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Intelligent Retail Forecasting System for New Clothing Products Considering Stock-out

General problems of the fibre and textile industries

Authors:

  • Huang He
    College of Fashion and Design, Donghua University, Shanghai, P. R. China
  • Huang He
    Faculty of Management, McGill University, Montreal, Canada
  • Liu Qiurui
    College of Public Administration, Huazhong Agricultural University, Wuhan, P. R. China

Nr DOI: 10.5604/01.3001.0010.1704

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Abstract:

Improving the accuracy of forecasting is crucial but complex in the clothing industry, especially for new products, with the lack of historical data and a wide range of factors affecting demand. Previous studies more concentrate on sales forecasting rather than demand forecasting, and the variables affecting demand remained to be optimized. In this study, a two-stage intelligent retail forecasting system is designed for new clothing products. In the first stage, demand is estimated with original sales data considering stock-out. The adaptive neuro fuzzy inference system (ANFIS) is introduced into the second stage to forecast demand. Meanwhile a data selection process is presented due to the limited data of new products. The empirical data are from a Canadian fast-fashion company. The results reveal the relationship between demand and sales, demonstrate the necessity of integrating the demand estimation process into a forecasting system, and show that the ANFIS-based forecasting system outperforms the traditional ANN technique.

Tags:

intelligent forecasting system, demand estimation, stock out, adaptive neuro fuzzy inference system, new clothing product.

Citation:

Huang H, Liu Q. An Intelligent Retail Forecasting System for New Clothing Products Considering Stock-out. FIBRES & TEXTILES in Eastern Europe 2017; 25, 1(121): 10-16. DOI: 10.5604/01.3001.0010.1704

Published in issue no 1 (121) / 2017, pages 10–16.

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