IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v59y2021i24p7491-7515.html
   My bibliography  Save this article

Machine learning for demand forecasting in the physical internet: a case study of agricultural products in Thailand

Author

Listed:
  • Anirut Kantasa-ard
  • Maroua Nouiri
  • Abdelghani Bekrar
  • Abdessamad Ait el cadi
  • Yves Sallez

Abstract

Supply chains are complex, stochastic systems. Nowadays, logistics managers face two main problems: increasingly diverse and variable customer demand that is difficult to predict. Classical forecasting methods implemented in many business units have limitations with the fluctuating demand and the complexity of fully connected supply chains. Machine Learning methods have been proposed to improve prediction. In this paper, a Long Short-Term Memory (LSTM) is proposed for demand forecasting in a physical internet supply chain network. A hybrid genetic algorithm and scatter search are proposed to automate tuning of the LSTM hyperparameters. To assess the performance of the proposed method, a real-case study on agricultural products in a supply chain in Thailand was considered. Accuracy and coefficient of determination were the key performance indicators used to compare the performance of the proposed method with other supervised learnings: ARIMAX, Support Vector Regression, and Multiple Linear Regression. The results prove the better forecasting efficiency of the LSTM method with continuous fluctuating demand, whereas the others offer greater performance with less varied demand. The performance of hybrid metaheuristics is higher than with trial-and-error. Finally, the results of forecasting model are effective in transportation and holding costs in the distribution process of the Physical Internet.

Suggested Citation

  • Anirut Kantasa-ard & Maroua Nouiri & Abdelghani Bekrar & Abdessamad Ait el cadi & Yves Sallez, 2021. "Machine learning for demand forecasting in the physical internet: a case study of agricultural products in Thailand," International Journal of Production Research, Taylor & Francis Journals, vol. 59(24), pages 7491-7515, December.
  • Handle: RePEc:taf:tprsxx:v:59:y:2021:i:24:p:7491-7515
    DOI: 10.1080/00207543.2020.1844332
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2020.1844332
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207543.2020.1844332?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Martin, Simon & Rasch, Alexander, 2024. "Demand forecasting, signal precision, and collusion with hidden actions," International Journal of Industrial Organization, Elsevier, vol. 92(C).
    2. Anna Borucka, 2023. "Seasonal Methods of Demand Forecasting in the Supply Chain as Support for the Company’s Sustainable Growth," Sustainability, MDPI, vol. 15(9), pages 1-21, April.
    3. Suriyan Jomthanachai & Wai Peng Wong & Khai Wah Khaw, 2024. "An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 741-792, February.
    4. Qingyan Zhou & Hao Li & Youhua Zhang & Junhong Zheng, 2023. "Product Evaluation Prediction Model Based on Multi-Level Deep Feature Fusion," Future Internet, MDPI, vol. 15(1), pages 1-16, January.
    5. Fábio Polola Mamede & Roberto Fray da Silva & Irineu de Brito Junior & Hugo Tsugunobu Yoshida Yoshizaki & Celso Mitsuo Hino & Carlos Eduardo Cugnasca, 2023. "Deep Learning and Statistical Models for Forecasting Transportation Demand: A Case Study of Multiple Distribution Centers," Logistics, MDPI, vol. 7(4), pages 1-19, November.
    6. Arnab Mitra & Arnav Jain & Avinash Kishore & Pravin Kumar, 2022. "A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach," SN Operations Research Forum, Springer, vol. 3(4), pages 1-22, December.

    More about this item

    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:taf:tprsxx:v:59:y:2021:i:24:p:7491-7515. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    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.