Proposed Data Envelopment Analysis Model Research Articles

Airline efficiency and environmental impacts – Data envelopment analysis

Airline efficiency has been a research interest for decades. While early airline efficiency research focused primarily on revenue generation and profitability, growing airline social responsibility is driving greater investment into understanding and improving the environmental impact on airline efficiency. This study developed a two-phase, two-stage model using a data envelopment analysis (DEA) approach to simultaneously evaluate airline operations for available seat mile (ASM) generation, revenue passenger mile (RPM) generation, carbon dioxide emissions abatement, and revenue generation on a sample of thirteen airlines. Efficiency evaluation was performed for the years between 2013 and 2015, between U.S. and non-U.S. carriers, and between full-service carriers (FSCs) and low-cost carriers (LCCs). Results indicated more accurate measurement of airlines’ overall efficiency using the proposed DEA model, which included operational and cost factors as input variables and environmental impact as both the input and output variables in the model. Service and environmental factors were found to be significant in determining airline efficiency, with environmental abatement affecting the overall efficiency of airline performance both inside and outside the U.S. when emission reduction effort was properly accounted for. The findings provided theoretical and managerial implications in the assessment of airline efficiency with a special emphasis on incorporating environmental impact in the overall evaluation.

Impact of Smart, Green, Resilient, and Lean Manufacturing System on SMEs’Performance: A Data Envelopment Analysis (DEA) Approach

In the present era of the fourth industrial revolution, small and medium enterprises (SMEs) are adopting smart, green, resilient, and lean (SGRL) practices to enhance their performance and achieve sustainability. For SMEs to perform well in their supply chains and satisfy customers, the impact of the combined effects of SGRL manufacturing on SMEs’ performance needs to be studied. Although SGRL manufacturing has been studied independently in order to understand its impact on SMEs’ performance, there is still a need for significant research on its combined effect. The objective of the present work is to evaluate the performance of SMEs and to understand the combined effect of SGRL manufacturing on SMEs’ performance. This research applied the data envelopment analysis (DEA) methodology to evaluate 30 SMEs identified in the northern region of India. A DEA model was developed that considers environmental, operational, and social performances as output criteria while considering SGRL practices as input criteria. Sixteen decision-making units (DMUs) were identified as inefficient using the DEA approach and one of them was considered for a case study for comparison with efficient SMEs. The case study employed a Strength, Weakness, Opportunity, and Threat (SWOT) analysis to provide remedial action to one of the selected underperforming SMEs, i.e.,SME11. The strengths, weaknesses, opportunities, and threats of SME11 were identified and strategies were suggested by benchmarking SME11 with one of the efficient SMEs, i.e., SME23. The findings of this research work will help policymakers, owners, and managers of SMEs take necessary actions and enhance their performance by adopting the proposed DEA model using SGRL manufacturing practices.

Evaluating performance of an untargeted urban food security scheme in India

PurposeThe purpose of this study is to evaluate the efficiency of the relative performances of Mother’s canteen across the regions of Tamil Nadu and find out the determinants of inefficiencies in the scheme.Design/methodology/approachAn untargeted food security scheme called Amma (Mother's) canteen was started in Tamil Nadu, India, with an aim to provide the urban poor with hygienic and healthy food at an affordable price. Along with secondary data, interviews were conducted to understand the operational details of Mother's canteen. Data envelopment analysis (DEA) was used to find the relative efficiency of the scheme operated by nine corporations.FindingsBased on the daily expenditure, number of meals served and revenue, seven of nine corporations were found to be inefficient. Further, sensitivity analyses found that among six procurement variables, procurement (quantity and price) of black gram and cooking oil were determinants of inefficiency.Research limitations/implicationsAs an untargeted scheme, the cost of delivering service-based evaluation was used for performance evaluation. Policymakers could use centralized procurement instead of open market procurement at the corporation level and standardized ingredients' usage (quantity) to further reduce the cost of the food security scheme.Practical implicationsThe proposed DEA model may be used by policymakers to empirically evaluate the food security scheme's delivery effectiveness across various corporations in a region. Inefficient branches are identified here with empirical support for further performance improvement changes.Originality/valueThere are limited number of studies evaluating untargeted schemes. This paper presents the challenges of evaluating an untargeted scheme which allows self-selection of beneficiaries. The outcome of this study will help in identifying inefficient corporations, and further, improve the performance and cost of delivering untargeted food security scheme.

