Had Rock Case Study
Case Study: Hard Rock Cafe 1. Describe three different forecasting applications at Hard Rock. Name three other areas in which you think Hard Rock could use forecasting models.
The first forecasting application that Hard Rock uses is the point-of-sale system (POS), they can analyze sales data, maintain a sales history, and improve their pricing of products. The second application Hard Rock uses is the 3-year weighted moving average to help evaluate managers and to set their bonuses. And the third application Hard Rock uses is multiple regression to help figure out how to set up the menu.
Managers can compute the impact on demand of other menu items if the price of one item is changed. Three other areas Hard Rock could use forecasting models is seasonal forecasting for the menu, customer satisfaction with/without entertainment, and new menu items and its impact. 2. What is the role of the POS system in forecasting at Hard Rock? The POS System counts every person who walks through the door. The system gathers information from what the customers’ buy or even if they just walk in. From this transaction, they then compile statistics on the average consumer.
The statistics combined with data on weather, conventions and food/beverage costs affect the finalized forecasts. Since most of Hard Rock’s information is all gathered into one POS system, it becomes their core of all their strategies and basics for forecasting. 3. Justify the use of the weighting system used for evaluating managers for annual bonuses. Using the weighting system, Hard Rock can more accurately predict sales and the bonuses act as an incentive for managers to exceed previous years sales.
The three-year model helps to ensure that managers will strive to make sure the company does well in the long-term to maximize future earnings. 4. Name several variables besides those mentioned in the case that could be used as good predictors of daily sales in each cafe. Some variables that can help as good predictors of daily sales would be the age demographic that comes to the stores and the times the come, vacations and holiday times, and when competitors have sales or offers. . At Hard Rock’s Moscow restaurant, the manager is trying to evaluate how a new advertising campaign affects guest counts. Using data for the past 10 months (see table) develop a least squares regression relationship and then forecast the expected guest count when advertising is $65,000. Data: MONTH| 1| 2| 3| 4| 5| 6| 7| 8| 9| 10| Guest count (in thousands)| 21| 24| 27| 32| 29| 37| 43| 43| 54| 66| Advertising (in $ thousands)| 14| 17| 25| 25| 35| 35| 45| 50| 60| 60| Advertising (in $ thousands)| Guest Count (in thousands)| x^2| xy| | | 14| 21| 196| 294| | | 17| 24| 289| 408| | | 25| 27| 625| 675| | | 25| 32| 625| 800| | | 35| 29| 1225| 1015| | | 35| 37| 1225| 1295| | | 45| 43| 2025| 1935| | | 50| 43| 2500| 2150| | | 60| 54| 3600| 3240| | | 60| 66| 3600| 3960| | Sum| 366| 376| 15910| 15772| | | | | | | | | | | y=a+bx| | | x| 36. 6| | investment| 65000| | y| 37. 6| | # of Guests| 60307| | b| 0. 800| | | | | a| 8. 34| | | | | | | | | | |