Standard Data Envelopment Analysis Research Articles

Weight-restricted approach on constant returns to scale DEA models: Efficiency of internet banking in Turkey

The performance measurement of decision-making units (DMUs) can be optimised by strategically choosing the optimal input and output weights. This approach circumvents the potential complexities associated with establishing a common basis for ranking homogeneous units and managing the results from multiple efficient frontiers. Efficient frontier ranking is simplified by limiting the weights based on the intrinsic or technical connections between relevant inputs and outputs. It also creates a coherent framework that coordinates various considerations across all under-evaluated DMUs and variables. This method effectively examines the complexities related to the inputs and outputs of homogeneous units using conventional data envelopment analysis (DEA) models to evaluate the efficiency of these units.In the banking sector, the relationship between inputs and outputs is complex. This complexity requires careful attention, particularly in cases where the existence of one input or output is conditional on the existence of another. This dynamic is accentuated by the integration of advanced fintech solutions, which inherently provide significant benefits to banking operations. The use of appropriate analytical models for banking data is important. The synergy between advanced financial technologies and analytical methods has reached its peak in reducing losses and strengthening profits. This, in turn, facilitates the optimal allocation of financial resources to growth-oriented projects.The empirical application of weight-restricted DEA models to Turkey's mobile-internet-banking sector underscores their efficacy. These modified models contrast with the standard DEA versions, which often encounter challenges stemming from the allocation of weights to pivotal variables. This study demonstrates the tangible benefits of our approach and its potential to revolutionise the efficiency assessment landscape in the banking sector.The proposed unconventional model which is applied to Turkish Internet banking for the period extending from 2006 to 2018 and which takes a comprehensive weight restriction approach to DEA and encompasses all relevant input and output variables, resulted in strictly positive weights and a reduced count of inflated efficient units along with their scores. This is practically beneficent to banking sector in so far as it facilitates decision-making and related economic and financial planning, and as it proves to be a reliable benchmark by local or international banks intending to provide or develop Internet banking services.

Efficiency and Stability of Zakat Institutions in Malaysia and Indonesia: DEA Window Analysis

The purpose of this study is to measure the efficiency and stability of zakat institutions in Indonesia and Malaysia. There are 14 zakat institutions that will be analyzed using data envelopment analysis (DEA) window analysis during the period of 2016 to 2020. The result shows that the average efficiency of zakat institution is 78% during the observation period. The inefficiency of zakat institutions from both countries is caused by the less-than-optimal distribution of zakat funds. The best performing zakat institutions are Selangor, BSMU, BRI Foundation, and Baznas because they have the highest efficiency and stability. This study has filled the gap in previous studies that measure the efficiency of zakat institutions with the standard DEA approach. Zakat institution managers can identify potential improvements in input and output variables to achieve maximum level of efficiency.

Assessing environmental and operational efficiencies: a multi-objective optimisation problem in a two-stage network data envelopment analysis

Abstract The environmental efficiency of industries plays an important role in economic development of countries. Accordingly, dividing the internal network structure of industries into two sub-processes, including green and operational stages enables decision-makers to assess both of the efficiencies simultaneously. Such assessment can be implemented using a non-parametric methodology termed data envelopment analysis (DEA). Standard DEA models consider the whole system of decision-making units (DMUs) as a single process (i.e., black-box). The black-box approach ignores modelling of the internal network structure of the assessed DMUs. This issue tackled by network DEA models since it considers the internal network structure of DMUs. In the network DEA, the efficiency evaluation of system stages is essential to identify its overall efficiency, resulting to a multi-objective optimisation problem. Therefore, the network DEA is a widely welcomed methodology proposed for solving multi-objective problems. This paper assesses the operational and environmental efficiencies of a network structure system by converting the multi-objective optimisation problem into a linear single objective function. In this investigation, a technique of tri-objective function problem is proposed. The proposed technique transforms into a single objective function by keeping one objective function and shifting the other two objective functions into the model’s constraints. The applicability and usefulness of the proposed technique have been tested using a data set of 20 industries. The developed approach provides valuable evaluations to decision-makers to rank DMUs by considering their green and operational efficiency simultaneously.

