IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-694-9_23.html

The Deep Learning Approach In Demand Forecasting In The Supply Chain Process Of Ice Creams At The Grocery Store Firm

In: Proceedings of the International Conference on Emerging Challenges: Sustainable Strategies in the Data-Driven Economy (ICECH 2024)

Author

Listed:
  • Tran Luu Thi

    (University of Economics, The University of Danang)

  • Tran Tien Huy

    (University of Economics, The University of Danang)

  • Ngo Thi Trang Thuy

    (University of Economics, The University of Danang)

  • Phan Tien Thanh

    (University of Economics, The University of Danang)

  • Huu Thuan Thang Nguyen

    (University of Economics, The University of Danang)

  • Nguyễn Thị Dung

    (University of Economics, The University of Danang)

  • Lê Thị Long Châu

    (Danang Vocational Tourism College)

  • Nguyen Nhat Minh

    (RMIT University)

  • Nguyen Ho Thanh Dat

    (University of Economics, The University of Danang)

  • Hoang Van Hai

    (University of Economics, The University of Danang)

Abstract

Research purpose: This study investigates the application of a Deep Feedforward Network (DFN) for demand forecasting and customer willingness-to-pay predictions in the retail sector. Research design, approach, and method: Using real-world data from VinMart and other retail stores, the DFN was tested for its ability to predict order quantities across multiple locations. Main findings: While the model delivered promising results, training separate networks for individual stores proved more effective than using a single network for all stores, due to varying input significance levels. Data limitations, such as the lack of extensive historical records, affected accuracy. Additionally, the DFN was used to predict customer willingness-to-pay, with inputs gathered through quantitative research. While the model showed potential, its success is highly dependent on data quality. Practical/managerial implications: Future research should explore alternative deep learning architectures and incorporate more diverse variables to enhance accuracy. This study underscores the importance of robust data for optimizing supply chain decisions in retail.

Suggested Citation

  • Tran Luu Thi & Tran Tien Huy & Ngo Thi Trang Thuy & Phan Tien Thanh & Huu Thuan Thang Nguyen & Nguyễn Thị Dung & Lê Thị Long Châu & Nguyen Nhat Minh & Nguyen Ho Thanh Dat & Hoang Van Hai, 2025. "The Deep Learning Approach In Demand Forecasting In The Supply Chain Process Of Ice Creams At The Grocery Store Firm," Advances in Economics, Business and Management Research, in: Dinh Nguyen Van & Nguyen Nguyen Danh & Ngoc Luu Thi Minh & Mai Nguyen Phuong (ed.), Proceedings of the International Conference on Emerging Challenges: Sustainable Strategies in the Data-Driven Economy (ICECH 2024), pages 332-345, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-694-9_23
    DOI: 10.2991/978-94-6463-694-9_23
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:advbcp:978-94-6463-694-9_23. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.