Journal Article

A survey of machine learning techniques for food sales prediction

Tsoumakas, Grigorios

2019

Overview

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.

• Publisher: Springer Nature B.V
• Subject: Algorithms / Artificial intelligence / Bakeries / Bakery products / Companies / Decision analysis / Decision making / Food / Food processing industry / Forecasting / Groceries / Grocery industry / Industrial enterprises / Machine learning / Manufacturing / Predictions / Production planning / Restaurants / Retail stores / Sales / Sales forecasting / Supermarkets / Time series / Variables