Output-oriented Data Envelopment Analysis Model Research Articles

Sustainable approach towards alternatives for the use of iron ore tailings in the construction sector using Data Envelopment Analysis methodology.

Iron ore tailings (IOTs) need to be properly managed to mitigate the environmental, social, and economic impacts of mining activities. To cope with this issue, we use data envelopment analysis (DEA) to evaluate alternatives for using IOT in the construction sector. The classical and weight restriction output-oriented DEA models were used in this analysis. The results show that the ranking of alternatives depends on the aspect being evaluated. Concrete block is the most environmentally friendly alternative when analysing both models. For both social and economic aspects, ceramics produced better results in the classical model, whereas Portland cement showed better outcomes in the weight restriction model. In this sense, the results suggest great potential for the use of IOT in the construction sector, enabling the reduction of risks and social and environmental impacts of tailings dams.

The Efficiency of Alternative and Conventional Energy Exchange-Traded Funds: Are Clean Energy Exchange-Traded Funds a Safer Asset?

This paper examines the efficiency of alternative energy equity Exchange-Traded Funds (ETFs) and conventional energy equity ETFs from 2018 to 2020, utilizing a combination of an output-oriented Slack-Based Data Envelopment Analysis (DEA) model and cluster analysis. In the context of an output-oriented DEA model, efficiency is defined as the ability of an ETF to maximize its outputs (annualized average return; environmental, social responsibility, and corporate governance; and net asset value) given a fixed level of inputs (expense ratio and beta). The findings indicate that alternative energy ETFs have the potential for long-term outperformance compared to conventional energy ETFs in terms of efficiency. However, during financial crises, the performance differences between the two types of ETFs diminish, with no significant outperformance observed in either category. The expense ratio and net asset value are identified as key factors influencing the efficiency of both ETF types. Additionally, social and governance metrics have a notably stronger positive impact on conventional energy ETFs relative to alternative energy ETFs, highlighting the increasing significance of these factors in financial asset performance.

How to promote the sustainable development of strategic emerging industries: Evidence from Guangzhou

The strategic emerging industry (SEI) can effectively cope with high energy consumption, high pollution and low efficiency in traditional production mode, which attracts high attention. However, few studies quantitatively analyze the input-output relationships and the sustainable development level of SEIs. Moreover, existing method frameworks fail to consider both the current situation and development potential of SEIs. To deal with the above research gaps, a novel static-dynamic intuitionistic fuzzy evaluation framework integrating output-oriented data envelopment analysis model, improved Malmquist model, triangular intuitionistic fuzzy number, clustering method and Tobit regression model is first proposed. 120 SEIs from Guangzhou are selected for empirical study. Results reveal that all SEIs have almost no expected movement in operational cost but obvious redundancies in employee expenditure with a standard deviation of 1.771. It's urgent to optimize the employment, assessment and salary systems for staff. Moreover, SEI sustainable development scores vary from 0.473 to 1. It's necessary to strengthen cultivation of excellent SEIs and pay attention to supervision of the weak. Research findings can not only guide local government and industry associations to make scientific evaluation, but also help SEIs carry out self-inspections, which can further promote sustainable development of the entire industry.

Efficiency of Turkish Health Management Departments through Data Envelopment Analysis

The purpose of this study was to determine which of the input and output variables made a statistically significant difference in the efficiency status of undergraduate departments offering Health Management education in Turkey, to identify areas where inefficient departments could improve, and to determine which of the input and output variables made a statistically significant difference in the efficiency status of inefficient departments. The output-oriented data envelopment analysis model was employed in the efficiency analysis. As a decision-making unit, there were 43 Health Management departments. Mann-Whitney U test was used to analyse whether or not there was a statistically significant difference between the efficiencies of the departments based on the input and output variables. In Turkey, 15 of 43 state universities providing formal undergraduate education in the field of Health Management are fully efficient. Also, there was a significant difference in terms of the “number of completed projects” and the “number of papers in journals screened within the scope of Web of Science Core Collection (WOSCC)” variables. No study examining the efficiency of Health Management departments has been found in the literature. Department administrators are encouraged to increase their publications and look into ways to design more initiatives.

