A Comparative Study on the Forecast of Fresh Food Sales Using Logistic Regression, Moving Average and Bpnn Methods

Different prediction methods give different performance predictions when used for daily fresh food sales forecasting. Logistic Regression (LR) is a good choice for binary data, the Moving Average (MA) method is good for simple prediction, while the Back-Propagation Neural Network (BPNN) method is good for long term data. In this study we develop and compare the performance of three sales forecasting models, based on the above three prediction methods, for the forecasting of fresh food sales in a point of sales (POS) database for convenience stores. Fresh food is characterized by two factors: its short shelf-life and its importance as a revenue producer for convenience stores. An efficient forecasting model would be helpful to increase sales volume and reduce waste at such stores. The correctness of the prediction rate is a good way to compare the efficacy of different models which is the method used here. The research results reveal that LR performs better than the other methods although MA is better suited to the management of convenience stores.