Application Of Data Envelopment Analysis Research Articles

Diversification and Efficiency Assessment of Japanese Major Private Railways Using Data Envelopment Analysis and the Malmquist Index

Passenger transportation in Japan’s main metropolitan areas is operated by the JR companies, which were privatized and divested from Japan National Railways (JNR) in 1987 and by 16 major private railway companies with large-scale operations. Although their core business is transportation, the major private railway companies have adopted a strategy of diversification, and they engage, e.g., in real estate and distribution businesses. This study examines the relationship between the degree of business diversification and the production efficiency of Japan’s major private railway companies from the perspective of a future business model. To this aim, this study applies data envelopment analysis combined with the Malmquist index to the data of the railway companies from 1987 to 2019. We focus on four phases of activities: cost, operational resource, operational output, and total revenue. This study is the first to analyze the diversified management of Japanese railroad companies by evaluating their production efficiency and its changes over time. The results of the analysis reveal that, while all companies’ earnings have generally increased, the less diversified they are, the more they struggle to optimize personnel and other overhead expenses and resources, lowering production efficiency.

Trajectories of persistence in exporting: a capability approach

Purpose The purpose of this paper is answering two research questions: What are the trajectories of persistence in exporting followed by different groups of firms? What factors relate to each trajectory of persistence in exporting? The authors propose and test a framework that links operational and marketing firms’ capabilities to different trajectories of persistence in exporting. Design/methodology/approach Using a dataset of 2,913 firms over 14 years from the annual manufacturing survey in Colombia (AMS), the authors explored the trajectories of persistence in exporting. The authors applied data envelopment analysis to measure operational and marketing capabilities and group-based trajectory modeling to discover and link such trajectories to firms’ capabilities. Findings The authors identified four trajectories of persistence in exporting. Also, the authors found that while the interplay between marketing and operational capabilities relates positively to the non-exporting trajectory and negatively to the persistent trajectory, operational capabilities relate positively to the erratic trajectory and negatively to the slow growth trajectory of persistence in exporting. Meanwhile, marketing capabilities do not relate to any trajectory. Research limitations/implications Policymakers should help firms develop marketing and operational capabilities to compete globally to motivate them to export and persist in exporting. Policymakers should avoid stimulating firms to reinforce learned and familiar capabilities that cannot leverage desirable trajectories of persistence in exporting. Originality/value The authors introduce the trajectories of persistence in exporting, providing a fresh perspective for analyzing exporting behavior over time. The authors have also proposed and tested a unique framework that links operational and marketing firms’ capabilities to these trajectories, thus contributing to the existing body of knowledge on exporting behavior by firms from emerging markets.

Efficiency in higher education institutions: an analysis of data envelopment analysis applications

Efficiency in higher education institutions: an analysis of data envelopment analysis applications

Efficiency in higher education institutions: an analysis of data envelopment analysis applications

Efficiency in higher education institutions: an analysis of data envelopment analysis applications

Measuring Relative Efficiency of Life Insurance Companies with Data Envelopment and Fuzzy Data Envelopment Analysis

Insurance sector in India is one of the flourishing sectors of the economy. The insurance sector plays a vital role as it minimizes the risk and mobilizes the resource. Studying the efficiency of the insurance companies is highly important because it helps in determining how insurance companies respond to the various challenges and how many of them are likely to survive those challenges in the event of their occurrence. So, it becomes necessary to analyse the performance of life insurance companies in India with the application of Data Envelopment Analysis (DEA) and its extension Fuzzy Data Envelopment Analysis (FDEA). The empirical results of the Conventional DEA models and Fuzzy DEA models are computed to trace the performance of decision making unit at a possibility level. The computed fuzzy efficiency scores need to be ranked in order to determine how each DMU performs relative to the other DMUs in an uncertain environment. Chen and Klein (1997) ranking method were applied to rank the fuzzy numbers. To compare the two ranking methods the concept of Spearman rank correlation is applied. From this study the author infer that fuzzy DEA is more efficient than conventional DEA (CRS and VRS) Approach.

