# Case Study - Vintage Restaurant

590 Words Oct 8th, 2014 3 Pages
Case Problem: The Vintage Restaurant is on Captiva Island, a resort community near Fort Myers, Florida. The restaurant, which is owned and operated by Karen Payne, has just completed its third year of operation. During that time, Karen has sought to establish a reputation for the restaurant as a high-quality dining establishment that specializes in fresh seafood. The efforts by Karen and her staff have proven successful, and her restaurant has become one of the best and fastest-growing restaurants on the island. Karen has concluded that to plan for the growth of the restaurant in the future, she needs to develop a system that will enable her to forecast food and beverage sales by month for up to one year in advance. Karen has the following …show more content…
The regression equation is given as: y = 146.5x + 1958.7
For the fourth year, the total sales are obtained by plugging in x = 4 in the above equation.
[pic](in thousands of dollars)
The average monthly sales during the fourth year, therefore, is 2544.7/12 = 212.058 (in thousands of dollars).
The forecast for a particular month (say July) is calculated by multiplying the average monthly sales forecast by that month’s (July’s) seasonal index. For the month of July, it will be 0.831*212.058 = 176.22 (in thousands of dollars).
The monthly forecasts for the 12 months of the fourth year are as shown below:
Month (yr.4) |January |February |March |April |May |June |July |August |September |October |November |December | |S. Index |1.398 |1.293 |1.322 |1.023 |1.043 |0.798 |0.831 |0.865 |0.636 |0.725 |0.874 |1.192 | |Forecast |296.45755 |274.19143 |280.3411 |216.9357 |221.1768 |169.2226 |176.2205 |183.4305 |134.8691 |153.7423 |185.339 |252.7735 | |
Suppose the actual January sales for the fourth year turn out to be \$295,000. The forecasted January sales are \$296,458.
Error between actual and forecasted sales = \$296,458 - \$295,000 = \$1458
Percentage Error = [pic]
This is an extremely small percentage error. Karen does not have to worry about this error and she can be assured that her forecast model is extremely…