Previsão de demanda de veículos automotores através do modelo de regressão linear múltipla

This study aimed to model a equation for the demand of automobiles and light commercial vehicles, based on the data from February 2007 to July 2014, through a multiple regression analysis. The literature review consists of an information collection of the history of automotive industry, and it has contributed to the understanding of the current crisis that affects this market, which consequence was a large reduction in sales. The model developed was evaluated by a residual analysis and also was used an adhesion test - F test - with a significance level of 5%. In addition, a coefficient of determination (R2) of 0.8159 was determined, indicating that 81.59% of the demand for automobiles and light commercial vehicles can be explained by the regression variables: interest rate, unemployment rate, broad consumer price index (CPI), gross domestic product (GDP) and tax on industrialized products (IPI). Finally, other ten samples, from August 2014 to May 2015, were tested in the model in order to validate its forecasting quality. Finally, a Monte Carlo Simulation was run in order to obtain a distribution of probabilities of future demands. It was observed that the actual demand in the period after the sample was in the range that was most likely to occur, and that the GDP and the CPI are the variable that have the greatest influence on the developed model

Desenvolvimento de modelo auto-regressivo para previsão em pronto-atendimento

The 1988 constitution makes an life is a supreme good when increased health as the fundamental condition requiring that all ill patient has the right to be treated in a public hospital (CF, art. 196). In this sense, the goal of this work is to generate a weekly forecast of hospital care by means of an advanced prediction model. It is expected that the model of self-regressivas seasonal moving averages SARIMA generate reliable and adherent to issue forecasts analyzed, thus enabling better resource allocation and more efficient hospital management

Métodos de previsão auto-regressivo aplicado a uma série de volume de produção de caminhões

The national truck fleet has expanded strongly in recent decades. However, due to fluctuations in the demand that the market is exposed, it needed up making more effective strategic decisions of automakers. These decisions are made after an evaluation of guaranteed sales forecasts. This work aims to generate an annual forecast of truck production by Box and Jenkins methodology. They used annual data for referring forecast modeling from the year 1957 to 2014, which were obtained by the National Association of Motor Vehicle Manufacturers (Anfavea). The model used was Autoregressive Integrated Moving Average (ARIMA) and can choose the best model for the series under study, and the ARIMA (2,1,3) as representative for conducting truck production forecast

Modelagem auto-regressiva na previsão de demanda dos produtos de uma empresa do setor químico

Considering the high competitiveness in the industrial chemical sector, demand forecast is a relevant factor for decision-making. There is a need for tools capable of assisting in the analysis and definition of the forecast. In that sense, the objective is to generate the chemical industry forecast using an advanced forecasting model and thus verify the accuracy of the method. Because it is time series with seasonality, the model of seasonal autoregressive integrated moving average - SARIMA generated reliable forecasts and acceding to the problem analyzed, thus enabling, through validation with real data improvements in the management and decision making of supply chain

Estimativa do potencial de acréscimo da oferta de energia no setor sucroalcooleiro paulista a partir de cogeração e gaseificação do bagaço de cana-de-açúcar com utilização de ciclo combinado

This work evaluates the existing potential in the state of Sao Paulo for the generation of electrical energy using the sugar cane bagasse as fuel. As the bagasse is a by-product of the sugarcane and alcohol industry and it is produced in large scale in the country, mainly in the state of Sao Paulo, it is important to develop researches that aim the best utilization of this input. In order to determine its potential, at first, a study was conducted considering the utilization of the cogeneration, which is a common practice in the plants of the sector. However, it was concluded that the cogeneration could provide a higher quantity of energy if more modern technologies and more efficient processes were used. Another study to estimate the potential considered a system of gasification of the sugar cane bagasse integrated with the combined cycle (BIG/GTCC). It was concluded that this technology can provide a considerable increase in the electrical supply. In this work it was also developed an energetic study based on real data from a plant located in the state of Sao Paulo. A thermodynamic analysis was done in the existing equipment of the cogeneration section of the plant. And another analysis was done considering the implementation of the BIG/GTCC technology to the cogeneration system. Comparing the results of both settings, it was concluded that the utilization of the sugar cane bagasse integrated to a combined cycle increased considerably the efficiency in the generation of electricity of the plant, increasing more than six times its production capacity of electrical energy

Previsão da capacidade de carga de estacas escavadas em solo arenoso da região de Araquari-SC

In Geotechnical engineering the foundation projects depend on the bearing capacity and the acceptable displacements. One of the possible ways to predict the bearing capacity of foundations is through semi-empirical statistical methods which correlate in-situ tests (SPT and CPT). The piles breaking loads are defined by the interpretation of the load x head displacement curve and the experimental data acquired through the load test. In this work it is studied the behavior of bored piles executed in the Araquari/SC region, comparing the bearing capacity values predicted by the methods DECOURT & QUARESMA MODIFICADO (1996), AOKI & VELLOSO MODIFICADO MONTEIRO (2000), MILITITISKY E ALVES (1985), DECOURT & QUARESMA (1978), MÉTODO DE AOKI & VELLOSO (1975) e PHILOPANNAT (1986), with the results of the load test, evaluating their differences and discussing parameters that have direct effects on the prediction