Integrated Data Envelopment Analysis Research Articles

Performance measurement and management in complex environments: a system of systems approach for the public sector

Issues related to rapidly changing digital environments and turbulent or complex settings, such as the public sector, often hinder successful implementations of Performance Measurement and Management (PMM) systems. Despite the availability of PMM methods, the challenge of implementing successful systems in turbulent settings persists. This article introduces a novel PMM framework employing a System of Systems (SoS) approach, integrating Data Envelopment Analysis and Machine Learning to guide organisational changes by enhancing the efficiency of Decision-Making Units (DMU). Validated by a case study within the Brazilian Navy, the framework demonstrates a significant capacity to interpret the impact of DMU characteristics on operational efficiency and draw actionable insights. Our findings suggest that leveraging SoS principles emphasises adaptability and ongoing improvement through a data-driven process, suggesting the framework’s potential to navigate through complexity by enabling more effective management practices and decision-making. Future research directions include longitudinal studies and the incorporation of technologies to support managerial decision-making.

Modelling for working capital efficiency: integrating SBM-DEA and artificial neural networks in Indian manufacturing

Purpose This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN). Design/methodology/approach A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME. Findings Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME. Originality/value The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.

The underlying signals of efficiency in European universities: a combined efficiency and machine learning approach

ABSTRACT This study explores the critical determinants of efficiency in European Higher Education Institutions (HEIs). The contemporary landscape presents a wide range of challenges and opportunities, necessitating unprecedented levels of efficiency. Our primary objective is to unravel the fundamental elements that drive the efficiency of European HEIs. To achieve this, we employ a novel methodological approach, integrating Data Envelopment Analysis (DEA) with a Machine Learning technique, specifically Feature Selection. This combination sheds new light on areas previously less explored in DEA, including the intricate interplay among variables. Our empirical investigation contributes to the academic discourse by presenting a comprehensive, multi-country analysis from a multifaceted viewpoint. We assess the efficiency levels of European universities, highlighting the influence of factors such as student fee funding, Ph.D. program intensity, and international mobility on achieving high efficiency scores. The findings of this study have some implications for policy-making, suggesting strategies to enhance the efficiency levels of HEIs.

An integrated data envelopment analysis and regression tree method for new product price estimation

An integrated data envelopment analysis and regression tree method for new product price estimation

Ensemble learning method for classification: Integrating data envelopment analysis with machine learning

In classification tasks with large sample sets, the use of a single classifier carries the risk of overfitting. To overcome this issue, an ensemble of classifier models has often been shown to outperform the use of a single “best” model. Given the rich variety of classifier models available, the selection of the high-efficiency classifiers for a given task dataset remains an urgent challenge. However, most of the previous classifier selection methods only focus on the measurement of classification output performance without considering the computational cost. This paper proposes a new ensemble learning method to improve the classification quality for big datasets by using data envelopment analysis. It contains the following two stages: classifier selection and classifier combination. In the first stage, the commonly used classifiers are evaluated on the basis of their performance on resource consumption and classification output performance using the range directional model (RDM); then, the most efficient classifiers are selected. In the second stage, the classifier confusion matrix is evaluated using the data envelopment analysis (DEA) cross-efficiency model. Then, the weight for the classifier combination is determined to ensure that classifiers with higher performance have greater weights based on the cross-efficiency values. Experimental results demonstrate the superiority of the cross-efficiency model over the BCC model and the benchmark voting method in model ensemble. Furthermore, our method has been shown to save more computational resources and yields better results than existing methods.

