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

Details

Serval ID
serval:BIB_98D052C4EDFC
Type
Article: article from journal or magazin.
Collection
Publications
Title
Forecasting (un-)seasonal demand using geostatistics, socio-economic and weather data
Journal
International Journal of Business Forecasting and Marketing Intelligence
Author(s)
Babongo F., Chavez-Demoulin V., Hameri A.P., Niemi T., Appelqvist P.
ISSN
1744-6635
1744-6643
Publication state
In Press
Peer-reviewed
Oui
Language
english
Abstract
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.
Keywords
Demand forecasting, seasonal products, socio-economic features, weather, geostatistics, kriging, Semivariogram
Create date
09/01/2019 17:10
Last modification date
21/08/2019 5:12
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