Technical efficiency in Chile’s higher education system: A comparison of rankings and accreditation

Improving the productivity of higher education in every nation's economy is one of the main challenges faced in the current environment of competition and shrinking public funds. The effective use of resources is a crucial issue in Chile's higher education reform. Data envelopment analysis (DEA) has been widely applied to measure efficiency in universities, sometimes focused on teaching or research. Universities function as a complex production process in which teaching and research are linked in the internal structure, share some inputs, and continue across multiple periods. To deal with this complexity, we developed a new DEA model that incorporates network structures, carryover activities, and shared inputs in a dynamic approach. We applied this model to a set of 33 Chilean universities and compared the results to their rankings and accreditation status. Our proposed DEA model has advantages: (a) there are no subjective criteria, and (b) the model considers the internal structure of the university production model, as well as the inputs and outputs over time. The objectivity of the model allows us to evaluate overall efficiency in terms of the quantity and quality of teaching and research, removing exogenous criteria and judgments regarding the performance of higher education institutions. This new quantitative approach could generate disaggregated data to analyze efficiency improvement over time or serve as a benchmarking tool according to the universities' characteristics.

Analyzing the sustainability of China’s industrial sectors: A data-driven approach with total energy consumption constraint

Tight energy constraints and severe environmental pollution are two main problems affecting economic growth and sustainable development. As an important economic growth mode, green growth seeks to achieve both economic growth and environmental sustainability. Previous studies on the efficiency evaluation of green growth ignored fixed-sum energy consumption; this is a significant omission because this constraint is general in China: the government has set targets to reduce energy consumption by 20% and 16% per unit of GDP during its the eleventh (2006-2010) and twelfth (2011-2015) five-year plans. To fill this gap, we propose a new data envelopment analysis (DEA) model to analyze the green growth degree of China’s industrial sectors. The proposed DEA model considers the constraint of total energy consumption in order to conform to the policy of energy and emission reduction. Previous theoretical studies on fixed-sum output construct the common equilibrium efficient frontier (CEEF) only by considering the minimal reduction strategy for the fixed-sum output of all decision making units (DMUs), while ignoring each DMU’s adjustment strategy for fixed-sum input/output. In this paper, we construct a new CEEF by considering each industrial sector’s adjustment of energy consumption. The new CEEF not only guides the industrial sectors to alter their energy consumption structure to achieve green growth but also allows a more accurate evaluation of the energy and environmental efficiency (EEE) of industrial sectors. Last, our proposed model is applied to analyze the green growth and EEE of 36 of China’s industrial sectors.

Cost-efficiency measurement for two-stage DEA network using game approach: an application to electrical network in Iran

This study proposes a two-stage game–data envelopment analysis (DEA) approach for cost-efficiency (CE) measurement using centralized and Stackelberg game models. Each decision-making unit (DMU) is proposed to make up two-stage network structures (or processes), where all the first-stage outputs are the only second-stage inputs. The main contribution of this study is the development of cooperative and non-cooperative models for CE measurement and implies a unique CE decomposition. We apply centralized and Stackelberg approaches for cooperative and non-cooperative assumption between two stages. We proposed a simplified version of the two-stage DEA network CE model. Applying the proposed model for CE calculation reduces both the number of constraints and variables, resulting in a sharp reduction in computational requirements. The applicability of the presented model is demonstrated in the context of studying the output of the electrical network in Iran. The results of the case study show that in a two-stage network, the proposed DEA model can provide accurate estimates of CE. This paper aids the two-stage network structures to control the costs by weak DMUs management. To the authors’ knowledge, this paper is the first research on the CE in a network by the DEA approach so that the relationships between the internal stages of the network are considered.

