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First half of Forecasting and Second Half of Forecasting


Hard Rock Cafe—from one pub in London in 1971 to more than 110 restaurants in more than 40 countries today, became 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. Its short-term sales forecasts are conducted each month, by cafe, and then aggregated for a headquarters view.

Designing forecast system

Before using forecasting techniques to analyze operations management problems, a manager must make three decisions: (1) what to forecast, (2) what type of forecasting technique to use, and (3) what type of tool to use. We discuss each of these decisions before examining specific forecasting techniques.

Deciding the area of forecasting

Although some sort of demand estimate is needed for the individual goods or services produced by a company, forecasting total demand for groups or clusters and then deriving individual product or service forecasts may be easiest. Also, selecting the correct unit of measurement (e.g., product or service units or machine-hours) for forecasting may be as important as choosing the best method.

Different forecasting applications used at Hard Rock

The forecaster's objective is to develop a useful forecast from the information at hand with the technique appropriate for the different characteristics of demand. This choice sometimes involves a trade-off between forecast accuracy and costs, such as software purchases, the time required to develop a forecast, and personnel, training. Two general types of forecasting techniques are used for demand forecasting: qualitative methods and quantitative methods. Lucent's CDP process uses a combination of both methods. Qualitative methods include judgment methods, which translate the opinions of man­agers, expert opinions, consumer surveys, and sales-force estimates into quantitative estimates. Quantitative methods include causal methods and time-series analysis. Causal methods use historical data on independent variables, such as promotional campaigns, economic conditions, and competitors' actions, to predict demand. Time-series analysis is a statistical approach that relies heavily on historical demand data to project the future size of demand and recognizes trends and seasonal patterns. Hsiao.J.C. and D.SCleaver, 1982 Management Science, Houghton Mufflin Company, Boston.


A key factor in choosing the proper forecasting approach is the time horizon for the decision requiring forecasts. Forecasts can be made for the short term, medium term, and long term. SHORT TERM: In the short term managers typ­ically are interested in forecasts of demand for individual products or services. There is little time to react to errors in demand forecasts, so forecasts need to be as accurate as possible for planning purposes. Time-series analysis is the method most often used for short-term forecasting. It is a relatively inexpensive and accurate way to generate the large number of forecasts required.

Although causal models can be used for short-term forecasts, they are not used extensively for this purpose because they are much more costly than time-series analysis and require more time to develop. In the short term, operations managers rarely can wait for development of causal models, even though they may be more accurate than time-series models. Finally, managers use judgment methods for short-term forecasts when historical data are not available for a specific item, such as a new product. However, these forecast techniques also are more expensive than forecasts generated from time-series analysis.

MEDIUM TERM: The time horizon for the medium term is three months to two years into the future. The need for medium-term forecasts relates to capacity planning. The level of forecast detail required is not as great as for the short term. Managers typically forecast total sales demand in dollars or in the number of units of a group (or family) of similar products or services. Causal models are commonly used for medium-term fore­casts. These models typically do a good job of estimating the timing of turning points, as when slow sales growth will turn into rapid decline, which is useful to operations managers in both the medium and the long term.

Some judgment methods of forecasting also are helpful in identifying turning points. As we mentioned earlier, however, they are most often used when no historical data exist. Time-series analysis typically does not yield accurate results in the medium or long term primarily because it assumes that existing patterns will continue in the future. Although this assumption may be valid for the short term, it is less accurate over longer time horizons.

LONG TERM: For time horizons exceeding two years, forecasts usually are developed for total sales demand in dollars or some other common unit of measurement (e.g., barrels, pounds, or kilowatts). Accurate long-term forecasts of demand for individual products or services not only are very difficult to make but also are too detailed for long-range planning purposes. Three types of decisions—facility location, capacity planning, and process choice—require market demand estimates for an extended period into the future. Causal models and judgment methods are the primary techniques used for long-term forecasting. However, even mathematically derived causal-model forecasts have to be tempered by managerial experience and judgment because of the time horizon involved and the potential consequences of decisions based on them.

