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

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process

Authors: Julie Bibaud-Alves 1 ; Philippe Thomas 2 and Hind El Haouzi 2

Affiliations: 1 Université de Lorraine, CRAN, UMR 7039, Campus Sciences, BP 70239, 54506 Vandœuvre-lès-Nancy Cedex, France, CNRS, CRAN, UMR7039, France, Parisot Meubles, 15 Avenue Jacques Parisot, 70800 Saint-Loup-sur-Semouse and France ; 2 Université de Lorraine, CRAN, UMR 7039, Campus Sciences, BP 70239, 54506 Vandœuvre-lès-Nancy Cedex, France, CNRS, CRAN, UMR7039 and France

ISBN: 978-989-758-384-1

Keyword(s): Sales Forecasting, Neural Networks, Multilayer Perceptron, Seasonality, Overfitting, Time Series.

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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Paper citation in several formats:
Bibaud-Alves, J.; Thomas, P. and El Haouzi, H. (2019). Demand Forecasting using Artificial Neuronal Networks and Time Series: Application to a French Furniture Manufacturer Case Study.In Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2019) ISBN 978-989-758-384-1, pages 502-507. DOI: 10.5220/0008356705020507

@conference{ncta19,
author={Julie Bibaud{-}Alves. and Philippe Thomas. and Hind Bril El Haouzi.},
title={Demand Forecasting using Artificial Neuronal Networks and Time Series: Application to a French Furniture Manufacturer Case Study},
booktitle={Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2019)},
year={2019},
pages={502-507},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008356705020507},
isbn={978-989-758-384-1},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2019)
TI - Demand Forecasting using Artificial Neuronal Networks and Time Series: Application to a French Furniture Manufacturer Case Study
SN - 978-989-758-384-1
AU - Bibaud-Alves, J.
AU - Thomas, P.
AU - El Haouzi, H.
PY - 2019
SP - 502
EP - 507
DO - 10.5220/0008356705020507

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