Using weather data to improve demand forecasting for seasonal products

Détails

ID Serval
serval:BIB_DD18C469B614
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Using weather data to improve demand forecasting for seasonal products
Périodique
International Journal of Services and Operations Management
Auteur(s)
Babongo F., Appelqvist P., Chavez-.Demoulin V., Hameri A.P., Niemi T.
ISSN
1744-2370
1744-2389
Statut éditorial
Publié
Date de publication
2018
Peer-reviewed
Oui
Volume
31
Numéro
1
Pages
53-76
Langue
anglais
Résumé
In seasonal business, manufacturers need to make major supply decisions up to a year before delivering products to retailers. Traditionally, they make those decisions based on sales forecasts that in turn are based on previous season's sales. In our research, we study whether demand forecasts for the upcoming season could be made more accurate by taking into account the weather of the previous sales season. We use a ten-year dataset of winter sports equipment (e.g. skis, boots, and snowboards) sales in Switzerland and Finland, linked with daily meteorological data, for developing and training a generalised additive model (GAM) to predict demand for the next season. Results show a forecasting error reduction of up to 45% when including meteorological data from the past season. In our case, the value of this reduction in the forecasting error corresponds to around 2% of total sales. The results contribute to the theory of stochastic inventory control by showing that taking into account external disturbances, in this case the fluctuations in weather, improves forecasting accuracy in situations where the lag between ordering and demand is around one year.
Mots-clé
demand forecasting, seasonal products, newsvendor model, generalised additive model, GAM
Création de la notice
19/05/2016 21:40
Dernière modification de la notice
21/08/2019 5:14
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