Title: Using weather data to improve demand forecasting for seasonal products

Authors: Flora Babongo; Patrik Appelqvist; Valérie Chavez-Demoulin; Ari-Pekka Hameri; Tapio Niemi

Addresses: Department of Operations, Faculty of Business and Economics, Anthropole, University of Lausanne, CH-1015 Lausanne, Switzerland ' Amer Sports Corporation, Mäkelänkatu 91, P.O. Box 130, FI-00601 Helsinki, Finland ' Department of Operations, Faculty of Business and Economics, Anthropole, University of Lausanne, CH-1015 Lausanne, Switzerland ' Department of Operations, Faculty of Business and Economics, Anthropole, University of Lausanne, CH-1015 Lausanne, Switzerland ' Department of Operations, Faculty of Business and Economics, Anthropole, University of Lausanne, CH-1015 Lausanne, Switzerland

Abstract: 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.

Keywords: demand forecasting; seasonal products; newsvendor model; generalised additive model; GAM.

DOI: 10.1504/IJSOM.2018.094183

International Journal of Services and Operations Management, 2018 Vol.31 No.1, pp.53 - 76

Received: 29 Jun 2016
Accepted: 12 Nov 2016
Published online: 22 Aug 2018 *

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