Forecasting women's apparel sales using mathematical modeling

Traditionally, statistical time series methods like moving average (MA), auto‐regression (AR), or combinations of them are used for forecasting sales. Since these models predict future sales only on the basis of previous sales, they fail in an environment where the sales are more influenced by exogenous variables such as size, price, color, climatic data, effect of media, price changes or campaigns. Although, a linear regression model can take these variables into account its approximation function is restricted to be linear. Soft computing methods such as fuzzy logic, artificial neural networks (ANNs), and genetic algorithms offer an alternative taking into account both endogenous and exogenous variables and allowing arbitrary non‐linear approximation functions derived (learned) directly from the data. In this paper, two approaches have been investigated for forecasting women's apparel sales, statistical time series modeling, and modeling using ANNs. Four years' sales data (1997‐2000) were used as backcast data in the model and a forecast was made for 2 months of the year 2000. The performance of the models was tested by comparing one of the goodness‐of‐fit statistics, R2, and also by comparing actual sales with the forecasted sales of different types of garments. On an average, an R2 of 0.75 and 0.90 was found for single seasonal exponential smoothing and Winters' three parameter model, respectively. The model based on ANN gave a higher R2 averaging 0.92. Although, R2 for ANN model was higher than that of statistical models, correlations between actual and forecasted were lower than those found with Winters' three parameter model.