Input-oriented Data Envelopment Analysis Research Articles

Improvement of the irrigation performance in Water Users Associations integrating data envelopment analysis and multi-regression models

Irrigation performance assessment is particularly important in the Water Users Associations (WUAs) operating in Calabria (Southern Italy), where collective irrigation service suffers from poor performances both from an operative and economic point of view. For many years Data Envelopment Analysis (DEA) has been proposed for the diagnosis of Water Users Association performance; however, the number and type of related performance indicators must be selected with caution to avoid misleading and unrealistic results.In this paper, we propose to apply DEA to a limited but significant set of performance indicators and to couple it to Multiple Regression Analysis by Principal Component Regression (PCR). The proposed methods were applied to evaluating the system operation and financial performances of ten of the eleven WUAs operating in Calabria (Southern Italy) to indicate potential improvements.The analysis of the current performance indicators collected throughout five years (2011–2015) showed that in Calabrian WUAs the irrigation service is underutilised, and water delivered to crops is always in excess; the cost recovery of WUAs is very low, because of staff costs and low fee collection. DEA identified five inefficient WUAs and took the remaining five organisations as reference for performance improvement. The input-oriented DEA coupled to PCR has suggested reducing water usage, management and personnel costs and water fees, by increasing the irrigated area and the irrigation service coverage. The output–output oriented DEA coupled to PCR predicted a high increase of the cost recovery capacity of the inefficient WUAs, but in this case the improved scenario required an abnormal increase (10-fold) of the irrigated area, which may be basically unfeasible.Overall, the integration of DEA with multi-regression models and their implementation in the case study, using a limited set of easy-to-survey performance indicators, appears to be a powerful and easy tool for decision makers in the irrigation sector.

Swiss Cantonal Banks: A DEA Efficiency and Productivity Analysis

This paper applies a data envelopment analysis (DEA) to study the efficiency and productivity changes in the Swiss cantonal bank sector in the period 2006-2014. The efficiency analysis is conducted by applying the production input-oriented DEA variable returns to scale model in a three-stage procedure. The productivity is studied by estimating a DEA-based Malmquist Productivity Index (MPI) that provides evidence of increasing productivity growth on average for the sector in the studied period. The main source of productivity growth as per the components of the Banker, Charnes and Cooper (BCC) MPI model is related to a frontier-shift (technological innovation) rather than to improvements in the technical efficiency. The decreasing average DEA scores in the post-global financial crisis period of 2008-2014 further support this finding. In the second stage of the efficiency analysis, the environmental factors influencing the productivity growth are analysed by conducting a general method of moments (GMM) regression. The results provide evidence of a positive and statistically significant relationship between the stock of residential buildings per canton and technical efficiency. In the third stage, the environmental variables from the second-stage regression are included within the constraints of the first-stage DEA model as proposed by Ray [1]. The third-stage DEA scores support the evidence of slightly decreasing average post-global crisis technical efficiency. The overall average technical efficiency in the Swiss cantonal banking sector, however, remains at a relatively high level in the studied period.

Determinants of tomato farmers efficiency in Mymensingh district of Bangladesh: Data Envelopment Analysis approach

Tomato is one of the major vegetables in Bangladesh that experienced massive productivity growth after independence. Nevertheless, farmers are struggling to find out optimal input combination in their farm that causes inefficient input use. Therefore, this study estimated the factors affecting efficiency of tomato farmers in Mymensingh of Bangladesh. Input oriented Data Envelopment Analysis (DEA) was employed for measuring efficiency while Tobit regression model was used to estimate the factors affecting efficiency. A total of 60 tomato farmers were selected using random sampling technique. Mean technical efficiency for tomato farmers was 0.83 implies that tomato farmers can reduce their input use by 17%. Education, training and high yielding variety adoption had positive effect on efficiency while age of tomato farmer’s had negative effect on efficiency. Efficiency increased with the farmer’s education, training and variety adoption. Farmer’s adopting local high yielding variety was more efficient than that of exotic high yielding variety. In addition, efficiency of farmers reduced with their age. Improvement of tomato farmer’s efficiency is possible if farmers received education, training and local high yielding variety.J. Bangladesh Agril. Univ. 16(1): 93-97, April 2018

Allocating on coal consumption and CO2 emission from fair and efficient perspective: empirical analysis on provincial panel data of China.

