Forecasting (un-)seasonal demand using geostatistics, socio-economic and weather data

Détails

ID Serval
serval:BIB_98D052C4EDFC
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Forecasting (un-)seasonal demand using geostatistics, socio-economic and weather data
Périodique
International Journal of Business Forecasting and Marketing Intelligence
Auteur(s)
Babongo F., Chavez-Demoulin V., Hameri A.P., Niemi T., Appelqvist P.
ISSN
1744-6635
1744-6643
Statut éditorial
In Press
Peer-reviewed
Oui
Langue
anglais
Résumé
Accurate demand forecasts are essential to supply chain management. We study the spatial demand variation of seasonal and unseasonal sport goods and demonstrate how demand forecast accuracy can be improved by using geostatistics and linking socio-economic and weather data with order line specific supply chain transactions. We found that the socio-economic features impact the demand of both seasonal and unseasonal products and unseasonal products are impacted more. Weather conditions affect only seasonal products. Cross-validation analyses show that using external information improves demand forecasting accuracy by reducing forecasting error up to 48%. The results can be applied both to the operational demand planning process and to the strategy used when making location-based decisions on supply chain actions, for example, deciding locations for new stores or running marketing campaigns.
Mots-clé
Demand forecasting, seasonal products, socio-economic features, weather, geostatistics, kriging, Semivariogram
Création de la notice
09/01/2019 17:10
Dernière modification de la notice
21/08/2019 5:12
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