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We adopt a super slack-based measurement (SBM) data envelopment analysis (DEA) model to estimate the efficiency of five biggest freight rail operators in Russia, which are included in the top 30 freight rail operators in terms of two dimensions – financial and operational efficiency during 2013–2017. The result shows that the private companies characterized by high financial and operational efficiency, while the Rossiiskye Zheleznye Dorogi (RZD) subsidiaries characterized by sufficiently low financial and operational efficiency scores. And the result also presents that operational efficiency score of operators handling universal rolling stock is higher than financial efficiency scores. In contrast, financial efficiency scores of operators handling special rolling stock is higher than operational efficiency scores. rail freight operators in addition to a special rolling stock park should have a universal rolling stock park for higher profitability. State-owned companies and its subsidiary operate inefficiently in the midst of a market economy in Russia. Rail freight operators for a higher level of financial efficiency should be transferred to the private sector.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

This paper aims to assess the application of seven statistical and data mining techniques to second-stage data envelopment analysis (DEA) for bank performance.

Different statistical and data mining techniques are used to second-stage DEA for bank performance as a part of an attempt to produce a powerful model for bank performance with effective predictive ability. The projected data mining tools are classification and regression trees (CART), conditional inference trees (CIT), random forest based on CART and CIT, bagging, artificial neural networks and their statistical counterpart, logistic regression.

The results showed that random forests and bagging outperform other methods in terms of predictive power.

This is the first study to assess the impact of environmental factors on banking performance in Middle East and North Africa countries.

This paper presents an alternative approach to evaluating the overall efficiency and performance of Taiwanese container ports. Specifically, a parallel activity with series structure concept in the form of data envelopment analysis (MNDEA) is used to construct a model that applies to three different activities: harbor management, stevedoring and warehousing operations. We will further divide each activity into two process types, production processes and services processes. We will also adopt a Delphi survey approach and use the Analytic Network Process (ANP) to identify these processes’influence dependence and their degree of importance for the MNDEA model setting. An empirical application demonstrates the performance of Taiwanese container ports by using MNDEA with window analysis techniques via the directional distance functionThe results demonstrate that the application is effective in indicating and/or suggesting resource-adjustments, while considering which undesirable output levels and shared inputs were involved. The results also present directions for possible improvements in workplace efficiency.

The purpose of this paper is to improve the estimation of the production frontier in cases where outliers exist. We focus on the case when outliers appear above the true frontier due to measurement error.

The authors use stochastic data envelopment analysis (SDEA) to allow observed points above the frontier. They supplement SDEA with assumptions on the efficiency and show that the true frontier in the presence of outliers can be derived.

This paper finds that the authors’ maximum likelihood approach outperforms super-efficiency measures. Using simulations, this paper shows that SDEA is a useful model for outlier detection.

The model developed in this paper is original; the authors add distributional assumptions to derive the optimal quantile with SDEA to remove outliers. The authors believe that the value of the paper will lead to many citations because real-world data are often subject to outliers.

This paper aims to define the efficiency achieved by urban transport companies in Spain concerning the resources they use, considering the type of management used for implementation, public-private, and size.

This study consisted of an analysis of the efficiency of 229 public-private urban transport operators during the period 2012–2021 using Data Envelopment Analysis, the Malmquist Index and inference estimators to determine productivity, efficiency change into Pure Technical Efficiency Change (PTECH), and scale efficiency change.

Based on the efficiency analysis, the authors concluded that of the 229 companies studied, more than 35 were inefficient in all analysed periods. Considering the sample used, direct management is considered significantly more efficient. It cannot be concluded that the size of these companies influences their efficiency, as the data show unequal development behaviours in the studied years.

This study provides arguments on whether there is a significant difference between the two types of management in the urban transport sector. It also includes firm size as a study variable, which has not been previously considered in other studies related to urban transport efficiency. Efficiency should be a crucial factor in determining funding allocation in this sector, as it encourages operators to optimize and improve their services.

This paper aims to propose an integration of the analytic hierarchy process (AHP) and data envelopment analysis (DEA) methods in a multiattribute grey relational analysis (GRA) methodology in which the attribute weights are completely unknown and the attribute values take the form of fuzzy numbers.

