Data Envelopment Analysis Procedure Research Articles

Technical efficiency and managerial ability in two-stage production processes with undesirable products: A case on Asian banks

Performance improvement and managerial ability estimation using benchmarking tools and data envelopment analysis (DEA) in banking and financial sectors have attracted considerable attention among researchers and bank managers. Although there are many efforts in two-stage DEA in banks, few studies have been conducted to measure managerial ability and technical efficiency of two-stage DEA in the banking sector. This contribution proposes a two-stage DEA procedure to estimate technical efficiency and managerial ability in the banking sector in six Asian countries. The empirical results obtained for selected Asian banks revealed that variables total capital adequacy ratio, total assets and number of branches have a positive impact on the efficiency of the deposit gathering section. Moreover, we observed that the number of ATMs has a negative impact on the efficiency of the sales and services section. Another finding is that in all these six countries, the sales and service section has performed better than the deposit gathering section.

A firm-specific Malmquist productivity index model for stochastic data envelopment analysis: an application to commercial banks

In the data envelopment analysis (DEA) literature, productivity change captured by the Malmquist productivity index, especially in terms of a deterministic environment and stochastic variability in inputs and outputs, has been somewhat ignored. Therefore, this study developed a firm-specific, DEA-based Malmquist index model to examine the efficiency and productivity change of banks in a stochastic environment. First, in order to estimate bank-specific efficiency, we employed a two-stage double bootstrap DEA procedure. Specifically, in the first stage, the technical efficiency scores of banks were calculated by the classic DEA model, while in the second stage, the double bootstrap DEA model was applied to determine the effect of the contextual variables on bank efficiency. Second, we applied a two-stage procedure for measuring productivity change in which the first stage included the estimation of stochastic technical efficiency and the second stage included the regression of the estimated efficiency scores on a set of explanatory variables that influence relative performance. Finally, an empirical investigation of the Iranian banking sector, consisting of 120 bank-year observations of 15 banks from 2014 to 2021, was performed to measure their efficiency and productivity change. Based on the findings, the explanatory variables (i.e., the nonperforming loan ratio and the number of branches) indicated an inverse relationship with stochastic technical efficiency and productivity change. The implication of the findings is that, in order to improve the efficiency and productivity of banks, it is important to optimize these factors.

Project Management Efficiency Measurement with Data Envelopment Analysis: A Case in a Petrochemical Company

The research question this study poses is how to measure the efficiency of project management activities. The purpose of this article is to quantify the efficiency of the execution of a project portfolio managed by a project management office (PMO) structure. The research subject is a PMO operating within a petrochemical manufacturing company in southern Brazil. The research method is quantitative modeling. The study employed data envelopment analysis (DEA) to calculate the relative efficiencies of projects in three classes according to complexity over a period of four years. Each project is a decision-making unit (DMU), as required by the DEA procedure. One novelty is the calculation of cost- and time-weighted efficiency values, which slightly differ from the average. The main results indicate that the average efficiency for classes of projects roughly stands between 40 and 80%. The results also indicate a learning process guided by the PMO, as the average efficiency increased over three years in two classes of projects, according to the prioritization imposed by the office. The study also pointed out that the most influential variables in determining project efficiency are accuracy in meeting deadlines and the time planned for completion. The most important implication is that, from now on, the company has a theoretical foundation to justify focusing further efforts on reducing and controlling time to completion, not only cost and scope conformity, to increase overall project efficiency. Future research should prioritize investigating management techniques that increase the likelihood of completing projects within their deadlines.