This paper considers a problem of how to allocate resource effectively and equitably among provinces. To address the problem, a total factor resource input-oriented data envelopment analysis (DEA) model is used to evaluate the energy and environmental efficiency for 30 provinces in China during 2009-2013 in this paper. Based on the evaluation results, from efficient and fair perspective, a revised DEA-based resource allocation model is established. It is worth pointing out that the model takes the input orientation and output orientation into account at the same time and can be used to allocate coal consumption and carbon emission by 2020 for 30 provinces in China. Results indicate that if the Chinese government wants to fulfill the CO2 emission reduction targets of 40-45% by 2020, and coal consumption intensity reduction target during 13th Five-Year Plan, inefficient provinces will undertake more coal consumption and carbon emission intensity reduction obligation share. And provinces with historical high coal consumption and high CO2 emission intensity will have greater potential of coal consumption and carbon emission intensity reduction. In addition, this paper set several scenarios of gross domestic product (GDP) growth rate, under the scenarios analysis, finds the growth rate of GDP has negative effect on reduction of coal consumption and carbon dioxide emissions intensity. This research provides more realistic practical significance for achieving sustainable economic development.

DEA BASED APPROACH TO SET ENERGY EFFICIENCY TARGET UNDER PAT FRAMEWORK: A CASE OF INDIAN CEMENT INDUSTRY

Aim: Propose a Data Envelopment Analysis (DEA) based approach to set energy efficiency targets under the Perform, Achieve and Trade (PAT) framework of the Bureau of Energy Efficiency (BEE) Design / Research methods: We adopt input-oriented non-controllable DEA model with variable return to scale DEA Conclusions / findings: Due to the implication of in-house energy conversion, we estimate separate energy efficiency targets based on “purchased energy” as well as “process energy”. The later accounts for energy finally used in the production process after in-house energy conversion. Originality / value of the article: The Bureau of Energy Efficiency (BEE) in India has introduced a market based energy efficiency mechanism under the Perform, Achieve and Trade (PAT) framework. Under this mechanism, energy efficiency certificates can be traded across eight identified sectors thus bringing cost effectiveness to achieve the energy efficiency targets. To implement the scheme, differentiated energy efficiency targets have been set using baseline specific energy consumption. This approach does not account for technical and operational aspects like vintage, scale, output mix and input mix. This study proposes an alternative target setting method based on Data Envelopment Analysis (DEA) which takes into account some of the above mentioned technical and operational differences across the industrial plants. A comparative assessment highlights the efficacy of DEA methodology in implementation of the PAT scheme. We estimate energy efficiency targets based on “purchased energy” as well as “process energy”, i.e. that used finally in the production process. Implications of the research: BEE may adopt the suggested approach to set energy efficiency targets for subsequent cycles under the Perform, Achieve and Trade (PAT) framework.

Measuring Technical Efficiency in Rice Productivity Using Data Envelopment Analysis

The study has made an attempt to measure technical efficiency (TE) in rice productivity in Odisha during the period of 2011–13. An input-oriented data envelopment analysis (DEA) has estimated the average TE score at 79.10 per cent, indicating overuse of critical input resources to the tune of 20.90 per cent. The model found seed and fertilizers use can be reduced to the extent of 4.14 kg/ha and 26.58 kg/ha, respectively, without affecting the current productivity. Malmquist Index analysis for panel data on TE change indicates regress in efficiency for 14 districts, progress for 13 districts and no change for 3 districts. Nevertheless, the mean value of TE change over the study period was found to be 1, pointing neither progress nor regress in input resource utilization. The observed technical inefficiencies in resource use may be attributed to four important factors: (a) very small-sized operational land holding; (b) better off-farm income-generating activities and fluctuations of agricultural product prices; (c) traditional mindset of older farmers; and (d) low degree of involvement of farmers with the cooperative bodies. The DEA results suggest need for adoption of more efficient resource management practices in the state’s rice cultivation scenario.