An integrated data envelopment analysis framework for operational efficiency assessment in Brazilian international airports

International airports play a significant role in a country’s economic development. We propose an integrated framework for operational efficiency analysis using Data Envelopment Analysis, Windows Analysis, and Malmquist Index to identify inefficiencies and areas of improvement in the air transportation industry. This study analyzed 23 Brazilian international airports from 2010 to 2021, using the DEA Window Analysis (DEAWA) combined with the Index (MI). The airports were grouped geographically in southern Brazil. The integration of these methods brought an important insight to the evaluation processes, even for those previously analyzed using DEA. The analyses found that COVID-19 potently influenced the drop in the efficiency index. No airport was fully efficient over the analyzed periods. Private airports achieved an efficiency index slightly higher than public ones, but the difference of the efficiency index by public–private management type is not clear. This type of analysis provides a strategic and comparative tool, making it possible to understand better the weaknesses of the air transportation system.

Analyzing the Shift in China’s Cultural Industries: From Economic Growth to Social Enrichment

Cultural industries constitute a crucial part of the sustainable economy. In accordance with the principles of socialist public ownership nations, the economic benefits of cultural industries should be ultimately transformed into more significant social benefits. Guided by the policies and requirements of the Chinese government concerning the social benefits of cultural industries, this paper innovatively integrates Data Envelopment Analysis (DEA) and Tobit models to empirically analyze the social benefits and their influencing factors within China’s cultural industries. The findings indicate that the social benefits of China’s cultural industries are currently in a state of diminishing returns to scale, with fiscal support and educational levels significantly enhancing the industry’s social benefits. This paper recommends that policymakers aiming to convert the economic benefits of cultural industries into social benefits more systematically and effectively should focus on enhancing the quality of industry outputs.

CROSS-BARGAIN-EEF EVALUATION APPROACH FOR CO2 EMISSION EFFICIENCY IN CHINA’S CONSTRUCTION INDUSTRY WITH FIXED-SUM UNDESIRABLE OUTPUT

The rapid urbanization in China has led to a substantial expansion of construction activities, causing notable energy consumption and a consequent rise in CO2 emissions. This study proposes an approach to assess CO2 emission efficiency in China’s construction industry by integrating data envelopment analysis cross-efficiency and the Nash bargaining game, considering CO2 as a fixed-sum undesirable output. The study identifies Jiangsu and Beijing as top performers, establishing benchmarks for other provinces. In contrast, Inner Mongolia displays the lowest efficiency. Generally, most provinces exhibit room to improve environmental efficiency in the construction sector. The study provides policy implications and tailored suggestions for specific provinces.

Multi-Objective Optimization of Cutting Parameters for Polyethylene Thermoplastic Material by Integrating Data Envelopment Analysis and SWARA-Based CoCoSo Approach

This paper presents a comprehensive study on the multi-objective optimization of cutting parameters for Polyethylene (PE) thermoplastic material utilizing a CO2 laser. Recognizing the pivotal role of precise and efficient cutting in various sectors, from packaging to biomedical engineering, we integrate two potent analytical methodologies - Data Envelopment Analysis (DEA) and Step-Wise Weight Assessment Ratio Analysis (SWARA)-based Comprehensive Criteria Score Optimization (CoCoSo) approach. The cutting parameters chosen for the study were material thickness, power, and cutting speed. The experiments were conducted according to the Taguchi L18 orthogonal array. Measurements of surface roughness and kerf width were performed to examine the cutting quality. Additionally, another response variable, material removal rate, was calculated. By integrating Data Envelopment Analysis and SWARA-Based CoCoSo approach, the experimental condition that yielded the lowest surface roughness, kerf width, and the highest material removal rate was determined. The optimum experimental condition was found to be 4 mm material thickness, 80 W laser power, and 15 mm/s cutting speed. This work, therefore, paves the way for the innovative application of these combined methodologies in enhancing the production processes of PE and other thermoplastic materials, with clear implications for cost-effectiveness and sustainability in the manufacturing sector.