Planning for potential increases in disbursements and risk of managed funds conditional on desired short-term performance levels

ABSTRACT As increasing disbursements (such as costs and fees) and risk (such as portfolio risk and leverage risk) may affect managed fund performance, analysts go beyond risk-adjusted return measures for performance appraisal. A methodology that assesses performance in a multidimensional framework is data envelopment analysis (DEA). A variant of DEA is inverse DEA. In this paper, inverse DEA is applied to determine output (investment income and benefit payments) targets for a given fund to perform at a desired efficiency level when increase in disbursements and risk at known levels is envisaged. An output-oriented inverse DEA model assuming variable returns to scale is formulated with theoretical underpinning. The proposed modelling framework ensures that, when an input augmented fund with estimated output targets is included in the observed fund set, the frontier of best performance established with the observed fund set does not change. The inverse DEA model is applied to a sample of Australian superannuation funds to demonstrate how a given fund may obtain pathways to improve its performance under different input-augmentation scenarios. As different pathways suggest feasible output targets, fund managers may find them valuable in forward planning.

The Impact of COVID-19 on Bank Efficiency in the Western Balkans: A DEA Approach

Abstract This paper aims to assess and compare the relative efficiency of commercial banks in six Western Balkan developing countries (North Macedonia, Serbia, Montenegro, Bosnia and Herzegovina (B&H), Kosovo1, and Albania), using the leading nonparametric methodology Data Envelopment Analysis (DEA) for the period 2016-2020, and investigating the impact of the COVID-19 pandemics on the performance of banking systems in these countries. The output-oriented DEA model has been implemented with interest expenses and non-interest expenses as inputs and interest revenue and non-interest revenue as outputs. Our findings show that the average efficiency of the six Western Balkan banking systems differs, and Kosovo’s banking system has noted the highest average efficiency in the whole observed period (2016–2020). In contrast, the banking system in Bosnia and Herzegovina noted the lowest average efficiency. The COVID-19 pandemic decreased the relative efficiency of the banking sectors in all six of the Western Balkan countries observed, except Kosovo. However, additional research that includes all pandemic years is recommended to assess the impact of the COVID-19 pandemic on the banking systems in the Western Balkans. This study provides invaluable insights for academic members, banking management and regulatory bodies, governments, and the interested public. It is the first empirical study that includes a glimpse of the first impact of the COVID-19 pandemic on banking systems in the developing countries of the Western Balkans.

A novel algorithm for evaluating the configurations of omni-channel distribution network considering transparency and consensus formation

In this paper, an algorithm has been developed to evaluate the performance of omni channel distribution network configurations based on data envelopment analysis (DEA) models and consensus formation. At first, bi-objective input- and output-oriented simultaneous DEA models are developed to evaluate the configurations with respect to two common criteria, i.e., the service and cost, and the transparency criterion which is defined based on the blockchain technology. Then, a consensus is formed on the evaluation results, based on a risk-based optimisation model. The proposed models are solved by fuzzy goal programming. In addition, the application of the proposed models is demonstrated through a case study in the context of the Indian retail industry. The computational results extracted from the optimisation models developed in our paper showed that the transparency criterion is very effective in the configurations evaluations and scores.

Reconciling productivity, profitability and sustainability of small-holder sugarcane farms: A combined life cycle and data envelopment analysis

