Title: Efficiency of football clubs in Premier League Objectives: The aim of this diploma thesis is to investigate the technical efficiency of Premier League clubs in the 2015/2016 season using a Data Envelopment Analysis approach. Determine which clubs worked efficiently and identify weaknesses of individual teams. Methods: The efficiency of each Premier League club is analysed by Data envelopment analysis (DEA). The efficiency is computed for input oriented CCR model (constant returns to scale) and BCC model (variable returns to scale). Results: The technical efficiency of each club in the 2015/2016 Premier League season is evaluated in the practical part of this thesis, with eight clubs achieving maximum efficiency for both CCR and BCC models. That indicates that the competition as a whole is highly effective. Keywords: Premier League, technical efficiency data envelopment analysis, CCR, BCC

Reducing energy demand and carbon emissions from the UK housing stock through efficiency improvements is the focus of policy interest. The 2008 UK Climate Change Act set legally binding targets of an 80% reduction in greenhouse gas emissions against a 1990 baseline. The majority of emissions in the residential sector are carbon dioxide emissions arising from energy used for heating homes and water, cooking, lighting and electrical appliances. The sector s contribution to total UK emissions is significant and therefore reducing energy use in homes is an important factor if the UK is to meet its targets. In this research an initial survey of studies of the residential sector has been conducted to review factors considered to influence energy use and related emissions in UK housing. Further review identified energy and climate change policy instruments and structural change in the energy supply sector between 1970 and the present. A subsequent time-line of policy and events describes the changing, historical policy landscape related to energy efficiency improvements in the sector. As a result of these reviews, a need to better understand how householders have responded to technical energy efficiency improvements in housing, and the influence of social and economic factors, was identified as a research gap. In order to model householders historical behaviour Data Envelopment Analysis (DEA) was identified as an innovative approach for this field of research as a potential means to measure sector efficiency in a new way. The analysis has two stages. In the first, DEA is used to measure the relative efficiency with which the UK housing sector has managed its energy use and related emissions to deliver energy services such as space heating and lighting to householders. In the second stage, multiple regressions are used to examine whether the variability over time in the efficiency measure can be explained by policy interventions, energy market developments, and economic and social factors. DEA is a method for modelling the relative performance efficiency with which an observed sample converts measurable inputs to quantitative outputs. In this research, samples consist of annual observations of the UK housing stock, using data largely taken from DECC s UK housing energy fact file. An efficiency frontier of performance enveloping the observed sample points as closely as possible is constructed through DEA mathematical programming. The core of the analysis lies in identifying relevant quantitative input and output measures from available data. A range of measures of comfort and energy service levels to represent energy service outputs, and household energy and emissions data to represent inputs are examined in the analysis. The result is a timeline of efficiency performance that can be related to socio-economic change and the history of policy interventions. The analysis shows that the efficiency of the UK housing stock to manage its energy use and related emissions has not followed the steady upward trend that might have been expected from technical innovation. There is evidence of rebound effects over time, with householders behaviour in response to technical efficiency improvements acting to raise comfort levels rather than lower energy usage. Nevertheless, statistically significant roles can be identified for factors such as income, price and tenure which have implications for policy design and control and lead to a number of policy recommendations.

The purpose of this diploma thesis is to describe an application of data envelopment analysis to the performance assessment of bank branches. The bank has used, in the first half of the year 2007, two DEA models: DEA model with the purchasing power as an implicit parameter and DEA model with the economic potential as an explicit parameter. I have used for the calculations different models, which are stated in the literature, (i.e. CCR model or BCC model), because in data received from the bank was the influence of the purchasing power already involved. Next purpose of this thesis is to summarize theoretical basis of DEA models and also this theoretical basis put together with existing foreign studies.

This master thesis deals with efficiency assessment of shop network of a leading Mobile Network Operator, by which it utilizes an innovative approach called Data Envelopment Analysis (DEA). The following three areas are in main focus of this thesis: advantages of DEA modelling compared to common approaches to Decision Making Units efficiency assessment and extensive summary of majority of DEA models, which are relevant for solving the given problem; followed by a comprehensive model of efficiency assessment of shop network of a Mobile Network Operator, which is unique to this thesis from both theoretical and practical standpoints; and last but not least a detailed analysis of the shop network including recommendations.

