Basic Data Envelopment Analysis Models Research Articles

Operational performance model for banks: a dynamic data envelopment approach

PurposeOperational performance is critical for the banking sector for both managers and other stakeholders as it strongly affects the overall performance of the banking system. Traditional performance measures such as ratio analysis encountered certain shortcomings. At this juncture, data envelopment analysis (DEA) approaches are increasingly applied in bank efficiency studies. However, basic DEA models ignored the interactions between consecutive terms and focused primarily on measuring performance independently for each study period. All this is required to develop an operational performance model that can enable the long-term decision model.Design/methodology/approachAn attempt has been made to develop a dynamic DEA within a non-radial category to measure interconnection activities considering non-performing loans as an undesirable link. This study uses the Indian banking dataset from 2015 to 2019. The study's research design directs three directions: ‘comparison of the dynamic DEA with the traditional static DEA model, areas of inefficiencies that are investigated for each factor using the factor efficiency index and the robustness results highlighting the performance difference between bank categories.'FindingsComparing with static DEA results, the study confirms that the dynamic model best measures long-term operational performance due to the linkage between consecutive terms. The efficiency analysis concludes that the input factor that requires the most improvement is ‘fixed assets' and ‘deposits'. The output factor that needs the most progress is ‘non-interest income'. The robustness of the developed model is proven by ownership categories present within the Indian banking system. At a significance level of 10%, the result of both the separate and dynamic model for privately owned banks is significantly better than that of publicly owned banks.Originality/valueThis paper proposes an operational efficiency model for Indian banks in line with undesirable output. The mean factor efficiency analysis related to non-radial DEA modelling enhances managerial flexibilities in determining improvement initiatives.

Combined Shannon's entropy and DEA: case of Indian hotel and restaurant sector

A non-parametric linear programming-based data envelopment analysis (DEA) has been broadly applied in many sectors to measure the performance of decision-making units (DMUs). Basic DEA models such as CCR and BCC can bifurcate the DMUs into efficient and inefficient sets but cannot further identify the most efficient DMUs. Numerous methods have been proposed to address this issue and allotted ranks to DMUs. The main objective of the current paper is to implement an integrated DEA with Shannon's entropy to measure efficiencies and to rank hotels and restaurants (H&R) operating in India. The present study is carried out by integrating an orientation independent CCR, orientation dependent BCC, and non-oriented SBM with Shannon's entropy. The combined DEA-Shannon's entropy method provides comprehensive efficiency scores (CES) to all DMUs. The adopted methods have been applied to the selected 45 H&R companies in India for the year 2019-2020.

Efficiency Evaluation of Regional Environmental Management Systems in Russia Using Data Envelopment Analysis

The concept of eco-efficiency has recently become an issue of great importance due to the growing trend of environmental degradation, and many approaches based on Data Envelopment Analysis (DEA) are used in the literature to evaluate the environmental performance of economic systems. However, research to date has paid little attention to the possibility of extending the DEA approach to the problem of measuring the economic feasibility of eco-efficiency improvement. The main aim of this study is to evaluate the efficiency of investments focused on improving the eco-efficiency of the regional economy in Russia using the DEA approach. The various types of costs for environmental protection measures are considered as inputs and the annual decrease in specific environmental impacts of the regional economy are considered as outputs of DEA models. This is different from previous research, which generally focused on environmental efficiency only, omitting the integration of economic aspects in eco-efficiency measures. This study compares three different modifications of basic DEA models in the context of technical complexity and practical feasibility. The results show that the efficiency of regional environmental management in many Russian regions has a great potential for improvement. From a practical point of view, the Slack-Based Measure (SBM) model provides the most accurate results for policy applications. Unlike other ratings, the DEA-SBM model may stimulate an optimization of environmental protection spending and the introduction of technological and organizational eco-innovations.