CONTEXTSustainable crop production is critical to address the grand challenges of climate change and food security, and eco-efficiency is increasingly used to assess systems sustainability. Sugarcane is a major high biomass, high input sugar and bioenergy crop but with a relatively large carbon footprint. China is the third largest sugarcane producing country. However, its production eco-efficiency remains unclear. A reliable assessment of sugarcane crop production eco-efficiency is necessary to identify the most important targets and effective strategies to improve its economic and environmental outputs. OBJECTIVESThis study aimed to i) establish an eco-efficiency assessment model framework suitable for sugarcane crop production system in general, ii) quantify the eco-efficiency of sugarcane production in China, and iii) propose strategies to improve sugarcane crop productivity, sustainability and economic profit, while reducing its environmental cost. METHODSHere, based on farmer survey data, we combined life cycle assessment (LCA) and data envelopment analysis (DEA) to determine eco-efficiency and sustainability of small-holder sugarcane farm production system in southern China. Six modelling approaches, including both input- and output-oriented, were used to assess sugarcane agriculture eco-efficiency. RESULTS AND CONCLUSIONSAn output-oriented DEA model with yield and profit as desirable and carbon footprint as undesirable outputs proved to be superior to the other approaches for sugarcane production eco-efficiency assessment. The estimated average eco-efficiency of plant (first year) and ratoon (second year) crops were 0.803 and 0.869, respectively. Under this model, yield and profit were increased by 16.6% and 180% for plant crop, and 9.62% and 38.2% for ratoon crop, respectively, with a substantial reduction, up to 28%, in carbon footprint. The farm performance analysis indicated that the most eco-efficient farmers had large farms with crop rotation systems, lower fertiliser input, a relatively higher education, more acceptance of new farming practices and products, and better access to agriculture resources, compared with eco-inefficient farmers. SIGNIFICANCEAdopting the model outputs reported here will help eco-inefficient farmers to narrow their productivity gap, increase profit and make Chinese sugarcane farming more eco-efficient. This is the first report of eco-efficiency and sustainability assessment in sugarcane production using LCA + DEA framework, with potential for adopting it to other agricultural systems.

External Two Stage DEA Analysis of Bank Efficiency in West Balkan Countries

Since the beginning of the application of the Data Envelopment Analysis (DEA) model in various areas of the economy, it has found its wide application in the field of finance, more specifically banks, in the last few years. The focus of this research was to determine the sustainability of the intermediate function of banks, especially in recent years when interest rates on deposits have been at a minimum level. The research was divided into two parts, wherein the first part determined the efficiency of the intermediate function of banks in the countries of the Western Balkans in the period from 2015 to 2019. The second part approached the regression analysis in which we determined the influence of the bank size, type of bank, and mergers and acquisitions (M&A) activity on the defined efficiency. In the first stage we applied the output-oriented DEA model using deposits, labor costs, and capital as input variables; on the other side, we used loans and investments as output variables. We used data from the revised financial statements of the banks operating in Serbia, Bosnia and Herzegovina, Montenegro, North Macedonia, and Albania. The results of our study showed that there is a difference in efficiency levels between countries and within countries in the considered time period. Furthermore, Tobit regression analysis showed a significant and negative influence of the bank type and M&A on relative technical efficiency of banks, and a positive and significant relationship between bank size and relative efficiency. These findings suggest that large commercial banks can sustain on the West Balkan market. It is to be expected that less efficient small banks will be taken over by large and more efficient banks.

ASSESSMENT OF THE PERFORMANCE OF LOGISTICS VILLAGES OPERATED BY THE TURKISH STATE RAILWAYS USING MCDM AND DEA METHODS

Increasing competition with globalization brought along the cost problem arising from logistics activities. In this context, logistics villages play an active role in reducing costs. Logistics villages provide significant benefits to users as areas where goods from different modes of transport are transferred, arranged and prepared for transportation, and all logistics-related activities are gathered in one region. Logistics villages have begun to be established by The Turkish State Railways (TSR-Türkiye Cumhuriyeti Devlet Demiryolları) using government resources after 2006 in Turkey. The aim of this study is to assess the logistics performances of the eight logistics villages in operation by analyzing their efficiencies using Multi Criteria Decision Making (MCDM) and Data Envelopment Analysis (DEA) methods. In the study, Entropy-based EDAS, MAUT and MOOSRA methods have been used. The efficiency scores of logistics villages were calculated using output-oriented DEA models. According to CCR and BCC models, İstanbul (Halkalı) and Uşak logistics villages were found to be efficient.

