Data Envelopment Analysis Technique Research Articles

Avaliação do comportamento dos bancos brasileiros em transações de fusão e aquisição

Abstract This study aims to determine the impact of an M&A on the performance of the consolidating bank in relation to its competitors in both the intermediation and profitability approaches, in addition to proposing an M&A ranking metric for a more accurate performance analysis. Although some studies assess the performance of banks in M&As, the explanation of their impact on institutions in the market can be better explored by proposing a financial-operational metric, different from valuations by the stock market, complemented with the point of view of organizational strategies that appear in the literature as essential for the continuity of the resourcefulness of the topic. Assessing the consequences of bank M&As and understanding their intentions provides better management of both institutions and the country's socioeconomic aspects. It also provides guidelines and new tools for performance evaluation for academics, managers, and government officials on M&A processes. The research method is quantitative-qualitative. In the quantitative approach, the data envelopment analysis (DEA) technique with a second stage of multiple linear regression was used. The database was formed by information from the banks' financial statements from 2000 to 2018 (representing the CAMELS), supplemented with macroeconomic and market structure information. In the qualitative approach, the impact of these regressed variables was determined in the quantitative stage for four case studies, which are the Big4 banks that represent approximately 87% of the Brazilian financial market. Although some studies deal with the topic of the relationship between efficiency and M&As, this study shows that there is no clear and direct relationship between M&As and the performance (efficiency) of banks in the market. The results suggest that banks carry out M&As for reasons other than the search for efficiency, leaving efficiency as a consequence of the operation. This article contributes to the proposal of a new metric for measuring the efficiency of M&As, whose function is to establish a ranking and relativize the analysis of banks' performance in relation to the market as a whole, allowing the visualization of the relative effect vis-à-vis competitors.

The relationship between firm size and efficiency: why does default on bank loans matter?

This paper presents an investigation of the interconnection between firm size and efficiency under the financial constraints lens. Specifically, we used the Data Envelopment Analysis (DEA) technique to measure the efficiency of a sample of large, medium-sized and small private Italian firms, using the firms’ default risk as an undesirable output. Our findings indicate that larger companies perform better than medium-sized and smaller companies in terms of efficiency (across all business profiles), including default on bank loans. Based on indicators widely employed to characterize the bank-firm relationship, our study demonstrates the need to improve the efficiency of the Italian entrepreneurial system, consisting mainly of small companies, through their dimensional growth.

Efficiency effects of public hospital closures in the context of public hospital reform: a multistep efficiency analysis.

In the wake of hospital reforms introduced in 2011 in Turkey, public hospitals were grouped into associations with joint management and some shared operational and administrative functions, similar in some ways to hospital trusts in the English National Health Service. Reorganization of public hospitals effect hospital and market area characteristics and existence of hospitals. The objective of this study is to examine the effect of closure on competitive hospital performances. Using administrative data from Turkish Public Hospital Statistical Yearbooks for the years 2005 to 2007 and 2014 to 2017, we conducted a three-step efficiency analysis by incorporating data envelopment analysis (DEA) and propensity score matching techniques, followed by a difference-in-differences (DiD) regression. First, we used bootstrapped DEA to calculate the efficiency scores of hospitals that were located near hospitals that had been closed. Second, we used nearest neighbour propensity score matching to form control groups and ensure that any differences between these and the intervention groups could be attributed to being near a hospital that had closed rather than differences in hospital and market area characteristics. Lastly, we employed DiD regression analysis to explore whether being near a closed hospital had an impact on the efficiency of the surviving hospitals while considering the effect of the 2011 hospital reform policies. To shed light on a potential time lag between hospital closure and changes in efficiency, we used various periods for comparison. Our results suggest that the efficiency of public hospitals in Turkey increased in hospitals that were located near hospitals that closed in Turkey from 2011. Hospital closure improves the efficiency of competitive hospitals under hospital market reforms. Future studies may wish to examine the efficiency effects of government and private sector collaboration on competition in the hospital market.

Comperative Efficiency using Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) in the Banking Industry

This study’s objective is to employ data envelopment analysis (DEA) and stochastic frontier analysis (SFA) to investigate the efficiency accomplishments of Indonesian commercial banking from 2018 to 2019. The first method of measuring efficiency employing a non-parametric data envelopment analysis (DEA) technique reveals that the average efficiency of 71 banks fell from 2018 (0.82) to 2019 (0.81). According to DEA findings, major banks outperform small banks on average. According to the approximated SFA Cobb-Douglas (CD) function, interest expenditure and labor expense have a positive and considerable influence on interest income. This occurs when deposit interest rates rise, banks gain interest revenue by raising lending rates, and banks increase non-interest income. According to the SFA of the Cobb-Douglas function, many banks are inefficient, particularly the first to 49th banks that arise from small banks. The Gamma value is near one (0.999), while the LR test yields a significant result of 36.14. The Cobb-Douglas SFA model is therefore applicable. The efficiency performance findings from the two models above reveal the same thing: large banks are more efficient than small banks.

