the efficiency behavior of the companies obtained by applying the proposed model for its numerical illustration.

Literature survey on DEA in the insurance industry with a focus on identification of research hotspots with text mining

DEA is a frequently used non-parametric methodology for measuring the relative efficiency of Decision-Making Units (DMUs) that use the same inputs to produce the same outputs. Emrouznejad and Yang (2018) provided a literature survey on DEA with 10,300 peer-reviewed journal articles from 1978 to the end of 2016. Our article focuses on DEA applications in the insurance industry in convergence with the existing relevant literature as Kaffash et al (2020), who have surveyed 132 DEA articles in the insurance industry for the period from 1993 to 2018. We include particular keyword analyses necessary to identify research hotspots in different periods. This article aims to conduct a bibliometric analysis of DEA-published documents (articles in journals and book chapters) in the insurance industry from 1993 to 2021, focusing on identifying research hotspots based on keyword co-occurrence analysis. We have analyzed published documents from relevant databases, such as Scopus, Web of Science, Ebsco and ProQuest. We use descriptive analytics and text mining as the main methods in our analysis. We provide descriptive statistics for articles per year and category of the insurance industry, geographical distribution, top five journals and authors by citations, and citation analysis. An additional qualitative factor of our article is in-depth keyword co-occurrence analysis by using text mining to identify research hotspots in the insurance industry. Our analysis aims to contribute to researchers and insurance practitioners as an empirical and applicative point for initiating and developing research.

A Computational Procedure Using Robust Ranking Method to Formulate and Solve Fuzzy DEA Models

In some real-world applications, performance assessment often has to be considered under uncertainty with fuzzy input and output. Data envelopment analysis (DEA) is a new and effective approach to measuring the relative efficiency of decision-making units concerned with performance evaluation. Recently, DEA has studied with fuzzy input and output. In this paper, we present a computational procedure using a robust ranking method to formulate and solve fuzzy DEA models. Based on this approach, we convert the fuzzy DEA input and output from fuzzy form to crisp. Also, a case study has been presented as in [6] to assess the performance of eight manufacturing enterprises with fuzzy data.

Research on the Efficiency of China's Marine Industry Financial Support Based on DEA

Cao, Y.L., 2020. Research on the efficiency of china's marine industry financial support based on DEA. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 217-219. Coconut Creek (Florida), ISSN 0749-0208.Financial support is the driving force and means for the development of marine industry. Improving the efficiency of financial support is the key to financial support. With the increasingly fierce market competition, only by relying on high-quality products and high customer satisfaction can enterprises survive in the fierce competition. Among many evaluation methods, Data Envelopment Analysis (DEA), as a non-parametric statistical method for effectively dealing with multi-input and multi-output problems, has received more and more attention in evaluating the relative efficiency of decision-making units. The adjustment and optimization of financial structure have been proved by theory and practice to promote economic growth, and financial support plays a decisive role in the development of marine industry. China's financial structure is still in the stage of scale expansion to promote the marine economy. In order to promote the improvement of marine industry, we should turn to the intensive mode of efficiency improvement. This paper extends from financial support to the study of financial support efficiency, and puts forward the method of using DEA model to evaluate and analyze the financial support efficiency of marine industry.

Methodology for calculating critical values of relevance measures in variable selection methods in data envelopment analysis

The selection of input and output variables is a key step in evaluating the relative efficiency of decision-making units (DMUs) in data envelopment analysis (DEA). In this paper, we present a methodology based on Monte Carlo simulations and bootstrapping for calculating the critical values of relevance measures in variable selection methods in DEA. Additionally, we define a set of metrics to study the methods’ performance when using such critical values. We conducted an extensive simulation study, applying the proposed methodology to two variable selection methods in 28 single-output model specifications (i.e., different number of inputs and DMUs in the DEA model) under multiple scenarios, varying factors related to the functional form of the production function, the probability of an input being relevant in the model, the probability distribution of the inputs, and the theoretical efficiencies of the DMUs. The simulation study shows that (i) our proposed methodology yields consistent results for the two methods studied, in terms of the generated critical values and the performance metrics, and (ii) for most model specifications, the critical values can be estimated with a linear model with a high adjusted R2, using factors related to the input probability distribution and the probability of an input being relevant as independent variables. Furthermore, we describe and compare the performance of the two methods studied, provide guidelines for using our methodology and the results presented in this paper, and propose suggestions for future research.

