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

Um Modelo de otimização inteira mista na programação de produção de mangueiras hidráulicas

This work deals with a problem of mixed integer optimization model applied to production planning of a real world factory that aims for hydraulic hose production. To optimize production planning, a mathematic model of MILP Mixed Integer Linear Programming, so that, along with the Analytic Hierarchy process method, would be possible to create a hierarchical structure of the most import criteria for production planning, thus finding through a solving software the optimum hose attribution to its respective machine. The hybrid modeling of Analytic Hierarchy Process along with Linear Programming is the focus of this work. The results show that using this method we could unite factory reality and quantitative analysis and had success on improving performance of production planning efficiency regarding product delivery and optimization of the production flow