Efficiency Evaluation of Business in IoT Supply Chains by Network DEA

<p>Recently Internet of things (IoT) and its applications are emerging as a momentous trend in industry. Numerous hardware and software providers have been entering the intense market competition in IoT related industries. Correspondingly, the attention on evaluation of IoT industry is growing. However, it is a main theme as how to consider the multiple dimensions and dependencies among the criteria in IoT supply chains simultaneously. By considering internal processes in DMUs as well as their interactions, this study designs the evaluation methods with network data envelopment analysis (NDEA) and multi-objective programming (MOP) techniques. This work intends to estimate the efficiency of IoT businesses from the perspectives of R&D, manufacturing, sales and finance, and the overall performance. The proposed models are implemented with empirical case studies in IoT supply chains in Taiwan. The results show the usefulness and validity of the proposed methods in evaluating IoT related business.</p> <p> </p>

Enhanced Efficiency in Fog Computing: A Fuzzy Data-Driven Machine Selection Strategy

With the rapid proliferation of IoT and Cloud networks and the corresponding number of devices, handling incoming requests has become a significant challenge. Task scheduling problems have emerged as a common concern, necessitating the exploration of new methods for request management. This paper proposes a novel approach called the Fuzzy Inverse Markov Data Envelopment Analysis Process (FIMDEAP). Our method combines the strengths of the Fuzzy Inverse Data Envelopment Analysis (FIDEA) and Fuzzy Markov Decision Process (FMDP) techniques to enable the efficient selection of physical and virtual machines while operating in a fuzzy mode. We represent data as triangular fuzzy numbers and employ the alpha-cut method to solve the proposed models. The paper provides a mathematical optimization model for the proposed method and presents a numerical example for illustration. Furthermore, we evaluate the performance of our method in a cloud environment through simulations. The results demonstrate that our approach outperforms existing methods, namely PSO + ACO and FBPSO + FBACO, in terms of key metrics, including energy consumption, execution cost, response time, gain of cost, and makespan.

Profitability Analysis Of Selected IT Companies In India Using Data Envelopment Analysis (DEA): A Comprehensive Study

The IT sector in India plays a vital role in driving the country's economy, contributing significantly to its growth and employment landscape. As of 2023, it constitutes around 8% of India's total GDP, employing over 4.5 million professionals directly and supporting an estimated 10 million indirect jobs. With export revenue reaching approximately $194 billion, the IT sector stands as one of the largest contributors to India's foreign exchange reserves. This sector has experienced robust growth, with a projected annual growth rate of 7-9%. This growth can be attributed to its relentless focus on digitalization, innovation, and technology-driven solutions, enabling it to foster economic development, attract investments, and drive digital transformation across various industries. Conducting a profitability analysis of IT companies in India holds significant importance. It provides valuable insights into their financial performance and viability, assisting stakeholders in making informed decisions. Investors and policymakers can assess the efficiency and effectiveness of these companies in generating profits and maximizing returns on investments. Profitability analysis also aids in resource allocation, risk management, and policy formulation to create an enabling environment for IT sector growth. Additionally, profitability analysis allows companies to benchmark their performance, identify areas for improvement, and enhance competitiveness. By evaluating financial indicators, stakeholders can make informed decisions regarding expansion, diversification, and strategic partnerships. Ultimately, profitability analysis serves as a crucial tool for decision-making, resource allocation, and sustainable growth within the IT sector. Therefore, this study aims to analyze the profitability of six selected IT companies in India from 2008-09 to 2021-22 using the Data Envelopment Analysis (DEA) technique. By employing DEA, the study aims to provide valuable insights into the financial performance of these companies and contribute to the existing understanding of profitability dynamics within the Indian IT sector.

Road Safety Efficiency on Interurban Roads in Spain.

The slowdown in economic development caused by traffic accidents in Spain, together with the disparity in the risk of death or injury due to traffic accidents in its provinces, makes it necessary to evaluate their road safety performance. Therefore, the objectives of the present research are, on the one hand, to find out the level of road safety efficiency of Spanish provinces in the period 2014-2018. On the other hand, it is also aimed to determine to what extent the annual efficiency change is originated by pure changes in efficiency or by technological changes. To achieve both objectives, the nonparametric technique of Data Envelopment Analysis and the Malmquist Index have been used based on the consideration of the Safety Performance Indicators (SPI) as the inputs of the process. The research findings show changes in the level of road safety efficiency in each of the years of the study period. Thus, it is possible to identify a geographical delimitation of the causes that generate changes in efficiency in recent years. A contraction in efficiency and technological progress is identified in part of the Spanish provinces in the north, northeast, and south of Spain.