APPLICATION OF DATA ENVELOPMENT ANALYSIS FOR MEASURING SERVICE QUALITY FROM DISTRIBUTORS’ PERSPECTIVE IN SUPPLY CHAIN

Abstract: Vietnam’s textile and apparel sector has achieved fast and sustainable growth over the past years and played an important role in national socio-economic development. The export value of textile and garment products in recent years has ranked number two in the country’s total export revenue. In this scenario, an attempt was made to examine the service quality at the manufacturer – distributor interface of the textile supply chain and provide clear guidelines for benchmarking of service quality in multi-unit services. A sample of 144 distributors from Small and Medium Enterprises (SMEs) in major regions of South Vietnam was selected. Exploratory Factor Analysis was used to identify the critical factors of service quality. This research applies the data envelopment analysis (DEA) approach to the computation of a measure of overall service quality and benchmarking when measuring service quality with the Service Performance model. Dealing with the five dimensions of Service Performance (SERVPERF) as outputs, the proposed approach uses DEA as a tool for multiple criteria decision making (MCDM), in particular, the pure output DEA model without inputs. Data envelopment analysis measures the relative efficiency of decision-making units (DMUs) and identifies a set of corresponding efficient DMUs that can be used as benchmarks for the improvement of inefficient DMUs. The findings shed valuable insights on measures and critical underlying dimensions of service quality in the context of the supply chain in the textile industry, specifically from the distributor perspective. The results also give the best performer in textile SMEs and set the benchmarking guideline within each group among SEMsKeywords: service quality, data envelopment analysis, SERVPERF

Evaluating the efficiency of structural funds: An application in the competitiveness of SMEs across different EU beneficiary regions

The funds dedicated to the cohesion policy are aimed at attaining the convergence of wealth levels across the European Union (EU) member states. These constitute the second-highest group of expenditures in the EU budget and thus their evaluation assumes a prominent role. Their assessment can be carried out through different tools and methodologies, all of them with their own merits and flaws. For instance, macroeconomic models allow assessing the potential impact of EU funds on economic growth, but disregard management issues. Other modelling approaches consider direct econometric estimations which might bring biased estimates of the impacts of Structural Funds if some variables are omitted. A different possible tool is Data Envelopment Analysis (DEA), which is a non-parametric method to measure the relative efficiency of Decision-Making Units (DMUs), allowing for the consideration of multiple inputs and outputs in global performance evaluation. In this work we employ the Value-Based DEA approach, which combines DEA with Multiple Criteria Decision Aiding (MCDA) and enables performing a robustness analysis of the results in face of uncertain information, considering the main factors that might influence the efficiency of the implementation of Structural Funds. Specifically, we address the assessment of its application in the competitiveness of small and medium-sized enterprises (SMEs), in different beneficiary regions of the EU. Our analysis helped in the identification of best practices, sources of inefficiency and gaps regarding the benchmark regions/countries, thus leading to shape opportunities for improvement. Overall, we were able to ascertain that national inefficient programmes mainly need to reduce their dependence on EU co-funding to become efficient, whereas almost all inefficient regional programmes need to improve their capacity of execution. Nevertheless, differences were found on the factors that require adjustments when considering regions’ categories.