Data envelopment analysis using the binary-data

PurposeThe purpose of this paper is to propose the data envelopment analysis (DEA) model that can be used as binary-valued data. Often the basic DEA models were developed by assuming that all of the data are non-negative. However, there are situations where all data are binary. As an example, the information on many diseases in health care is binary data. The existence of binary data in traditional DEA models may change the behavior of the production possibility set (PPS). This study defines a binary summation operator, expresses the modified principles and introduces the extracted PPS of axioms. Furthermore, this study proposes a binary integer programming of DEA (BIP-DEA) for assessing the efficiency scores to use as an alternate tool in prediction.Design/methodology/approachIn this study, the extracted PPS of modified axioms and the BIP-DEA model for assessing the efficiency score is proposed.FindingsThe binary integer model was proposed to eliminate the challenges of the binary-value data in DEA.Originality/valueThe importance of the proposed model for many fields including the health-care industry is that it can predict the occurrence or non-occurrence of the events, using binary data. This model has been applied to evaluate the most important risk factors for stroke disease and mechanical disorders. The targets set by this model can help to diagnose earlier the disease and increase the patients’ chances of recovery.

Combined Role of Manpower Ethics and Physical Space Facilities of Bank Branches in Productivity Analysis

Productivity growth and efficiency improvements are the major sources of economic development. Pure efficiency, scale efficiency, and technology are basic factors, and rules and regulations and balance are recently known factors affecting the productivity growth. This paper focuses on the effect of manpower ethics and physical space facilities of bank branches as two factors affecting the Malmquist productivity index in the bank branches evaluation. The proposed model uses assurance region weight restrictions to increase discrimination power of basic data envelopment analysis models for the constant return to scale technologies. The validity of the proposed model and the extended Malmquist index is confirmed with an empirical case study of 74 branches of aspecializedbank in thehousing sector of Iran in two time periods of 2017 and 2018. The results for both traditional and extended indicesand the effects of manpower ethics and physical space facilities are analyzed.

Efficiency measurement to identify the best efficient unit in the presence of dual-role factors

Efficiency measurement is crucial to management for performance evaluation of several decision-making units (DMUs) in order to find the most efficient unit. Data envelopment analysis (DEA) is a practical and popular method that has been used widely for efficiency measurement in many applications. Thus, in the literature of DEA, several mixed integer DEA models are developed to efficiency measure and find the best efficient unit. In some situations, some factors named dual-role factors may be simultaneously considered as both inputs and outputs. However, a little attention has been paid in the literature of DEA to these types of factors. The current paper develops a new mixed integer linear DEA model to identify the best efficient DMU in the presence of dual-role factors. The basic DEA models consider different roles for each dual-role factor, and also solve at least one model for each DMU to determine the best unit. However, the developed approach in this paper considers a unique role for each dual-role factor, and also determines the best unit through by solving just one mixed integer DEA model. Two numerical examples are used to illustrate the profits and applicability of the developed approach.

New approach for ranking efficient DMUs based on Euclidean norm in data envelopment analysis

Data envelopment analysis (DEA) is a widely used technique for measuring the relative efficiencies of decision-making unit (DMUs) with multiple inputs and multiple outputs. In many applications, ranking of DMUs is an important and essential procedure to decision makers in DEA, especially when there are extremely efficient DMUs. Basic DEA models usually give the same efficiency score for some DMUs. Hence, it is necessary to rank of all extreme DMUs. The main purpose of this study is to propose an appropriate method in order to overcome the drawbacks in several methods for ranking DMUs based on the DEA concept. In the present paper, we propose a model for ranking extreme efficient DMUs in DEA by super efficiency technique and Euclidean norm (l2-norm). The presented method in this paper is able to overcome the existing obstacles in some methods. As regards, the proposed model is into nonlinear programming form, a linear model is suggested to approximate the nonlinear model.

A heuristic method for choosing 'virtual best' DMUs to enhance discrimination power of augmented DEA model

Despite its intrinsic advantageous features as a tool for increasing discrimination power of the basic DEA (data envelopment analysis) model, augmented DEA has two main drawbacks including the presence of unrealistic efficiency scores and the presence of great distance between its efficiency scores and scores obtained by primary model. In this regard, this paper extends a heuristic method for dealing with both issues and improving the power of augmented DEA model in performance evaluation. Since different virtual DMUs lead to different results for ranking, the hierarchical clustering algorithm is applied in this study to select the best virtual DMUs in order to reduce the possibility of having inappropriate efficiency scores. Finally, to demonstrate the superiority of the proposed approach over previous approaches in literature, two numerical examples are provided.

