The topic of the diploma thesis is analysis of the factors influencing steel consumption in the European Union and modelling a forecast of steel consumption´s development. The core of the thesis is mapping of the European steel market with an accent on the develompent of the steel consumption. The thesis deals with an analysis of the quantitative methods used on the planning of the steel consumption in the chosen steel company and consequently evaluates the results of the particular model at the forecasting of steel consumption in the European Union for next 5 years.

The subject of my thesis is to analyze the predictions of tax revenues in ten municipalities of comparable size. The main aim of my thesis is to evaluate the accuracy of predictions for selected municipal tax revenues and see if you can not refine their expert estimation using appropriate statistical methods. A sub-goal is to characterize in detail the various components of the budget revenues, and analyze their size and structure in selected municipalities. Another important sub-goal is to compare the communities to highlight their differences and common elements of municipal budget process. These information are based on interviews at municipalities. The result of the analysis is that the optimal use of prediction methods differ not only income from income, but from city to city. For income tax paid by employees appeared in some cases, reliable prediction of the city, in other cities it was exponential smoothing. For the tax on personal income from independent activities is clearly the most accurate regression analysis, which refines the prognosis of this tax by up to several tens of percent. Although the error of prediction of the city in property taxes was none too small, this approach has remained the most accurate. Only three cities have been more accurate by using exponential smoothing.

This study tests a model which suggests that the external forces on a firm, the size and age of the firm, the industry and competitive market in which a firm operates, the level of leverage of the firm, as well as whether it has good or bad news regarding future earnings have an impact on whether a firm provides precise forecasts in its MD&A. Furthermore, the model suggests that firms providing precise forward-looking statements in the MD&A have lower forecast errors. Using 2SLS, the proposed model is tested using forward-looking statements regarding sales, earnings per share, cash flow, and capital expenditures extracted from the 2004 and 2005 annual reports of firms listed on the 2002 Fortune 500 list.

A previsão de demanda desempenha um papel de fundamental importância dentro das organizações, pois através dela é possível obter uma declaração antecipada do volume demandado no futuro, permitindo aos gestores a tomarem decisões mais consistentes e alocarem os recursos de modo eficaz para atender esta demanda. Entretanto, a eficiência na tomada de decisões e alocação dos recursos requer previsões cada vez mais acuradas. Diante deste contexto, a combinação de previsões tem sido amplamente utilizada com o intuito de melhorar a acurácia e, consequentemente, a precisão das previsões. Este estudo tem por objetivo fazer a adaptação de um modelo de previsão para estimar a demanda de produtos destinados à suplementação animal através da combinação de previsões, considerando as variáveis que possam impactar na demanda e a opinião de especialistas. O trabalho está estruturado em dois artigos, sendo que no primeiro buscou-se priorizar e selecionar, através do Processo Hierárquico Analítico (AHP), variáveis que possam impactar na demanda para que estas pudessem ser avaliadas na modelagem via regressão do artigo 2. Por sua vez, no segundo artigo, realizou-se a adaptação do modelo composto de previsão idealizado por Werner (2004), buscando uma previsão final mais acurada. Os resultados obtidos reforçam que as previsões, quando combinadas, apresentam desempenhos superiores para as medidas de acurácia MAPE, MAE e MSE, em relação às previsões individuais. / The demand prediction has a role of fundamental importance inside the organizations, because trough it is possible to obtain a previous declaration of the demanded amount in the future, allowing the managers to take more consistent decisions and to allocate the resources in an efficient manner in order to satisfy this demand. However, the efficiency in the support decision and resource allocation demands accurated predictions. So, the combination of predictions have been used with the aim of improving the accuracy and, consequently, the precision of the prediction. This study has as objective to do an adaptation of a prediction model to estimate the demand of products designated to animal supplementation through the combination of prediction, considering the variables that can impact in the demand and in the expert opinion. The work is structured in two papers, considering that the first searches to priorize and select through the Analitic Hierarch Process (AHP), variables that can impact in the demand, so they could be evalute in the regression modelling of the paper 2. By the way, in the second paper, it was done an adaptation of the composed prediction model proposed by Werner (2004), searching for a more accurated final prediction. The obtained results reinforce that the prediction, when combined, present superior performance to the accuracy metrics MAPE, MAE and MSE, in relation to the individual predictions.