Analysis of the Influence of Transportation Infrastructure on Socio-Environmental Efficiency: Countries Case Studies

The literature suggests that a country’s economic development is directly related to the efficiency of its transportation system. While the importance of a well-functioning transport system for economic growth and poverty reduction is widely acknowledged, there is limited quantitative research demonstrating this relationship. Many studies have separately applied data envelopment analysis (DEA) to analyze highways, airports, railways, and harbors. However, they have yet to analyze all modes and their relationship with economic development comprehensively. Consequently, this paper seeks to address this gap in the existing literature. The objective of this study is to demonstrate that transportation system. To this end, we employ DEA and consider a range of operational variables, including airports, railways, highways, and harbors. Furthermore, we examine the relationship between transportation efficiency and several economic and social indicators, including gross domestic product (GDP), human development index (HDI), and carbon dioxide (CO2) emissions from the transportation system. Our findings reveal that Brazil exhibits the lowest relative efficiency when compared with the G-7 countries and South America. Additionally, it demonstrates medium efficiency within the BRICS group (Brazil, Russia, India, China, and South Africa).

Environmental sustainability performance of US airlines: implications of financial performance and technical efficiency

Air transportation significantly contributes to global CO2 emissions. The US Aviation Climate Action Plan introduced in November 2021 aims to decarbonize the aviation sector by 2050. Aligned with this initiative, our study applies Data Envelopment Analysis and fixed-effect panel regression to empirically explore how financial performance and technical efficiency impact Environmental Sustainability Performance (ESP) in the airline industry. We curated panel data of nine US passenger airlines from 2010 to 2019 to examine three key areas: the impact of financial performance on environmental sustainability performance, the influence of efficiency on environmental sustainability performance, and the relationship between flight stage length and environmental sustainability performance. Our findings indicate that improved Financial Performance, higher technical efficiency, and longer stage lengths positively contribute to enhanced environmental sustainability performance. Our study provides valuable insights for managers and policymakers, emphasizing the pivotal role of financial stability in achieving environmental goals within the airline industry. It underscores the intricate connection between economic viability and sustainability, offering guidance for policymakers seeking to balance financial success with environmental goals.

Adapting Efficiency Analysis in Health Systems: A Scoping Review of Data Envelopment Analysis Applications During the COVID-19 Pandemic.

Objective: To synthesize the current evidence base concerning the application of Data Envelopment Analysis (DEA) in healthcare efficiency during the COVID-19 pandemic using a scoping review of 13 primary studies. Methods: We consulted databases including Web of Science (WoS) and Scopus, as well as manual search entries up to September 2022. Included studies were primary applications of DEA for assessing healthcare efficiency during the COVID-19 pandemic. Key findings derived from thematic analysis of repeating pattern observations were extracted and tabulated for further synthesis, taking into consideration the variations in DMU definitions, the inclusion of undesirable outputs, the influence of external factors, and the infusion of advanced technologies in DEA. Results: The review observed a diverse application of DMUs, ranging from healthcare supply chains to entire national health systems. There was an evident shift towards incorporating undesirable outputs, such as mortality rates, in the DEA models amidst the pandemic. The influence of external and non-discretionary factors became more pronounced in DEA applications, highlighting the interconnected nature of global health challenges. Notably, several studies integrated advanced computational methods, including machine learning, into traditional DEA, paving the way for enhanced analytical capabilities. Conclusions: DEA, as an efficiency analysis tool, has exhibited adaptability and evolution in its application in the context of the COVID-19 healthcare crisis. By recognizing the multifaceted challenges posed by the pandemic, DEA applications have grown more comprehensive, integrating broader societal and health outcomes. This review provides pivotal insights that can inform policy and healthcare strategies, underscoring the importance of dynamic and comprehensive efficiency analysis methodologies during global health emergencies.

Efficiency analysis in bi-level on fuzzy input and output

To enhance the conventional framework of data envelope analysis (DEA), a novel hybrid bi-level model is proposed, integrating fuzzy logic with triangular fuzzy numbers to effectively address data uncertainty. This model innovatively departs from the traditional DEA’s ’black box’ approach by incorporating inter-organizational relationships and the internal dynamics of decision-making units (DMUs). Utilizing a modified Russell’s method, it provides a nuanced efficiency analysis in scenarios of ambiguous data. The study aims to enhance the accuracy and applicability of Data Envelopment Analysis in uncertain data environments. To achieve this, a novel hybrid bi-level model integrating fuzzy logic is presented. Validated through a case study involving 15 branches of a private Iranian bank, the model demonstrates improved accuracy in efficiency assessments and paves the way for future research in operational systems uncertainty management. The results indicated that, among the 15 branches of a private Iranian bank analyzed for the year 2022, branches 1, 10, and 11 demonstrated leader-level efficiency, while branch 3 exhibited follower-level efficiency, and branch 1 achieved overall efficiency. These branches attained an efficiency rating of E++, signifying a high level of efficiency within the model’s parameters.

