Increasing discrimination of DEA evaluation by utilizing distances to anti-efficient frontiers

This paper develops three DEA performance indicators for the purpose of performance ranking by using the distances to both the efficient frontier and the anti-efficient frontier to enhance discrimination power of DEA analysis. The standard DEA models and the Inverted DEA models are used to identify the efficient and anti-efficient frontiers respectively. Important issues like possible intersections of the two frontiers are discussed. Empirical studies show that these indicators indeed have much more discrimination power than that of standard DEA models, and produce consistent ranks. Furthermore, three types of simulation experiments under general conditions are carried out in order to test the performance and characterization of the indicators. The simulation results show that the averages of both the Pearson and Spearman correlation coefficients between true efficiency and indicators are higher than those of true efficiency and efficiency scores estimated by the BCC model when sample size is smal

An Evaluation of Cross-Efficiency Methods, Applied to Measuring Warehouse Performance

In this paper method and practice of cross-efficiency calculation is discussed. The main methods proposed in the literature are tested not on a set of artificial data but on a realistic sample of input-output data of European ware- houses. The empirical results show the limited role which increasing automation investment and larger warehouse size have in increasing productive performance. The reason is the existence of decreasing returns to scale in the industry, resulting in sub-optimal scales and inefficiencies, regardless of the operational performance of the facilities. From the methodological perspective, and based on a multidimensional metric which considers the capability of the various methods to rank warehouses, their ease of implementation, and their robustness to sensitivity analyses, we conclude to the superiority of the classic Sexton et al. (1986) method over recently proposed, more sophisticated methods

Performance Management of Supply Chain Sustainability in Small and Medium-sized Enterprises Using a Combined Structural Equation Modelling and Data Envelopment Analysis

Although the contribution of small and medium-sized enterprises (SMEs) to economic growth is beyond doubt, they collectively affect the environment and society negatively. As SMEs have to perform in a very competitive environment, they often find it difficult to achieve their environmental and social targets. Therefore, making SMEs sustainable is one of the most daunting tasks for both policy makers and SME owners/managers alike. Prior research argues that through measuring SMEs’ supply chain sustainability performance and deriving means of improvement one can make SMEs’ business more viable, not only from an economic perspective, but also from the environmental and social point of view. Prior studies apply data envelopment analysis (DEA) for measuring the performance of groups of SMEs using multiple criteria (inputs and outputs) by segregating efficient and inefficient SMEs and suggesting improvement measures for each inefficient SME through benchmarking it against the most successful one. However, DEA is limited to recommending means of improvement solely for inefficient SMEs. To bridge this gap, the use of structural equation modelling (SEM) enables developing relationships between the criteria and sub-criteria for sustainability performance measurement that facilitates to identify improvement measures for every SME within a region through a statistical modelling approach. As SEM suggests improvements not from the perspective of individual SMEs but for the totality of SMEs involved, this tool is more suitable for policy makers than for individual company owners/managers. However, a performance measurement heuristic that combines DEA and SEM could make use of the best of each technique, and thereby could be the most appropriate tool for both policy makers and individual SME owners/managers. Additionally, SEM results can be utilized by DEA as inputs and outputs for more effective and robust results since the latter are based on more objective measurements. Although DEA and SEM have been applied separately to study the sustainability of organisations, according to the authors’ knowledge, there is no published research that has combined both the methods for sustainable supply chain performance measurement. The framework proposed in the present study has been applied in two different geographical locations—Normandy in France and Midlands in the UK—to demonstrate the effectiveness of sustainable supply chain performance measurement using the combined DEA and SEM approach. Additionally, the state of the companies’ sustainability in both regions is revealed with a number of comparative analyses

The curse of dimensionality of decision-making units: A simple approach to increase the discriminatory power of data envelopment analysis

