Objectives

• Utilize the necessary and truthful information for analysis.
• Formulate the problem with a correct POM model or techniques.
• Explore all possible alternatives and investigate each one of them.
• Make decisions given all the information.

Background

With the growth of the Hard Rock Caf franchisefrom one pub in London in 1971 to more than 129 restaurants in more than 40 countries todaycame a corporate-wide demand for better forecasting. Hard Rock Caf 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 cafe, 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 cafe's 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 cafe. The general managers of individual cafes tap into the same database to daily forecast for their sites. A cafe 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 cafe is located. The daily forecast is further broken into hourly sales, which drives employee scheduling. An hourly forecast of $5500 in sales, for example, 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 forecast and actual sales are then examined to see why errors occurred.

Hard Rock Caf doesn't limit its use of forecasting tools to sales. To evaluate managers and set bonuses, a three-year weighted moving average is applied to cafe sales. If cafe 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 two years ago in reaching his moving average.

An even more sophisticated application of statistics is found in Hard Rock Caf'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 $7.99 to $8.99, they 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 centre section driving higher sales volumes. When an item such as a hamburger is moved off the centre to one of the side flaps, the corresponding effect on related items, say French fries, is determined.

Requirement

Using the information about Hard Rock Caf provided, report that addresses the following areas:

• Describe three different forecasting applications at Hard Rock Cafe, and why you think they are used the way they are. Name three other areas in which you think Hard Rock could use forecasting models.
• The role of the POS system in forecasting at Hard Rock Cafe.
• Justify the use of the weighting system used for evaluating managers for annual bonuses.
• Name several variables besides those mentioned in the case that could be used as good predictors of daily sales in each cafe.
• At Hard Rock Cafe's Moscow location, the manager is trying to evaluate how a new advertising campaign affects guest counts. Using data for the past 10 months (see the table), develop a least squares regression relationship and then forecast the expected guest count when advertising is $65 000.