Sales forecasting by artificial neural networks for the apparel retail chain stores

Sales forecasting with high accuracy is crucial in many industries. Especially, in fastmoving consumer goods, retail and apparel industries, the products are not tailor-made and the products must be produced and made available in chain stores to the customers, in advance. Therefore, for sales and operations planning, forecast information is required. However, traditionally, time series based forecasting techniques are used that merely consider the seasonality, trend, auto-regressive and cyclic factors. This type of forecasting is not suitable especially in cases where many other factors affect the product sales. In apparel retail industry, special factors such as promotions, special days, weather (temperature), and location of the store may affect the product demands of the chain stores. So, in this study, artificial neural net-works are developed and used for sales forecasting of a product family of a real chain store, in Turkey. The stores exist in many cities, and some of the cities have much more stores than the other cities. Some of the stores identified among these are studied. The past sales, sales price and promotion data of selected stores are used. In addition to these, store information and weather temperature data are included in the model. Sales are estimated by artificial neural networks. As a result of the study, the most appropriate network structure has been obtained, and a high sales forecasting performance has been reached.