Unveiling the potential of hotel mergers: A hybrid DEA approach for optimizing sector-wide performance in the hospitality industry

Mergers and acquisitions (M&A) in the hospitality sector involve consolidating assets among hotels through partnerships. While data envelopment analysis (DEA) has been widely used for hotel efficiency analysis, little attention has been paid to hotel M&A. In this paper, a hybrid DEA methodology consisting of two stages is proposed to identify optimal matches among hotels to enhance sector performance. The initial stage employs an inverse Data Envelopment Analysis (IDEA) model to evaluate the maximum gains that could potentially be generated from pairwise consolidations among hotels. A DEA procedure that incorporates a standard DEA model and a greedy heuristic is developed in the second stage to identify the optimal pairs of hotel mergers. The optimal merger strategy for the entire hotel industry is determined from the complete set of hotels under consideration.The pertinence of the proposed methodology is shown through a sample of 58 hotels from the Sultanate of Oman

Technical Efficiency of Chemical Fertilizers Use and Agricultural Yield: Evidence from India

The agriculture sector is a significant contributor to food security and employment in India, where sustainable yield in agriculture is a prime concern. The heavy and improper use of chemical fertilizers persists in technically inefficient agricultural production. This study attempts to evaluate the technical inefficiency of chemical fertilizers' use and measure the potential minimization of fertilizer input without compromising the agricultural yield level. The study uses secondary data from 2006 to 2015 of 28 Indian states and empirically analyses the efficiency of chemical fertilizers use and their impact on agricultural yield by incorporating slack-based Data Envelopment Analysis (DEA). Three chemical fertilizers, namely; Nitrogen (N), Phosphorus (P), Potassium (K), and gross irrigated land area, have been taken as input variables, and States Total Food-grain (STFG) has been taken as the output variable. The conventional slack-based DEA procedure may have bias efficiency estimates, therefore in the second step Double Bootstrap DEA procedure is followed to correct the biasness of efficiency scores which further checks the moderating relation between efficiency scores and agricultural credit using #algorithm1 and #algorithm2 of Simar and Wilson (2007). Findings indicate that fertilizer K has a higher possibility to decrease, followed by P and N. Evidence from double bootstrap establishes a positive relationship between agricultural credit and yield. Hence, farm-level policies, budgetary implications of agricultural credit, and awareness about the proper use of fertilizers will help reduce chemical fertilizer intensiveness in the production and enhance the farmers’ income through input saving strategy.

Intensive Care Unit Services Preparedness for the Pandemic: An Efficiency Analysis.

Intensive care unit (ICU) services efficiency and the shortage of critical care professionals has been a challenge during pandemic. Thus, preparing ICUs is a prominent part of any pandemic response. The objective of this study is to examine the efficiencies of ICU services in Turkey right before the pandemic. Data were gathered from the Public Hospital Statistical Year Book for the year 2017. Analysis are presented at hospital level by comparing teaching and non-teaching hospitals. Bootstrapped data envelopment analysis procedure was used to gather more precise efficiency scores. Three analysis levels are incorporated into the study such as, all public hospitals (N = 100), teaching (N = 53), non-teaching hospitals (N = 47), and provinces that are providing high density of ICU services through the country (N = 54). Study results reveal that average efficiency scores of ICU services obtained from teaching hospitals (eff = 0.65) is higher than non-teaching (eff = 0.54) hospitals. After applying the bootstrapping techniques, efficiency scores are significantly improved and the difference between before and after bootstrapping results are statistically significant (P < .05). Province based analysis indicates that, ICU services efficiencies are high for provinces located in southeast part of the country and highly populated places, such as İstanbul. Evidence-based operational design that considers the spatial distribution of health resources and effective planning of critical care professionals are critical for efficient management of intensive care. Study results will be helpful for health policy makers to deeply understand dynamics of critical care.

Technical Efficiency of Regional Public Hospitals in China Based on the Three-Stage DEA.

Many countries are facing the increasing cost of healthcare services and the low efficiency of public hospitals. These issues are also evident in China. This paper offers a comprehensive assessment of the efficiency of public hospitals operating in China’s 31 regions. The impact of the third round of reform of the health system in 2009 is assessed based on the three-stage data envelopment analysis procedure. The time period from 2011 to 2018 is covered in this study. Due to different functions performed by the public hospitals and other ones, the number of patients with infectious diseases is incorporated as an output variable reflecting the social function. The outpatient visits and inpatient visits are considered to reflect the outputs related to the private functions. The results imply an increase in the mean efficiency of public hospitals from 0.927 to 0.981 after taking the impact of environmental variables and statistic noise into account. These results indicate that the efficiency of public hospitals is dependent in the operational environment. There are 11 regions whose hospitals operate on the efficiency frontier during the whole period covered. Therefore, the Chinese government should reasonably design and apply the regulatory tools to improve the efficiency of public hospitals.

