Predicting women's apparel sales by soft computing

Abstract

In this research, forecasting models were built based on both univariate and multivariate analysis. Models built on multivariate fuzzy logic analysis were better in comparison to those built on other models. The performance of the models was tested by comparing one of the goodness-of-fit statistics, R 2, and also by comparing actual sales with the forecasted sales of different types of garments. Five months sales data (August-December 2001) was used as back cast data in our models and a forecast was made for one month of the year 2002. 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. An R2 of 0.93 was obtained for multivariate analysis (0.75 for univariate analysis), which is significantly higher than those of 0.90 and 0.75 found for Single Seasonal Exponential Smoothing and Winters' three parameter model, respectively. Yet another model, based on artificial neural network approach, gave an R2 averaging 0.82 for multivariate analysis and 0.92 for univariate analysis.