Had Rock Case Study

Topics: Regression analysis, Forecasting, Linear regression, Prediction / Pages: 3 (527 words) / Published: Oct 22nd, 2012
Case Study: Hard Rock Café 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 café.
Some variables that

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