Oncology services efficiency in the age of pandemic: A jackknife and bootstrap sensitivity analysis for robustness check of DEA scores

BackgroundDeveloping countries face great challenges in health care because of changing disease dynamics and the increasing burden of chronic diseases such as cancers. Thus, effective operational design of health services is critical to better manage scarce health care resources. This study aimed to examine the spatial distribution of the efficiency of Turkey’s oncology services. MethodsData was collected from the 2017 Public Hospitals Statistical Yearbook, and a total of 55 provinces with advanced centers for cancer were analyzed. This study applied Charnes, Cooper, and Rhodes’s input-oriented data envelopment analysis (DEA) and performed jackknifing for robustness check of DEA scores. ResultsThe iteration procedure generated four models. The final model included 38 decision-making units (DMUs), and 50 % of provinces were found to have efficient oncology services. The final model’s average conventional efficiency score was 0.79. Next, bootstrapped DEA procedure was incorporated into the final model to gather bias-corrected efficiency scores. After applying the bootstrapping approach, efficiency scores are significantly improved and the difference between conventional and bias-corrected efficiency scores are statistically significant (U = 475; p < 0.05). ConclusionsGeographic planning of cancer care services is a relevant principle in health operations design that requires specific health service configurations for preparedness of health crisis such as pandemic. The results highlighted that health policymakers must be aware of regional imbalances and eliminate them to provide advanced oncology care services for population groups in poor areas of the country. Policy summaryHealth policy makers should prioritize a balanced geographical distribution of professional oncology services to provide vulnerable groups better access to critical care.

Data envelopment analysis based procedure for selection and ranking of ground motion prediction equations

One of the influential components in the probabilistic seismic-hazard analysis (PSHA) is selecting appropriate ground motion prediction equations (GMPEs). To handle the source of epistemic uncertainty, the most common approach is considering several GMPEs in a logic tree framework. In such an approach, the selected GMPEs and the weights assigned for each one are usually determined by experts, mostly according to gut feeling. In order to reduce the role of subjectivity in selecting GMPEs, some statistical criteria based on goodness-of-fit measures were developed in recent years. In this study, in order to incorporate different criteria in the ranking procedure, we present to use data envelopment analysis (DEA) as a multi-criteria efficiency evaluation framework. One of the advantages of the DEA procedure is that it is able to determine the efficiency or inefficiency of each GMPE. Also, the DEA methodology is able to handle the situation in which several target intensity measures such as spectral amplitude in different structural periods are interested to be considered in the ranking procedure. In this study, the DEA procedure is applied to the Central Iran seismotectonic province. The results show that the DEA can well evaluate the efficiency of GMPEs, and can be used as supplements to existing ranking procedures.

An optimistic-pessimistic DEA model based on game cross efficiency approach

The ranking of the decision making units (DMUs) is an essential problem in data envelopment analysis (DEA). Numerous approaches have been proposed for fully ranking of units. Majority of these methods consider DMUs with optimistic approach, whereas their weaknesses are ignored. In this study, for fully ranking of the units, a modified optimistic–pessimistic approach, which is based on game cross efficiency idea is proposed. The proposed game like iterative optimistic-pessimistic DEA procedure calculates the efficiency scores according to weaknesses and strengths of units and is based on non-cooperative game. This study extends the optimistic-pessimistic DEA approach to obtain robust rank values for DMUs. The proposed approach yields Nash equilibrium solution, thus overcomes the problem of non-uniqueness of the DEA optimal weights that can possibly reduce the usefulness of cross efficiency. Finally, in order to verify the validity of the proposed model and to show the practicability of algorithm, we apply a real-world example for selection of industrial R&amp;D projects. The proposed model can increase the discriminating power of DMUs and can fully rank the DMUs.