Efficiency assessment in European air traffic management—A fundamental analysis of data, models, and methods

The European air traffic management system is characterized by its dynamic and diverse nature. To effectively address future challenges, it is crucial to enhance efficiency and resilience within the system. As an integral part of the air traffic system, air navigation services operate as natural monopolies and are subject to regulatory measures. While operational and academic studies regularly address performance benchmarking, no comparative analysis is currently evaluates the different approaches. This paper introduces a fundamental analysis for evaluating the technical efficiency of air traffic control at the air navigation service provider (ANSP) level. Our research encompasses the development of an economic model, the application of various analytical methods, and the careful selection of units for evaluation. Our findings demonstrate that the standard Data Envelopment Analysis (DEA) method is highly effective. Additionally, we emphasize that not all ANSPs shall be included in the analysis, and we caution against incorporating monetary inputs and outputs in the model.

Efficient water use and reusing processes across Spanish regions: A circular data envelopment analysis with undesirable inputs

Efficiency is a fundamental element of the transition towards a sustainable use of natural resources among which water has a prominent role. Spain is among the European countries facing considerable water shortage problems, particularly within its southern regions. The current paper introduces an efficiency evaluation model based on Data Envelopment Analysis (DEA). The model incorporates a subset of outputs as undesirable inputs to account for the circularity of the production process by means of treated and reused water. The main differences existing between the circular approach proposed, a standard two-stage DEA model and a dynamic Slacks-Based Measure process will be carefully illustrated. In particular, the drawbacks faced by these latter techniques to consistently incorporate circularity into their optimization structures will be demonstrated both formally and numerically. The malleability of the current approach and its capacity to generate consistent identifiable categories among the regions evaluated within a circular production setting will be studied in detail. The analysis performed demonstrates significant structural differences in production capacity and efficiency across regions, with the inefficient ones displaying a considerable subpar use of resources through their production processes. The prevalence of inefficiency in several northern regions is partially compensated through their treatment and reutilization of water. However, this is not the case for the southern ones, which face stricter constraints and more inefficient production processes. The subsequent implications for efficiency are examined and policies aimed at enhancing the structural quality of the corresponding production systems suggested.

Unveiling the potential of hotel mergers: A hybrid DEA approach for optimizing sector-wide performance in the hospitality industry

Mergers and acquisitions (M&A) in the hospitality sector involve consolidating assets among hotels through partnerships. While data envelopment analysis (DEA) has been widely used for hotel efficiency analysis, little attention has been paid to hotel M&A. In this paper, a hybrid DEA methodology consisting of two stages is proposed to identify optimal matches among hotels to enhance sector performance. The initial stage employs an inverse Data Envelopment Analysis (IDEA) model to evaluate the maximum gains that could potentially be generated from pairwise consolidations among hotels. A DEA procedure that incorporates a standard DEA model and a greedy heuristic is developed in the second stage to identify the optimal pairs of hotel mergers. The optimal merger strategy for the entire hotel industry is determined from the complete set of hotels under consideration.The pertinence of the proposed methodology is shown through a sample of 58 hotels from the Sultanate of Oman

Tourism Efficiency: Bootstrap-Data Envelopment and Tobit Panel Data Analysis

Tourism is an important sector for countries, not only for cultural but also for economic activities. The tourism sector, which operates effectively, contributes to the development of the country's economy. Therefore, the study aims at calculating tourism efficiency and identifying factors that influence it. The study discussed 18 European countries that are among the world's major destination centers. Firstly, tourism efficiency scores were calculated with the data covering the period 2002-2019. Inputs are the number of tourists and tourism expenditures, and output is tourism revenues. Tourism sector efficiency was calculated with the standard Data Envelopment Analysis (DEA) model. Because of the possible statistical limitations of the DEA method, analysis was repeated with the Bootstrap-DEA method. The resulting efficiency scores were used as dependent variables in the Tobit model. The variables including per capita income, digitalization, energy consumption, financial development, political stability, and life expectancy at birth were handled as new trends of tourism efficiency in the Tobit panel data analysis. Boostrap DEA results, which yield more accurate results, gave efficiency results for a smaller number of countries than the efficiency analysis performed with standard DEA. Tobit panel data analysis results showed that income per capita, digitalization, political stability, and life expectancy at birth enhanced tourism efficiency. In the study, unlike the literature, tourism efficiency was not considered at the level of companies, but at the level of countries. In addition to the standard DEA analysis, the Bootstrap DEA method was used, which yielded superior results. Additionally, not only the efficiency values were calculated in the study, but also the factors affecting the tourism efficiency were determined.