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.

Evaluating the technical efficiency of Saudi Arabian telecommunications companies using window DEA and AHP techniques

PurposeThis study aims to evaluate the efficiency of the three telecommunications companies in Saudi Arabia: Saudi Telecom Company (STC), Mobily and Zain over the period of 2010–2019. This evaluation is a step toward improving the performance of the Saudi telecommunications sector.Design/methodology/approachThree multicriteria decision-making (MCDM) techniques were used to calculate technical efficiency. These techniques include the traditional data envelopment analysis (DEA), window DEA and analytical hierarchy process (AHP). The three inputs used were total assets, operating expenses and capital expenditures, whereas the two outputs were sales revenue and total stockholders’ equity.FindingsSTC was ranked first using the three techniques, followed by Zain, and then Mobily. According to the DEA window analysis, these three companies were all efficient only in 2012. The efficiency was high in the initial years, 2010–2013, when it was above 0.90, and it dropped below 0.90 in the subsequent years, 2014–2019. In addition, the efficiency of STC remained high, with an average of 0.990. However, the average efficiencies of Zain and Mobily during this period were 0.807 and 0.804, respectively.Originality/valueThis is the first study to use the three MCDM techniques to evaluate the performance of telecommunications providers. The results show that window DEA is better than the other two techniques at evaluating performance over time, as it has a higher discrimination power than either the traditional DEA or AHP.

Sustainable Development and Public Transport in Haryana

This study examined the efficiency of public transport in Haryana from 2015-16 to 2020-21 in cognisance of the sustainable development aspect. The depots of the state roadways are considered as decision-making units. The study employed the Data Envelopment Analysis (DEA) technique for estimating the scores of the overall technical efficiency (OTE) and pure technical efficiency (PTE) of decision-making units (DMUs) by using three inputs— fleet size, total staff and bus utilisation. The study considered the total daily passengers transported and effective kilometres covered for the output. Further, along with the efficiency measurement, each depot’s total number of accidents has been collected to measure safety. The findings estimated the mean value of the PTE score for all depots, which turns out to be 0.91, indicating that to improve the managerial efficiency of the entire public transport of the state, the depots need to increase the output by 9 per cent. The average OTE score of all selected units is 0.89, reflecting that these DMUs have 89 per cent worked efficiently from 2015-16 to 2020-21. The efficiency score is based on output-oriented issues, meaning that inputs will be constant for total efficiency. The study recommends the promotion of public transport based on sustainability and efficiency in the State of Haryana.

Food bank operations: review of operation research methods and challenges during COVID-19

Food banks have played a crucial role in mitigating food insecurity in affluent countries for over four decades. Throughout the years, academics have researched food banks for a variety of operational problems, resulting in several research papers on the topic. However, despite significant academic interest, the operational challenges and optimization of food bank operations remain under-researched. This study aims to conduct a systematic literature review on food bank operations and provide evidence-based recommendations for addressing prevalent challenges, and provide decision-makers with practical recommendations. In addition, this investigation seeks to investigate the impact of the COVID-19 pandemic on food bank operations. We conducted a comprehensive analysis of academic publications on food bank operations using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) in order to get a deeper comprehension of the problems confronting food bank operations. Using a keyword search strategy with the logical operators “AND” and “OR,” two search methods were utilized to identify relevant articles on food bank operations management, supply chain, distribution, and production in our first search. In our second search, we discovered articles in the “Operations Research & Management Science” (OR &MS) category of Web of Science containing food bank-related keywords such as food charity, food donation, and food aid. The database searches yielded 246 hits, and the article content was scanned to eliminate irrelevant articles by removing non-English articles and duplicated studies, leaving 55 articles for further examination. Our extensive examination of Operations Research (OR) methodologies reveals that Mixed-Integer Linear Programming (MILP) models are the most commonly used methodology, followed by Linear Program (LP), Dynamic Program (DP), and Data Envelopment Analysis (DEA) techniques. The key findings of this study emphasize the operational challenges food banks encountered during and after the COVID-19 pandemic, including supply chain disruptions, increased demand, and volunteer shortages. To address these issues, effective solutions, including the management of food donations and volunteer scheduling, were proposed. Our findings have practical implications for decision-makers in food bank management, highlighting the importance of adopting evidence-based solutions. Finally, Limitations and prospective research directions in food bank management are discussed, with an emphasis on the need for ongoing research in this crucial area.

