In this paper we propose an approach to implement environmental standards into Data Envelopment Analysis (DEA) and in this way to measure their regulatory impact on eco-efficiency of firms. As one standard feature of basic DEA models (as e.g. CCR from Charnes et al. (1978)) lies in the exogeneity of inputs, desirable and undesirable outputs, it is not possible to introduce environmental constraints for these parameters directly into basic DEA models. Therefore, we use a bounded-variable way, which allows constraints on the efficiency frontier. The regulatory impact is assessed as difference in eco-efficiency scores before and after fictive introduction of an environmental standard. Furthermore, we distinguish between weak and strong disposability of undesirable outputs and develop corresponding models. Assessing the regulatory impact of environmental standards in advance provides support for environmental policy makers in choosing appropriate instruments and in adjusting the intensity of regulation. (authors' abstract)

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This thesis focuses on combining data envelopment analysis (DEA) and multi-objective linear programming (MOLP) methods to set targets by referencing peers' performances and decision-makers' (DMs) preferences. A large number of past papers have proven the importance of a company having a target; however, obtaining a feasible but challenging target has always been a difficult topic for companies. Since DEA was proposed in 1978, it has become one of the most popular performance assessment tools. The performance possibility set and efficient frontier established by DEA provide solid and scientific reference information for managers to evaluate an individual's efficiency. Based on the successful experience of DEA in performance assessment, many scholars have mentioned that DEA can be used to set appropriate targets as well; however, traditional DEA models do not include DMs' preference information that is crucial to a target-setting process. Therefore, several MOLP methods have been introduced to include DMs' preferences in the target-setting process based on the DEA efficient frontier and performance possibility set. The trade-off-based method is one of the most popular interactive methods that have been incorporated with DEA. However, there are several gaps in the current research: (1) the trade-off-based method could take so many interactions that no DMs could finish the interactive process; (2) DMs might find it very difficult to provide the preference information required by MOLP models; and (3) DMs cannot have an intuitive view in terms of the efficient frontier. Regarding the gaps above, this thesis proposes three new trade-off-based interactive target-setting models based on the DEA performance possibility set and efficient frontier to improve DMs' experience when setting targets. The three models can work independently or can be combined during the decision-making process. The piecewise linear model uses a piecewise linear assumption to simulate DMs' real utility function. It gradually narrows down the region that could contain DMs' most-preferred solution (MPS) until it reaches an acceptable range. This model could help DMs who have limited time for interaction but want to have a global view of the entire efficient frontier. This model has also been proven very helpful when DMs are not sensitive to close efficient solutions. The prioritized trade-off model provides a new way for a DM to know about the efficient frontier, which allows the DM to explore the efficient frontier following the preferred direction with a series of trade-off tables and trade-off figures as visual aids. The stepwise trade-off model focuses on situations where the number of objectives (outputs/inputs for the DEA model) is quite large and DMs cannot provide all indifference trade-offs between all the objectives simultaneously. To release the DMs' burden, the stepwise model starts from two objectives and gradually includes new objectives in the decision-making process, with the assumption that the indifference trade-offs between previous objectives are fixed, until all objectives are included. All three models have been validated through numerical examples and case studies of a Chinese state-owned bank to help DMs to explore their MPS in the DEA production possibility set.

Made available in DSpace on 2014-06-12T17:37:02Z (GMT). No. of bitstreams: 2 arquivo7456_1.pdf: 3892394 bytes, checksum: 3124b935349872a3620686eb040b53b5 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2005 / Esta tese tem por finalidade avaliar as práticas de análise de projetos de desenvolvimento do Sistema Finor (Fundo de Investimentos do Nordeste), em seus critérios de seleção e análise de pleitos, no período de 1962 até o ano de 2001. Depois da exposição do histórico do incentivo, e da própria experiência da Sudene (Superintendência do Desenvolvimento do Nordeste), foi executada uma análise dos critérios de seleção dos projetos que abrangeu desde o "34-18", passando pelo Finor debêntures, até o fechamento do incentivo para novos projetos, no ano de 2001. Foram feitas aplicações de modelos não-paramétricos e paramétricos na seleção e análise de projetos de desenvolvimento como sugestões de aplicação ao sistema. Um dos empregos contou com o DEA Data Envelopment Analysis e o outro envolveu a investigação do custo de oportunidade, dos projetos Finor, com o auxílio de modelos de regressão (método dos mínimos quadrados) e de indicadores financeiros. Diante das amplas demandas apresentadas o órgão teve dificuldade de superálas, isto se configurou nos problemas de operação, os desafios políticos e a necessidade de avanços técnicos do incentivo Finor

Tese de mestrado integrado. Engenharia Industrial e Gestão. Universidade do Porto. Faculdade de Engenharia. 2011