An unsupervised learning-based generalization of Data Envelopment Analysis

In this paper, we introduce an unsupervised machine learning method for production frontier estimation. This new approach satisfies fundamental properties of microeconomics, such as convexity and free disposability (shape constraints). The new method generalizes Data Envelopment Analysis (DEA) through the adaptation of One-Class Support Vector Machines with piecewise linear transformation mapping. The new technique aims to reduce the overfitting problem occurring in DEA. How to measure technical inefficiency through the directional distance function is also introduced. Finally, we evaluate the performance of the new technique via a computational experience, showing that the mean squared error in the estimation of the frontier is up to 83% better than the standard DEA in certain scenarios.

A bootstrapping dynamic two-stage SBM model: An application to assess industrial water use and health risk systems

Despite the great number of applications of bootstrapping data envelopment analysis (DEA) with a one-stage structure, there are limited attempts for approximating the distribution of the DEA estimator considering the two-stage structure across multiple periods. This research develops the dynamic two-stage non-radial DEA model based on smoothed bootstrap and subsampling bootstrap. Then, we run the proposed models on assessing the efficiency of China's industrial water use and health risk (IWUHR) systems and compare them with the bootstrapping results on standard radial network DEA. The results are as follows. (1) The proposed non-radial DEA model based on smoothed bootstrap can adjust original over-estimated and under-estimated values. (2) China's IWUHR system has good performance, and its HR stage performs better than the IWU stage for 30 provinces from 2011 to 2019. The poor performance of the IWU stage in Jiangxi and Gansu needs to be noticed. The provincial differences of the detailed bias-corrected efficiencies expand in the later period. (3) The rankings of IWU efficiency in the three regions are in agreement with that of HR efficiency: eastern, western, and central regions in this order. Particular attention should be paid to the downward trend of the bias-corrected IWUHR efficiency in the central region.

Limiting flexibility in nonparametric efficiency evaluations: An ex post k-centroid clustering approach

An attractive feature of standard data envelopment analysis (DEA) is that decision-making units (DMUs) are put in their best possible light. This is accomplished by not imposing any restrictions on how the inputs and outputs can be weighted together. Unfortunately, this flexibility also has drawbacks. The underlying best practice production structure is typically very complex, which can make it difficult to understand efficiency evaluations. This flexibility may also motivate DMUs to pick specialized input‒output combinations simply to appear more efficient, particularly in applications where they are rewarded based on their DEA performance. In this paper, we therefore propose an approach that retains the fundamental principles of DEA while reducing flexibility in the evaluations. Our approach entails performing ex post k-centroid clustering of DMUs according to their input‒output mix patterns. The resulting mixed-integer programming problem seeks to put the centroids (as prototypes of their generated clusters) in their best possible light. This clustering enables us to work with a less complex underlying technology, which simplifies the evaluations and enhances their interpretability. Furthermore, it limits the possibility of DMUs inflating their performance by selecting extreme input‒output combinations. We demonstrate our approach with a simple numerical example and a complex data set involving Brazilian electricity distribution companies, providing clear and realistic illustrations of its application.

