Demand forecasting in restaurants using machine learning and statistical analysis
Takashi Tanizaki, Tomohiro Hoshino, Takeshi Shimmura, Takeshi Takenaka · Procedia CIRP · 2019
In this paper, demand forecasting in restaurants using machine learning is proposed. Many researches have been proposed on demand forecasting technology using POS data. However, in order to make demand forecasts at a real store, it is necessary to establish a store-specific demand forecasting model in consideration of various factors such as the store location, the weather, events, etc. Therefore, we constructed a demand forecasting model that functionally combines the above mentioned data using machine learning. In this paper, the demand forecasting model using machine learning and the verification result of the model using real store data is discussed.