DEA Based Approach to Set Energy Efficiency Target Under Pat Framework: A Case of Indian Cement Industry

Aim: Propose a Data Envelopment Analysis (DEA) based approach to set energy efficiency targets under the Perform, Achieve and Trade (PAT) framework of the Bureau of Energy Efficiency (BEE) Design / Research methods: We adopt input-oriented non-controllable DEA model with variablereturn to scale DEA Conclusions / findings: Due to the implication of in-house energy conversion, we estimate separate energy efficiency targets based on “purchased energy” as well as “process energy”. The later accounts for energy finally used in the production process after in-house energy conversion. Originality / value of the article: The Bureau of Energy Efficiency (BEE) in India has introduced a market based energy efficiency mechanism under the Perform, Achieve and Trade (PAT) framework. Under this mechanism, energy efficiency certificates can be traded across eight identified sectors thus bringing cost effectiveness to achieve the energy efficiency targets. To implement the scheme, differentiated energy efficiency targets have been set using baseline specific energy consumption. This approach does not account for technical and operational aspects like vintage, scale, output mix and input mix. This study proposes an alternative target setting method based on Data Envelopment Analysis (DEA) which takes into account some of the above mentioned technical and operational differences across the industrial plants. A comparative assessment highlights the efficacy of DEA methodology in implementation of the PAT scheme. We estimate energy efficiency targets based on “purchased energy” as well as “process energy”, i.e. that used finally in the production process. Implications of the research: BEE may adopt the suggested approach to set energy efficiency targets for subsequent cycles under the Perform, Achieve and Trade (PAT) framework

A Novel Method of Evaluating Key Factors for Success in a Multifaceted Critical Care Fellowship Using Data Envelopment Analysis.

The current system of summative multi-rater evaluations and standardized tests to determine readiness to graduate from critical care fellowships has limitations. We sought to pilot the use of data envelopment analysis (DEA) to assess what aspects of the fellowship program contribute the most to an individual fellow's success. DEA is a nonparametric, operations research technique that uses linear programming to determine the technical efficiency of an entity based on its relative usage of resources in producing the outcome. Retrospective cohort study. Critical care fellows (n = 15) in an Accreditation Council for Graduate Medical Education (ACGME) accredited fellowship at a major academic medical center in the United States. After obtaining institutional review board approval for this retrospective study, we analyzed the data of 15 anesthesiology critical care fellows from academic years 2013-2015. The input-oriented DEA model develops a composite score for each fellow based on multiple inputs and outputs. The inputs included the didactic sessions attended, the ratio of clinical duty works hours to the procedures performed (work intensity index), and the outputs were the Multidisciplinary Critical Care Knowledge Assessment Program (MCCKAP) score and summative evaluations of fellows. A DEA efficiency score that ranged from 0 to 1 was generated for each of the fellows. Five fellows were rated as DEA efficient, and 10 fellows were characterized in the DEA inefficient group. The model was able to forecast the level of effort needed for each inefficient fellow, to achieve similar outputs as their best performing peers. The model also identified the work intensity index as the key element that characterized the best performers in our fellowship. DEA is a feasible method of objectively evaluating peer performance in a critical care fellowship beyond summative evaluations alone and can potentially be a powerful tool to guide individual performance during the fellowship.

Integrated Analysis of Healthcare Efficiency: A Systematic Review.

Data Envelopment Analysis (DEA) has been used as a performance measurement tool in efficiency assessment of healthcare systems. However, over the years, researchers and health practitioners presented the theoretical and methodological limitations of DEA that limits the full view of healthcare efficiency. To address these limitations, a commonly used strategy is to integrate other statistical methods and techniques with DEA to provide better efficiency evaluation. This paper reviews 57 studies with DEA applications in the healthcare industry to illustrate the integrated analysis of healthcare efficiency. With DEA as the central method, regression models in conjunction with statistical tests are commonly used. Input-oriented radial DEA models using predominantly capacity-related inputs and activity-related outputs and following either constant return to scale or variable return to scale assumptions are mostly applied to measure healthcare efficiency.

How to assess sustainability of countries via inverse data envelopment analysis?