Modeling Retail Supply Chain Efficiency: Exploration and Comparative Analysis of Different Approaches

Retail supply chains are key on any market. Their significance has long been recognized in the literature and in practice. Various factors such as pandemics, economic crises, wars, and natural disasters have further increased interest in this area. However, the most recent research has focused more on resilience, sustainability, energy consumption, and a circular economy, while the efficiency of logistics processes has been almost completely overlooked. Logistics process efficiency in retail supply chain is a fundamental principle without which all mentioned performances cannot have desired values. This gap is precisely the main motivation of this research. In this paper, research models in literature are presented which can be used, with some modifications, to measure the efficiency of the retail supply chain. The models were based on the data envelopment analysis (DEA) approach. Four main groups were identified: standard DEA models, efficiency decomposition models, network models, and game-theory-based models. In the second part of the paper, various approaches were tested on a real example of a trading company operating in Serbia. Seven supply chains were observed, each consisting of a distribution center (DC) and retail store (RS). Variables used were the number of pallet places, logistics costs, number of deliveries, accuracy of deliveries, and turnover. The results showed the advantages and disadvantages of different approaches in real examples. The main contributions of this paper lie in unique approaches to measuring the efficiency of the retail supply chain. The paper creates an excellent foundation for future research and measurements on real systems, which is equally useful for researchers and industry experts.

How to peel a data envelopment analysis frontier: A cross-validation-based approach

Data Envelopment Analysis (DEA) presents the typical characteristics of a data-driven approach with the specific objective of determining technical efficiency and production frontiers in Engineering and Microeconomics. However, by construction, the frontier estimator generated by DEA suffers from overfitting problems; something that contrasts with currently accepted models in machine learning. In this regard, DEA can be seen as a preliminary stage of a more complex approach, where the aim is to avoid overfitting in order to determine a proper description of the underlying Data Generating Process that is behind the generation of the observations in a production process. In this paper, we introduce a possible solution to overcome the overfitting problem associated with DEA that is based on cross-validation. This process “peels” the standard DEA frontier (removing certain supporting hyperplanes) until a new convex technology, which also satisfies free disposability in inputs and outputs but not the principle of minimal extrapolation, is determined. Our approach is tested by resorting to a computational experience. Additionally, we illustrate how the new method could be used as a complement to the standard DEA technique through an empirical application based on a PISA (Programme for International Student Assessment) dataset.

Economic complexity and human development: comparing standard and slack-based data envelopment analysis models

Economic complexity and human development: comparing standard and slack-based data envelopment analysis models

A mixed-integer slacks-based measure data envelopment analysis for efficiency measuring of German university hospitals

Standard Data Envelopment Analysis (DEA) models consider continuous-valued and known input and output statuses for measures. This paper proposes an extended Slacks-Based Measure (SBM) DEA model to accommodate flexible (a measure that can play the role of input and output) and integer measures simultaneously. A flexible measure’s most appropriate role (designation) is determined by maximizing the technical efficiency of each unit. The main advantage of the proposed model is that all inputs, outputs, and flexible measures can be expressed in integer values without inflation of efficiency scores since they are directly calculated by modifying input and output inefficiencies. Furthermore, we illustrate and examine the application of the proposed models with 28 university hospitals in Germany. We investigate the differences and common properties of the proposed models with the literature to shed light on both teaching and general inefficiencies. Results of inefficiency decomposition indicate that “Third-party funding income” that university hospitals receive from the research-granting agencies dominates the other inefficiencies sources. The study of the efficiency scores is then followed up with a second-stage regression analysis based on efficiency scores and environmental factors. The result of the regression analysis confirms the conclusion derived from the inefficiency decomposition analysis.

An inverse data envelopment analysis model to consider ratio data and preferences of decision-makers

Abstract Inverse data envelopment analysis (DEA) determines the optimal level of inputs and/or outputs of decision-making units (DMUs) to reach efficiency targets. This paper presents a new inverse DEA model for determining minimum inputs for working capital management. The proposed model is employed in the Indian textile industry to calculate working capital efficiency. Given the working capital efficiency, the decision maker’s preferences will be estimating the change in inputs when outputs increase. Furthermore, unlike the standard inverse DEA model, this article discusses the inverse DEA model when negative ratio data exist. The DEA model requires additional attention when ratio data are present; therefore, a novel inverse DEA ratio model is proposed. The input targets obtained from the proposed model are less than the standard inverse DEA model. Also, the proposed model is a closer estimate of the production probability set for ratio data.

