Improving envelopment in data envelopment analysis

Data envelopment analysis defines the relative efficiency of a decision making unit (DMU) as the ratio of the sum of its weighted outputs to the sum of its weighted inputs allowing the DMUs to freely allocate weights to their inputs/outputs. However, this measure may not reflect a DMU's true efficiency as some inputs/outputs may not contribute reasonably to the efficiency measure. Traditionally, to overcome this problem weights restrictions have been imposed. This paper offers a new approach to this problem where DMUs operate a constant returns to scale technology in a single input multi-output context. The approach is based on introducing unobserved DMUs, created by adjusting the output levels of certain observed relatively efficient DMUs, reflecting a combination of technical information of feasible production levels and the DM's value judgments. Its main advantage is that the information conveyed by the DM is local, with reference to a specific observed DMU. The approach is illustrated on a real life application. © 2003 Elsevier B.V. All rights reserved.

Ranking efficient decision-making units in data envelopment analysis using fuzzy concept

This paper introduces a new mathematical method for improving the discrimination power of data envelopment analysis and to completely rank the efficient decision-making units (DMUs). Fuzzy concept is utilised. For this purpose, first all DMUs are evaluated with the CCR model. Thereafter, the resulted weights for each output are considered as fuzzy sets and are then converted to fuzzy numbers. The introduced model is a multi-objective linear model, endpoints of which are the highest and lowest of the weighted values. An added advantage of the model is its ability to handle the infeasibility situation sometimes faced by previously introduced models.

Estimating the efficiency of healthcare facilities providing HIV/AIDS treatment in Zambia:a data envelopment approach

Zambia and many other countries in Sub-Saharan Africa face a key challenge of sustaining high levels of coverage of AIDS treatment under prospects of dwindling global resources for HIV/AIDS treatment. Policy debate in HIV/AIDS is increasingly paying more focus to efficiency in the use of available resources. In this chapter, we apply Data Envelopment Analysis (DEA) to estimate short term technical efficiency of 34 HIV/AIDS treatment facilities in Zambia. The data consists of input variables such as human resources, medical equipment, building space, drugs, medical supplies, and other materials used in providing HIV/AIDS treatment. Two main outputs namely, numbers of ART-years (Anti-Retroviral Therapy-years) and pre-ART-years are included in the model. Results show the mean technical efficiency score to be 83%, with great variability in efficiency scores across the facilities. Scale inefficiency is also shown to be significant. About half of the facilities were on the efficiency frontier. We also construct bootstrap confidence intervals around the efficiency scores.

Influential DMUs and outlier detection in data envelopment analysis with an application to health care

This paper explains some drawbacks on previous approaches for detecting influential observations in deterministic nonparametric data envelopment analysis models as developed by Yang et al. (Annals of Operations Research 173:89-103, 2010). For example efficiency scores and relative entropies obtained in this model are unimportant to outlier detection and the empirical distribution of all estimated relative entropies is not a Monte-Carlo approximation. In this paper we developed a new method to detect whether a specific DMU is truly influential and a statistical test has been applied to determine the significance level. An application for measuring efficiency of hospitals is used to show the superiority of this method that leads to significant advancements in outlier detection. © 2014 Springer Science+Business Media New York.

A non-parametric data envelopment analysis approach for improving energy efficiency of grape production

Grape is one of the world's largest fruit crops with approximately 67.5 million tonnes produced each year and energy is an important element in modern grape productions as it heavily depends on fossil and other energy resources. Efficient use of these energies is a necessary step toward reducing environmental hazards, preventing destruction of natural resources and ensuring agricultural sustainability. Hence, identifying excessive use of energy as well as reducing energy resources is the main focus of this paper to optimize energy consumption in grape production.In this study we use a two-stage methodology to find the association of energy efficiency and performance explained by farmers' specific characteristics. In the first stage a non-parametric Data Envelopment Analysis is used to model efficiencies as an explicit function of human labor, machinery, chemicals, FYM (farmyard manure), diesel fuel, electricity and water for irrigation energies. In the second step, farm specific variables such as farmers' age, gender, level of education and agricultural experience are used in a Tobit regression framework to explain how these factors influence efficiency of grape farming.The result of the first stage shows substantial inefficiency between the grape producers in the studied area while the second stage shows that the main difference between efficient and inefficient farmers was in the use of chemicals, diesel fuel and water for irrigation. The use of chemicals such as insecticides, herbicides and fungicides were considerably less than inefficient ones. The results revealed that the more educated farmers are more energy efficient in comparison with their less educated counterparts. © 2013.

Ranking efficiency units in DEA using bootstrapping an applied analysis for Slovenian farm data

This article explores how data envelopment analysis (DEA), along with a smoothed bootstrap method, can be used in applied analysis to obtain more reliable efficiency rankings for farms. The main focus is the smoothed homogeneous bootstrap procedure introduced by Simar and Wilson (1998) to implement statistical inference for the original efficiency point estimates. Two main model specifications, constant and variable returns to scale, are investigated along with various choices regarding data aggregation. The coefficient of separation (CoS), a statistic that indicates the degree of statistical differentiation within the sample, is used to demonstrate the findings. The CoS suggests a substantive dependency of the results on the methodology and assumptions employed. Accordingly, some observations are made on how to conduct DEA in order to get more reliable efficiency rankings, depending on the purpose for which they are to be used. In addition, attention is drawn to the ability of the SLICE MODEL, implemented in GAMS, to enable researchers to overcome the computational burdens of conducting DEA (with bootstrapping).