The following thesis documents application of Data Envelopment Analysis method on the sample of eight banks which were functioning on the Czech banking market during the period 2007 -- 2010 in order to measure their technical efficiency. Thesis consists of two major parts -- theoretical and practical one. The first part includes main principles, areas of application of the method and theoretical base of the DEA which will be used in the practical part. Second part presents brief description of the trends on the Czech banking market and it's development during the period 2007 -- 2010. Practical part also contains results of the measurement, its interpretation and analysis of main reasons of banks' inefficiency.

Diploma thesis deals with the evaluation of the efficiency using DEA models focusing on the use of dedicated software tools. There are four chapters in the thesis where different approaches to efficiency are briefly presented. The method of data envelopment analysis (DEA) with its modifications is described in detail -- basic models, CCR, BCC and additive model. The main part of the work is presented in the second part of this thesis where I describe how to work with each program and summarize its advantages and disadvantages. The last chapter applies prior knowledge on real situation -- the evaluation of the efficiency of the regional police stations in the Czech Republic.

The implementation of the Basel II capital adequacy framework promoted internally modelled risk parameters and allowed banks to build their own models. The recent crisis pointed at the gaps in the Basel II Accord, seeing banks having trouble to deal with lack of liquidity and higher default rates. The minimum regulatory capital held by the banks turned out to be insufficient and banks started looking for other techniques to better quantify the risks they are exposed to. Model accuracy is a key objective to meet the capital adequacy requirements while facing severe economic conditions. The purpose of this thesis is to suggest a new approach to credit modelling. Data envelopment analysis (DEA) can overcome some the difficulties that the banks deal with. The key opportunity in using DEA and its modifications is in the fact that this method does not require prior information about the classification between good and bad units and only requires financial and other data about the client in question. This thesis analyses the performance of DEA applied on a real world portfolio of corporate loans compared to the two standard methods used in the banking sector. Logistic regression is the most popular method, having few restrictions and providing output in the form of a probability of default. The second method is the discriminant analysis giving similar results to the logistic regression but being based on more assumptions. The model is validated by comparing the model output with the actual status and its predictive power evaluated.