Application of DEA Models to Develop Strategies for Improving the Energy Efficiency of National Economies: Focusing on SDGs and Best Practices in European Countries

Objective: This study aims to develop a strategy for improving the energy efficiency of national economies through the application of DEA (Data Envelopment Analysis) models. The research focuses on adopting the best practices from European countries in the context of SDGs. Theoretical Framework: The research is grounded in the framework of sustainable development and energy efficiency, focusing on the UN Sustainable Development Goals (SDG 12). DEA models are utilized to assess energy efficiency across various European economies, considering economic, environmental, and energy-related factors. Method: DEA models were applied to assess energy efficiency by analyzing input variables such as energy intensity, CO₂ emissions, and energy consumption. The model used a comparative analysis of 38 European countries, with special attention to the weights (λ) in the VRS model, allowing for variable returns to scale. The data were sourced from Eurostat and the World Bank. Results and Discussion: The results indicated that countries like Ireland and Albania serve as key reference units for Ukraine, highlighting the effectiveness of transitioning to renewable energy sources and reducing dependence on fossil fuels. The analysis demonstrated that inefficient countries could improve their performance by following the strategies of their reference units, specifically in reducing energy consumption and emissions. Research Implications: The study provides practical insights into how less efficient countries, like Ukraine, can enhance their energy policies by adopting successful practices from European nations, ultimately contributing to global sustainability goals. Originality/Value: This research contributes to the literature by offering a structured methodology for enhancing energy efficiency in national economies through DEA modeling. It emphasizes the significance of renewable energy in sustainable development and presents a comparative framework applicable to a broad range of countries.

Measuring, interpreting and monitoring economic efficiency in South Australia’s Spencer Gulf and West Coast Prawn Fisheries

Fishers and fishing vessels are not homogeneous, with their level of catch affected by the choice of physical inputs such as engine size, boat size and the type of fishing technology employed, as well as less tangible factors such as skipper skill and experience. Economic output also vitally depends upon the value of the product in the market and the cost of inputs. These differences in the ability of individual fishers to catch fish, and create economic returns, can be assessed through the application of efficiency analysis. This paper provides an empirical examination of efficiency indicators in the South Australian Spencer Gulf and West Coast Prawn Fisheries. Applying data envelopment analysis (DEA) to a unique vessel-level dataset that includes both quantity and value information, we estimate measures of efficiency in these input-controlled fisheries where effort decisions are made in real-time using a participative management approach. We find that quantity and value-based efficiency measures are relatively high but that the variability of value-based measures is increasing. This increasing heterogeneity in value, despite the homogeneity in quantities, has potential implications for pressures on cooperative management going forwards as fishers pursue different business strategies. Comparing the measures of efficiency to direct measures of individual profit, we find a divergence: technical and cost measures of efficiency are more strongly positively related to short-run economic performance while revenue-efficiency is more strongly positively correlated with long-run economic performance, suggesting that different efficiency metrics may be required for short- and long-run monitoring.

Hybrid method for benchmarking the operating costs of Brazilian energy distributors

Purpose: This study aims to propose a model for benchmarking evaluation, applied to the Brazilian regulatory system in establishing efficient operating costs for electricity distribution concessionaires. Theoretical framework: The application of data envelopment analysis to the regulation of electrical energy distribution is discussed, elucidating how different regulatory models accompany this modality. Methodology: The proposed methodology is based on a benchmarking evaluation model that integrates the associated use of Data Envelopment Analysis and Stochastic Frontier Analysis, through a methodology that establishes the evaluation of efficiency in Three Stages. Findings: The results of the performance assessment were expressed in terms of management efficiency, in which the effects of the operational environment and statistical noise are controlled, resulting in a rigorous measure of efficiency, by introducing manageable and unmanageable variables into the calculation directly from efficiency. Originality: The methodology allows to adjust operational costs, by levelling the operating environment of each electricity distribution concessionaire before repeating the DEA analysis, making the concessionaires' performance more coherent with the characteristics of the Brazilian market. Keywords: Electricity Distribution; Efficient Operating Costs; Benchmarking; Data Envelopment Analysis (DEA); Stochastic Frontier Analysis (SFA).

