Coffee Sales Prediction for Vending Machines Based on ARIMA Model

The global coffee market is booming and consumer preferences are refined. Vending machines have significant potential in coffee sales. However, predicting coffee sales to balance supply and demand, reduce inventory, and improve customer satisfaction is a crucial issue for the industry. This paper presents a study on the prediction of coffee sales in vending machines using the Autoregressive Integrated Moving Average (ARIMA) model. The analysis is based on a comprehensive dataset of daily coffee sales records, which provides valuable insights into consumer behavior and sales trends. The ARIMA model is employed to capture the temporal dependencies and patterns within the sales data. The fitted model is then validated using the Ljung-Box Q-test to ensure that the residuals are uncorrelated, indicating a good fit. The results show that the ARIMA model has a high prediction accuracy, with a Root Mean Square Error (RMSE) of 68.9416, and can roughly predict future coffee sales. This study contributes to the literature on time series analysis and forecasting in the retail industry, particularly in the context of automated vending machines. The findings have practical implications for vending machine operators, who can leverage the predictions to optimize inventory management, sales strategies, and customer service.