Measuring the Efficiency of Banks' Performance Based on Economic Policies Using Data Envelopment Analysis

Due to long-term and all-compassing sanctions and the instability of political engagements with the other countries, Iran's economy more than ever should adjust its governance system to minimize economic Problems. As the most important financial institution, banks play an important role in the national economy. Considering the present and future economic conditions, the banks management always tries to improve banking services, marketing, innovation in service delivery, competition with other banks, considering the current and future economic conditions. Therefore, one of the goals of banks is to increase economic efficiency. Data envelopment analysis is a mathematical model to evaluate the relative efficiency of decision-making units. In this study, the efficiency of 83 first-class branches of Mellat Bank was analyzed using data envelopment analysis during 2016 and 2017 with an input-based approach and assuming a variable return to scale. In order to achieve efficiency, three inputs of effective deposits, loan, delayed debts, and an input of profit and loss were used. Finally, the efficiency of the branches was calculated using the Malmquist index. The research data were collected through the bank's annual report and analyzed using Lingo and Eviwes software. The present study is aimed to obtain the effectiveness of the bank's management using a three-stage data envelopment analysis. According to the results obtained after adjusting the environmental factors and statistical disturbance and comparing with the efficiency obtained before adjusting the inputs, it was found that environmental variables have a significant effect on the effectiveness of bank branches. According to the results of this research, it can be concluded that in many cases, the ineffectiveness of managers can be related to environmental and uncontrollable factors of managers, rather than to the manager's qualification.

Türk Bankacılık Sektörünün Veri Zarflama Analizi İle Etkinliğinin Ölçülmesi (Measurement of the Efficiency of Turkish Banking Sector with Data Envelopment Analysis)

Purpose – In this study data envelopment analysisone of the non-parametric methods used in quantification of relative efficiency of decision-making unit at multiple input multiple output processes is used for the purpose of quantification of efficiency values of 24 banking companies acting in Turkish banking sector. Design/methodology/approach – In this study total active, total deposit, branch number and interest expenses are selected to be input parameter whereas credits, net period profit, and interst incomes are used as outpuit parameters. At the application DEAP software pack is employed to analyze with the aid of input aimed CCR model. Results – As a result of the study, it was determined that the general efficiency ratio of the banks is 95.7%. When the efficiency levels of the banks according to their capital structures have been examined, it has been found that participation banks are not effective, only one of the private capital banks has the effective level, and the banks with foreign capital are highly effective. Discussion – At a glance to previous studies on data envelopment analysis of Turkish banking sector, the most efficient bank groups are similar in terms of employed input and output parameters, mean efficiency levels obtained from analysis, effective bank numbers and capital. In this respect main findings of the study correspond to former studies. Most of the studies present the most effective source utilizing banks to be the foreign-invested banks.

Two-stage DEA for Bank Efficiency Evaluation Considering Shared Input and Unexpected Output Factors

With the increasingly fierce market competition, only by relying on high-quality products and high customer satisfaction can enterprises survive in the fierce competition. Among many evaluation methods, Data Envelopment Analysis (DEA), as a non-parametric statistical method to effectively deal with multi-input and multi-output problems, has received more and more attention in evaluating the relative efficiency of decision-making units. In the process of bank efficiency evaluation based on DEA method, there will be a situation that banks have both dual role factors and unexpected output factors. The Two-stage DEA model provides an effective analysis method to solve the problem of bank efficiency evaluation of complex organizational structure. In order to evaluate the efficiency of unexpected output with uncertain information, a stochastic DEA model of unexpected output is established.

A new uncertain DEA model and application to scientific research personnel

Data envelopment analysis (DEA), which has been widely used since it was introduced by Charnes et al. (J Econom 30:91–107, 1985), is an effective method for evaluating the relative efficiency of decision-making units. DEA models require accurate input data and output data; however, if no sample is available to estimate accurate data, then uncertain DEA is introduced. This paper reports on several new studies on uncertain DEA using the Hurwicz criterion, which attempts to find the intermediate area between extremes. Some uncertain DEA models, as well as their crisp equivalent models, are presented. Then, the Hurwicz ranking method is proposed based on these models, which can give an evaluation to all the decision-making units. By varying the parameter $$\beta $$ in the Hurwicz criterion, which reflects the optimism of the decision maker, the new ranking method can exhibit various forms. Finally, an application to scientific personnel is provided to prove the advantage of the proposed method.

