Question

1.A) In abut 5 lines explain how associative methods of forecasting differ from Time series methods of forecasting. B) Which one would you use to explain how the demand for pork might affect the demand for beef? 2. What all does Hard Rock Cafe's Point of Sale (POS) system capture? At what level is the information aggregated? 3. A) In about 5 lines, discuss the key differences between Time Series method of forecasting and Qualitative Methods of forecasting. B) Name 3 methods of forecasting under each method.

Forecasting at Hard Rock Cafe With the growth of Hard Rock Cafe 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 such food items as beef, chicken, and pork. It's short-term sales forecasts are conducted each month, by cafe, and then aggregated for 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 cafe's door. The sale of each entree represents one customer; the entree 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 prepare a 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 $5,500 in sales translates into 19 workstations, which are further broken down into a specific number of waitstaff, hosts, bartenders, and kitchen staff. Computerized scheduling software plugs in people based on their availability. Variances between forecasts 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 cafe sales. If cafe genera 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 $7.99 to $8.99, Hard Rock can predict the effects 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, he corresponding effect on related items, say french fries, is determined.

Answer 1

Ans 1A)

Associative techniques of Forecasting are based on the identification of related variables that can be used to predict the behavior of dependent variable. Thus the sales of pork may be related to the price per unit of pork as well as other substitutes like meat, beef etc. Essentially its involves development of equation that summarizes the effect of predictor variables by using regression lines.

Forecasting techniques based on time series data are based on the assumption that future values can be predicted based on past data. This involves identifying the trends, seasonality, cycles present in the series.

Ans 1B) Associative method of Forecasting is used to predict the sales of pork may be related to the price per unit of pork as well as other substitutes like meat, beef etc