Handling negative data in slacks-based measure data envelopment analysis models

This paper proposes slacks-based measure (SBM) data envelopment analysis (DEA) models that handle negative data. Unlike existing negative data allowable DEA models, the proposed SBM DEA models are consistent with ordinary SBM models and units invariant, they handle various types of returns to scale, and they avoid division by zero. These new SBM DEA models transform original negative inputs and outputs into positive counterparts based on a newly defined “base point”. Hence, these models are referred to as the BP-SBM DEA models. In addition to the basic BP-SBM DEA models, this research further develops data-oriented and application-oriented BP-SBM DEA-type models for different application problems involving negative data. Numerical examples are provided to illustrate various aspects and implementation details of these models.

A data envelopment analysis model for evaluating the performance of taxi operators: a case study in Harbin, China

ABSTRACTThis paper measures the performance of taxi operators using data envelopment analysis (DEA) and the improvement of performance based on benchmarks. This includes basic DEA models and a cross efficiency model. In the present work, a sample set of 20 taxi operators are considered, and an evaluation indicator framework is constructed, which mainly involves the identification of decision-making units (DMUs) and the determination of input-output variables. The combined results of multiple DEA models are used to evaluate the performance of taxi operators in a case study of Harbin. The results highlight the significance of the combined application of multiple DEA models, especially a cross-efficiency model that can obtain more objective efficiency values. Finally, the benchmarking method is used to capture the achievements of best-performing operators and provide valuable improvement recommendations for inefficient operators. Conclusions provide a suitable basis for the operation and management of the taxi market.

Efficiency of acute public hospitals in the region of Murcia, Spain.

Aim: To evaluate the level of efficiency of public acute hospitals situated in the region of Murcia (Spain). Materials & methods: Data from nine acute general hospitals of Murcia's Health Service (SMS by its Spanish acronym) were analyzed over the 2012-2014period. The data were extracted from the In-patient Health Establishment Statistics of the Ministry of Health, Social Services and Equality, from the National Health Service (SNS) portal and the SMS portal. To this end, the data envelopment analysis (DEA)-window method was used, since this extension of the basic DEA model allows to compare the efficiency of a small number of units over different years and analyze changes in efficiency over time. In addition, the model was complemented by smooth bootstrapping and a superefficiency analysis to improve the quality of the data interpretation. Four inputs were used (number of beds, number of operating rooms, personnel costs and operating costs), two undesirable outputs (average stay and rate of return) and three desirable outputs (weighted discharges, emergencies and surgical interventions). Results: The average level of inefficiency was 1.58% over the study period, with a good evolution between 2012 (3.53%) and 2014 (0.20%). This improvement was also reflected in the number of efficient hospitals that rose from two in 2012 to eight in 2014. Moreover, the slack levels detected were small. Conclusion: The management of the public hospitals analyzed was favorable, both regarding average level of efficiency and the number of hospitals qualified as efficient. However, the analysis revealed several ways to increase efficiency by reducing specific inputs and nondesirable outputs (mainly operating and personnel costs as well as average length of stay) while increasing desirable outputs (mostly the number of surgical interventions). To finish, specific policy measures are suggested to improve the performance of these hospitals.

Efficiency measurement to identify the best efficient unit in the presence of dual-role factors

Efficiency measurement is crucial to management for performance evaluation of several decision-making units (DMUs) in order to find the most efficient unit. Data envelopment analysis (DEA) is a practical and popular method that has been used widely for efficiency measurement in many applications. Thus, in the literature of DEA, several mixed integer DEA models are developed to efficiency measure and find the best efficient unit. In some situations, some factors named dual-role factors may be simultaneously considered as both inputs and outputs. However, a little attention has been paid in the literature of DEA to these types of factors. The current paper develops a new mixed integer linear DEA model to identify the best efficient DMU in the presence of dual-role factors. The basic DEA models consider different roles for each dual-role factor, and also solve at least one model for each DMU to determine the best unit. However, the developed approach in this paper considers a unique role for each dual-role factor, and also determines the best unit through by solving just one mixed integer DEA model. Two numerical examples are used to illustrate the profits and applicability of the developed approach.

