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Food waste is a global issue that has a significant impact on the environment and economy of nations. It is aware that the causes of food waste are complex and varied, arising from several points in the food chain, including production, distribution, retail, and consumption, particularly in the bakery sector. Furthermore, because bread has a limited shelf life and goes bad after only three days, a lot of it is wasted. Model prediction is believed to be one of the ways to tackle this issue as by predicting the sales in the future, managers will be able to manage the daily production to reduce wastage. In this paper, sales data from a French bakery store is gathered, and SARIMA, XGBoost, and Random Forest is implemented to generate an accurate model to predict the sales in the upcoming year. This is motivated by the need to reduce the amount of bread wasted every day, month, or year. The result shows that XGBoost outperforms both SARIMA and Random Forest with the lowest RMSE value at 123.61. The research results can help retailers and bakeries reduce the amount of bread wastage globally through better inventory management as well as bread production to improve both profitability as well as the environment.

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Sales Prediction for Solving Food Wastage with Machine Learning Algorithms on French Bakery Shop

  • 会议论文

  • 发表日期：

    2025年

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  • Steven Yenardi,
  • Adeline Sneha John Crisastum,
  • Raja Rajeswari A./P. Ponnusamy,
  • N. R. Wilfred Blessing

摘要

Food waste is a global issue that has a significant impact on the environment and economy of nations. It is aware that the causes of food waste are complex and varied, arising from several points in the food chain, including production, distribution, retail, and consumption, particularly in the bakery sector. Furthermore, because bread has a limited shelf life and goes bad after only three days, a lot of it is wasted. Model prediction is believed to be one of the ways to tackle this issue as by predicting the sales in the future, managers will be able to manage the daily production to reduce wastage. In this paper, sales data from a French bakery store is gathered, and SARIMA, XGBoost, and Random Forest is implemented to generate an accurate model to predict the sales in the upcoming year. This is motivated by the need to reduce the amount of bread wasted every day, month, or year. The result shows that XGBoost outperforms both SARIMA and Random Forest with the lowest RMSE value at 123.61. The research results can help retailers and bakeries reduce the amount of bread wastage globally through better inventory management as well as bread production to improve both profitability as well as the environment.