In this paper, two input-oriented and output-oriented inverse semi-oriented radial measures are presented. Such models are applied to determine resource allocation and investment strategies for assessing sustainability of countries. Our proposed models can deal with both positive and negative data. In our proposed inverse input-oriented data envelopment analysis (DEA) model, optimal inputs are suggested while outputs and efficiency score of decision-making unit (DMU) under evaluation are unchanged. Similarly, in our proposed inverse output-oriented DEA model, optimal outputs are proposed while inputs and efficiency score of DMU under evaluation are kept unchanged. For the first time, we propose two new inverse DEA models to handle resource allocation and investment analysis problems given sustainable development aspects in the presence of negative data. A case study is given for assessing sustainability of countries.

Group decision making with interval fuzzy preference relations based on DEA and stochastic simulation

This paper proposes an integrated approach to group decision making with interval fuzzy preference relations using data envelopment analysis (DEA) and stochastic simulation. A novel output-oriented CCR DEA model is proposed to obtain the priority vector for the consistency fuzzy preference relation, in which each of the alternatives is viewed as a decision-making unit. Meanwhile, we design a consistency adjustment algorithm for the inconsistent fuzzy preference relation. Furthermore, we build an optimization model to get the weights of each fuzzy preference relation based on maximizing group consensus. Then, an input-oriented DEA model is introduced to obtain the final priority vector of the alternatives. Finally, a stochastic group preference analysis method is developed by analyzing the judgments space, which is carried out by Monte Carlo simulation. A numerical example demonstrates that the proposed method is effective.

Considerations for using data envelopment analysis for the assessment of radiotherapy treatment plan quality

PurposeThe operations research method of data envelopment analysis (DEA) shows promise for assessing radiotherapy treatment plan quality. The purpose of this paper is to consider the technical requirements for using DEA for plan assessment.Design/methodology/approachIn total, 41 prostate treatment plans were retrospectively analysed using the DEA method. The authors investigate the impact of DEA weight restrictions with reference to the ability to differentiate plan performance at a level of clinical significance. Patient geometry influences plan quality and the authors compare differing approaches for managing patient geometry within the DEA method.FindingsThe input-oriented DEA method is the method of choice when performing plan analysis using the key undesirable plan metrics as the DEA inputs. When considering multiple inputs, it is necessary to constrain the DEA input weights in order to identify potential plan improvements at a level of clinical significance. All tested approaches for the consideration of patient geometry yielded consistent results.Research limitations/implicationsThis work is based on prostate plans and individual recommendations would therefore need to be validated for other treatment sites. Notwithstanding, the method that requires both optimised DEA weights according to clinical significance and appropriate accounting for patient geometric factors is universally applicable.Practical implicationsDEA can potentially be used during treatment plan development to guide the planning process or alternatively used retrospectively for treatment plan quality audit.Social implicationsDEA is independent of the planning system platform and therefore has the potential to be used for multi-institutional quality audit.Originality/valueTo the authors’ knowledge, this is the first published examination of the optimal approach in the use of DEA for radiotherapy treatment plan assessment.

Benchmarking sustainability of urban water infrastructure systems in China

The broad scope and definition of sustainability has perplexed assessment of water infrastructure systems, especially for the purpose of directing engineering practices when quantified criteria are desired. An input-oriented data envelopment analysis (DEA) was improved to benchmark the relative sustainability of the water infrastructure of 157 cities in China. The DEA calculates a single sustainability score using seven inputs and five outputs that represent the economic, resource and environmental dimensions of sustainability. Overall, 69 out of the 157 sampled systems obtained high sustainability scores. Eight specific efficiency indicators based on individual DEA input to output ratio were evaluated to identify the causes of performance differences. Compared to water supply systems, the performance of wastewater treatment plants has greater influence on the sustainability score of the overall system. For all systems, the sustainability scores are more sensitive to sludge production and electricity consumption than capital investment and removal efficiency of treatment processes. The DEA provides guidelines to cities for setting priorities in order to meet specific sustainability criteria. Statistical analysis indicates that the overall sustainability score primarily depends on the system scale, meteorological conditions such as air temperature and rainfall, and source water quality.