Data envelopment analysis (DEA) is a technique for identifying the best practices of a given set of decision-making units (DMUs) whose performance is categorized by multiple performance metrics that are classified as inputs and outputs. Although DEA is regarded as non-parametric, the sample size can be an issue of great importance in determining the efficiency scores for the evaluated units, empirically, when the use of too many inputs and outputs may result in a significant number of DMUs being rated as efficient. In the DEA literature, empirical rules have been established to avoid too many DMUs being rated as efficient. These empirical thresholds relate the number of variables with the number of observations. When the number of DMUs is below the empirical threshold levels, the discriminatory power among the DMUs may weaken, which leads to the data set not being suitable to apply traditional DEA models. In the literature, the lack of discrimination is often referred to as the “curse of dimensionality”. To overcome this drawback, we provide a simple approach to increase the discriminatory power between efficient and inefficient DMUs using the well-known pure DEA model, which considers either inputs only or outputs only. Three real cases, namely printed circuit boards, Greek banks, and quality of life in Fortune’s best cities, have been discussed to illustrate the proposed approach

A novel combination of Cased-Based Reasoning and Multi Criteria Decision Making approach to radiotherapy dose planning

In this thesis, a set of novel approaches has been developed by integration of Cased-Based Reasoning (CBR) and Multi-Criteria Decision Making (MCDM) techniques. Its purpose is to design a support system to assist oncologists with decision making about the dose planning for radiotherapy treatment with a focus on radiotherapy for prostate cancer. CBR, an artificial intelligence approach, is a general paradigm to reasoning from past experiences. It retrieves previous cases similar to a new case and exploits the successful past solutions to provide a suggested solution for the new case. The case pool used in this research is a dataset consisting of features and details related to successfully treated patients in Nottingham University Hospital. In a typical run of prostate cancer radiotherapy simple CBR, a new case is selected and thereafter based on the features available at our data set the most similar case to the new case is obtained and its solution is prescribed to the new case. However, there are a number of deficiencies associated with this approach. Firstly, in a real-life scenario, the medical team considers multiple factors rather than just the similarity between two cases and not always the most similar case provides with the most appropriate solution. Thus, in this thesis, the cases with high similarity to a new case have been evaluated with the application of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This approach takes into account multiple criteria besides similarity to prescribe a final solution. Moreover, the obtained dose plans were optimised through a Goal Programming mathematical model to improve the results. By incorporating oncologists’ experiences about violating the conventionally available dose limits a system was devised to manage the trade-off between treatment risk for sensitive organs and necessary actions to effectively eradicate cancer cells. Additionally, the success rate of the treatment, the 2-years cancer free possibility, has a vital role in the efficiency of the prescribed solutions. To consider the success rate, as well as uncertainty involved in human judgment about the values of different features of radiotherapy Data Envelopment Analysis (DEA) based on grey numbers, was used to assess the efficiency of different treatment plans on an input and output based approach. In order to deal with limitations involved in DEA regarding the number of inputs and outputs, we presented an approach for Factor Analysis based on Principal Components to utilize the grey numbers. Finally, to improve the CBR base of the system, we applied Grey Relational Analysis and Gaussian distant based CBR along with features weight selection through Genetic Algorithm to better handle the non-linearity exists within the problem features and the high number of features. Finally, the efficiency of each system has been validated through leave-one-out strategy and the real dataset. The results demonstrated the efficiency of the proposed approaches and capability of the system to assist the medical planning team. Furthermore, the integrated approaches developed within this thesis can be also applied to solve other real-life problems in various domains other than healthcare such as supply chain management, manufacturing, business success prediction and performance evaluation

Data envelopment analysis as a benchmarking application for humanitarian organizations