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 2009

Since 2008, the world has been in recession. As daily news outlets report, this crisis has prompted many small businesses and large corporations to file for bankruptcy, which has grave global social implications. Despite government intervention and incentives to stimulate the economy that have put nations in hundreds of billions of dollars of debt, and have reduced the prime rates to almost zero, efforts to combat the increase in unemployment rate as well as the decrease in discretionary income have been troublesome. It is a vicious cycle: consumers are apprehensive of spending due to the instability of their jobs and ensuing personal financial problems; businesses are weary from the lack of revenue and are forced to tighten their operations which likely translates to layoffs; and so on. Cautious movement of cash flows are rooted in and influenced by the psychology of the players (stakeholders) of the game (society). Understandably, the complexity of this economic fallout is the subject of much attention. And while the markets have recovered much of the lost ground as of late, there is still great opportunity to learn about all the possible factors of this recession, in anticipation of and bracing for one more downturn before we emerge from this crisis. In fact, there is no time like today more appropriate for research in bankruptcy prediction because of its relevance, and in an age where documentation is highly encouraged and often mandated by law, the amount and accessibility of data is paramount – an academic’s paradise! The main objective of this thesis was to develop a model supported by Data Envelopment Analysis (DEA) to predict the likelihood of failure of US companies in the retail-apparel industry based on information available from annual reports – specifically from financial statements and their corresponding Notes, Management’s Discussion and Analysis, and Auditor’s Report. It was hypothesized that the inclusion of variables which reflect managerial decision-making and economic factors would enhance the predictive power of current mathematical models that consider financial data exclusively. With a unique and comprehensive dataset of 85 companies, new metrics based on different aspects of the annual reports were created then combined with a slacks-based measure of efficiency DEA model and modified layering classification technique to capture the multidimensional complexity of bankruptcy. This approach proved to be an effective prediction tool, separating companies with a high risk of bankruptcy from those that were healthy, with a reliable accuracy of 80% – an improvement over the widely-used Altman bankruptcy model having 70%, 58% and 50% accuracy when predicting cases today, from one year back and from two years back, respectively. It also provides a probability of bankruptcy based on a second order polynomial function in addition to targets for improvement, and was designed to be easily adapted for analysis of other industries. Finally, the contributions of this thesis benefit creditors with better risk assessment, owners with time to improve current operations as to avoid failure altogether, as well as investors with information on which healthy companies to invest in and which unhealthy companies to short.

In the recent years most of the studies related to the hospital efficiency focus on the issues of the ownership, size, physician team work, and clinical performance. It is certain that nursing units are the major profit centers for hospitals. However, the relative efficiency of nursing units regarding the resource use and outputs is seldom investigated. Therefore the aim of this study is to investigate the influence of each input and output item on the relative efficiency using Data Envelopment Analysis (DEA). The research samples included 44 units of two government owned hospital accredited as medical centers in 2002. The research samples are divided into 5 groups. The inputs items include full time nurse, continuous education hours, cost expand, specialties, patient care hours and workload. The output items are length of stay, number of admission, occupancy rate and total patient days. Every nursing unit is considered as a Decision Making Unit (DMU). The data were collected from Jan. to Dec. 2002 to evaluate the overall efficiency, technical efficiency and scale efficiency. Efficiency reference sets were found to be reference for inefficiency nursing units. The ways to improve the resources inputs or outputs of those inefficient nursing units were suggested using scale variable analysis. The influence of each input and output variable on the relative efficiency were assessed using sensitivity analysis. The results are summarized as follows:. 1. Overall inefficiency nursing units: A hospital has 4 (20%), B hospital has 5 (20%), both A and B hospitals have 9 (21%), medical nursing units have 5(17%)and surgical nursing units have 7 (47%). 2. Technical inefficiency nursing units: A hospital has 2 (10%), B hospital has 2 (8%), both A and B hospital have 3(6%), medical nursing units has one (3%)and surgical nursing units have 4(27%). 3. Scale inefficiency nursing units: A hospital has 4(20%), B hospital has 5 (20%), both A and B hospital have 9 (21%), medical nursing units have 5 (17%)and surgical nursing units have 7(47%). 4. Relative inefficiency nursing units with overall efficiency value between 0.9-1 belong to the marginal inefficiency units. The overall inefficiency of nursing units due to scale inefficiency. 5. From slack variable analysis the first three inputs needed to reduced are continue education hours, patient care hours and number of full time nurse. The outputs needed to increase are the number of admission and the length of stay. In this study, the input items to evaluate the efficiency of nursing units are mainly based on the data of clinical productivity. However, the quality of nursing care, the index of patient satisfaction, the number of medical equipments and the standard on patient care activities are not considered. It is highly suggested that service index and equipment allocation should be considered while evaluating efficiency.