POS system in forecasting at Hard Rock


When adequate historical data are lacking, as when a new product is introduced or technology is expected to change, firms rely on managerial judgment and experience to generate forecasts. Judgment methods can also be used to modify forecasts generated by quantitative methods, as is done with Lucent's forecasting process. In this section, we discuss four of the more successful methods currently in use: sales-force estimates, executive opinion, market research, and the Delphi method.


Sometimes the best information about future demand comes from the people closest to the customer. Sales-force estimates are forecasts compiled from estimates of future demands made periodically by members of a company's sales force. This approach has several advantages.

The sales force is the group most likely to know which products or services customers will be buying in the near future, and in what quantities.

Sales territories often are divided by district or region. The forecasts of individual sales-force members can be combined easily to get regional or national sales.   Salespeople may not always be able to detect the difference between what a customer "wants" (a wish list) and what a customer "needs" (a necessary purchase).  If the firm uses individual sales as a performance measure, salespeople may underestimate their forecasts so that their performance will look good when they exceed their projections or may work hard only until they reach then-required minimum sales.

Name several variables that would be good predictors of daily sales and how could this information be gathered and used at each café


Causal methods are used when historical data are available and the relationship between the factor to be forecasted and other external or internal factors can be identified. These relationships are expressed in math­ematical terms and can be very complex. Causal methods provide the most sophisticated forecasting tools and are very good for predicting turning points in demand and preparing long-range forecasts. Although many causal methods are available, we focus here on lin­ear regression, one of the best-known and most commonly used causal methods.

In linear regression, one variable, called a dependent variable, is related to one or more independent variables by a linear equation. The dependent variable, such as demand for doorknobs, is the one the manager wants to forecast. The independent variables, such as advertising expenditures and new housing starts, are assumed to affect the dependent variable and thereby "cause" the results observed in the past. A linear regression line relates to the data. In technical terms, the regression line minimizes the squared deviations from the actual data.

In the simplest linear regression models, the dependent variable is a function of only one independent variable, and therefore the theoretical relationship is a straight line:

Y = a + bX        •';-':    '

Y = dependent variable X = independent variable a = Y-intercept of the line b = slope of the line

The objective of linear regression analysis is to find values of a and b that minimize the sum of the squared deviations of the actual data points from the graphed line. Computer programs are used for this purpose. For any set of matched observations for Y and X, the program computes the values of a and b and provides measures of fore­cast accuracy. Three measures commonly reported are the sample correlation coeffi­cient, the sample coefficient of determination, and the standard error of the estimate.

The sample correlation coefficient, r, measures the direction and strength of the relationship between the independent variable and the dependent variable. The value of r varies from -1.00 to +1.00. A correlation coefficient of +1.00 implies that period-by-period changes in direction (increases or decreases) of the independent variable are always accompanied by changes in the same direction by the dependent variable.

How is forecasting carried out as an effective weapon in a company?

Executive opinion- It is a fore­casting method in which the opinions, experience, and technical knowledge of one or more managers are summarized to arrive at a single forecast.


When a new product or service is contemplated, the sales force may not be able make accurate demand estimates. Executive opinion is a forecasting method in w the opinions, experience, and technical knowledge of one or more managers are summarized to arrive at a single forecast. As we discuss later, executive opinion can be use to modify an existing sales forecast to account for unusual circumstances, such as a new sales promotion or unexpected international events. Executive opinion can ate be used for technological forecasting. The quick pace of technological change make keeping abreast of the latest advances difficult Executive opinion can I costly because it takes valuable executive time. When actual sales are much lower than the forecasts, everyone blames someone else for the extra inventory that was created. Hence, the key to effective use of executive opinion is to ensure that the forecast reflects not a series of independent modifications but consensus among executives on a single forecast.


Market research is a systematic approach to determine consumer interest in a product or service by creating and testing hypotheses through data-gathering surveys.