Energy audit and management of environmental GHG emissions based on multi-objective genetic algorithm and data envelopment analysis: An agriculture case

This study aimed to address the challenges of energy consumption and environmental emissions in the mushroom production sector through an energy audit analysis and life cycle assessment approach. The main objective of the study was to estimate energy and environmental indicators, optimize their outputs using multi-objective genetic algorithm and data envelopment analysis methods, and identify opportunities for energy savings in the mushroom production process. The energy flow analysis revealed that the total input energy for mushroom production was 1022537.82 MJ/m2, while the total output energy was only 11125.94 MJ/m2, resulting in an energy use efficiency rate of 0.01, indicating significant energy imbalance and inefficiency. Electrical energy consumption had the largest share of total consumed energy, approximately 80.6%. The life cycle assessment results showed that the mushroom production chain emits about 8.50 × 10+3 kg of CO2 GHG. The results of energy optimization demonstrate that between 6.5% and 25% reductions in energy utilization can be achieved during mushroom production. Among the input energies, compost had the largest quota in energy savings of almost 7% and 26% by data envelopment analysis and multi-objective genetic algorithm techniques, respectively. Thus, it is recommended to implement the multi-objective genetic algorithm technique to identify opportunities for energy savings and reduce environmental emissions in the mushroom production sector, which can lead to significant energy savings and contribute to environmental sustainability while reducing operating costs.

Efficiency of subnational government expenditure towards elementary education and nutrition: Bootstrap data envelopment analysis for India

The need of translating increasing outlays into better outcomes on various public programmes has attracted the attention of researchers worldwide to focus more on the quality of public spending, often assessed in terms of its efficiency. The present article is a contribution to the existing literature on the subject in that it assesses the efficiency of government expenditure of Indian states for two most basic social services—elementary education and nutrition during 2014–2015 and 2018–2019 using data envelopment analysis and Malmquist productivity index techniques, in both input‐oriented as well as output‐oriented settings under variable returns to scale assumption; it constructs the average performance index for children at elementary education and malnutrition index for women and children to utilise them as output indicators; and it adopts a robust bootstrap truncated regression procedure to avoid various econometric issues while estimating the impact of selected environmental factors on bias‐corrected inefficiency scores. The major findings of this article seem to suggest that, while states should be provided greater flexibility in their fiscal operations, they have to complement their fiscal autonomy with good governance to improve the efficiency of their spending on basic social services. Towards this end, states like Bihar, Jharkhand and those belonging to North‐Eastern and Himalayan regions need special attention with a different set of development policies.

State-wise Efficiency of Agricultural Production in Inda: Data Envelopment Efficiency Approach

Agricultural sector in India remains of vital importance to the Indian economy contributing to the overall economic growth through supplies of food and raw materials and providing livelihood support to a substantial share of the population of the country. Indian agriculture is currently facing numerous issues like low productivity, ensuring food security and farmer’s income. Efficiency of agriculture in India is very much connected with these issues and has been a topic of extensive research and analysis. The present study attempts to measure the efficiency of agricultural production of the selected twenty major states of India. The efficiency in agricultural production of the states have been evaluated using the non-parametric technique of data envelopment analysis (DEA). The study reveals that there is a wide variation in efficiency scores of agricultural production across the states with an average efficiency score being 0.6. The Tobit regression analysis reveals that the states with higher literacy, higher cropping intensity have higher efficiency levels. The study outcomes from this research will be useful for policymakers to adopt right policy in the direction of sustainable agricultural development.

A non-parametric synthesize measure for corporate governance: empirical evidence from Indian banks

The purpose of the present study is to craft a corporate governance index for Indian banks by eliminating the subjectivity in assigning weights. The present framework includes six corporate governance dimensions, which include the attributes related to the board committee, audit committee, risk management committee, remuneration committee and transparency of the banks. The study considers measuring the index value for corporate governance framework via a novel extension of the classical data envelopment analysis technique known as the benefit of doubt analysis, as this approach assigns the weights objectively to the different dimensions of corporate governance and thereafter provides a synthesised score to be termed as the corporate governance index. Further, the present study attempts to know any variation in the index score when the whole sample is divided into sub-sample for two different groups in terms of duality and non-duality of CEO leadership board and other is based on the large and small size of banks. The analysis reveals that non-duality boards and small-size banks are more adaptable to the governacne norms. The analysis further documented that in an emerging economy effective implementation of governance norms is not only beneficial in protecting the interest of the shareholders but also contributes to the business performance. Thus, based on the novel analysis the study provides few policy implications in terms of identifying the key areas for an effective corporate governance framework.