Humanitarian aid organizations are under tremendous pressure and competition for donor funds to sustain their operations. However, donor contribution levels have remained relatively stagnant over the past five years and are unlikely to grow in the foreseeable future. Additionally, donor policies and mandates have added pressure on humanitarian aid organizations to comply with new and more complex requirements. Many humanitarian aid organizations work in some of the most challenging areas of the world, where conflict, famine, environmental, economic, and cultural challenges are prevalent. Given all these factors, a novel form of performance and efficiency measurement is needed to evaluate the performance of humanitarian aid organizations. This study addressed the possible use of Data Envelopment Analysis that measures the efficiency of an organization’s country programs. Limited funding from donors, competition, and the humanitarian imperative to reach people in need requires humanitarian aid organizations to become better and more effective stewards of donor contributions. This study used a mixed-methods approach to compare and evaluate the efficiency of the country portfolios of a humanitarian aid organization using DEA. The DEA models used are CRS and VRS using an output orientation. This study used an explanatory sequential design. First, a quantitative approach using DEA was employed to compare the efficiency of an organization’s country portfolios. Second, a qualitative effort consisted of a focus group of DEA researchers who have performed DEA on humanitarian aid programs. The focus group addressed the views, perspectives, and issues of conducting DEA within the humanitarian sector. The DEA study was conducted in three phases. A sample of 19 country portfolios was used in this study. The results showed that 10% of the countries were efficient in the aggregate under a CRS model, and 20% using a VRS model. The focus group provided insights and perceptions of DEA from a practical perspective. These were categorized from technical requirements and communications with a client. The challenge in the humanitarian sector is that DEA is not a well known methodology. An explanation is often required on what DEA can do for an organization and its limitations. Additionally, an explanation was often needed for a client to understand how decision making units (DMUs), variables, and DEA techniques can be used to support a humanitarian aid organization

Fishing for solutions. Environmental and operational assessment of selected Galician fisheries and their products

Fishing is the only hunting activity which is still maintained on an industrial level to sustain worldwide food demand. Currently, worldwide fisheries are suffering a series of hazards linked to overexploitation and increasing human demand for protein, causing a wide range of environmental impacts on marine ecosystems, such as stock depletion or ecosystem disruption. Moreover, the fishing industry has grown to an extent where the environmental burdens associated with on board and on land operational activities, such as fuel consumption by vessels or wastewater generated by canning factories, are also becoming important environmental concerns. From a regional perspective, Galicia (NW Spain), the main fishing region in the European Union (EU) in terms of landed fish and economic turnover, does not escape these global threats. Additionally, Galicia supplies the rest of Spain and other EU countries with important amounts of fresh and processed seafood

Rankings na DEA baseados em DMUs virtuais - aplicação ao setor segurador

Mestrado em Contabilidade e Gestão das Instituições FinanceirasOs modelos clássicos da metodologia DEA (Data Envelopment Analysis) permitem, em geral, classificar várias DMUs (Decision Making Units) como eficientes, revelando-se incapazes de discriminar essas DMUs e, consequentemente, gerar um ranking completo para todas as DMUs, o que constitui uma limitação desta metodologia. Para ultrapassar tal limitação, várias classes de métodos e modelos para a obtenção de rankings na DEA têm sido desenvolvidos. Nesta dissertação apresenta-se um estudo sobre uma dessas classes, designada de DMUs virtuais, que utiliza este tipo de DMUs para integrar as eficiências otimista e pessimista e, deste modo, gerar um ranking completo sobre as DMUs reais. Este estudo inclui a aplicação a um caso real, mais concretamente, à avaliação e estabelecimento de rankings de dezoito seguradoras do ramo não vida, que operavam em Portugal no ano de 2019.The classic models of the DEA (Data Envelopment Analysis) methodology allow, in general, to classify several DMUs (Decision Making Units) as efficient, revealing themselves incapable of discriminating these DMUs and, consequently, generating a complete ranking for all DMUs, which constitutes a limitation of this methodology. To overcome this limitation, several classes of methods and models for obtaining rankings in DEA have been developed. This dissertation presents a study of one of these classes, called virtual DMUs, which uses this type of DMU to integrate optimistic and pessimistic efficiencies and, thus, generate a complete ranking of real DMUs. This study includes the application to a real case, more specifically, to the assessment and establishment of rankings of eighteen non-life insurance companies, which operated in Portugal in the year 2019.info:eu-repo/semantics/publishedVersio