Conventional procedurevis-à-visbootstrap-based corrections of efficiency analyses

PurposeThis paper aims to evaluate the performance of basic metals industry in India and analyze its determinants, using data envelopment analysis (DEA) method. It also intends to compare the results through conventional two-stage and bootstrap-based inferences.Design/methodology/approachConsidering technical efficiency as a measure of performance, this paper specifically investigates whether the participation of a firm in the global market affects its performance. The conventional two-stage procedure is used to test the export intensity and firm performance nexus. The bootstrap-based algorithms (by Simar and Wilson, 2007) are used to correct the bias and serial correlation issues involved in the conventional approach.FindingsThe result shows a negative relation between export intensity and firm performance while following the conventional procedure. Even after accounting for serial correlation, the relation remains more or less similar to that of conventional analysis. However, a strong negative relation between export intensity and firm performance is not observed in a more reliable inference obtained after correcting for possible bias as well as serial correlation.Research limitations/implicationsThis paper is based on cross-sectional analysis, and a more reliable result can be obtained by considering a larger sample and longer period.Originality/valueThis paper shows how the conventional two-stage procedure may result in misleading inferences due to bias in the estimation of efficiency scores and the serial correlation during the second stage inferential analysis. This paper also empirically exemplifies how the double bootstrap DEA procedure can overcome these limitations of the conventional two-stage approach.

Airport efficiency in Pakistan - A Data Envelopment Analysis with weight restrictions

This paper investigates 12 major airports in Pakistan for potential cost inefficiencies. We identify inefficiencies by benchmarking the productive performance of airports using Data Envelopment Analysis (DEA) and data for 2012. To improve the ability of DEA to differentiate performance levels, we impose restrictions on the possible weights of inputs and outputs in the DEA procedure. The definition of these weight restrictions is based on additional information on feasible production trade-offs and relative input prices. To the best of our knowledge, this paper provides the first application of predefined weight restrictions in a DEA analysis of airport efficiency. The results suggest that there are cost inefficiencies at several airports, which are mainly caused by overstaffing and overinvestment in capacity. Furthermore, we find that the operational scale of most airports is inefficiently small, so that increases in traffic will result in declining unit costs.

Data envelopment analysis procedure with two non-homogeneous DMU groups

The classic data envelopment analysis (DEA) model is used to evaluate decision-making units' (DMUs) efficiency under the assumption that all DMUs are evaluated with the same criteria setting. Recently, new researches begin to focus on the efficiency analysis of non-homogeneous DMU arose by real practices such as the evaluation of departments in a university, where departments argue for the adoption of different criteria based on their disciplinary characteristics. A DEA procedure is proposed in this paper to address the efficiency analysis of two non-homogeneous DMU groups. Firstly, an analytical framework is established to compromise diversified input and output (IO) criteria from two non-homogenous groups. Then, a criteria fusion operation is designed to obtain different DEA analysis strategies. Meanwhile, Friedman test is introduced to analyze the consistency of all efficiency results produced by different strategies. Next, ordered weighted averaging (OWA) operators are applied to integrate different information to reach final conclusions. Finally, a numerical example is used to illustrate the proposed method. The result indicates that the proposed method relaxes the restriction of the classical DEA model, and can provide more analytical flexibility to address different decision analysis scenarios arose from practical applications.