Orientador: Wagner dos Santos Oliveira / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química / Made available in DSpace on 2018-08-18T13:14:22Z (GMT). No. of bitstreams: 1 Carvalho_RicardoVenicioCuzziolde_M.pdf: 4759667 bytes, checksum: 68667164cdd33de1d01a9e41b3288d85 (MD5) Previous issue date: 2011 / Resumo: A indústria em geral e particularmente o segmento plástico enfrentam uma época de competitividade global e acirrada. Além disso, pressões sociais como a questão ambiental, eficiência energética e busca por produtos de qualidade são fatores que impulsionam a demanda por inovações e aprimoramentos constantes. Dentro do mercado dos polímeros, os plásticos de engenharia representam a fatia de materiais com melhor potencial para estes aprimoramentos. É objetivo deste trabalho avaliar as principais tendências de melhoria de propriedades, novas tecnologias de modificação e reforços, novas aplicações potenciais e volumes de consumo para os representantes dessa classe de materiais para os próximos 20 a 30 anos. O primeiro material selecionado para este estudo foi o Nylon 66, devido à sua grande versatilidade de modificação e aplicação além de ser um dos principais representantes dos polímeros de engenharia. O polipropileno também foi selecionado devido à sua grande versatilidade e possibilidade de utilização em aplicações de engenharia, especialmente na forma de compostos. A técnica de prospecção tecnológica utilizada foi o método Delphi que se baseia em um questionário enviado a especialistas da área em rodadas consecutivas para a obtenção de um consenso. O anonimato dos participantes, análise estatística dos resultados e feedback aos respondentes são as principais características deste método. Um questionário envolvendo os aspectos tecnológicos e mercadológicos foi elaborado e enviado aos respondentes em duas rodadas. São observadas nos resultados obtidos tendências de crescimento no mercado de polímeros de engenharia em aplicações de alta temperatura e em substituição aos materiais convencionais como os metais e cerâmicas, principalmente no mercado automotivo, industrial e eletroeletrônico. Avanços em propriedades como performance termo-mecânica e balanço entre rigidez e tenacidade além de inovações nas tecnologias de manufatura e moldagem dos polímeros de engenharia foram indicados como tendências futuras. Medidas para superar obstáculos em custos , definição de políticas públicas e privadas que viabilizem atividades de reciclagem, capacitação e mão de obra e o alinhamento entre indústria, academia e governo serão fundamentais para o sucesso dos polímeros de engenharia nas suas mais diversas aplicações / Abstract: Industry in general and the plastics segment in particular are facing a time of global and strong competition. Also, social claims like environmental care, energy efficient technologies and the need for high quality products are boosting innovations and improvements. On the plastics market, engineering polymers are the best suited materials for these improvements. It is the objective of this work to evaluate the tendencies for improved properties, new modification and reinforcement technologies, new potential applications and volumes of commercialization for this class of materials over the next 20 to 30 years. The first material selected for the research was Nylon 66 due to its huge ease of modification and versatility for application, in addition it is one of the most important engineering polymers. Polypropilene was also selected due to its versatility and possibility of utilization in engineering applications, specially in compounds. Delphi method was used as the research technique, it is based on a questionnaire sent to experts in polymers in successive rounds as a means of achieving consensus. Anonymity of participants, statistical evaluation of results and feedback to the experts are the main characteristics of this method. A questionnaire covering technological and market aspects was developed and sent to the participants in two rounds. It is seen in the results a tendency of growth in the market of engineering polymers for high temperature applications and in traditional materials replacement, like metals and ceramics, specially in the automotive, industrial and electro-electronic markets. Improvements in properties like thermomechanical performance and balance between stiffness and toughness in addition to technology innovations in manufacturing and molding of engineering polymers were pointed as future tendencies. Measures to overcome cost issues, public and private policy implementation to facilitate recicling, work force qualification and alignment between industry, academy and government will be vital for the success of enginering polymers in their various applications / Mestrado / Ciencia e Tecnologia de Materiais / Mestre em Engenharia Química

This study contributes to the mergers and acquisitions as well as the informational transparency literature – by examining the relationship between a firm’s analysts' forecast error/ informational uncertainty and M&A outcomes. Contrary to our conventional wisdom, we find that an acquiring firm with more forecast errors and informational uncertainty (firm risk, as expressed by stock return variation) tends to have more favorable abnormal market reactions. Whereas a target firm with more forecast errors and informational uncertainty tends to have less favorable abnormal market reactions. As the relation between acquirer forecast errors and informational uncertainty looks counter-intuitive, we further delve into this issue. We find that, in general, firms with higher analysts' forecast errors and informational uncertainty tend to make fewer acquisitions, which implies that firms with lower informational quality are more selective in their acquisitions. Further, we find that the positive relationship between forecast error/ informational uncertainty and CAR is primarily driven by non-public target acquisitions. In the sub-sample analyses - where we consider only public target firms, our results show that acquirers with higher forecast errors and uncertainty end up acquiring targets with higher forecast errors and weaker firm performance. These findings offer some plausible explanation for the non-significant relation between acquirer analysts' forecast errors/ informational uncertainty and M&A market reactions. It appears that market participants are less enthusiastic about public target acquisitions by acquirers with more inferior informational quality.