Integrating Data Envelopment Analysis and Machine Learning Approaches for Energy Optimization, Decreased Carbon Footprints, and Wheat Yield Prediction Across North-Western India

Integrating Data Envelopment Analysis and Machine Learning Approaches for Energy Optimization, Decreased Carbon Footprints, and Wheat Yield Prediction Across North-Western India

Risk identification for business blockchain-based technology adoption via an advanced fuzzy MRDM architecture

Blockchain technology holds considerable amount of potential for all types of industries by executing transactions in a verifiable, efficient, and permanent channel. It has been widely viewed as a standard requirement for making industry ready for the future, but when it comes to practical applications, it still arouses numerous risks/challenges that need to be addressed. Therefore, it is essential to address this gap and establish a comprehensive and effective practical framework to align the information technology revolution with sustainable value creation. The purpose of this research is to realize to what extent an enterprise legacy system’s transformation benefits a blockchain-based system and to minimize its specific risk through a hybrid fuzzy MRDM (multiple rule-based decision making) model that integrates data envelopment analysis with rough set theory (DEA-RST) and the fuzzy DEMATEL approach grounded on a questionnaire derived from domain experts. We aim to point out the inherent risks of blockchain-based technology adoption and to assist senior engineers in designing or adopting a suitable architecture for practical operation and planning of any future integration and development. The potential risk evaluation of business blockchain adoption reveals that the priority improvement sequence based on dimensions is smart contract risk, value transfer risk, and standard risk. Furthermore, law and regulation are the most critical criteria.

Assessment of truck driver safety efficiency based on data envelopment analysis

This paper introduces a safety efficiency evaluation framework that integrates Data Envelopment Analysis (DEA) and Super-Efficiency Data Envelopment Analysis (SE-DEA) for quantitatively assessing the safety of truck drivers. The framework utilizes data from onboard equipment to capture drivers’ dangerous behaviors and poor driving states. Subsequently, it evaluates safety efficiency using the DEA combination model, providing safety rankings. Improvement suggestions are offered to underperforming drivers based on slack variable values. The proposed method is compared with EWM-Topsis and Critic-Topsis models using fixed weights, demonstrating better stability. The study’s outcomes enable freight companies to identify high-risk drivers and offer personalized, targeted safety training.

Economy and carbon dioxide emissions effects of energy structures in China: Evidence based on a novel AHP-SBMDEA model

With the completion of the poverty alleviation task in China, the Chinese economy has made great breakthroughs. Meanwhile, the implementation of Chinese "dual carbon" goal has entered an important window period of both strategic opportunities and difficulties. The emission of carbon dioxide (CO2) leads to the greenhouse effect, which affects the sustainable development of society. Therefore, this paper proposes an improved analytic hierarchy process (AHP) with Slacks-based measure integrating data envelopment analysis (SBMDEA) (AHP-SBMDEA) based efficiency evaluation model for different provincial-level administrative regions in China, which is used to analyze and optimize the Chinese energy structure of different provincial-level administrative regions. With coal, gasoline, petroleum, fuel oil, diesel, crude oil and natural gas as inputs, the per capita Gross Domestic Product (GDP) as the desired output and the CO2 as the undesired output, after linear weighted calculation of the results of the AHP-SP model and the SBMDEA model, the energy efficiency evaluation model of different provincial administrative regions in China is established. The results show that there is a large difference in efficiency between Chinese northern and southern regions, and the overall efficiency of southern regions is higher than that of northern regions. In addition, it is easier to find out the optimal energy efficiency benchmark, which can provide suggestions and help to guide the energy consumption input structure improvement of other inefficient provincial-level administrative regions. Furthermore, the energy structure can also be adjusted through the redundant variable calculated by the SBMDEA model. For example, Sichuan Province can reduce the consumption of the coal, the gasoline, the diesel, the fuel oil, the crude oil and the natural gas by 5269.95 ×10000 tons (WT), 513.01 WT, 59.05 WT, 34.37 WT, 4,73.33 WT and 130.37 billion cubic meters (BCM), respectively. Its CO2 emissions can be reduced by 1925.4 WT, and raise the efficiency of energy utilization in the province.

