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Optimization of Restaurant Operations and Food Waste Management Through Day-Specific Sales Forecasting Using ANFIS

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  • Ribin Varghese Pazhamannil

    (Presidency University)

Abstract

The inability to accurately predict daily sales hinders restaurant managers from efficiently managing food ingredients and raw materials. Accurate sales forecasting enables better control over stock levels, reducing food waste and ensuring the timely use of perishable products. This study applies an Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict day-specific sales patterns for the restaurant TB2, taking into account factors such as holidays, seasonal trends, and marketing campaigns. Main effects plots generated using Minitab showed that Saturday had the highest dine-in sales, while Sunday recorded the highest online and total sales. Furthermore, festive seasons were found to enhance both dine-in and online sales volumes, while advertisements and public holidays had a significant positive effect on dine-in sales but only a limited impact on online sales. The accuracy of the ANFIS model was evaluated using the root mean square error and the coefficient of determination. The root mean square error between the predicted and actual total sales was 4589.21, with a coefficient of determination of 0.804, indicating strong predictive performance. The ANFIS model proves effective in forecasting restaurant sales for any given future date, enabling better operational management and food waste reduction.

Suggested Citation

  • Ribin Varghese Pazhamannil, 2025. "Optimization of Restaurant Operations and Food Waste Management Through Day-Specific Sales Forecasting Using ANFIS," SN Operations Research Forum, Springer, vol. 6(3), pages 1-18, September.
    • Handle: RePEc:spr:snopef:v:6:y:2025:i:3:d:10.1007_s43069-025-00541-x
    DOI: 10.1007/s43069-025-00541-x
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    References listed on IDEAS

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    1. Kaffash, Sepideh & Nguyen, An Truong & Zhu, Joe, 2021. "Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 231(C).
    2. Ravindra Andukuri & Ch Maheswara Rao, 2024. "Application of Fuzzy CODAS for the Optimal Selection of Condition Monitoring Equipment in Industrial Rotating Machinery," SN Operations Research Forum, Springer, vol. 5(4), pages 1-34, December.
    3. Abhishek Yadav, 2024. "A Comparative Study of Time Series, Machine Learning, and Deep Learning Models for Forecasting Global Price of Wheat," SN Operations Research Forum, Springer, vol. 5(4), pages 1-24, December.
    4. Posch, Konstantin & Truden, Christian & Hungerländer, Philipp & Pilz, Jürgen, 2022. "A Bayesian approach for predicting food and beverage sales in staff canteens and restaurants," International Journal of Forecasting, Elsevier, vol. 38(1), pages 321-338.
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