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

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Abstract

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.

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Acknowledgments

The authors would like to thank the editor for the significant contributions and the reviewers for providing valuable comments and fruitful suggestions.

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Authors and Affiliations

  1. SQC and OR Unit, Indian Statistical Institute, 203, B.T. Road, Kolkata, 700 108, India

    Prasun Das & Subhasis Chaudhury

Authors
  1. Prasun Das
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  2. Subhasis Chaudhury
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Corresponding author

Correspondence to Prasun Das.

Appendix: Weights and biases for the optimum network 5-10-8-1

Appendix: Weights and biases for the optimum network 5-10-8-1

Tables 6, 7, 8, 9, 10, and 11.

Table 6 Weights (forward) for 5–10
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Table 7 Weights (forward) for 10–8
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Table 8 Weights (forward) for 8–1
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Table 9 Weights (feedback) for 10–10
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Table 10 Weights (feedback) for 8–8
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Table 11 Bias values
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Das, P., Chaudhury, S. Prediction of retail sales of footwear using feedforward and recurrent neural networks. Neural Comput & Applic 16, 491–502 (2007). https://doi.org/10.1007/s00521-006-0077-3

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  • DOI: https://doi.org/10.1007/s00521-006-0077-3

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