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

Published Online:pp 103-124

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.


demand forecasting, seasonal products, socio-economic features, weather, geostatistics, kriging, semivariogram