Fashion Forecasting Example: Hope Sweden

Abstract: Hope was founded in 2001 by Ann Ringstrand and Stefan Söderberg. Since then the company has grown rapidly. It has won many prestigious design awards and is considered one of the most promising Swedish fashion companies. To gain control of their expansion they needed a sales forecasting tool. The search for a suitable method started with Hope’s sales order history. Due to very short and irregular records showing no noticeable patterns, the information had to be left aside and considered as of no use for forecasting future sales. A sales forecasting benchmarking study was carried out among Hope’s competitors. It revealed how little faith was put into forecasting when it came to fashion. Production orders are always made upon known demand in Hope’s segment of the industry and that is why no one of the interrogated companies even considered forecasting. The theoretical study depicts fashion as an unpredictable and volatile industry where few rules apply. To unite the empirical findings of fashion articles with quantitative forecasting techniques has due to many factors shown to be difficult. A quantitative method requires often 20 time periods, for Hope corresponding to 10 years of history. An article rarely lasts more than a season and it would consequently have to be linked, subjectively, to a similar item. Furthermore the conditions are changing rapidly. Yesterday was yesterday and today the circumstances are new. The retail buyer function is essential to Hope’s sales forecasting. In the end it determines the sales results. Its function was closely investigated in the pursuit of universal behaviour that could be the foundation of a forecasting tool. The procurement investigation brought a buyer portrait far from the analytic and calculating purchaser in the little existing literature. Instead he was impulsive and intuitively deciding his shop’s assortment and quantities. According to retail buyers, sales history is of little use in the fast moving fashion business. They do not employ mathematical models, however still their experience is founded on in store sell-through figures. As the sales records available to Hope include the retailers’ forecasting error, they should not be utilised. The conclusion is that in order to improve forecasting methods, a closer relationship with the retailers is required. Even then, other precautions are necessitated to reduce the risk of predicting the volatile fashion market. By continuously sharing inventory numbers, two-ways, Hope can anticipate a sell out and restart its production in time. The importance of the forecast is thus reduced through an open and more flexible supply chain.