Application of data envelopment analysis models in supply chain management: a systematic review and meta-analysis

Supply chain management aims to designing, managing and coordinating material/product, information and financial flows to fulfill the customer requirements at low costs and thereby increasing supply chain profitability. In the last decades, data envelopment analysis has become the main topic of interest as a mathematical tool to evaluate supply chain management. While, various data envelopment analysis models have been suggested to measure and evaluate the supply chain management, there is a lack of research regarding to systematic literature review and classification of study in this field. Regarding this, some major databases including Web of Science and Scopus have been nominated and systematic and meta-analysis method which called “PRISMA” has been proposed. Accordingly, a review of 75 published articles appearing in 35 scholarly international journals and conferences between 1996 and 2016 have been attained to reach a comprehensive review of data envelopment analysis models in evaluation supply chain management. Consequently, the selected published articles have been categorized by author name, the year of publication, technique, application area, country, scope, data envelopment analysis purpose, study purpose, research gap and contribution, results and outcome, and journals and conferences in which they appeared. The results of this study indicated that areas of supplier selection, supply chain efficiency and sustainable supply chain have had the highest frequently than other areas. In addition, results of this review paper indicated that data envelopment analysis showed great promise to be a good evaluative tool for future evaluation on supply chain management, where the production function between the inputs and outputs was virtually absent or extremely difficult to acquire. The facility of multiple inputs and multiple outputs of the data envelopment analysis model was definitely an attractive one to most researchers and, therefore, the data envelopment analysis procedure had found many applications beyond supply chain management into organization and industry.

A Measure of Well-Being Across the Italian Urban Areas: An Integrated DEA-Entropy Approach

In recent years, there has been an increasing proliferation of initiatives focusing on the concept of quality of life and well-being. At the centre of these studies there is the recognizing that the GDP offers only a partial perspective of factors affecting people’s lives. Following this line of the research, this paper is aimed at computing the well-being efficiencies of a sample of Italian Province capital cities, using a methodological approach that combines data envelopment analysis (DEA) with Shannon’s entropy formula. To avoid subjectivity in choosing a representative set of variables that proxy the phenomenon under study, we rely on the theoretical framework adopted by the Italian National Institute of Statistics (ISTAT) within the equitable and sustainable well-being (BES) project. The dashboard of indicators included in the analysis are related to the Ur-BES initiative, promoted by ISTAT to implement the BES framework at cities level. In a first step of the analysis, an immediate focus on separate dimensions of urban well-being is obtained by summarizing the plurality of available indicators through the building of composite indices. Next, the adopted integrated DEA–Shannon entropy approach has permitted to increase the discriminatory power of DEA procedure and attain a more reliable profiling of Italian Province capital cities well-being efficiencies. The results show a marked duality between the Northern and Southern cities, highlighting important differences in many aspects of human and ecosystem well-being.

Benchmarking superannuation funds based on relative performance

In this article, we assess fund performance using data envelopment analysis (DEA). For each inefficient fund, DEA provides a set of role model funds whose best practices may be emulated for performance improvement. We find that the role models of most inefficient funds consist entirely of funds different from their own type. To overcome this situation, we suggest a multi-step DEA procedure. The procedure starts by categorizing funds on a hierarchical basis. We establish the hierarchy based on the frequency of efficient funds that belong to each fund type. Thereafter, a set of role model funds for each inefficient fund is found by pooling the funds in its own category and the funds that belongs to the categories at the lower levels in the hierarchy and applying DEA. This procedure is repeated by augmenting the pool with funds at the next higher level and so on until all the sampled funds are included. At each step, a set of role models is identified. An inefficient fund can thus reach the efficient frontier in stages. Statistical evidence suggests that membership and proportion of risky assets may have a negative association, and the fund size may have a positive association with fund performance.

자료포락 분석을 이용한 지방 사립 전문대학교 학과의 효율성 비교 및 성과 달성 목표수준 정의

This paper compares efficiency results and performance targets for academic departments in a local private college using DEA (Data Envelopment Analysis). Because of an aging society, a smaller school-age population entering colleges, and enhanced accreditation standards by the government, colleges and universities are not recruiting and retaining sufficient students and therefore are struggling for survival. In contrast to popular four-year undergraduate universities concentrated in Seoul and its satellite cities, retaining students is critical for the survival of local private colleges in poor or remote regions. Therefore, it is very important to identify the factors involved in the retention of students in the various departments of a college. However, given the different characteristics of the departments, it is difficult to identify one unique or robust set of standards to evaluate their performance. The purpose of this paper is to maximize student retention capabilities by ensuring that additional resources are assigned to efficient DMUs, while, inefficient DMUs are given benchmarked targets. Based on previous studies and college accreditation standards, this paper presents indices to be used in evaluating the efficiency of academic departments in a college. In evaluating relative efficiency, this paper uses the output-oriented BCC model. To define target levels to be achieved for efficient DMU, a multi-stage DEA procedure is used.

