Abstract
Accurate demand forecasting is critical for any small and medium-sized manufacturer. Limited structured data sources commonly prevent small and medium-sized manufacturers from improving forecasting accuracy, affecting overall performance. We classified products, then implemented a hybrid forecasting method and compared the outcome with Exponential smoothing, ARIMA, LSTM, and ANN forecasting techniques. Numerical results demonstrate that a selection of forecasting methods is not independent of product categorization. For slow-moving products, careful consideration is required. The hybrid ARIMA-ANN method can outperform some existing techniques and lead to higher prediction accuracy, by capturing both linear and nonlinear variations.
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Wahedi, H. et al. (2022). Improving Accuracy of Time Series Forecasting by Applying an ARIMA-ANN Hybrid Model. In: Kim, D.Y., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action. APMS 2022. IFIP Advances in Information and Communication Technology, vol 663. Springer, Cham. https://doi.org/10.1007/978-3-031-16407-1_1
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