Comprehensive evaluation of renewable energy technical plans based on data envelopment analysis

Nowadays different kinds of renewable energy resources have got rapid development. For renewable energy planning, it is meaningful to assess the comprehensive performance of different candidate renewable energy schemes and distinguish the optimal. In view of this, based on data envelopment analysis(DEA), this paper presents a comprehensive evaluation method of renewable energy technical plans. Firstly, multiple indexes from different aspects are taken into consideration to establish the index system. And in the index processing stage, intuitionistic fuzzy number theory is adopted to describe the fuzziness and ambiguity in qualitative index and convert it into quantitative calculation. Then, super-efficiency DEA model is used to determine the relative ranking of different plans based on the concept of relative efficiency. Finally, a real case of renewable energy development in 2020 from a province in China is used to illustrate the feasibility and effectiveness of the proposed method in this paper. Results show that this proposed method can overcome the shortcoming of traditional basic DEA model and get more objective evaluation results and provide beneficial references for renewable energy planning and development.

Healthcare efficiency assessment using DEA analysis in the Slovak Republic

A regional disparity is becoming increasingly important growth constraint. Policy makers need quantitative knowledge to design effective and targeted policies. In this paper, the regional efficiency of healthcare facilities in Slovakia is measured (2008–2015) using data envelopment analysis (DEA). The DEA is the dominant approach to assessing the efficiency of the healthcare system but also other economic areas. In this study, the window approach is introduced as an extension to the basic DEA models to evaluate healthcare technical efficiency in individual regions and quantify the basic regional disparities and discrepancies. The window DEA method was chosen since it leads to increased discrimination on results especially when applied to small samples and it enables year-by-year comparisons of the results. Two stable inputs (number of beds, number of medical staff), three variable inputs (number of all medical equipment, number of magnetic resonance (MR) devices, number of computed tomography (CT) devices) and two stable outputs (use of beds, average nursing time) were chosen as production variable in an output-oriented 4-year window DEA model for the assessment of technical efficiency in 8 regions. The database was made available from the National Health Information Center and the Slovak Statistical Office, as well as from the online databases Slovstat and DataCube. The aim of the paper is to quantify the impact of the non-standard Data Envelopment Analysis (DEA) variables as the use of medical technologies (MR, CT) on the results of the assessment of the efficiency of the healthcare facilities and their adequacy in the evaluation of the monitored processes. The results of the analysis have shown that there is an indirect dependence between the values of the variables over time and the results of the estimated efficiency in all regions. The regions that had low values of the variables over time achieved a high degree of efficiency and vice versa. Interesting knowledge was that the gradual addition of variables number of MR, number of CT and number of medical devices together, to the input side did not have a significant impact on the overall estimated efficiency of healthcare facilities.

A double-DEA framework to support decision-making in the choice of advanced manufacturing technologies

PurposeThe purpose of this paper is to propose a methodological framework that combines several data envelopment analysis (DEA) models to deal with the problem of evaluating and ranking advanced manufacturing technologies (AMTs) without introducing any subjectivity in the analysis.Design/methodology/approachThe methodology follows a two-phase procedure. First, the relative efficiency of every technology is calculated by implementing different DEA cross-efficiency models generating the same number of high-order indicators as efficiency vectors. Second, high-order indicators are used as outputs in a SBM-DEA super-efficiency model to obtain a comprehensive DEA-like composite indicator.FindingsThe framework is implemented to evaluate a sample of flexible manufacturing systems. Comparing it to other methods, results show that the methodology provides reliable information for AMTs selection and effective support to management decision-making.Originality/valueThis paper contributes to the body of knowledge about the utilization of DEA to select AMTs. The framework has several advantages: a discriminating power higher than the basic DEA models; no subjective judgment relative to weights necessary to aggregate single indicators and choice of aggregation function; no need to perform any transformation normalizing original data; independence from the unit of measurement of the DEA-like composite indicator; and great flexibility and adaptability allowing the introduction of further variables in the analysis.

Measuring sources of economic growth in OECD countries

Growing socioeconomic inequalities and deepening polarization among and within nations indicate a major risk of political, social and economic instability. Policymakers need to deepen their awareness and understanding of the circumstances and find useful guidance and examples to inspire their effective qualitative and quantitative policies. This paper empirically investigates the relative dynamic socio-economic efficiency of thirty OECD countries using Data Envelopment Analysis (DEA) methodology. As an extension to the basic output-oriented DEA models with variable returns-to-scale, window analysis is employed. The appropriate design of window length is also proposed in the study. In the first step, the relative efficiency of the countries was measured by four economic indicators. In the second step, four new indicators were added, covering social, institutional and environmental dimensions. It is found that performance rankings change, in some cases, very significantly and that the overall relative performance of the OECD countries increases when the set of economic indicators is extended. DOI: http://dx.doi.org/10.5755/j01.ee.28.4.18502