A regulatory robust conditional approach to measuring the efficiency of wholesale water supply and wastewater treatment services

A benchmarking evaluation instrument was designed with a European Union country regulatory authority for water supply and wastewater treatment services to determine the efficient operating cost of its service providers that operated in the wholesale market segment in the 2017–2021 period. To this end, the non-parametric Data Envelopment Analysis technique was adapted to a robust and conditional approach. The results point to similar mean efficiency scores between water supply and wastewater services in the five-year period, despite the greater heterogeneity in the latter. Furthermore, the estimated potential cost savings for both services ranged from about 2% to 3%.

Multiparameter flood hazard, socioeconomic vulnerability and flood risk assessment for densely populated coastal city

In the current study, flood risk assessment of densely populated coastal urban Surat City, on the bank of the lower Tapi River in India, was conducted by combining the hydrodynamic model-based flood hazard and often neglected socioeconomic vulnerability. A two-dimensional (2D) hydrodynamic (HD) model was developed using physically surveyed topographic data and the existing land use land cover (LULC) of the study area (5248 km2). The satisfactory performance of the developed model was ascertained by comparing the observed and simulated water levels/depths across the river and floodplain. The 2D HD model outputs with geographic information system (GIS) applications were further used to develop probabilistic multiparameter flood hazard maps for coastal urban city. During a 100-year return period flood (Peak discharge = 34,459 m3/s), 86.5% of Surat City and its outskirt area was submerged, with 37% under the high hazard category. The north and west zones are the worst affected areas in Surat City. The socioeconomic sensitivity and adaptive capacity indicators were selected at the city's lowest administrative (ward) level. The socioeconomic vulnerability was evaluated by employing the robust data envelopment analysis (DEA) technique. Fifty-five of 89 wards in Surat City, covering 60% of the area under the jurisdiction of the Municipal Corporation, are highly vulnerable. Finally, the flood risk assessment of the city was conducted using a bivariate technique describing the distinctive contribution of flood hazard and socioeconomic vulnerability to risk. The wards adjoining the river and creek are at high flood risk, with an equal contribution of hazard and vulnerability. The ward-level hazard, vulnerability, and risk assessment of the city will help local and disaster management authorities to priorities high risk areas while planning flood management and mitigation strategies.

Application of the data envelopment analysis technique to measure the environmental efficiency of the 27 countries of the European Union during the period 2012–2020

In the last decade, we have experienced a changing geopolitical context that has caused changes in the energy context. In addition, human activity contributes to global warming or sea level rising, i.e., climate change. A set of action policies have been implemented to continue fighting against this environmental situation (such as the Paris Agreement, the COP27, or the European Green Deal for 2030); therefore, it is necessary to determine whether we are on the right track. It is compulsory to develop predictive models that accurately analyze the current status and the already path undertaken. To this end, this article analyzes the environmental efficiency of the 27 countries of the European Union (excluding the UK) using the so-called data envelopment analysis (DEA). In particular, economic (GDP and GDP per capita), environmental (CO2 and CH4 emissions), electricity production data, the volume of vehicles, and the industrial production rate of the different countries were collected to calculate environmental efficiency. Once these data were collected, the environmental efficiency was calculated using two methods based on the DEA. The results show that out of the 27 countries, only 12 have a relatively high environmental efficiency, although it could be improved, implementing a set of corrections. However, other countries have a low eco-efficiency performance and they must improve in the coming years. We can highlight that rich countries are closer to achieving high environmental efficiency than less developed countries.Graphical Political map of the European Union indicating the average eco-efficiencywith colors of the 27 countries of the DEA method.

Does Your Company Engage Efficiently on Social Media? Improving Performance Using DEA Model and Visualization

ABSTRACT: Despite an implosion in the use of social media, quantifying the efficiency of customer engagement continues to be a major challenge for most companies. In this study, we propose a data envelopment analysis (DEA) model to measure the relative operational efficiency of companies' customer engagement on social media based on non-financial inputs and outputs. We collected data from the Facebook and Twitter pages of 56 fast-food companies over 12 months in 2020, and used DEA, cluster analysis, and visualization techniques to perform a comparative efficiency analysis. Our study identified four distinct clusters of companies and mapped them to the corresponding stages of the social media maturity (SMM) model. By integrating the DEA model with the SMM, we provide a novel mechanism for assessing social media engagement efficiency. Furthermore, based on the results of our analysis, we offer strategic and tactical level recommendations to inefficient companies for improving their social media engagement performance.