On the Output Orientation of the Benefit-of-the-Doubt-Model

While Benefit-of-the-Doubt (BoD) models, as used in the context of composite indicator construction, seek to aggregate outputs only, they are formally equivalent to an input-oriented Data Envelopment Analysis (DEA) model in multiplier form. Strictly read, this introduces conceptually ambiguous results, as input adjustments have no real significance in the context at hand. At heart of this ambiguity lies a double interpretation of what is essentially a binding constraint in BoD’s underlying linear-fractional program. Moreover, there is a direct, reciprocal relation between the BoD-model and an earlier output-oriented DEA model introduced by Lovell et al. (Eur J Oper Res 87:507–518, 1995) for similar purposes. Although these models are essentially similar, I also show that there are instances (i.e. when adding additional weight restrictions) in which the results of one alternative are easier to communicate. The models surveyed in this paper are complemented with a short application on human development data for females living in Brussels municipalities.

Determinants and resource use efficiency of “better cotton”: An innovative cleaner production alternative

The resource constraints along with socioeconomic attributes and capabilities of farmers cause variations in the adoption of agricultural technologies. These variations represent an inefficiency source in complete utilization of management practices and epitomize a lack of achieving full potential offered by “better cotton” – an innovative cleaner production alternative. Therefore, this research focused on identifying the potential areas of improvement in “better cotton” farmers' input use by investigating the technical and economic efficiencies as well as the factors determining the resource use efficiency. Technical, allocative, and economic efficiencies of “better cotton” were analyzed using input-oriented data envelopment analysis. Factors affecting technical and economic efficiencies were identified using bootstrapped truncated regression. There was variation in technical efficiency and it was positively influenced by farmers’ education, farming experience, and drainage status, whereas it was negatively influenced by the number of household members involved in farming and the area under cotton. The mean economic efficiency of 56% indicated that at the current productivity level inefficient farmers can reduce their costs by 44%. The economic efficiency was negatively affected by the number of household members involved in farming and the intensity of using crop management practices. Thus, efforts should be made to improve input use efficiency of particularly those “better cotton” farmers who are currently operating inefficiently. This efficiency analysis aims to provide recommendations to policy makers and promoters of “better cotton” as to which practices and inputs should be focused while implementing “better cotton” as a cleaner alternative to conventional cotton.

Assessment of emergency department efficiency using data envelopment analysis

ABSTRACTDespite the important role of emergency department (ED) performance measurement, commonly used metrics remain disaggregated and are not standardized. The objectives of this study are to develop an aggregated performance measure that enables benchmarking EDs with respect to technical and scale efficiencies, and to investigate significant exogenous factors affecting the technical efficiency of EDs. To our best knowledge, this is the first study that examines the scale and technical efficiencies of EDs and helps to address hospital redesign/reengineering. This study formulated input-oriented data envelopment analysis (DEA) models that involve three inputs and three outputs and derived efficiency scores for individual EDs. The DEA analysis indicated that many EDs may not need to modify the size of their operations to improve efficiency. Instead, they may need to focus their efforts on re-engineering their processes to use their inputs more efficiently. The logistic regression analysis demonstrated that additional functional areas within the ED, length of stay, and percent of patients who arrive by ambulance were associated with the technical efficiency of EDs. This research is significant in that hospitals can use these models as benchmarking tools, and the findings can be a basis to redesign EDs with respect to critical hospital resources for performance improvement.

A window-DEA based efficiency evaluation of the public hospital sector in Greece during the 5-year economic crisis.

The main objective of this study was to apply the non-parametric method of Data Envelopment Analysis (DEA) to measure the efficiency of Greek NHS hospitals between 2009–2013. Hospitals were divided into four separate groups with common characteristics which allowed comparisons to be carried out in the context of increased homogeneity. The window-DEA method was chosen since it leads to increased discrimination on the results especially when applied to small samples and it enables year-by-year comparisons of the results. Three inputs -hospital beds, physicians and other health professionals- and three outputs—hospitalized cases, surgeries and outpatient visits- were chosen as production variables in an input-oriented 2-year window DEA model for the assessment of technical and scale efficiency as well as for the identification of returns to scale. The Malmquist productivity index together with its components (i.e. pure technical efficiency change, scale efficiency change and technological scale) were also calculated in order to analyze the sources of productivity change between the first and last year of the study period. In the context of window analysis, the study identified the individual efficiency trends together with “all-windows” best and worst performers and revealed that a high level of technical and scale efficiency was maintained over the entire 5-year period. Similarly, the relevant findings of Malmquist productivity index analysis showed that both scale and pure technical efficiency were improved in 2013 whilst technological change was found to be in favor of the two groups with the largest hospitals.