A new two-stage data envelopment analysis (DEA) model for evaluating the branch performance of banks

The purpose of this paper is to provide a framework for evaluating the overall performance of bank branches in terms of profitability efficiency and effectiveness by means of a two stage data envelopment analysis (DEA) model. DEA is a linear programming problem approach for evaluating efficiency of decision making units (DMU) that have multiple inputs and outputs. In this paper, we use a two stage DEA model for efficiency evaluation of DMUs. In the two stage model all the outputs from the first stage are the only inputs to the second stage, in addition to the inputs to the first stage and the outputs from the second stage. The outputs from the first stage to the second stage are called intermediate measures. Many papers have regressed non parametric estimate of efficiency in the two stage DEA procedure. All of these studies have several problems in the benchmark or projection unit and present the DMU with relative efficiency. Most of these papers do not present an efficient benchmark unit and cannot evaluate the relative efficiency unit. In this paper we review these studies and then create a two stage model that does not have these problems. Our model presents the efficient benchmark unit and also presents DMU with relative efficiency. In the next step we apply our two stage model in bank branches of a large commercial bank in Iran. This study aggregates profitability efficiency and effectiveness into the overall performance. Some relations between profitability efficiency and effectiveness are very important. Without these relations we cannot obtain superior insight about overall performance. So we assume that profitability efficiency and effectiveness are dependent, and with this assumption we design a two stage DEA model. This study shows the importance of profitability efficiency and effectiveness in the overall performance in bank branches in Iran. Key words: Banking, process efficiency, organizational effectiveness, data envelopment analysis (DEA), banks, banks performance.

A modified DEA model to estimate the importance of objectives with an application to agricultural economics

This paper demonstrates a connection between data envelopment analysis (DEA) and a non-interactive elicitation method to estimate the weights of objectives for decision-makers in a multiple attribute approach. This connection gives rise to a modified DEA model that allows us to estimate not only efficiency measures but also preference weights by radially projecting each unit onto a linear combination of the elements of the payoff matrix (which is obtained by standard multicriteria methods). For users of multiple attribute decision analysis the basic contribution of this paper is a new interpretation in terms of efficiency of the non-interactive methodology employed to estimate weights in a multicriteria approach. We also propose a modified procedure to calculate an efficient payoff matrix and a procedure to estimate weights through a radial projection rather than a distance minimization. For DEA users, we provide a modified DEA procedure to calculate preference weights and efficiency measures that does not depend on any observations in the dataset. This methodology has been applied to an agricultural case study in Spain.

Water Utility Efficiency Assessment Using a Data Envelopment Analysis Procedure

Data envelopment analysis is used to compare the relative efficiencies of potable water utilities using the United States Environmental Protection Agency community water system survey data. Three ownership types (private for profit, private not for profit, and public), and two types of supply sources (ground and surface) are compared. Statistically significant results indicating efficiency advantages of certain utility types are found, and clear trends towards certain utility types are identified. The findings indicate that public utilities are most efficient overall, followed by private not-for-profit utilities, with private for-profit utilities being least efficient. Except for a few cases of very large supply demands, utilities employing groundwater sources were generally more efficient than those using surface water sources. A brief investigation into the marginal return on information obtained from using additional measurement variables to measure utility performance is presented. Additional ranking information can be obtained by using more discrete measurement variables, but with diminishing marginal returns. This presentation of public water utility efficiency evaluation should prove useful as a tool for guiding ownership policies and water source development strategies and demonstrates the utility of publicly available data sets.