Análise quantitativa de demanda em uma empresa siderúrgica

Companies, in general, operate due to the work of its subsystems and this is possible thanks to operational planning that tries to promote the best way to integrate them without giving up the mission, vision and values of the company. The main purpose of a business is to serve customers, but to be successful must take into account other factors such as survival, profitability, growth and operational standpoint to use their resources effectively. The use of tools to support the process of analysis of management decision-making is gaining importance in the context of competitiveness in the world market. The objective of this paper is to present a simulation of the process of forecasting demand to obtain optimal results for a steel company, compare the actual results with order entry and assess the magnitude of the error

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