Forecasting is a powerful tool that can enable companies to save millions in revenue every year if the forecast is good enough. The problem lies in the good enough part. Many companies today use Excel topredict their future sales and trends. While this is a start it is far from optimal. Seco Analytics aim to solve this issue by forecasting in an informative and easy manner. The web application uses the ARIMA analysis method to accurately calculate the trend given any country and product area selection. It also features external data that allow the user to compare internal data with relevant external data such as GDP and calculate the correlation given the countries and product areas selected. This thesis describes the developing process of the application Seco Analytics.

Nowadays, there is a wide range of forecasting methods and forecasters encounter several challenges during selection of an optimal method for volatility forecasting. In order to make use of wide selection of forecasts, this thesis tests multiple forecast combination methods. Notwithstanding, there exists a plethora of forecast combination literature, combination of traditional methods with machine learning methods is relatively rare. We implement the following combination techniques: (1) simple mean forecast combination, (2) OLS combination, (3) ARIMA on OLS combined fit, (4) NNAR on OLS combined fit and (5) KNN regression on OLS combined fit. To our best knowledge, the latter two combination techniques are not yet researched in academic literature. Additionally, this thesis should help a forecaster with three choice complication causes: (1) choice of volatility proxy, (2) choice of forecast accuracy measure and (3) choice of training sample length. We found that squared and absolute return volatility proxies are much less efficient than Parkinson and Garman-Klass volatility proxies. Likewise, we show that forecast accuracy measure (RMSE, MAE or MAPE) influences optimal forecasts ranking. Finally, we found that though forecast quality does not depend on training sample length, we see that forecast...

A developed seamless extreme heat validation approach (Ford et al. 2018) is applied to three Subseasonl Experiment’s (SubX’s) medium-range forecast models, which arethe U.S. National Oceanic and Atmospheric Administration’s Earth System Research Laboratory FIM-iHYCOM (ESRL), the U.S. National Aeronautics and Space Administration’s Earth System Research Laboratory’s Goddard Earth Observing System Atmosphere-Ocean General Circulation Model, Version 5 (GMAO), and the U.S. National Centers for Environmental Prediction’s Global Ensemble Forecast System, version 11 (GEFS), for evaluating their heat wave predictability. Moreover, two land surface initializations, green vegetation fraction (GVF) and heat fluxes (LE/H), of each model are evaluated for understanding the interaction between heat wave predictability and the inconsistencies in the terrestrial segment of land-atmosphere feedbacks. The validation approach shows the overestimated autocorrelation of maximum temperature heat waves causing (1) the lowest reliability and overestimation of heat waves hindcasts, (2) lower heat wave hindcast skill of ensemble mean, and (3) higher discrimination between heat wave hindcast and observations of each ensemble member over lead times for all three models. Both ESRL and GEFS present the relationship between GVF and heat wave hindcast is positive, but negative relationship is shown on the GMAO. In addition, both ESRL and GEFS modelsunderestimate latent heat flux, but overestimate sensible heat flux in the Midwest. Therefore, for both ESRL and GEFS models, the relationship between heat wave and sensible heat fluxes (or GVF) is positive, and negative for the relationship between heat wave and latent heat flux (or evapotranspiration). In contrast, the GMAO model overestimates both latent and sensible heat fluxes in the Midwest. Therefore, for the GMAO model, the relationship between heat wave and latent/sensible heat fluxes (or GVF) is positive, and negative for the relationship between heat wave and evapotranspiration.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The retail sector is a growing industry, however with serious problems associated with inventories such as stock breakage. This article proposes a collaborative model applying the S&OP methodology to reduce stock breakages in a Peruvian company in the retail sector through a purchasing plan designed by the interaction and participation of different actors in charge of the process. The results of the model are measured by the percentage of stock breaks, the demand forecast error and the increase in sales. In the diagnosis of the problem two factors were identified that cause the stock breaks. The first is caused by the delay that exists in the replenishment of inventories, due to the bad programming of delivery of products between the distribution center and the stores. The second is related to the insufficient amount of purchases caused by not properly categorizing the products, poor forecast and not having safety inventory policies. A simulation resulted in a 17% stock breakage reduction, a 17% forecast error decrease, and a 15% sales increase.