Monitorização de produção com geração de relatórios e alertas

Abstract

The analysis of time series are always present in the most different domains, due to the great ease of representing events in relation to time. Some of the factors that can be investigated in the time series are the description of the behavior of the series and/or the realization of forecasts of future values of the series. The main objective of this issertation was to find a predictive model that could present satisfactory future values for business decision making. The methodology used consisted of three main steps that were: the definition of the problem, followed by the preparation of the data with an investigation on these data to then create a predictive model and finally present the results. It was identified in the research on the data that most of the products were no longer being produced, so that k-means was used to group products with regular demand and thus to proceed with the creation of the predictive models, where the algorithms of integrated autoregressive seasonal moving average (SARIMA), multilayer perceptron (MLP) and random forest (RF) were tested. The best results were presented by the Random Forest algorithm, with a determination coefficient of approximately 88%, with an mean absolute eror of approximately 189 product units.