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

General problems of the fibre and textile industries


  • 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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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.


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


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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