Prediction of Restaurant Sales during High Demand States Using Population Statistical Data

Predicting the future sales volume is a critical task in operating a restaurant as it helps to determine the number of staff and ingredients to have at any time and decide when to start preparing food. Future sales can typically be predicted according to the demand cycle; however, it is difficult to predict an immediate increase in demand because it is out of the demand cycle. To tackle this issue, this study proposes a method for predicting the next-hour future sales volume based on population statistical data, in addition to historical sales and current and historical weather data. The proposed method combines the results of two models, one predicting the sales volume, while the other determining whether the future demand will become high or low. The proposed method was evaluated using actual restaurant data collected in collaboration with a major Japanese restaurant company. The results demonstrate that the prediction accuracy can be improved by 1.45% compared to the prediction model of sales volume without combination of demand classification when the sales volume is high.