Had Rock Case Study

Case Study gravely stone Cafe 1. Describe three divergent presage applications at embarrassing judder. defecate three other(a) areas in which you deal heavily Rock could recitation omen frameworks. The first forecasting application that Hard Rock uses is the point-of-sale arranging (POS), they end analyze sales data, maintain a sales history, and improve their pricing of products. The plump for application Hard Rock uses is the 3-year weighted moving fair to do value managers and to set their bonuses. And the third application Hard Rock uses is multiple atavism to help figure out how to set up the menu.Managers can compute the impact on demand of other menu items if the equipment casualty 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 individual who straitss through the door. The system gathers training from what the customers secure 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 nourishment/beverage costs affect the finalized forecasts. Since near of Hard Rocks information is all gathered into one POS system, it becomes their meaning of all their strategies and basics for forecasting. 3. Justify the use of the weighting system used for evaluating managers for yearly bonuses. Using the weighting system, Hard Rock can more accurately counter 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 arrest sure the company does well in the long-term to maximize future earnings. 4. address several variables besides those mentioned in the chemise that could be used as equi table predictors of daily sales in from each one 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 vacation times, and when competitors have sales or offers. . At Hard Rocks capital of the Russian Federation restaurant, the manager is trying to evaluate how a new advertising campaign affects knob counts. Using data for the past 10 months (see table) develop a least squares regression relationship and then forecast the expect guest count when advertising is $65,000. selective information MONTH 1 2 3 4 5 6 7 8 9 10 node count (in thousands) 21 24 27 32 29 37 43 43 54 66 announce (in $ thousands) 14 17 25 25 35 35 45 50 60 60 Advertising (in $ thousands) leaf node Count (in thousands) x2 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 summation 366 376 15910 15772 y=a+bx x 36. 6 investment 65000 y 37. 6 of Guests 60307 b 0. 800 a 8. 34