This research has been organized to proceed along the following steps: computing the grey relational coefficients for alternatives with respect to each attribute using a fuzzy GRA methodology. Grey relational coefficients provide the required (output) data for additive DEA models; computing the priority weights of attributes using the AHP method to impose weight bounds on attribute weights in additive DEA models; computing grey relational grades using a pair of additive DEA models to assess the performance of each alternative from the optimistic and pessimistic perspectives; and combining the optimistic and pessimistic grey relational grades using a compromise grade to assess the overall performance of each alternative.

The proposed approach provides a more reasonable and encompassing measure of performance, based on which the overall ranking position of alternatives is obtained. An illustrated example of a nuclear waste dump site selection is used to highlight the usefulness of the proposed approach.

This research is a step forward to overcome the current shortcomings in the weighting schemes of attributes in a fuzzy multiattribute GRA methodology.

This study aims at evaluating the technical efficiency (TE) of healthcare systems in the Arab region and exploring the key factors that affect the efficiency performance.

The study applies a two-stage Data Envelopment Analysis (DEA) approach to a sample of 20 Arab countries. In the first stage, a DEA model is used to calculate the TE scores of the examined healthcare systems in 2019 and 2010, following both the output and input orientations of efficiency. In the second stage, a censored Tobit model is estimated to investigate the determinants of healthcare efficiency.

DEA results of 2019 indicate that achievable efficiency gains of the Arab countries range from 0.4% to 16% under the output and input orientations, respectively. Six countries are efficient under both orientations. Although the average efficiency scores of the Arab countries have deteriorated between 2010 and 2019, Djibouti and Sudan had the greatest efficiency improvements between the two years. Bahrain, Mauritania, Morocco and Qatar proved to be efficient in 2010 and 2019 under the two orientations of efficiency and according to the two DEA specifications followed. The Tobit model reveals that corruption and government health expenditure tend to have an adverse impact on healthcare efficiency.

The author evaluates healthcare efficiency and healthcare's efficiency determinants in the Arab countries. Regardless Arab countries' diversity, these countries are facing common health challenges, including diminishing role of governments in healthcare financing; increased out-of-pocket healthcare spending; poor healthcare outputs and prevalence of health inequities resulting from weak governance institutions. Comparing the efficiency of healthcare systems between 2010 and 2019 gives insights on the potential impact of the Arab spring uprisings on healthcare efficiency. Moreover, examining the determinants of healthcare efficiency allows for better understanding of how to improve the efficiency of healthcare systems in the region.

This paper uses various Data Envelopment Analysis (SBM-DEA) approaches to study the efficiency of major airlines in Asia-Pacific region. To evaluate the operation efficiency of fourteen major airlines in Asia-Pacific region from 2003-2011, Available Seat Kilometers(ASK), Available Ton Kilometers(ATK), the number of employees are used as input factors, Revenue Passenger Kilometers(RPK), Revenue Ton Kilometers(RTK), the amount of Sales are used as output factors.

The non-radial SBM-DEA (Slacks-based Measure of Efficiency) model was able to provide a more comprehensive efficiency of combining economic performance and regional difference. And it was also able to capture slack values in input excess and output shortage.

The results demonstrate that Korea and Japan airlines are operated efficiently and could be regarded as the benchmarking airlines. On the other hand, most of the China and ASEAN airlines are deemed to be inefficient. Also analyzing slacks may be more suitable way for the evaluation or suggestion of an improvement scheme for the inefficient airlines. The excess of labor is the major cause of the airlines’ inefficiency.

In today’s multi-channel distribution environment, it is crucial for the competitiveness of retail stores and companies to improve efficiency of dedicated logistics centers (DLCs) servicing types of retail outlets such as large retail-stores, super supermarkets, and convenient stores. This paper derives efficiency improvement strategies for DLCs through efficiency analyses. To this end, this paper empirically analyzes the efficiency of DLCs by applying DEA (data envelopment analysis) approaches using real data from a Korean distribution company. In detail, this paper analyzes operations, finance, and service efficiencies of DLCs and performs a comparison analysis on the efficiency between DLCs. Finally, this paper discusses the analysis results considering the DLCs’ characteristics and derives managerial and operational implications.