Factors affecting technical efficiency of small-scale apple farms in Balochistan Plateau, Pakistan

In Balochistan, Agriculture is dominated by small-scale farmers and the apple production sector is confronted with the chronic constraint of low output per unit of input. Although some agricultural policies have been implemented in this province, studies on effectiveness and impacts of such policies over apple production and farmers’ performance are still limited. This study is an effort to bridge the research gap on this potential crop in the plateau by investigating technical and scale efficiency of 181 officially designated small-scale apple farmers in mountainous district Mastung in Balochistan; then explored factors that underlie differences in production inefficiency. A two-stage input-oriented Data Envelopment Analysis (DEA) methodology was employed to evaluate technical and scale efficiency followed by truncated bootstrapped regression framework to analyze the correlative determinants to efficiency. Average technical and scale efficiencies of larger landholders were higher than medium and small landholders. Analysis indicated that there is still ample scope for inefficient sample farmers to reduce the input use by 33% without compromising the given yield level. Maximum contributions to the total input savings were from the use of urea, farm yard manure (FYM) and labor-use. Farmer’s experience, farm ownership, irrigation constraints, and farmer’s access to trainings, credit and extension services significantly affected apple farmers’ technical efficiency. Based on the findings of this study, strategies are suggested to enhancing efficiencies by farmers’ capacity development through effective extension services and trainings and provision of adequate credit.

Technical and scale efficiency in public and private Irish nursing homes – a bootstrap DEA approach

This article provides methodological and empirical insights into the estimation of technical efficiency in the nursing home sector. Focusing on long-stay care and using primary data, we examine technical and scale efficiency in 39 public and 73 private Irish nursing homes by applying an input-oriented data envelopment analysis (DEA). We employ robust bootstrap methods to validate our nonparametric DEA scores and to integrate the effects of potential determinants in estimating the efficiencies. Both the homogenous and two-stage double bootstrap procedures are used to obtain confidence intervals for the bias-corrected DEA scores. Importantly, the application of the double bootstrap approach affords true DEA technical efficiency scores after adjusting for the effects of ownership, size, case-mix, and other determinants such as location, and quality. Based on our DEA results for variable returns to scale technology, the average technical efficiency score is 62%, and the mean scale efficiency is 88%, with nearly all units operating on the increasing returns to scale part of the production frontier. Moreover, based on the double bootstrap results, Irish nursing homes are less technically efficient, and more scale efficient than the conventional DEA estimates suggest. Regarding the efficiency determinants, in terms of ownership, we find that private facilities are less efficient than the public units. Furthermore, the size of the nursing home has a positive effect, and this reinforces our finding that Irish homes produce at increasing returns to scale. Also, notably, we find that a tendency towards quality improvements can lead to poorer technical efficiency performance.

Assessing the Efficiency of Small-Scale and Bottom Trawler Vessels in Greece

This study explores the technical and scale efficiency of two types of Greek fishing vessels, small-scale vessels and bottom trawlers, using a bias-corrected input-oriented Data Envelopment Analysis model. Moreover, the associations between efficiency scores and vessel’s and skipper’s characteristics are also explored. The results indicate that small-scale vessels achieve a very low average technical efficiency score (0.42) but a much higher scale efficiency score (0.81). Conversely, bottom trawlers achieve lower scale but higher technical efficiency scores (0.68 and 0.73, respectively). One important finding of this study is that the technical efficiency of small-scale vessels, in contrast to trawlers, is positively associated with the experience of the skipper. In a looser context, it can be said that small-scale fisheries mainly rely on skill, whereas bottom trawlers rely more on technology. This study concludes that there is space for improvement in efficiency, mainly for small-scale vessels, which could allow the achievement of the same level of output by using reduced inputs.