A comparative benchmarking analysis of main Iberian container terminals: a DEA approach

Benchmarking is an important tool to organisations to improve their productivity, product quality, process efficiency or services. From Benchmarking the organisations could compare their performance with competitors and identify their strengths and weaknesses. This study intends to do a benchmarking analysis on the main Iberian Sea ports with a special focus on their container terminals efficiency. To attain this, the DEA (data envelopment analysis) is used since it is considered by several researchers as the most effective method to quantify a set of key performance indicators. In order to reach a more reliable diagnosis tool the DEA is used together with the data mining in comparing the sea ports operational data of container terminals during 2007.Taking into account that sea ports are global logistics networks the performance evaluation is essential to an effective decision making in order to improve their efficiency and, therefore, their competitiveness.

DEA does not like positive discrimination : a comparison of alternative models based on empirical data

Due to the existence of free software and pedagogical guides, the use of data envelopment analysis (DEA) has been further democratized in recent years. Nowadays, it is quite usual for practitioners and decision makers with no or little knowledge in operational research to run themselves their own efficiency analysis. Within DEA, several alternative models allow for an environment adjustment. Five alternative models, each of them easily accessible to and achievable by practitioners and decision makers, are performed using the empirical case of the 90 primary schools of the State of Geneva, Switzerland. As the State of Geneva practices an upstream positive discrimination policy towards schools, this empirical case is particularly appropriate for an environment adjustment. The alternative of the majority of DEA models deliver divergent results. It is a matter of concern for applied researchers and a matter of confusion for practitioners and decision makers. From a political standpoint, these diverging results could lead to potentially opposite decisions. Grâce à l'existence de logiciels en libre accès et de guides pédagogiques, la méthode data envelopment analysis (DEA) s'est démocratisée ces dernières années. Aujourd'hui, il n'est pas rare que les décideurs avec peu ou pas de connaissances en recherche opérationnelle réalisent eux-mêmes leur propre analyse d'efficience. A l'intérieur de la méthode DEA, plusieurs modèles permettent de tenir compte des conditions plus ou moins favorables de l'environnement. Cinq de ces modèles, facilement accessibles et applicables par les décideurs, sont utilisés pour mesurer l'efficience des 90 écoles primaires du canton de Genève, Suisse. Le canton de Genève pratiquant une politique de discrimination positive envers les écoles défavorisées, ce cas pratique est particulièrement adapté pour un ajustement à l'environnement. La majorité des modèles DEA génèrent des résultats divergents. Ce constat est préoccupant pour les chercheurs appliqués et perturbant pour les décideurs. D'un point de vue politique, ces résultats divergents conduisent à des prises de décision différentes selon le modèle sur lequel elles sont fondées.

Adjusting for the environment in DEA: a comparison of alternative models based on empirical data

Due to the existence of free software and pedagogical guides, the use of Data Envelopment Analysis (DEA) has been further democratized in recent years. Nowadays, it is quite usual for practitioners and decision makers with no or little knowledge in operational research to run their own efficiency analysis. Within DEA, several alternative models allow for an environmental adjustment. Four alternative models, each user-friendly and easily accessible to practitioners and decision makers, are performed using empirical data of 90 primary schools in the State of Geneva, Switzerland. Results show that the majority of alternative models deliver divergent results. From a political and a managerial standpoint, these diverging results could lead to potentially ineffective decisions. As no consensus emerges on the best model to use, practitioners and decision makers may be tempted to select the model that is right for them, in other words, the model that best reflects their own preferences. Further studies should investigate how an appropriate multi-criteria decision analysis method could help decision makers to select the right model.

Sustainable development in the BRICS countries: an efficiency analysis by data envelopment

There is much concern about the social and environmental impacts caused by the economic growth of nations. Thus, to evaluate the socio-economic performance of nations, economists have increasingly addressed matters related to social welfare and the environment. It is within the scope of this context that this work discusses the performance of countries in the BRICS group regarding sustainable development. The objective of this study regards evaluating the efficiency of these countries in transforming productive resources and technological innovation into sustainable development. The proposed objective was achieved by using econometric tools as well as the data envelopment analysis method to then create economic, environmental, and social efficiency rankings for the BRICS countries, which enabled to carry out comparative analyses on the sustainable development of those countries. The results of such assessments can be of interest for more specific scientific explorations.

Efficiency analysis of information technology and online social networks management : an integrated DEA-Model assessment

This paper analyses the relationship between productive efficiency and online-social-networks (OSN) in Spanish telecommunications firms. A data-envelopment-analysis (DEA) is used and several indicators of business ?social Media? activities are incorporated. A super-efficiency analysis and bootstrapping techniques are performed to increase the model?s robustness and accuracy. Then, a logistic regression model is applied to characterise factors and drivers of good performance in OSN. Results reveal the company?s ability to absorb and utilise OSNs as a key factor in improving the productive efficiency. This paper presents a model for assessing the strategic performance of the presence and activity in OSN.