CREDIT RISK AND PERFORMANCE EVALUATION OF COOPERATIVES IN REGION XI USING DATA ENVELOPMENT ANALYSES (DEA)

<p>This paper illustrated the methodology that indicates how the Standard and Negative Data Envelopment Analysis (DEA) techniques can be applied to predict likely failures in cooperatives. The general objective of this study is to develop a model that will provide a reasonably accurate means of evaluating the creditworthiness of cooperatives in Region 11 using information obtained from the Financial Statement submitted to the Cooperative Development Authority. The results showed that the Standard DEA could predict cooperatives that are likely to fail in combination with the Negative DEA. The proposed methodologies identify companies with a high risk of going bankrupt by looking at their total financial situation.</p><p><strong>JEL:</strong> E51; L10; L20</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0954/a.php" alt="Hit counter" /></p>

Unsupervised Detection of Misinformation in Financial Statements

Companies have incentives to hide, omit, or falsify the information reported in financial statements (FS) (e.g., Balance Sheet, Income Statement, Cash Flow Statement) to give a false impression of the company's financial health, assure investors or evade taxes. Typically, misinformation is introduced by changing FS elements e.g overstating the assets/profits or understating the liabilities/losses. Once detected, misinformation can have disastrous consequences for employees, investors, banks and government. It is important to identify such companies and the nature and extent of misinformation in their FS. Auditors or forensic accountants use complex investigative methods to detect instances of misinformation in FS. The effort intensive and subjective nature of these methods limits their capacity to effectively identify misinformation. We propose two novel unsupervised model-based anomaly detection (AD) techniques based on regression and kernel density estimates. We show they perform better than 15 standard AD techniques and data envelopment analysis for detection of suspicious FS on a real-world dataset of 4100 listed companies. Our approach provides specific suggestions regarding where the misinformation may be present, which helps in increasing the effectiveness of investigations.

Performance evaluation of two-stage production systems with time-lag effects: An application in the horticulture industry

In standard data envelopment analysis (DEA), it is assumed that inputs of a specific production period are used to generate outputs of the same period. However, in some practical examples, time-lag effects exist between inputs and outputs. The inputs of one period are used to generate outputs for several periods, or inputs of several periods are used to create outputs for one period. In this paper, we present some new DEA models for performance assessment of network production systems with time-lag effects. An empirical application in the horticulture sector in Iran shows the usefulness and capabilities of our proposed approach.

Audit Risk Evaluation Using Data Envelopment Analysis with Ordinal Data

This study examines the data envelopment analysis (DEA) model for audit risk evaluation which was initially developed by Bradbury and Rouse (2002) and reinterpreted by Davutyan and Kavut (2005). Bradbury and Rouse (2002) apply the standard DEA model for audit risk factors, including judgemental (ordinal) measures. In the presence of ordinal data, efficiency analysis in DEA requires appropriate models to be applied instead of the standard DEA model. Accordingly, audit risk evaluation based on the standard DEA model is not assessed appropriately because the risk factors are qualitative and ordinal measures. Hence, we employ an appropriate DEA model to accurately evaluate audit risk in the presence of ordinal data. In light of the prior two studies, our results demonstrate the appropriateness of the ordinal DEA model.

Weight profiles in cross-efficiency evaluation based on hypervolume maximization

In this paper, we propose and discuss a new model in order to obtain the weight profiles to be used in the calculation of cross-efficiency scores in Data Envelopment Analysis (DEA). In the standard DEA literature, total flexibility in the selection of weights has been strongly criticized due to the fact that some inputs and/or outputs may have no contribution (zero weights) to the efficiency scores. Although cross evaluation somewhat mitigates the effect of these unrealistic weighting schemes on the amalgamation of weights that standard DEA models produce, we propose a method that ensures strictly positive and balanced weights to be used in cross-efficiency evaluation. In particular, we propose an optimization model based on hypervolume maximization as a method for selecting a suitable weight profile for each evaluated unit to be used in cross-efficiency. The model is solved through Second-Order Cone Programming (SOCP). As a result, the solution obtained is unique and the Production Possibility Set (PPS) is not modified. Another advantage of the proposed method, among others, is that it does not need subjective expert opinions or full-dimension efficient facets of the polyhedral PPS, but if available, it can be easily incorporated into the model. The approach introduced is illustrated through a numerical example. We also present some theoretical results.