Case study:

Forecasting at Hard Rock Cafe

With the growth of Hard Rock Caf from one pub in London in 1971 to more than 145 restaurants in 60 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 locking in contracts for leather goods (used in jackets) and for food items such as beef, chicken, and pork. Its short-term sales forecasts are conducted each month, by caf, and then aggregated for a headquarters view. The heart of the sales forecasting system is the point-of-sale (POS) system, which, in effect, captures transaction data on nearly every person who walks through a cafs door. The sale of each entre represents one customer; the entre 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 caf. The general managers of individual cafes tap into the same database to prepare a daily forecast for their sites. A caf 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 caf 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 broke 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 forecast and actual sales are then examined to see why errors occurred. Hard Rock doesnt limit its use of forecasting tools to sales. To evaluate managers and set bonuses, a 3-year weighted moving average is applied to the caf sales. If caf general managers exceed their targets, a bonus is computed. Todd Lindsey, at corporate headquarters, applies weights of 40% to the most recent years sales, 40% to the year before, and 20% sales 2 years ago in reaching his moving average.

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

Question:

1. Briefly describe forecasting and give an appropriate example of forecasting relates to Hard Rock Case.

2. Describe FIVE (5) areas in which you think Hard Rock could use forecasting models according to the above case.

3. Analyse THREE (3) roles of POS system in forecasting at Hard Rock.

4. Explain THREE (3) primary components in forecasting.

5. Explain TWO (2) types of forecasting methods occurs in forecasting model.