Efficiency Analysis of Macedonian and Croatian Banking Sectors with DEA

The aim of this paper is to measure the relative efficiency of commercial banks in two developing countries, the Republic of North Macedonia and the Republic of Croatia under the operating (incomebased) approach by using the leading non-parametric methodology data envelopment analysis (DEA). We follow Banker et al. (2010) in the selection of the approach, variables (two inputs: interest expense and other operating expense, and two outputs: interest revenue and other operating revenue) and the model (output-oriented BCC DEA model) as in their first stage. The observed period is five years (2015-2019) and we use a balanced panel data for both samples (total of 65 Macedonian and 100 Croatian bank-year observations). Outliers are identified and excluded by using the Banker and Gifford (1988) super-efficiency procedure, and the BCC output-oriented model is rerun for both samples (total 55 Macedonian and 95 Croatian bank-year observations). We provide relative efficiency scores for each bank in both sectors, as well as an average score for the banking sectors. In addition, we analyse few banks for both sectors that have decreased or increased the efficiency, or show variable results over time. Besides, we explain how inefficient banks can improve the efficiency in future by setting targets for improvement. Our study provides valuable information for banking management and regulatory bodies.

Efficiency Assessment of Consumer’s Electronics Sector: Data Envelopment Analysis

ABSTRACT This article aims to assess the efficiency levels of the Indian consumer electronics sector listed companies. The efficiency levels of the Decision-Making Units (DMUs) have been assessed using a non-parametric technique, that is, Data Envelopment Analysis (DEA). The present study is based on panel data of a group of 16 companies listed on the Bombay Stock Exchange for the period March 31, 2014 to March 31, 2018. The study is based on an output-oriented DEA mechanism, that is, profit maximization and Production maximization instead of input-oriented DEA that emphasizes cost and input minimization. The Data Envelopment Analysis has been performed through DEA Excel Solver Pro and R-3.4.1 Software to analyze efficiency levels of the companies mentioned above. The companies’ performance for the period under review has assessed and compared through the use of the Malmquist Productivity Index (MPI) and Tobit Regression. There are very few known studies that have focused on assessing the efficiency level of Indian Consumer’ electronic market using output-oriented DEA models. As per our information, this study is unique in terms of analyzing the efficiency level of the Indian consumer electronic sector for 5 years. The DEA method, along with two other techniques, that is, Malmquist Productivity Index (MPI) and Tobit Regression, can be explained in detail and much more realistic compared to other techniques.

Efficiency of Wood-Processing Enterprises—Evaluation Based on DEA and MPI: A Comparison between Slovakia and Bulgaria for the Period 2014–2018

The ongoing transition to a low-carbon, sustainable forest-based economy, and the adoption of circular bioeconomy principles in the wood-processing industry is associated with the optimization of natural resources, application of environmentally sustainable production technologies, adoption of technological and organizational innovations, and increased economic efficiency and competitiveness. The implementation of all these measures can help to reach the biggest challenge of our time in the fight against climate change in a cost-effective and competitive way. The aim of this study was to estimate the technical efficiency of wood-processing companies in the Slovak Republic and the Republic of Bulgaria by applying data envelopment analysis (DEA) and the Malmquist productivity index (MPI), and to reveal some factors for efficiency improvements. The economic efficiency evaluation based on official data was performed using selected indices of four wood-processing companies in each country in the period 2014–2018. The study implemented an output-oriented DEA model with constant returns to scale as a nonparametric linear approach for measuring the efficiency of production decision-making units (DMUs). The results obtained revealed that the studied Slovak companies were more efficient with better management in terms of machinery planning and overhead utilization. Markedly, the Bulgarian companies achieved better materials management and current planning quality. Increased economic efficiency of wood-processing enterprises in both countries can be realized through investments in innovative technological improvements, and enhanced research and development activities.

Portuguese Primary Healthcare and Prevention Quality Indicators for Diabetes Mellitus - A Data Envelopment Analysis.