Measuring economic resilience of manufacturing organization leveraging integrated data envelopment analysis (DEA)-machine learning approach

ABSTRACT This study aims to assess the economic resilience of manufacturing firms through a combination of output-oriented data envelopment analysis (DEA) and machine learning techniques. The research draws on economic resilience factors identified in the literature and focuses on three categories: economic-related factors (financial flexibility, microeconomic market, macroeconomic stability), production-related factors (restoration of production, backup inventories, resource pooling/sharing), and management-related factors (diversification of activities, good governance (management), relocation). Using DEA, a mathematical approach, the study computes and analyzes the contributions of various components to economic resilience. The results of DEA normalization indicate that the highest weighted criteria are financial flexibility, good governance (management), and resource pooling (sharing). To gain a deeper understanding of the data structure, the K-means algorithm is employed for clustering and analysis. K-means clustering is a popular exploratory data analysis technique that aims to group samples into clusters of equal variances by minimizing inertia or the sum of squares within each cluster. The combination of these techniques with sensitivity analysis provides a novel analytical approach for policy formulation and decision-making. The findings have implications for practitioners and domain experts, offering valuable insights into enhancing economic resilience in the manufacturing sector.

Performance Evaluation of the Petrochemical Companies Accepted in Tehran Stock Exchange with Integrated Data Envelopment Analysis Approach with Common Set of Weight and Principal Component Analysis

Performance Evaluation of the Petrochemical Companies Accepted in Tehran Stock Exchange with Integrated Data Envelopment Analysis Approach with Common Set of Weight and Principal Component Analysis

Site Selection of Solar Power Plants Using Hybrid MCDM Models: A Case Study in Indonesia

Among developing countries in Asia, Indonesia has realized the importance of transitioning from fossil fuels to renewable energy sources such as solar power. Careful consideration must be given to the strategic placement of solar power installations to fully leverage the benefits of solar energy. This study proposes a methodology to optimize the site selection of solar power plants in Indonesia by integrating Data Envelopment Analysis (DEA), Fuzzy Analytic Hierarchy Process (F-AHP), and Fuzzy Measurement of Alternatives and Ranking according to Compromise Solution (F-MARCOS) models. The proposed methodology considers quantitative and qualitative criteria to evaluate potential locations for solar power plants. In the first stage, DEA is used to identify the most efficient locations based on quantitative measures such as solar radiation, land availability, and grid connectivity. In the second stage, qualitative factors such as technological, economic, environmental, and socio-political aspects are evaluated using F-AHP to prioritize the most important criteria for site selection. Finally, F-MARCOS ranks potential locations based on the selected criteria. The methodology was tested using data from Indonesia as a case study. The results show that the proposed hybrid model optimizes Indonesia’s solar power plant site selection. The optimal locations can contribute to a cost-effective long-term renewable energy supply nationwide. The findings from this study are relevant to policymakers, industry stakeholders, and researchers interested in renewable energy development and site selection. However, to promote sustainable solar energy development, governments and local authorities must also enact supportive policies and mechanisms that encourage the adoption and growth of renewable energy technologies in Indonesia.

Evaluating the Super-Efficiency of Working Capital Management Using Data Envelopment Analysis: Does COVID-19 Matter?

This study evaluates the working capital management (WCM) super-efficiency in UAE firms and determines how the COVID-19 crisis may affect WCM super-efficiency from a statistical perspective. Data envelopment analysis (DEA), difference tests, and Tobit regression techniques are used. Integration of DEA with Tobit regression is required to recognize the potential statistical consequence of COVID-19 on the super-efficiency of WCM. The results show that WCM super-efficiency is moderate and requires decision-makers' corrective measures to attain best practices. The study results show no significant difference in WCM super-efficiency, and COVID-19 does not significantly influence WCM super-efficiency. The results would rouse decision-makers' interests, who would look into the firm's efficiency and weak spots. They would then take major steps to improve efficiency and achieve the best practices. Stakeholders such as managers, financiers, and investors may need these results to monitor how firms manage their assets and liabilities. We contribute to the literature by investigating and evaluating firms' WCM super-efficiency pre and post the COVID-19 pandemic employing a unique technique based on DEA super-efficiency and examining whether COVID-19 has affected WCM super-efficiency to extend the scope of knowledge and stressing the importance of sound WCM, especially in periods of economic distress.