Analysis of Overall and Pure Technical Efficiency of Tourism in Europe

The aim of this paper is to assess the overall and pure technical efficiency of tourism in European countries in 2017 using the output-oriented Charnes, Cooper and Rhodes (1978, 1979), and Banker, Charnes and Cooper (1984) data envelopment analysis methodology. The countries were divided into two groups: European Union countries and non-European counties. We identified two input and two output variables for each group. Input variables were identified using the principal component analysis method, starting from fourteen pillars that measure the performance of countries in the field of travel and tourism, and are published in the regular reports of the World Economic Forum. Then, two output variables were defined (the total (direct and indirect) contribution of travel and tourism to gross domestic product, and the direct contribution of tourism to employment growth rate), to determine the relative efficiency of decision-making units within the formed homogeneous groups of countries, among which the group of Western Balkan countries was singled out. This analysis enabled us, based on relatively scarce potential, to propose guidelines for a tourism development strategy for the entire Western Balkans region.

A New Method for Solving Dual DEA Problems with Fuzzy Stochastic Data

Data envelopment analysis (DEA) is a widely used mathematical programming technique for measuring the relative efficiency of decision-making units which consume multiple inputs to produce multiple outputs. Although precise input and output data are fundamentally used in classical DEA models, real-life problems often involve uncertainties characterized by fuzzy and/or random input and output data. We present a new input-oriented dual DEA model with fuzzy and random input and output data and propose a deterministic equivalent model with linear constraints to solve the model. The main contributions of this paper are fourfold: (1) we extend the concept of a normal distribution for fuzzy stochastic variables and propose a DEA model for problems characterized by fuzzy stochastic variables; (2) we transform the proposed DEA model with fuzzy stochastic variables into a deterministic equivalent linear form; (3) the proposed model which is linear and always feasible can overcome the nonlinearity and infeasibility in the existing fuzzy stochastic DEA models; (4) we present a case study in the banking industry to exhibit the applicability of the proposed method and feasibility of the obtained solutions.

Multistage network data envelopment analysis: Semidefinite programming approach

Additive efficiency aggregation is one of the important techniques measuring the relative efficiency of decision-making units under network data envelopment analysis (DEA). However, the modelling of additive network DEA is limited to parametric methods to approximate optimal solutions in previous literature. Under multistage network structure, if some outputs leave the system from a given stage while others become inputs to the next stage and some new inputs enter at any stage, the additive network models become extremely nonlinear and are impossible to be solved by linear program. The current paper proposes to solve general additive two-stage models by using semidefinite programming, which is known as effective as linear program. We then extend the methodology to general multistage network structures (including serial processes, parallel processes, multistage processes with non-immediate successor flows, and multistage processes with feedbacks). A numerical data set and the case of regional R&D processes in China are revisited by using the newly developed approach.

Developing new methods for determining weights of components in network data envelopment analysis

Data envelopment analysis (DEA) is a powerful tool for measuring relative efficiency of decision-making units (DMUs). In many cases such DMUs have network structures with internal structures. Traditional DEA models, however, consider DMUs as black boxes without considering their internal structures. Furthermore, overall efficiency in multi component networks is based on efficiencies of their components. Cook et al. (2010) used the additive weighted average of components' efficiencies to calculate overall efficiency. They used the ratio of total weighted input of component to total weighted input of whole components as a weight of component. As an alternative approach, Faramarzi et al. (2014) proposed that the weights are the ratio of total weighted output at the ith component to total weighted output of whole components. In this paper, we propose three novel methods to obtain the weights of components. Then, to compare these three new methods and the methods proposed by Cook et al. (2010) and Faramarzi et al. (2014), we present a case study. Finally, using Spearman's rank correlation coefficient, we analyse the correlation among different approaches.