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A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm

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  • Wong, W.K.
  • Guo, Z.X.

Abstract

A hybrid intelligent (HI) model, comprising a data preprocessing component and a HI forecaster, is developed to tackle the medium-term fashion sales forecasting problem. The HI forecaster firstly adopts a novel learning algorithm-based neural network to generate initial sales forecasts and then uses a heuristic fine-tuning process to obtain more accurate forecasts based on the initial ones. The learning algorithm integrates an improved harmony search algorithm and an extreme learning machine to improve the network generalization performance. Extensive experiments based on real fashion retail data and public benchmark datasets were conducted to evaluate the performance of the proposed model. The experimental results demonstrate that the performance of the proposed model is much superior to traditional ARIMA models and two recently developed neural network models for fashion sales forecasting.

Suggested Citation

  • Wong, W.K. & Guo, Z.X., 2010. "A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm," International Journal of Production Economics, Elsevier, vol. 128(2), pages 614-624, December.
  • Handle: RePEc:eee:proeco:v:128:y:2010:i:2:p:614-624
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Fallah Tehrani, Ali & Ahrens, Diane, 2016. "Enhanced predictive models for purchasing in the fashion field by using kernel machine regression equipped with ordinal logistic regression," Journal of Retailing and Consumer Services, Elsevier, vol. 32(C), pages 131-138.
    2. Majd Kharfan & Vicky Wing Kei Chan & Tugba Firdolas Efendigil, 0. "A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches," Annals of Operations Research, Springer, vol. 0, pages 1-16.
    3. Bernardo Bertoldi & Chiara Giachino & Alberto Pastore, 2016. "Strategic pricing management in the omnichannel era," MERCATI E COMPETITIVIT, FrancoAngeli Editore, vol. 2016(4), pages 131-152.
    4. Shuyun Ren & Hau-Ling Chan & Tana Siqin, 2020. "Demand forecasting in retail operations for fashionable products: methods, practices, and real case study," Annals of Operations Research, Springer, vol. 291(1), pages 761-777, August.
    5. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    6. Ma, Shaohui & Fildes, Robert, 2020. "Forecasting third-party mobile payments with implications for customer flow prediction," International Journal of Forecasting, Elsevier, vol. 36(3), pages 739-760.
    7. NJ Matsoma & IM Ambe, 2016. "Factors Affecting Demand Planning in the South African Clothing Industry," Journal of Economics and Behavioral Studies, AMH International, vol. 8(5), pages 194-210.
    8. Lalou Panagiota & Ponis Stavros T. & Efthymiou Orestis K., 2020. "Demand Forecasting of Retail Sales Using Data Analytics and Statistical Programming," Management & Marketing, Sciendo, vol. 15(2), pages 186-202, June.
    9. Puchalsky, Weslly & Ribeiro, Gabriel Trierweiler & da Veiga, Claudimar Pereira & Freire, Roberto Zanetti & Santos Coelho, Leandro dos, 2018. "Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimization: An analysis of the soybean sack price and perishable products demand," International Journal of Production Economics, Elsevier, vol. 203(C), pages 174-189.
    10. Lean Yu & Zebin Yang & Ling Tang, 2016. "Prediction-Based Multi-Objective Optimization for Oil Purchasing and Distribution with the NSGA-II Algorithm," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(02), pages 423-451, March.
    11. Emmanuel Sirimal Silva & Hossein Hassani & Dag Øivind Madsen & Liz Gee, 2019. "Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends," Social Sciences, MDPI, Open Access Journal, vol. 8(4), pages 1-23, April.

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