Integration of Data Envelopment Analysis with Analytical Hierarchy Process and Entropy Measurement in the Optimization of Supplier Selection

Supplier selection is perceived as important decision-making process in any supply chain management. In this study, the best supplier for a company is being determined based on five main criteria chosen which are delivery, capacity, warranty, cost, and quality. The Analytic Hierarchy Process (AHP) and Entropy Measurement (EM) methods are integrated with Data Envelopment Analysis (DEA) was applied to set ranking and choose the best supplier as conventional DEA is not able to provide complete ranking among inefficient units. The mathematical modelling is executed using LINGO software. Supplier 3 has obtained efficient result of score 1 for both hybrid method and in Super Efficiency method as the most efficient supplier. Then, the results are validated using Spearman’s Rank Correlation Coefficient (SRCC) which shows positive correlation between both integrated methods. Finally, findings of this study indicate the feasibility of integrated AHP-DEA and EM-DEA for supplier selection with multiple criteria.

Does Local Government Competition Reduce Environmental Governance Performance? The Role of Public Value Conflict and Media Sentiment

With the increasing attention paid to environmental protection and sustainable development in various countries worldwide, the relationship between local government competition and environmental governance has become more subtle and complex. This paper provides new insight into their relationship based on public value theory and media sentiment perspective. Utilizing panel data from 2012 to 2019 in 216 cities in China, this study integrated Data Envelopment Analysis, Conflicting Attitudes Model, Computer-Aided Text Analysis, and machine learning-based sentiment analysis, as well as nonlinear mediation model to empirically test the relationships among local governments’ competition pressure, public value conflict, media sentiments, and environmental governance performance. The study found that: (1) Competition pressure and environmental governance performance exist in a “U-curved” relationship. (2) The core mechanism of the above relationship lies in the mediating role of public value conflict. Within a specific range, the public value conflict faced by local governments increases as competition pressure increases. This conflict would push local governments into a dilemma and induce them to commit misconduct. However, when competition pressure exceeds this range, the public value conflict faced by local governments will be weakened, leading environmental governance performance to rebound. (3) Negative media sentiments significantly alleviate the negative impact of public value conflict on environmental governance performance. This study helps researchers and policymakers recognize government competition’s influence on environmental governance from a public value perspective, with further exploration and confirmation of the moderating role of media sentiments. It also provides theoretical and policy enlightenment for rethinking the behavior logic of local government and solving the dilemma of local government environmental governance.

Applying Integrated Data Envelopment Analysis and Analytic Hierarchy Process to Measuring the Efficiency of Tourist Farms: The Case of Slovenia

This paper examines the efficiency of tourist farms in Slovenia by adopting an approach using a framework of non-parametric programming—Data Envelopment Analysis (DEA) and Analytic Hierarchy Process (AHP), combining the two because the DEA analysis by itself does not take into account all attributes, especially qualitative ones. The beforementioned two methods rank the farm tourism units with respect to their efficiency. By using the DEA method, an input- and output-oriented BCC and CCR model were introduced to upgrade the criteria by including the additional non-numerical criteria of the AHP. The results of the models showed that there are possible improvements on all levels of efficiency, as well as on the criteria of the additional offer of tourist farms, which were analyzed in the AHP model with additional criteria. According to the estimated efficiency, the ranking of tourist farms differed according to the two methods. Within the group of farms assessed as efficient by DEA, the AHP model allowed a more accurate ranking.