Applying Time Series Model in Customers Demand Forecast and Restaurant Revenue Management Strategy

• Main Authors: Tsai, Shao-Wen, 蔡紹文
• Other Authors: Chao, Chia-Yu
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/02132154278428606735

碩士 === 國立金門大學 ===  觀光管理學系 === 102 === With recent advancement in technology, the progress in database systems and the innovation and improvement of forecasting techniques, restaurant information system gradually transforms from materials management to revenue management. However, the data of restaurant operation retrieved from the POS can be used to build up analysis models through Business Intelligence solutions. In this research, the POS data of restaurants were imported into SQL SERVER to forecast the number of customers in each operating periods by time series model of Microsoft Business Intelligence solutions. Based on the forecast, the restaurant manager can use the skills of restaurant revenue management to implement the operational strategies concerned. The result shows that the forecasts of the customer demand of each period in the restaurant tend to match the real demand. The maximum average demand occurred at hour 18 on the weekend, while the minimum average demand occurred at hour 11 on Tuesdays and Thursdays.