Abstract Construction is an industry that manages various types of construction, including building, extending, remodeling, reconstructing, and demolishing buildings. There are two major types of construction business: One is public construction, and the other is commercial construction, totaled 391 million dollars. Due to the recession of real estate in recent five years, the demand of the private sector decreases dramatically, which leads to the chain reaction of overall sales decreasing in construction industry in Taiwan. However, the industry still has 255 billions of sales revenues generated from government projects each year as well as hundreds of billion dollars of revenues from high-speed railroad and Kaohsiung subway BOT project. As long as the gross profit reaches 500 million dollars, the profit margin will be above 10 percent. The major source of revenues for a construction company is done through bidding. There is only one winner in the bid. Therefore, to research and evaluate relative performance of each construction company in the bid is an important task. The purpose of this thesis is to evaluation the performance of 12 public construction companies using DEA analysis. The evaluation will focus on areas of liquidity ratios, debt and asset management ratios, profitability ratios, and overall performance of efficiency. Major conclusions from the study are as following: 1. Four financial ratios and overall performance of efficiency were derived from DEA analysis, which eliminate inconsistencies of ratio analysis to make evaluation results more relevant. 2. Among the evaluation criteria, including liquidity, debt and asset management, profitability ratios, evaluations of profitability ratios among firms has the biggest inconsistency, followed by asset management. 3. From the sensitivity analysis, we conclude that companies with stable degrees of efficiency, which means efficiency degrees with small variance, have relatively higher profitability. This indicates the management needs to enhance the overall performance in order to enjoy ample profit. 4. From the study of DEA, we find that DEA analysis pinpoint the slack of each variable to enable decision-makers to see clearly which variables (input or output) need to be increased or decreased. This helps them to plan for the companies¡¦ overall efficiency to be at peak. DEA analysis provides decision-makers insights by relatively comparing the differentiation of statistics. This enables them to make executable strategies. Results of examples in this thesis prove efficiencies for evaluation. The convenience of ratio analysis and the complete perspective offered by DEA analysis are two best advantages of these two evaluation methods.

With the initiation of national health insurance, the ecology of medicine and organization of hospitals in Taiwan underwent rapid change. The beginning of a global budget aggravated the impact of hospitals, especially the district hospitals which were already facing most of the pressure. Over half (51.61 %) of the above district hospitals chose to focus on long term care, nursing homes making up the great majority. This research evaluates the efficiency of two district hospital-based nursing homes by Data Envelopment Analysis (DEA). The purpose of this research includes: First, evaluate the efficiency of two district hospital-based nursing homes, including overall efficiency, purely technological efficiency and scale efficiency. Second, assess the relative efficiency relationships of the two nursing homes. Third, probe the differences between relatively efficient and relatively inefficient groups in input variable and output variable. Fourth, with the analysis results, suggest adjustments of input and output to every relative inefficiency groups, as the health care manager's reference. The research approach is to consult relevant documents and the purpose of this study is to choose input and output variables. These variables are screened by Pearson analysis for isotonicity relation. Process the study by DEA for relatively efficient analysis, then direct against the relatively inefficient groups for discussion of efficiency reference set and slack variable analysis. The result of study shows: First, overall efficiency: There are 6 relatively inefficient groups (37.5%) in the first nursing home; There are 8 groups relatively inefficiently attended to (50%) in the second nursing home; There are 18 groups relatively inefficient (56.25%) in the combined first and second nursing home groups. Second, purely technological efficiency: There are 4 groups relatively inefficiently attended to (25.0%) in the first nursing home; There are 5 groups relatively inefficiently attended to (31.25%) in the second nursing home; There are 15 groups relatively inefficient (46.9%) in the combined first and second nursing home groups. Third, scale efficiency: There are 6 groups relatively inefficiently attended to (37.5%) in the first nursing home; There are 8 groups relatively inefficiently attended to (50%) in the second nursing home; There are 18 groups relatively inefficient (56.25%) in the combined first and second nursing home groups. Fourth, the slack variable analysis: the input items that must improve are foreign caretaker labor hours, nurse assist labor hours and nurse labor hours into sequentially, the output one is most important in increasing the income range. In this research, nursing staff and attendants' labor hour can only represent the workforce attending to the unit, but besides personnel figures, personnel quality is a very important factor, too; as regards nursing efficiency, it is not only the number attending to the quantity of service, but also the quality: For example, the incidence of pressure sores, unexpected accidents, patient satisfaction, or staff members' satisfaction, etc. These will all influence the overall efficiency in DEA and remaining research to analyse further in the future. We hope the parameters, such as the make up of resident people in different areas, severity of disease, etc., could continue relevant research to influence the efficiency of nursing homes in the future.