Cross-country comparison of the efficiency of the European forest sector and second stage DEA approach

In this paper the relative efficiency of the forest sector of 28 EU/EFTA countries during the period 2010–2015 is assessed using Data Envelopment Analysis (DEA). Three non-discretionary inputs (persons employed, forest available for wood supply and initial growing stock) are considered. The outputs are roundwood production, gross value added and final growing stock. The proposed DEA model not only computes efficiency scores but also improvement targets. The countries with the lowest efficiency scores during the period under study are Greece, Bulgaria and Italy. In the second stage, a fractional regression model is fitted and the factors that have an influence on the estimated efficiency are identified. The factors that have an influence are GDP and belonging to the NORTH Europe and CENTRAL-WEST Europe regions. Quantitative estimates of the partial effects of these factors are provided. The results can contribute in providing guidance towards the best practice in roundwood production.

A non-parametric analysis of the effect of the configuration of competitive pillars on competitive efficiency

PurposeThis study aims to evaluate how the configuration of competitive pillars impacts businesses' competitive efficiency by using a non-parametric model, namely, data envelopment analysis (DEA), with a single constant input.Design/methodology/approachThe proposed DEA model evaluates technical inefficiency, which results from differences in the availability and allocation of resources, and configuration inefficiency, which we link to differences in the way businesses amalgamate their competitive pillars. The sample includes 115 Spanish businesses operating in manufacturing, construction, retail and knowledge-intensive business services (KIBS) sectors.FindingsThe results reveal that, on an average, firms can improve their overall competitive efficiency by 53.53%. The findings suggest that the configuration of competitive pillars has important implications for efficiency analyses: human capital and strategy are the most relevant aspects shaping competitive efficiency in manufacturing and construction firms; whereas innovation emerges as the most relevant competitive aspect driving competitive efficiency in KIBS firms.Originality/valueThe novelty of this study lies in the analysis of competitive efficiency in a model where efficiency can be explained by overall (industry-specific) competitive efficiency within the industry and by strategic choices on how resources and capabilities are combined within the business.

DEA Approach for Performance Assessment of Call Centre Agents

The paper focuses on a relatively new and prospective application of the data envelopment analysis (DEA) in the employee performance assessment. In the paper, a novel DEA approach is proposed for evaluating the performance of call centre agents, based on their relative efficiency. Since call centres handle a majority of customer-company interactions, performance of call centre agents largely influences the future success or failure of a company. To ensure the quality of customer service, permanent evaluation of call centre agents’ performance is essential. The proposed DEA model consisting of two input variables (wage and working time) and five output variables (quick-answer calls proportion, customer satisfaction, net first contact resolution, call quality and inbound contact handle time) has been tested on 55 call centre agents working at the call centre of one of the largest telecommunications operators in the Slovak Republic. After measuring the performance of each agent, based on the DEA models, the call centre agents’ performance was evaluated in the DEA matrix format. As a result, the call centre agents were divided into four groups: Stars, Cash Cows, Question Marks and Poor Dogs. Finally, based on the proposed approach, recommendations for call centre managers on how to improve or maintain the performance of each of these groups were drawn. The proposed approach provides a practical framework for call centre managers to assess the performance of the agents, and to plan and take steps to improve the quality of call centre services.