Diabetes mellitus (DM) is a worldwide public health priority. The increasing prevalence and the budget constraints force to have effective healthcare, especially at the primary healthcare (PHC) level. We aim to assess primary care efficiency considering the best use of human resources to produce optimal diabetes care in terms of prevention quality indicators (PQIs) rates across national ACES (health centre groupings). We conducted a two-stage data envelopment analysis (DEA) to assess the technical efficiency of 54 Portuguese primary care health centre groupings for the 2016-2017 biennium. In the first stage, efficiency scores were obtained through five output-oriented DEA models under vector return to scale (VRS) assumption, using three input variables representing key primary care human resources and one output representing each one of the five PQIs related to diabetes. In the second stage, Tobit regression models were estimated to assess the determinants of primary care efficiency in diabetes care. A total of 13 ACES reached the efficiency frontier. Better managing human resources could reduce PQI rates by 52.3% in 2016 and 49.1% in 2017. Higher proportion of patients under 65 years old and better controlled with a hemoglobin A1c (HbA1c) ≤6.5% were associated with better efficiency in diabetes care, whereas higher prevalence of DM and unemployment worsened hospitalizations rates by diabetes short-term complications and lower-extremity amputation. Inefficiency in DM care was found in most of the primary care settings which can substantially improve the avoidable hospitalization rates by DM using their current level human resources. These findings help to improve diabetes care by targeting human resources at primary care level, which should be integrated into performance assessments considering broader and integrated scopes.

Efficient and sustainable closed-loop supply chain network design: A two-stage stochastic formulation with a hybrid solution methodology

In recent years, consumers and legislators have pushed companies to design their supply chain networks to consider environmental and social impacts as an important performance outcome. Due to the role of resource utilization as a key component of logistics network design, another primary goal of design is ensuring available scarce resources are used as efficiently as possible across all facilities. To address efficiency issues in a sustainable closed-loop supply chain network, a stochastic integrated multi-objective mixed integer nonlinear programming model is developed in this paper, in which sustainability outcomes as well as efficiency of facility resource utilization are considered in the design of a sustainable supply chain network. In doing so, efficiency is assessed using a bi-objective output-oriented data envelopment analysis model. A hybrid three-step solution methodology is presented that creates a linear form of the original mixed integer nonlinear programming problem using piecewise McCormick envelopes approach. In the second step, an aggregated single objective programming model is derived by exploiting the multi-choice goal programming. Finally, a Lagrangian relaxation algorithm is developed to effectively solve the latter stochastic single objective mixed integer linear programming problem. The application of the proposed approach is investigated with data drawn from a case study in the electronics industry. This case study illustrates how firms may balance sustainability and efficiency in the supply chain network design problem. Further, it demonstrates the integration of efficiency results in improving economic aspects of sustainability as well as social responsibility outcomes, but also highlights the trade-offs that exist between efficiency and environmental impacts.

Measuring the efficiency of the healthcare sector in Poland – a window-DEA evaluation

PurposeThe main aim of the study was to measure and assess the efficiency of the healthcare system in Poland.Design/methodology/approachAn output-oriented Data Envelopment Analysis model with a 2-years window analysis extension was used between 2013 and 2018. The analysis was completed with a determination of the sources of productivity changes (between the first and last year of the study period) and factors that influence efficiency.FindingsEfficient regions have been identified and the spatial diversity in their efficiency was confirmed. The study identified individual efficiency trends together with “all-windows” best and worst performers. Using panel modeling, it was confirmed that the efficiency of health protection is influenced by, among others, accreditation certificates, the length of the waiting list or the number of medical personnel.Research limitations/implicationsAlthough the analysis was conducted at the voivodeship level (NUTS2), which was fully justified, it would be equally important to analyze data with a lower aggregation level. It would be extremely valuable from the perspective of difficulties faced by the healthcare system in Poland.Practical implicationsThe identification of areas and problems affecting the efficiency of the healthcare system in Poland may also be a hint for other countries with similar system solutions that also struggle with the same problems.Originality/valueThe paper explains the efficiency of the country's healthcare system while also paying attention to changes in its level, factors influencing it, spatial diversity and impact on the sector functioning.

