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An Intelligent Forecasting Model for Commodities in Retail Stores

In: Liss 2014

Author

Listed:
  • Donghua Chen

    (Beijing Jiaotong University)

  • Xiaomin Zhu

    (Beijing Jiaotong University)

  • Runtong Zhang

    (Beijing Jiaotong University)

  • Shen Haikuo

    (Beijing Jiaotong University)

Abstract

The inventory management is a key to meet the increasing daily demands of customers and reduce the unnecessary cost in retail stores. However, because of various characteristics of demands in retail stores, the traditional demand forecasting technologies don’t work well. In this paper, we use the modified K-means clustering analysis and a demand forecasting model with BP neural networks and grey model is proposed to make the prediction more intelligent and general. Making the comparative analysis between the predicted values and the actual values, the superiority of the proposed model is proved.

Suggested Citation

  • Donghua Chen & Xiaomin Zhu & Runtong Zhang & Shen Haikuo, 2015. "An Intelligent Forecasting Model for Commodities in Retail Stores," Springer Books, in: Zhenji Zhang & Zuojun Max Shen & Juliang Zhang & Runtong Zhang (ed.), Liss 2014, edition 127, pages 495-499, Springer.
    • Handle: RePEc:spr:sprchp:978-3-662-43871-8_72
    DOI: 10.1007/978-3-662-43871-8_72
    as

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