A Cross-Efficiency Evaluation Method Based on Evaluation Criteria Balanced on Interval Weights

Cross-efficiency evaluation approaches and common set of weights (CSW) approaches have long been suggested as two of the more important and effective methods for the ranking of decision making units (DMUs) in data envelopment analysis (DEA). The former emphasizes the flexibility of evaluation and its weights are asymmetric, while the latter focuses on the standardization of evaluation and its weights are symmetrical. As a compromise between these two approaches, this paper proposes a cross-efficiency evaluation method that is based on two types of flexible evaluation criteria balanced on interval weights. The evaluation criteria can be regarded as macro policy—or means of regulation—according to the industry’s current situation. Unlike current cross-efficiency evaluation methods, which tend to choose the set of weights for peer evaluation based on certain preferences, the cross-efficiency evaluation method based on evaluation criterion determines one set of input and output weights for each DMU. This is done by minimizing the difference between the weights of the DMU and the evaluation criteria, thus ensuring that the cross-evaluation of all DMUs for evaluating peers is as consistent as possible. This method also eliminates prejudice and arbitrariness from peer evaluations. As a result, the proposed cross-efficiency evaluation method not only looks for non-zero weights, but also ranks efficient DMUs completely. The proposed DEA model can be further extended to seek a common set of weights for all DMUs. Numerical examples are provided to illustrate the applications of the cross-efficiency evaluation method based on evaluation criterion in DEA ranking.

Modeling undesirable output with a DEA approach based on an exponential transformation: An application to measure the energy efficiency of Chinese industry

Abstract There is great interest in measuring energy efficiency of China's industry using data envelopment analysis (DEA) approaches. This paper proposes a new exponential transformation of undesirable outputs into desirable outputs as part of a DEA approach for calculating environmental efficiency by using all kinds of classic models. Different from the existing transformations, the exponential model should provide a more flexible way to deal with undesirable outputs, allowing the user to adjust the slope and distribution of the transformation function by setting a parameter. A strategy for maximizing the standard variance among the undesirable outputs after transformation is employed in this paper to determine the optimal parameter value for the exponential model. This paper empirically examines the energy performance of sectors in Chinese industry from 2010 to 2014 using the proposed DEA model and the results show that most sectors in Chinese industry have not performed well, especially the sectors concerned with energy extraction. Based on the results of the efficiency evaluation, this paper calculates how can each sector achieve efficiency improvement through energy conservation and emission reduction, and propose specific improvement target values for each sector accordingly.

Extended DEA model under type-2 fuzzy environment

Purpose The purpose of this paper is to combine the uncertain methods of type-2 fuzzy sets and data envelopment analysis (DEA) evaluation model together. A new type-2 fuzzy DEA efficiency assessment method is established. Then the proposed procedure is applied to the poverty alleviation problem. Design/methodology/approach The research method is the DEA model, which is an effective method for efficiency assessment of social–economic systems. Considering the existence of the same efficiency values that cannot be ranked in the proposed DEA model, the balance index is introduced to solve the ranking problem of decision-making units effectively. Findings The results show that the proposed method can not only measure the efficiency of the existence of uncertain information but also deal with the ranking of multiple efficient decision-making units. Originality/value This paper selects type-2 fuzzy DEA model to express a lot of uncertain information in efficiency evaluation problems. We use the parameter decomposition method of type-2 fuzzy programming or the type-2 expectation values indirectly. The balance index is proposed to further distinguish the multiple effective decision-making units. Furthermore, this paper selects rural poverty alleviation in Hainan Province as a case study to verify the feasibility of the method. The relative efficiency values in different years are calculated and analyzed.

A New Method for Solving Dual DEA Problems with Fuzzy Stochastic Data

Data envelopment analysis (DEA) is a widely used mathematical programming technique for measuring the relative efficiency of decision-making units which consume multiple inputs to produce multiple outputs. Although precise input and output data are fundamentally used in classical DEA models, real-life problems often involve uncertainties characterized by fuzzy and/or random input and output data. We present a new input-oriented dual DEA model with fuzzy and random input and output data and propose a deterministic equivalent model with linear constraints to solve the model. The main contributions of this paper are fourfold: (1) we extend the concept of a normal distribution for fuzzy stochastic variables and propose a DEA model for problems characterized by fuzzy stochastic variables; (2) we transform the proposed DEA model with fuzzy stochastic variables into a deterministic equivalent linear form; (3) the proposed model which is linear and always feasible can overcome the nonlinearity and infeasibility in the existing fuzzy stochastic DEA models; (4) we present a case study in the banking industry to exhibit the applicability of the proposed method and feasibility of the obtained solutions.