Group decision making based on the modified probability calculation method and DEA cross-efficiency with probabilistic hesitant fuzzy preference relations

In this paper, we propose a novel group decision making (GDM) method with probabilistic hesitant fuzzy preference relations (PHFPRs) based on the modified probability calculation method and data envelopment analysis (DEA) cross-efficiency. The primary advantage of the method is that it can reduce information loss and ensure the reliability of the decision-making result. A modified probability calculation method for hesitant fuzzy preference relations (HFPRs) is first proposed to obtain complete and normalized PHFPRs. Then, for the consistent additive preference relation (APR), we construct an output-oriented DEA model to derive the priority vector, in which each alternative is regarded as a decision-making unit (DMU). A DEA cross-efficiency model is further proposed to yield the priority vector, irrespective of whether the desired consistency level is achieved. Furthermore, a stochastic analysis method is developed to obtain the expected priority vector for any PHFPR, and a mathematical programming model is established to obtain the weight vector of decision makers based on group consensus. Finally, the procedure of the GDM method based on the modified probability calculation and the DEA cross-efficiency is designed. We also show the applicability and effectiveness of the proposed GDM method using illustrative examples.

Assessing the technical efficiency of Brazil's teaching hospitals using data envelopment analysis.

The general objective of this study was to assess the technical efficiency of Brazil's teaching hospitals using data envelopment analysis (DEA). To this end, a quantitative exploratory study was conducted with secondary data from the national health information system (DATASUS) using an output-oriented DEA model. The study population consisted of 29 large-sized teaching hospitals located in the country's Center-West, South, Southeast, and North regions. Twelve hospitals were shown to be on the efficient frontier (technically efficient) and 17 were off the frontier. Absolute efficiency values were calculated for the hospitals that were off the frontier, using benchmarks with weighting for benchmarking. Private for-profit hospitals were shown to be the most efficient, followed by private not-for-profit and public facilities. The findings of this study suggest that DEA has potential for assessing technical efficiency in hospital settings in relation to operational capacity.

The Efficiency of Waste Sector in Italy: An Application by Data Envelopment Analysis

Abstract With growing environmental legislation and mounting popular concern for the need to pursuing a sustainable growth, there has been an increasing recognition in developed nations of the importance of waste reduction, recycling and reuse maximization. This empirical study investigates both ecological and economic performances of urban waste systems in 78 major Italian towns for the years 2015 and 2016. To this purpose the study employs the non-parametric approach to efficiency measurement, represented by Data Envelopment Analysis (DEA) technique. More specifically, in the context of environmental performance we implement two output-oriented DEA models in order to consider both constant and variable returns to scale. In addition, we include an undesirable output – the total amount of waste collected – in the two models considered. The results show that there is variability among the municipalities analysed: Northern and Central major towns show higher efficiency scores than Southern and Islands ones.

Efficiency Analysis of Regional Innovation Development Based on DEA Malmquist Index

The aim of the work was to evaluate the dynamics of regional innovation development and compare the Russian regions according to their innovation efficiency, used resources, and achieved results. To estimate direct and indirect innovation effects, this study used the data on Russian regions according to variables of the innovative product volume, the share of high-tech products in the gross regional product (GRP) structure, the number of used patents, and investment in innovation activity for 2006–2017. To obtain a representative sample, a cluster analysis was applied as a preliminary step, which made it possible to select a group of regions that were most advanced in terms of their innovative development. Output-oriented data envelopment analysis models were applied for Malmquist Productivity Index calculation. The obtained results indicate the average growth of total factor productivity of regional innovation development over time. The main source of innovative development is largely derived from the economy of scale, while the effectiveness of regional innovation systems is basically increasing through broader resource bases, rather than through its effective utilization. The research findings can be applied to diagnose regional innovation effectiveness, justify public investment in research and development (R & D), and identify the priorities of regional innovation policy for specific regions.