Amer Sports (Finland)

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.

Purpose – The purpose of this paper is to study how variations in weather affect demand and supply chain performance in sport goods. The study includes several brands differing in supply chain structure, product variety and seasonality. Design/methodology/approach – Longitudinal data on supply chain transactions and customer weather conditions are analysed. The underlying hypothesis is that changes in weather affect demand, which in turn impacts supply chain performance. Findings – In general, an increase in temperature in winter and spring decreases order volumes in resorts, while for larger customers in urban locations order volumes increase. Further, an increase in volumes of non-seasonal products reduces delays in deliveries, but for seasonal products the effect is opposite. In all, weather affects demand, lower volumes do not generally improve supply chain performance, but larger volumes can make it worse. The analysis shows that the dependence structure between demand and delay is time varying and is affected by weather conditions. Research limitations/implications – The study concerns one country and leisure goods, which can limit its generalizability. Practical/implications – Well-managed supply chains should prepare for demand fluctuations caused by weather changes. Weekly weather forecasts could be used when planning operations for product families to improve supply chain performance. Originality/value – The study focuses on supply chain vulnerability in normal weather conditions while most of the existing research studies major events or catastrophes. The results open new opportunities for supply chain managers to reduce weather dependence and improve profitability.