Orientador: Andrea Leda Ramos de Oliveira / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Ciências Aplicadas / Made available in DSpace on 2018-08-27T16:39:38Z (GMT). No. of bitstreams: 1 Cicolin_LucasdeOliveiraMelo_M.pdf: 2559448 bytes, checksum: 5e698508c5714b5f7b557f9f29c79c1b (MD5) Previous issue date: 2015 / Resumo: O agronegócio é um elemento estratégico para a economia brasileira, especialmente pelos sucessivos aumentos de produtividade e pela manutenção do saldo positivo da balança comercial. Entretanto, a estrutura do sistema logístico nacional, que apoia tal atividade, não tem comportado o fluxo das commodities agrícolas no processo de abastecimento do mercado interno e da exportação. O Brasil é um dos maiores produtores de grãos do mundo, com capacidade de expandir sua participação no mercado internacional, mas demanda de sólido desenvolvimento dos fatores que influenciam a eficiência logística, transformando as vantagens competitivas e da produção em retorno econômico. Neste sentido, a avaliação de desempenho do processo logístico brasileiro se fez necessária para direcionar a atenção e os investimentos aos fatores críticos que limitam a capacidade de crescimento da competitividade brasileira, em especial da cadeia agroindustrial do milho, que além de abastecer outras cadeias produtivas, como a indústria de carnes e alimentos, contribui com o saldo da balança comercial através do seu volume exportado. Isto posto, o presente trabalho analisou o processo logístico de escoamento da produção de milho dos maiores estados produtores até os portos de exportação e os respectivos modais utilizados ¿ essas rotas foram chamadas de Unidades Tomadoras de Decisão ¿ DMU¿s, que tiveram sua eficiência relativa analisada. O estudo teve como objetivo principal o desenvolvimento de um sistema de medição de desempenho que avaliou a eficiência relativa dos processos logísticos de exportação do milho brasileiro. A metodologia utilizada foi a Análise por Envoltória de Dados - DEA na determinação das fronteiras de eficiência e referência de benchmarking. O Balanced Scorecard ¿ BSC foi utilizado como ferramenta no desdobramento da estratégia da cadeia logística e definição dos indicadores de medição de desempenho; sendo propostos quatro modelos de análise de eficiência: financeiro, de processos internos, de participação da rota e de desenvolvimento sustentável, visando responder "Qual o nível de eficiência dos processos logísticos brasileiro de exportação do milho, com relação aos custos, aos parâmetros ambientais, sociais e de desenvolvimento?". Como principais resultados do estudo foi possível definir como benchmarking a rota intermodal rodohidroviária do estado do Mato Grosso até os portos de Santarém ¿ PA e Manaus ¿ AM através do terminal de Miritituba ¿ PA que se apresentou como referência nos modelos de eficiência propostos e também foi analisada a eficiência relativa das principais rotas de escoamento do milho para exportação / Abstract: Agribusiness is a strategic element for the Brazilian economy, maintaining positive trade balance, especially by the successive increases in productivity. However, the infrastructure of the national logistics system, which supports such activity, has not behaved the flow of agricultural commodities in the supply of the internal market and export. Brazil is a major grain supplier of world, with capacity to expand its international market share, but demand development of the factors that influences the logistics efficiency, transforming the competitive advantages in production in economic returns. In this sense, the performance evaluation of the Brazilian logistics process was necessary to direct attention and investment to critical factors limiting the growth capacity of Brazilian competitiveness, particularly in the agro-industrial corn chain, which in addition to supplying other productive chains, as the meat and food industry, contributes to the balance of national trade through its export volume. This study examined the logistics process flow of corn production of the major producing brazilian states to the export ports and their modes used - these routes were called Decision-makers Units - DMU's, who had their relative efficiency analyzed. The study aimed to develop a performance measurement system which assessed the relative efficiency of logistics processes export of Brazilian corn. The methodology was the Data Envelopment Analysis - DEA in determining the boundaries of efficiency and benchmarking reference. The Balanced Scorecard - BSC was used as a tool in the deployment of supply chain strategy and defining performance measurement indicators; It is proposed four models of efficiency analysis: financial, internal process, use of the route and sustainable development in order to answer "What is the level of efficiency of Brazilian logistics exports processes of corn, with regard to costs, environmental parameters, and social development?". The main results of the study could be defined as benchmarking the intermodal route of Mato Grosso state to the ports of Santarém - PA and Manaus - AM through Miritituba terminal - PA that presented itself as a reference in the proposed efficiency models and was also analyzed the relative efficiency of the main routes of flow of corn for export / Mestrado / Pesquisa Operacional / Mestre em Pesquisa Operacional

Master of Science

Wind power becomes one of the most promising energy sources in the electricity generation sector in Texas over the past decade by declining levelized cost of wind energy. However, recent studies show that the wind farms in Texas are relatively less productive. Hence, this study aims to find out reasons of inefficiencies by constructing a two-stage performance assessment of wind farms in Texas. In the first stage of analysis, comprehensive input- and output-oriented Data Envelopment Analysis (DEA) models are applied to evaluate productive efficiencies of the 95 large utility-scale wind farms by using pre-determined three input and two output variables. The sensitivity analysis is provided for the robustness of the DEA models with different combinations of input and output variables of the original model. The slack analysis and projection data are obtained for inefficient wind farms to find out optimal input-output variables. Tobit regression models are conducted for the second stage of the analysis to investigate the reasons of inefficiencies. DEA results indicate that half of the wind farms were operated efficiently in Texas during 2016. 13 wind farms were performed at the most productive scale size, ten wind farms should reduce their operational size to improve production efficiency, and 72 wind farms have the notable potential to increase their production efficiency by expanding operational sizes with modern wind turbine technologies. The sensitivity analysis shows the importance of each input-output variables. Tobit regression models indicate that elevation of the site, rotor diameter, hub height, and brand of the turbine have significant contributions to the relative efficiency scores of the wind farms, and the age of turbine has a negative impact on the productive efficiency of the wind farms.