Prediction of retail sales of footwear using feedforward and recurrent neural networks

Fluctuation of sales over time is one of the major problems faced by most of the industries. To alleviate this problem management tries to base their plans on forecast of sales pattern, which are mostly adhoc and rarely provides solid foundation for the plans. This study makes an attempt to solve this problem by taking a neural network approach, at the process of sales of footwear, and arriving at an optimum neural network model. The algorithms used for developing such model through neural network are both feedforward and recurrent Elman network. The data used in this work are the weekly sales of footwear and the information about the seasonality of sales process. While solving the problem, the focus is on forecasting of weekly retail sales as per the requirement of management. This work would reduce the uncertainty existing in the short-term/middle term planning of sales and distribution logistics of footwear over different time horizons across the entire supply chain of footwear business.

[1]  Derek W. Bunn,et al.  Large neural networks for electricity load forecasting: Are they overfitted? , 2005 .

[2]  Jingtao Yao,et al.  Forecasting and Analysis of Marketing Data Using Neural Networks , 1998, J. Inf. Sci. Eng..

[3]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[4]  Ramin Yasdi.  Prediction of Road Traffic using a Neural Network Approach , 1999, Neural Computing & Applications.

[5]  Zhou Hongxing,et al.  Short-term load forecasting using neural networks combined with linear models , 2000, Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No.00EX393).

[6]  Abhijit S. Pandya,et al.  Foreign currency rate forecasting using neural networks , 2000, SPIE Defense + Commercial Sensing.

[7]  Murray Smith,et al.  Neural Networks for Statistical Modeling , 1993 .