Resumo

CRUZ R., René Santa  e  CORREA, Camila. Intermittent demand forecasting with time series methods and artificial neural networks: A case study. Dyna rev.fac.nac.minas [online]. 2017, vol.84, n.203, pp.9-16. ISSN 0012-7353.  https://doi.org/10.15446/dyna.v84n203.63141.

This article aims to study the intermittent demand forecasting for a specific type of spare part of a Brazilian refrigeration industry that commercialize its products in the Latin American market. Demand characterization is performed in terms of their intermittency and variability. Results are obtained with classical intermittent forecasting methods outside the sample: Croston, Syntetos-Boylan Approximation (SBA), Shale-Boylan-Johnston Correction (SBJ), Multiple Aggregation Prediction Algorithm (MAPA) and with Artificial Neural Networks (ANN) based models. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are used for comparison and selection of forecast model. The comparative analysis results shows that the predictions based on a simple three-layer ANN model trained with the Resilient Backpropagation algorithm present better performance. The calculations were performed using R software with RStudio, "forecast", "tsintermittent" and "neuralnet" libraries.

Palavras-chave : demand forecasting; intermittent demand; artificial neural networks..

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