Aplicação do valor presente ajustado (APV) para avaliação do fluxo de caixa de uma empresa do setor automotivo no sistema inovar auto

The automobile industry shows relevance inside the Brazilian industrial scenario since it contributes with the development of a significant chain of supply, distributors, workshops, publicity agencies and insurance companies in the internal market, aside from being one of the five biggest worldwide market. Thereby, the federal government decreed in Dec, 17th 2012 by Law nº 12.715 the Inovar-Auto Program. As the Adjusted Present Value (APV) is highly recommended, although not yet widespread to public politics of tax reduction, this work intends to apply the APV method on the cash flow analysis of an automobile sector's company, which has recently installed in national territory and wants to rely with governmental incentives proposed by Inovar-Auto Program. The developed work evaluates the company's current cash flow stochastically from mathematical modeling of variables such as price, demand and interest rate through probability distributions with the assist of Crystal Ball software, a Microsoft Excel Add-in, generating different scenarios from Monte Carlo Simulation. As results probabilities situations have been evaluated until the end of the Inovar-Auto's conducted period, in 2017. Beside APV others indicator such as Internal Rate of Return (IRR) and payback period were estimated for the investment project. For APV a sampling distribution with only 0.057% of risk, IRR of 29% were obtained and estimated project payback period was 4.13 years

Estudo da viabilidade de implantação do INOVAR-AUTO utilizando o método do fluxo de caixa sob risco

The automobile industry has a growing importance in the Brazilian economic environment. The industry moves a huge chain that encompasses manufacturers, suppliers of raw materials, auto parts dealers, gas stations, insurance companies, repair shops, tire stores, media companies, advertising agencies, among others. Because of this importance in the current economic environment in Brazil, the federal government, through Law No. 12715 of 17 December 2012 established a Program for the Promotion of Innovation and Densification in the Productive Chain of Motor Vehicles called INOVAR-AUTO in order to support technological development, innovation, safety, environmental protection, energy efficiency and quality of cars, trucks, buses and auto parts. The specific purpose of this study, a simulation for discussion of the viability of the program implementation using the Monte Carlo Simulation combined with the Cash-Flow-at-Risk was performed. To this end, an exploratory and documentary literature on the subject was held as well as a case study in a automobile company of Japanese origin

Processos de auto-regulação no curso de vida: controle primário e controle secundário

Este artigo discute conceitos relevantes à perspectiva do curso de vida, porém pouco difundidos no Brasil: controle primário e controle secundário. O primeiro se refere aos esforços que o indivíduo empreende para adaptar o ambiente às suas necessidades; o segundo, para se adaptar ao ambiente. Apresenta-se a formulação original dos conceitos como modelo de dois processos de controle, em oposição a modelos de processo único, como o do desamparo aprendido. Em seguida, discute-se revisão conceitual que trouxe modificação e ampliação para estes construtos, concebendo-os em um modelo bidimensional que articula controle primário e secundário com os conceitos de seleção e compensação. Nesse processo, apresentam-se contribuições no intuito de estimular a reflexão e expandir a discussão teórico-conceitual que envolve estes construtos.

Evidence of a Feeding Aggregation of Humpback Whales (Megaptera novaeangliae) Around Kodiak Island, Alaska

The known summer feeding range of the North Pacific humpback whale (Megaptera novaeangliae) extends from California, along the coasts of Oregon, Washington, and Alaska, into the Bering Sea, along the Aleutian Islands, the Sea of Okhotsk (Tomilin 1957), and to northern Japan (Rice 1977). In feeding areas of the northeastern Pacific Ocean, humpback whale photoidentification research has been concentrated off California (Calambokidis et al. 1993), southeastern Alaska (Darling and McSweeney 1985, Baker et al. 1986, 1992; Perry et al. 1990), Prince William Sound in Alaska (von Ziegesar 1992), the Oregon and Washington coasts (Calambokidis et al. 1993), and British Columbia (Darling and McSweeney 1985; Graerne Ellis, unpublished data). Results of these photoidentification studies have documented that individual whales tend to return to the same general areas in subsequent years (Darling and McSweeney 1985, Baker et al. 1986, Calambokidis et a(. 1996, von Ziegesar et al. 1994).

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