A survey of machine learning techniques for food sales prediction

Abstract

Food sales prediction is concerned with estimating future sales of companies in the food industry, such as supermarkets, groceries, restaurants, bakeries and patisseries. Accurate short-term sales prediction allows companies to minimize stocked and expired products inside stores and at the same time avoid missing sales. This paper reviews existing machine learning approaches for food sales prediction. It discusses important design decisions of a data analyst working on food sales prediction, such as the temporal granularity of sales data, the input variables to use for predicting sales and the representation of the sales output variable. In addition, it reviews machine learning algorithms that have been applied to food sales prediction and appropriate measures for evaluating their accuracy. Finally, it discusses the main challenges and opportunities for applied machine learning in the domain of food sales prediction.

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Notes

  1. 1.

    https://www.kaggle.com/c/favorita-grocery-sales-forecasting

  2. 2.

    http://www.foodtecsolutions.com/solutions/salesForecast

  3. 3.

    https://www.oracle.com/industries/retail/products/supply-chain-planning/demand-forecasting/index.html

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Acknowledgements

I would like to thank the anonymous reviewers for their constructive comments that helped me improve this work.

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Correspondence to Grigorios Tsoumakas.

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Tsoumakas, G. A survey of machine learning techniques for food sales prediction. Artif Intell Rev 52, 441–447 (2019). https://doi.org/10.1007/s10462-018-9637-z

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Keywords

  • Food
  • Sales prediction
  • Demand forecasting
  • Machine learning
  • Regression
  • Timeseries forecasting