Had Rock Case Study

Case Study leaden Rock Cafe 1. Describe three different forecasting applications at badly Rock. Name three other areas in which you think tough Rock could use forecasting models. The first forecasting application that ticklish Rock uses is the point-of-sale outline (POS), they earth-closet analyze gross sales data, discover a sales history, and improve their pricing of products. The second application Hard Rock uses is the 3-year weighted moving average to avail evaluate animal trainers and to mass 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 dissemble on demand of other menu facts if the price of wizard item is changed. Three other areas Hard Rock could use forecasting models is seasonal worker forecasting for the menu, customer satisfaction with/without entertainment, and new menu items and its impact. 2. What is the role of the POS carcass in forecasting at Hard Roc k? The POS System takes every(prenominal) person who walks through the door. The system gathers information from what the customers buy or evening if they just walk in. From this transaction, they then compile statistics on the average consumer.The statistics unite with data on weather, conventions and food/beverage costs affect the finalized forecasts. Since most of Hard Rocks 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 motivator for managers to exceed previous years sales.The three-year model helps to ensure that managers will get to 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 individually 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 Rocks Moscow restaurant, the manager is trying to evaluate how a new advertising campaign affects invitee counts. Using data for the past 10 months (see table) develop a least(prenominal) 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 thickening 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) 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 Sum 366 376 15910 15772 y=a+bx x 36. 6 in vestment 65000 y 37. 6 of Guests 60307 b 0. 800 a 8. 34