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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
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    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. 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.

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