Data Envelopment Analysis-based Scenario Selection for Sequencing Pattern in a Simulated Robotic Cell

In this study, the performance of suggested scenarios for part input sequences in a 3-machine robotic cell producing different parts is determined through the application of data envelopment analysis (DEA) and the Banker–Charnes–Cooper model. A single gripper robot supports the manufacturing process by loading and unloading products and moving them inside the system. This study addresses random machine failures and repairs to minimize cycle time based on two robot move cycles in a three-machine robotic cell and overall production costs. Here, simulation assists in the modeling of uncertainty and a simulation-based optimization approach is applied to find the best scenarios for sequencing patterns in the cell through several numerical examples using DEA. The results displayed that, efficient scenarios satisfying minimum time and cost, are those, in which the percentages of operations assigned to the machines are close to each other. This enables decision-makers in manufacturing systems to make precise selections of the optimal part sequencing pattern with the lowest production cost and cycle time for robotic cells.

Renewable Energy Generation Efficiency of Asian Economies: An Application of Dynamic Data Envelopment Analysis

Renewable Energy Generation Efficiency of Asian Economies: An Application of Dynamic Data Envelopment Analysis

Efficiency evaluation of waste management: A systematic literature review of DEA applications

This paper aims to implement a systematic literature review to survey the Web of Science scientific database and review papers with an emphasis on the application of Data Envelopment Analysis (DEA) in the efficiency evaluation of waste management. This approach includes the identification, screening, eligibility, and inclusion phase of the relevant papers. The article also provides a theoretical overview of waste management and the DEA methodology. Researching the Web of Science scientific database according to the keywords “waste management”, “waste disposal” and “data envelopment analysis” led to 12 full-text published papers written in English. Qualitative analysis of papers was carried out with an emphasis on the analyzed country, i.e. region, time frame, applied DEA model, and included input and output variables in the model. Research of this type complements the scientific literature on the importance of waste management and provides key guidelines to scientists, policymakers, and authorities on efficient waste management as one of the key problems of today's society.

A data envelopment analysis model for optimizing transfer time of ischemic stroke patients under endovascular thrombectomy

This study applies Data Envelopment Analysis (DEA) to optimize transfer times and futile transfers of eligible ischemic stroke patients receiving Endovascular Thrombosis (EVT) in Primary Stroke Centers (PSC) in Nova Scotia. The study aims to assess healthcare delivery in Nova Scotia over two periods. It seeks to improve stroke care for rural populations by examining nine inputs, including age and distance between PSCs and the Comprehensive Stroke Centre (CSC) that provided EVT treatment, concerning a single output variable: whether EVT is performed or not. In the first phase, 115 patients were treated as Decision-Making Units (DMUs) for ten PSCs by applying an input-oriented Variable Returns to Scale (VRS) assisted by super-efficiency analysis using the Python-based PyDEA tool. This tool is known for its unrestricted capacity to handle DMUs, inputs, and outputs. In the second phase, eight PSCs with low patient numbers were merged into four DMUs, each consisting of two PSCs. These two merged PSCs have limited patients, and the selected PSCs are also geographically close. Two PSCs have been kept separate because they had sufficient patient volume. In the first phase, VRS generated more reasonable efficiency scores for evaluation, while in the second phase, Constant Returns to Scale (CRS) outperformed VRS, yielding better results. In the initial stage of the second phase, ten PSCs were considered as six DMUs using the input-oriented CRS and VRS for 115 patients. Super-efficiency measures were applied in this stage to improve the evaluation process further. In the second part of the second phase, a comparison between the first period (2018–2019) and the second period (2020–2021) was conducted using the Malmquist Productivity Index (MPI), considering CRS and VRS to evaluate the relative efficiency and productivity change of six DMUs over time.