Improved DEA models in the presence of undesirable outputs and imprecise data: an application to banking industry in India

Data envelopment analysis (DEA) is a widely used non-parametric technique for measuring the performance of a homogeneous set of decision making units (DMUs). The basic DEA models are typically based on the conjecture of input minimization and output maximization, and are limited to crisp data. Therefore, the present paper focuses on the DEA models that can handle undesirable outputs and imprecise input–output data forms like intervals or ordinal relations or fuzzy numbers. These models measure the final efficiency of each DMU as an interval for interval and ordinal data, and a fuzzy number for fuzzy data. Moreover, comparison with the existing models show that the proposed models are more theoretically accurate, numerically efficient and measure less number of DMUs as efficient. In addition, some numerical examples with different data sets and an application to the banking industry in India are presented to validate effectiveness of the proposed models.

Malmquist Productivity Index In Ratio Data Envelopment Analysis

The selection of advanced production technologies and the use of data envelopment analysis (DEA) are important complex organizational decisions. The basic DEA models are designed for certain data, while inputs and outputs of real world problems are ratio. This is because the use of specific data is not possible or practical, and appropriately responsive to the given area. This article developed a model capable of computing the Malmquist Productivity Index (MPI) aiming to determine the progress or regression of the units under evaluation in the presence of such data. The proposed model was then applied to a numerical example, and the results were addressed.

An extended multiple criteria data envelopment analysis model

Several researchers have adapted the data envelopment analysis (DEA) models to deal with two inter-related problems: weak discriminating power and unrealistic weight distribution. The former problem arises as an application of DEA in the situations where decision-makers seek to reach a complete ranking of units, and the latter problem refers to the situations in which basic DEA model simply rates units 100% efficient on account of irrational input and/or output weights and insufficient number of degrees of freedom. Improving discrimination power and yielding more reasonable dispersion of input and output weights simultaneously remain a challenge for DEA and multiple criteria DEA (MCDEA) models. This paper puts emphasis on weight restrictions to boost discriminating power as well as to generate true weight dispersion of MCDEA when a priori information about the weights is not available. To this end, we modify a very recent MCDEA models in the literature by determining an optimum lower bound for input and output weights. The contribution of this paper is sevenfold: first, we show that a larger amount for the lower bound on weights often leads to improving discriminating power and reaching realistic weights in MCDEA models due to imposing more weight restrictions; second, the procedure for sensitivity analysis is designed to define stability for the weights of each evaluation criterion; third, we extend a weighted MCDEA model to three evaluation criteria based on the maximum lower bound for input and output weights; fourth, we develop a super-efficiency model for efficient units under the proposed MCDEA model in this paper; fifth, we extend an epsilon-based minsum BCC-DEA model to proceed our research objectives under variable returns to scale (VRS); sixth, we present a simulation study to statistically analyze weight dispersion and rankings between five different methods in terms of non-parametric tests; and seventh, we demonstrate the applicability of the proposed models with an application to European Union member countries.

LU Decomposition in DEA with an Application to Hospitals

A fundamental problem that usually appears in linear systems is to find a vector $$\mathbf{x}$$x satisfying $$\mathbf{Bx}=\mathbf{b}$$Bx=b. This linear system is encountered in many research applications and more importantly, it is required to be solved in many contexts in applied mathematics. LU decomposition method, based on the Gaussian elimination, is particularly well suited for spars and large-scale problems. Linear programming (LP) is a mathematical method to obtain optimal solutions for a linear system that is more being considered in various fields of study in recent decades. The simplex algorithm is one of the mostly used mathematical techniques for solving LP problems. Data envelopment analysis (DEA) is a non-parametric approach based on linear programming to evaluate relative efficiency of decision making units (DMUs). The number of LP models that has to be solved in DEA is at least the same as the number of DMUs. Toloo et al. (Comput Econ 45(2):323---326, 2015) proposed an initial basic feasible solution for DEA models which practically reduces at least 50 % of the whole computations. The main contribution of this paper is in utlizing this solution to implement LU decomposition technique on the basic DEA models which is more accurate and numerically stable. It is shown that the number of computations in applying the Gaussian elimination method will be fairly reduced due to the special structure of basic DEA models. Potential uses are illustrated with applications to hospital data set.