Hip fracture surgery efficiency in Israeli hospitals via a network data envelopment analysis

Data envelopment analysis (DEA) has been used previously for examining hospital efficiency, based on administrative data. Yet, previous DEA research devoted to quality assurance rarely considered medical processes or outcomes in efficiency studies. The goal of this study is to examine the relative efficiency of hip fracture surgery, based on clinical data reflecting medical process indicators and outcomes. To accomplish our goal, recent developments in DEA research were harnessed to model an output-oriented two-stage DEA network. The proposed DEA model has: two input variables reflecting the condition of the patient, fracture type and Charlson index; two intermediate variables reflecting clinical decisions, surgery within 48 h and usage of a drain for 1 day (rate); and two output variables reflecting the success of the surgery, survival rate after surgery and the rate of no infection. Using data from orthopedic wards in most of the acute Israeli hospitals (20 out of 22), no statistically significant correlation was found, either between the socio-economic index of patients who had hip fracture surgery and the relative efficiency scores produced by the two-stage network DEA model, or between efficiency and the geographical periphery status of the hospital. In addition to this, which points to a degree of social equality regarding hip fracture surgeries, we also compared the two-stage network model and related DEA models, providing several lemmas that represent the relationships between the various models mathematically.

Measuring environmental sustainability performance of freight transportation seaports in China: A data envelopment analysis approach based on the closest targets

AbstractBecause of China's rapid economic development, its freight transportation system has grown to become one of China's high‐pollution‐emission sectors. However, there are few studies that pay close attention to measuring and improving the environmental performance of China's freight transportation system, especially in regard to seaports. In this paper, data envelopment analysis (DEA) is applied to measure the environmental performance of freight transportation seaports. In addition, we also provide benchmarking information to point the way to improving environmental performance effectively. Our proposed DEA model is based on the closest targets, which satisfies the strong monotonicity and can yield the most relevant solution for the inefficient seaports. An empirical study of 21 of China's primary freight transportation seaports shows that most of them have relatively good environmental performance. Among the five coastal port groups, the Bohai‐rim port group had the best environmental performance, whereas the Pearl River port group had the worst. Our data show significant differences between the best and worst performances, indicating that more measures should be taken to balance and coordinate the development between the five coastal port groups.

Modeling fuzzy data envelopment analysis under robust input and output data

This paper offers a fuzzy optimization framework for data envelopment analysis (DEA) to evaluate the relative efficiency of decision making units (DMUs) with parametric interval-valued fuzzy variable-based inputs and outputs. The parametric interval-valued fuzzy variable-based inputs and outputs is employed to capture the uncertainty of data on the basis of professional judgements or empirical estimations. The DEA problem is formulated as fuzzy expectation model with credibility constraints. When the inputs and outputs are mutually independent parametric interval-valued triangular fuzzy variables, we investigate the parametric equivalent representations of expectation objective function and chance constraints. In order to find the optimal solution of our DEA model, a domain decomposition method is proposed. Finally, the numerical example on the sustainable supplier evaluation and selection problem is provided to demonstrate the efficiency of the proposed DEA model and domain decomposition method.

Benchmarking in the Supply Chain Using Data Envelopment Analysis

It is necessary to evaluate performance using a proper combination of all resources of supply chain in the best possible way to provide products and services in the market. One strategy to measure supply chain efficiency is the data envelopment analysis (DEA), which involves use of current inputs and outputs to evaluate supply chain performance. Therefore, the outcome of each DEA evaluation is not suitable for providing suitable benchmark for the future. Hence, managers are not able to improve the activities of their subset using the results of DEA model. For this purpose, we forecast data of units under evaluation using system dynamics simulation and then we present a proper model to formulate strategies for improving performance using the proposed DEA model. Finally, we implemented the designed algorithm in the milk industry of Fars province (Iran), and proper strategies for improving the efficiency of this industry were developed.

