IDEAS home Printed from https://ideas.repec.org/a/igg/joris0/v7y2016i2p1-21.html
   My bibliography  Save this article

Application of SARIMAX Model to Forecast Daily Sales in Food Retail Industry

Author

Listed:
  • Nari Sivanandam Arunraj

    (Deggendorf Institute of Technology, Deggendorf, Germany)

  • Diane Ahrens

    (Deggendorf Institute of Technology, Deggendorf, Germany)

  • Michael Fernandes

    (Deggendorf Institute of Technology, Deggendorf, Germany)

Abstract

During retail stage of food supply chain (FSC), food waste and stock-outs occur mainly due to inaccurate sales forecasting which leads to inappropriate ordering of products. The daily demand for a fresh food product is affected by external factors, such as seasonality, price reductions and holidays. In order to overcome this complexity and inaccuracy, the sales forecasting should try to consider all the possible demand influencing factors. The objective of this study is to develop a Seasonal Autoregressive Integrated Moving Average with external variables (SARIMAX) model which tries to account all the effects due to the demand influencing factors, to forecast the daily sales of perishable foods in a retail store. With respect to performance measures, it is found that the proposed SARIMAX model improves the traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) model.

Suggested Citation

  • Nari Sivanandam Arunraj & Diane Ahrens & Michael Fernandes, 2016. "Application of SARIMAX Model to Forecast Daily Sales in Food Retail Industry," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 7(2), pages 1-21, April.
    • Handle: RePEc:igg:joris0:v:7:y:2016:i:2:p:1-21
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJORIS.2016040101
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Irene Nandutu & Marcellin Atemkeng & Nokubonga Mgqatsa & Sakayo Toadoum Sari & Patrice Okouma & Rockefeller Rockefeller & Theophilus Ansah-Narh & Jean Louis Ebongue Kedieng Fendji & Franklin Tchakount, 2022. "Error Correction Based Deep Neural Networks for Modeling and Predicting South African Wildlife–Vehicle Collision Data," Mathematics, MDPI, vol. 10(21), pages 1-31, October.
    2. Krembsler, Jonas & Spiegelberg, Sandra & Hasenfelder, Richard & Kämpf, Nicki Lena & Winter, Thomas & Winter, Nicola & Knappe, Robert, 2024. "Fare revenue forecast in public transport: A comparative case study," Research in Transportation Economics, Elsevier, vol. 105(C).
    3. Navid Shirzadi & Fuzhan Nasiri & Ramanunni Parakkal Menon & Pilar Monsalvete & Anton Kaifel & Ursula Eicker, 2023. "Smart Urban Wind Power Forecasting: Integrating Weibull Distribution, Recurrent Neural Networks, and Numerical Weather Prediction," Energies, MDPI, vol. 16(17), pages 1-17, August.

    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:igg:joris0:v:7:y:2016:i:2:p:1-21. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.