Demand Forecasting using Artificial Neuronal Networks and Time Series: Application to a French Furniture Manufacturer Case Study

Julie Bibaud-Alves, Philippe Thomas, Hind El Haouzi

Abstract

The demand forecasting remains a big issue for the supply chain management. At the dawn of Industry 4.0, and with the first encouraging results concerning the application of deep learning methods in the management of the supply chain, we have chosen to study the use of neural networks in the elaboration of sales forecasts for a French furniture manufacturing company. Two main problems have been studied for this article: the seasonality of the data and the small amount of valuable data. After the determination of the best structure for the neuronal network, we compare our results with the results form AZAP, the forecasting software using in the company. Using cross-validation, early stopping, robust learning algorithm, optimal structure determination and taking the mean of the month turns out to be in this case study a good way to get enough close to the current forecasting system.

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