A New Sales Forecasting Model for International Restaurants

Competitive advantage for e-business requires more accurate information and precise decision to aid international companies to analyze and predict sales forecasting trend optimizing potential profits and reduce losses. We propose an improvement model to minimize the forecast error for the time series of daily sales information. We use real international restaurant data as the basis to show the performance of our sales prediction model. Multiple time series are considered in this model to vastly improve the forecasting outcome. Those data series are combined from EPARK Company and open data like weather, into a multi-series data, our forecast model extend the previous predecessor tremendously. Various residue computations during the process are compared and discussed. We applied the model for data from different area to compare the difference respectively. Result shows a proper selection of computation method is more dynamic than a fixed method for shops in different geographic area even within the same company. In addition, analysis shows significant error reduction in forecasting achieved when open data like weather information is included in the regression process. Thus international business can be more agile and flexible for just-in-time stock inventory and better resource allocation strategy.