HARD ROCK CAFE FORECASTING

HARD ROCK CAFE FORECASTING
FORECASTING AT HARD ROCK CAFÉ*

With the growth of Hard Rock Café – from one pub in London in 1971 to more than 110 restaurants in more than 40 countries today – came a corporate wide demand for better forecasting. Hard Rock uses long-range forecasting in setting a capacity plan and intermediate-term forecasting for looking in contracts for leather goods (used in jackets) and for such food items as beef, chicken, and pork. In short-term sales forecasts are conducted each month, by café, and then aggregated for a headquarters view.

The heart of the sales forecasting system is the point-of-sale system (POS), which, in effect, computes transactional data on nearly every person who walks through a café’s door. The sale of each entrée represents one customer; the entrée sales data are transmitted daily to the Orlando corporate headquarters’ database. There, the financial team, headed by Todd Lindsey, begins the forecast process. Lindsey forecasts monthly guest counts, retail sales, banquet sales, and concert sales (if applicable) at each café.

The general managers of individual cafes tap into the same database to prepare daily forecast for their sites. A café manager pulls up prior years’ sales for that day, adding information from the local Chamber of Commerce or Tourist Board on upcoming events such as a major convention, sporting event, or concert in the city where the café is located. The daily forecast is further broken into hourly sales, which drives employee scheduling. An hourly forecast of $5,500 in sales translates into 19 workstations, which are further broken down into a specific number of wait staff, hosts, bartenders, and kitchen staff. Computerized scheduling software plugs in people based on their availability Variances between the forecast and actual sales are then examined to see why errors occurred.

Hard Rock does not limit its use of forecasting tools to sales. To evaluate managers and set bonuses, a 3-year moving average is applied to café sales. If café managers exceed their targets, a bonus is computed. Tood Lindsey, at corporate headquarters, applies weights of 40% to the most recent year’s sales, 40% to the year before, and 20% to sales 2 years ago in reaching his moving average.

An even more sophisticated application of statistics is found in Hard Rock’s menu planning. Using multiple regression, managers can compute the impact on demand of other menu items if the price of one menu item is changed. For example, if the price for a cheeseburger increases from $7.99 to $8.99, Hard Rock can predict the effect this will have on sales of kitchen sandwiches, pork sandwiches, and salads. Managers do the same analysis on menu placement, with the center section driving higher sales volumes. When an item such as a hamburger is moved off the center to one of the side flap, the corresponding effect on related items, say French fries, is determined.

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*Adopted from Heizer, J. and Render, B., Operations Management, Prentice Hall, 10th Edition, 2011.
One of the typical forecasts produced in the Hard Rock’s corporate headquarter is the guest count forecast in various cafés and for the entire company. The historical data for the guest count (in thousands) in one of the largest Hard Rock Café’s located in Hawaii is presented below:

The financial team at Hard Rock’s headquarters would like to forecast the quest count in this restaurant in the second half of 2012.

Questions

1. Describe three forecasting applications at Hard Rock. Name other areas in which you think Hard Rock could use forecasting models.

2. What variables, besides time, can influence guest count? (example: weather, holidays, strikes, etc.)

3. What are historical data patterns of guest count?

4. What forecasting techniques would you suggest applying and why?

5. Apply FIVE forecasting techniques for this data, and identify the best forecast. (example: MAD, MSE, etc.)
Professional business analysis format. The following are some specific requirements:

In Question 1 describe three forecasting applications at Hard Rock. The description of each application should include the decisions affected by the forecasting, the forecast unit, time horizon, data collection procedure, and potential forecasting methods (could be qualitative or quantitative). In addition, please list other areas in which you think Hard Rock could use forecasting models.
In Question 2 identify and explain carefully about what variables, besides time, that may influence guest count.
In Question 3, 4, and 5 provide graphs or tables that are relevant to your discussion.
There is no need to provide print-out of detailed forecasting results unless without analysis or explanation. At least 5 forecasting techniques should be included in comparison and need to justify the selection process of the best technique.

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