Forecasting women's apparel sales using mathematical modeling

Celia Frank; Ashish Garg; Les Sztandera; Amar Raheja

doi:10.1108/09556220310470097

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International Journal of Clothing Science and Technology

ISSN: 0955-6222

Online from: 1989

Subject Area: Mechanical & Materials Engineering

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Forecasting women's apparel sales using mathematical modeling

Document Information:
Title:	Forecasting women's apparel sales using mathematical modeling
Author(s):	Celia Frank, (Philadelphia University, Philadelphia, PA, USA), Ashish Garg, (Philadelphia University, Philadelphia, PA, USA), Les Sztandera, (Philadelphia University, Philadelphia, PA, USA), Amar Raheja, (California State Polytechnic University, Pomona, CA, USA)
Citation:	Celia Frank, Ashish Garg, Les Sztandera, Amar Raheja, (2003) "Forecasting women's apparel sales using mathematical modeling", International Journal of Clothing Science and Technology, Vol. 15 Iss: 2, pp.107 - 125
Keywords:	Apparel, Computing, Forecasting, Modelling, Time series
Article type:	Research paper
DOI:	10.1108/09556220310470097 (Permanent URL)
Publisher:	MCB UP Ltd
Abstract:	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.