Case study – forecasting at hard rock cafe

With the growth of Hard Rock Cafe – from one pub in London in 1971 to more than 110 restaurants in more than 40 countries today – came a corporatewide 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 such food items as beef, chicken, and pork. Its 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, captures transaction 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 a 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 convert 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 stations, 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 forecast and actual sales are then examined to see why errors occurred.

Hard Rock doesn’t limit its use of forecasting tools to sales. To evaluate managers and set bonuses, a 3-year weighted moving average is applied to café sales. If café general managers exceed their targets, a bonus is computed. Todd 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 item is changed. For example, if the price of a cheeseburger increases from $6.99 to $7.99, Hard Rock can predict the effect this will have on sales of chicken 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 flaps, the corresponding effect on related items, say french fries, is determined.

Hard Rock’s Moscow Café

Month 1 2 3 4 5 6 7 8 9 10
Guest 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

 

Questions:

1.     Describe three different forecasting applications at Hard Rock. Also identify the nature (qualitative or quantitative forecasts) of the forecasting techniques at Hard Rock.

2.     What is the role of the POS system in forecasting at Hard Rock?

3.     Justify the use of the weighting system used for evaluating managers for annual bonuses.

4.     Using data for the past 10 months (see the table), what should you do to determine the best method or methods to forecast these date? Explain why if you think a time series model or a regression model is better suited here?

5.     Develop a least squares regression relationship and then forecast the expected guest count when advertising is $65,000. Please first show the procedure manually on a separate sheet, and then show the procedure using MS Excel.

1.     Name several variables besides those mentioned in the case that could be used as good predictors of daily sales in each café.

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