Market research may be used to forecast demand for the short, medium, and long term. Accuracy is excellent for the short term, good for the medium term, and only fair for the long term. Although market research yields important information, one short­coming is the numerous qualifications and hedges typically included in the findings. For example, a finding might be "The new diet burger product received good customer acceptance in our survey; however, we were unable to assess its longer-term acceptance once other competitor products make their appearance." Another is that the typical response rate for mailed questionnaires is poor. Yet another shortcoming is the possibility that the survey results do not reflect the opinions of the market. Finally, the survey might produce imitative, rather than innova­tive, ideas because the customer's reference point is often limited. T.F. Dodd, 1974, Sales Forecasting, Gower Press, England


This form of forecasting is useful when there are no his­torical data from which to develop statistical models and when managers inside the firm have no experience on which to base informed projections. A coordinator sends a question to each member of the group of outside experts, who may not even know who else, is participating. Anonymity is important when some members of the group tend to dominate discussion or command a high degree of respect in their fields. In an anonymous group, the members tend to respond to the questions and support their responses freely. The coordinator prepares a statistical summary of the responses along with a summary of arguments for particular responses. The report is sent to the same group for another round, and the participants may choose to modify their previous responses.

The Delphi method can be used to develop long-range forecasts of product demand and new-product sales projections. It can also be used for technological forecasting. The Delphi method can be used to obtain a consensus from a panel of experts who can devote their attention to following scientific advances, changes in society, governmental regulations, and the competitive environment. The results can provide direction for a firm's research and development staff.

The Delphi method has some shortcomings, including the following major ones:

The process can take a long time (sometimes a year or more). During that time, the panel of people considered to be experts may change, confounding the results or at least further lengthening the process.  Responses may be less meaningful than if experts were accountable for their responses.  There is little evidence that Delphi forecasts achieve high degrees of accuracy. However, they are known to be fair to good in identifying turning points in new-product demand.

How is forecasting carried out in your organization (be sure to specify the level you are discussing)? 2) How does that relate to product development and services it offers? 3) What are the difficulties your organization faces most in coming up with accurate forecasts? Could they improve their forecasts by using different methods?


Judgment forecasting is clearly needed when no quantitative data are available to use quantitative forecasting approaches. However, judgment approaches can be used in concert with quantitative approaches to improve forecast quality. Among the guide­lines for the use of judgment to adjust the results of quantitative forecasts are the fol­lowing -

Adjust Quantitative Forecasts When Their Track Record Is Poor and the Decision Maker Has Important Contextual Knowledge.    Contextual knowledge is knowledge that practitioners gain through experience, such as cause-and-effect relationships, environmental cues, and organizational information, that may have an effect on the variable being forecast. Often, these factors cannot be incorporated into quantitative forecasting approaches. The quality of forecasts generated by quantitative approaches also deteriorates as the variability of the data increases, particularly for time series. The more variable the data, the more likely it is that judgment forecasting will improve the forecasts. Consequently, the decision maker can bring valuable contextual information to the forecasting process when the quantitative approaches alone are inadequate.

Make Adjustments to Quantitative Forecasts to Compensate for Specific Events.    Specific events such as advertising campaigns, the actions of competitors, or international developments often are not recognized in quantitative forecasting and should be acknowledged when a final forecast is being made.

In the remainder of this chapter, we focus on the commonly used quantitative fore­casting approaches.


Since Hard Rock Café is a company founded at the end of 1971 based at a single site in London,  its growth is awesome. Within a very short time, the company has come to a great summit of success. From this modest start in 1971, the number and geographical spread of the customers have increased rapidly in part as a result of the company’s effective marketing strategies. These are very evident sources that the company has come through these desired objectives mentioned.




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2. T.F. Dodd, 1974, Sales Forecasting, Gower Press, England.
3. David .M. Georgoff and Robert Gmrdick, 1986, Manager’s Guide to forecasting, Harvard Business Review, January - February.
4. Dunn, R. And K.D.Ramasingh, 1981, Management Science, Macmillan Publishing Co. Inc. Bombay.
5. J.L.Riggs, Engineering Economics, McGraw-Hill.N.Y.2nd ed., 1982.Ch.3.
6. Burns.T. and Stalker.G,The Management of Innovations, London : Tavistock Publications,1961.
7. Foster, Douglas, 1982, Mastering Marketing, The Macmillan Press Ltd.
8. Goldhar.J.D.&MJelinek,1983 Plan for Economics for Scope, in Harvard Business Review, December.


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