Resource scheduling in a private cloud environment: an efficiency priority perspective

Purpose Resource scheduling is the study of how to effectively measure, evaluate, analyze, and dispatch resources in order to meet the demands of corresponding tasks. Aiming at the problem of resource scheduling in the private cloud environment, the purpose of this paper is to propose a resource scheduling approach from an efficiency priority point of view. Design/methodology/approach To measure the computational efficiencies for the resource nodes in a private cloud environment, the data envelopment analysis (DEA) approach is incorporated and a suitable DEA model is proposed. Then, based on the efficiency scores calculated by the proposed DEA model for the resource nodes, the 0-1 programming technique is introduced to build a simple resource scheduling model. Findings The proposed DEA model not only has the ability of ranking all the decision-making units into different positions but also can handle non-discretionary inputs and undesirable outputs when evaluating the resource nodes. Furthermore, the resource scheduling model can generate for the calculation tasks an optimal resource scheduling scheme that has the highest total computational efficiency. Research limitations/implications The proposed method may also be used in studies of resource scheduling studies in the environments of public clouds and hybrid clouds. Practical implications The proposed approach can achieve the goal of resource scheduling in private cloud computing platforms by attaining the highest total computational efficiency, which is very significant in practice. Originality/value This paper uses an efficiency priority point of view to solve the problem of resource scheduling in private cloud environments.

Assessing the scoring efficiency of a football match

Data Envelopment Analysis (DEA) has been widely used for efficiency assessment in many sports and specifically in football. This paper presents a novel approach to measuring the scoring efficiency of football teams in a match. It uses a parallel process network DEA model that takes into account the defensive and attacking variables of the two teams, their respective percentage of possession of the ball and also the team's economic value. The proposed approach provides estimates of how many more goals each team should have scored in each of the matches played in the season. By averaging the scoring efficiency in the different matches played, team scoring efficiencies for the season can be computed. Moreover, the proposed DEA model can be run after each match so that the changes in efficiency can be monitored throughout the season. This can help coaches and managers assess their team's past performance and plan ahead for each match. The proposed approach has been applied to Spanish First Division teams for the season 2013/2014 and a virtual final league table has been computed.

Proposing a hybrid method based on DEA and ANP for ranking organizational units and prioritizing human capital management drivers

Purpose – The purpose of this study is to present a hybrid analytic network process (ANP) and data envelopment analysis (DEA) method for ranking organizational units as well as prioritizing organization’s human capital management criteria. Design/methodology/approach – In the proposed DEA model, human capital management criteria are considered as the inputs and organizational commitment considered as the output of DEA model. Afterward, the organizational unit’s efficiency and the weight of human capital management criteria are evaluated through DEA and the data are considered as the basis of ANP model. Findings – The results of prioritizing the drivers of human capital management were showed that leadership practices, human capital management and learning capacity are the most important ones. Also, the findings proved that the proposed integrated DEA/ANP can be helpful in managerial issues. Research limitations/implications – Filling the super-matrix and designing the questionnaire of ANP have always been the challenge of scholars because of the large number of data it requires. One of the main advantages of the proposed hybrid method in this research is that it resolves the above-mentioned problem of ANP. Practical implications – The results of the proposed method may provide managers this opportunity to better analyze the condition of organizational units from human capital management perspective and focus on the most important activities in this context. Moreover, the proposed hybrid method can help scholars to better use both DEA and ANP techniques and obtain more reliable findings. Originality/value – In this study, DEA efficiency measures of units, and the weights of inputs and outputs of DEA model are used to fill the super-matrix of ANP. In addition to having a logical approach, this method provides more reliable results and enjoys the advantages of both ANP and DEA techniques.