Efficiency Measurement of State Transport Undertakings in India: An Application of DEA Methodologies

This article measures the technical efficiency of public transport sector in India. The study makes an attempt to provide an overview of the general status of the state transport undertakings (STUs) in terms of their productive efficiency. Data have been collected for 29 STUs for the year 2019–20. Technical efficiency of the STUs is measured by applying data envelopment analysis (DEA) technique with the use of five input and four output variables. Fleet size, total staff, fuel consumption breakdown and accident per lakh kilometre are considered as inputs and bus utilisation, passenger kilometres, load factor and passenger lead as outputs. On the basis of the status of technical efficiency, it is concluded that the performance of the STUs is good but still very far from the optimal level. The mean Charnes–Cooper–Rhodes (CCR) technical efficiency is 89.5 per cent and Banker, Charnes and Cooper (BCC) technical efficiency is 92.9 per cent. The CCR mean score implies STUs have the scope of producing the same output with the inputs 10.5 per cent lesser than their existing level, whereas that of BCC mean score implies 7.1 per cent. JEL Codes: 61, R41

Stage stochastic incremental data envelopment analysis models and applications

Data envelopment analysis (DEA) is a mathematical programming method that can evaluate the relative efficiency of multiple inputs and multiple outputs of a decision-making unit (DMU). The classical DEA model assumes that inputs and outputs are determined. However, there are some applications where the inputs–outputs are stochastic. In practice, it is important to evaluate stage performance. It is essential to eliminate the effect of preceding stage inputs (outputs) on stage performance in order to accurately assess stage performance. In this paper, we propose stage stochastic incremental DEA models that integrate two different kinds of inputs and outputs. The first kind of model takes into account the assessment of stage efficiency when determinate incremental inputs and stochastic incremental outputs are applied at the beginning and end of the stage. The second kind of model uses stochastic incremental inputs–outputs to evaluate stage efficiency. To verify the efficacy of the suggested models, the first kind of model is applied to assess the stage financing efficiency of 15 energy-saving and environmental protection clean enterprises (ESEPCEs). The second kind of model is applied in assessing the stage investment efficiency of 15 ESEPCEs. The empirical results show that the proposed models not only eliminate the effect of prior performance but also more accurately assess stage efficiency in a stochastic environment.

Constant returns-to-scale production technologies with fixed ratio inputs and outputs

Abstract In practical applications of data envelopment analysis, inputs and outputs are often stated as ratio measures, including various percentages and proportions characterizing the production process. Such ratio measures are inconsistent with the basic assumptions of convexity and scalability required by the conventional variable and constant returns-to-scale (VRS and CRS) models. This issue has been addressed by the development of the Ratio-VRS (R-VRS) and Ratio-CRS (R-CRS) models of technology, both of which can incorporate volume and ratio inputs and outputs. In this paper, we provide a detailed standalone development of the special case of the R-CRS technology, referred to as the F-CRS technology, in which all ratio inputs and outputs are of the fixed type. Such ratio measures can be used to represent environmental and quality characteristics of the production process that stay constant while simultaneously allowing the scaling of the volume of production. We illustrate the use of the F-CRS technology by an application in the context of school education.

Approach in inputs & outputs selection of Data Envelopment Analysis (DEA) efficiency measurement in hospitals: A systematic review.

The efficiency and productivity evaluation process commonly employs Data Envelopment Analysis (DEA) as a performance tool in numerous fields, such as the healthcare industry (hospitals). Therefore, this review examined various hospital-based DEA articles involving input and output variable selection approaches and the recent DEA developments. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was utilised to extract 89 English articles containing empirical data between 2014 and 2022 from various databases (Web of Science, Scopus, PubMed, ScienceDirect, Springer Link, and Google Scholar). Furthermore, the DEA model parameters were determined using information from previous studies, while the approaches were identified narratively. This review grouped the approaches into four sections: literature review, data availability, systematic method, and expert judgement. An independent single strategy or a combination with other methods was then applied to these approaches. Consequently, the focus of this review on various methodologies employed in hospitals could limit its findings. Alternative approaches or techniques could be utilised to determine the input and output variables for a DEA analysis in a distinct area or based on different perspectives. The DEA application trend was also significantly similar to that of previous studies. Meanwhile, insufficient data was observed to support the usability of any DEA model in terms of fitting all model parameters. Therefore, several recommendations and methodological principles for DEA were proposed after analysing the existing literature.