Forecasting can be broadly considered as a method or a technique for estimating many future aspects of a business or other operation. Planning for the future is a critical aspect of managing any organization, and small business enterprises are no exception. Indeed, their typically modest capital resources make such planning particularly important. In fact, the long-term success of both small and large organizations is closely tied to how well the management of the organization is able to foresee its future and to develop appropriate strategies to deal with likely future scenarios. Intuition, good judgment, and an awareness of how well the industry and national economy is doing may give the manager of a business firm a sense of future market and economic trends. Nevertheless, it is not easy to convert a feeling about the future into a precise and useful number, such as next year's sales volume or the raw material cost per unit of output. Forecasting methods can help estimate many such future aspects of a business operation.
"Perfect accuracy [in forecasting] is not obtainable," warned Richard Brealey and Stewart Myers in Principles of Corporate Finance. "If it were, the need for planning would be much less. Still the firm must do the best it can. Forecasting cannot be reduced to a mechanical exercise. Naive extrapolation or fitting trends to past data is of limited value. It is because the future is not likely to resemble the past that planning is needed. To supplement their judgement, forecasters rely on a variety of data sources and forecasting methods. For example, forecasts of the economic and industry environment may involve use of econometric models which take account of interactions between economic variables. In other cases the forecaster may employ statistical techniques for analyzing and projecting time series. Forecasts of demand will partly reflect these projections of the economic environment, but they may also be based on formal models that marketing specialists have developed for predicting buyer behavior or on recent consumer surveys to which the firm has access."
Forecasting methods have many practically applications for business establishments. For example, a number of important business decisions could conceivably be affected by the forecasted sales of a given product. Production schedules, raw material purchasing plans, policies regarding inventories, and sales quotas will be affected by such forecasts. Given these stakes, it is vitally important for the business to utilize accurate forecasting methodologies.
How should the business go about preparing the quarterly sales volume forecasts for the product in question, then? The firm will certainly want to review the actual sales data for the product in question for past periods. Suppose that the forecaster has access to actual sales data for each quarter over the 25-year period the firm has been in business. Using this historical data, the forecaster can identify the general level of sales. He or she can also determine whether there is a pattern or trend, such as an increase or decrease in sales volume over time. A further review of the data may reveal some type of seasonal pattern, such as peak sales occurring before a holiday. Thus by reviewing historical data over time, the forecaster can often develop an accurate understanding of the previous pattern of sales. Understanding such a pattern can often lead to better forecasts of future sales of the product. In addition, if the forecaster is able to identify the factors that influence sales, historical data on these factors (or variables) can also be used to generate forecasts of future sales volumes.
All forecasting methods can be divided into two broad categories: qualitative and quantitative. Many forecasting techniques use past or historical data in the form of time series. A time series is simply a set of observations measured at successive points in time or over successive periods of time. Forecasts essentially provide future values of the time series on a specific variable such as sales volume. Division of forecasting methods into qualitative and quantitative categories is based on the availability of historical time series data.
QUALITATIVE FORECASTING METHODS Qualitative forecasting techniques generally employ the judgment of experts to generate forecasts. A key advantage of these procedures is that they can be applied in situations where historical data are simply not available. Moreover, even when historical data are available, significant changes in environmental conditions affecting the relevant time series may make the use of past data irrelevant and questionable in forecasting future values of the time series. For example, historical data on gasoline prices would likely be of questionable value in determining future gasoline prices if other factors (oil boycotts, gasoline rationing programs, scientific breakthroughs in alternative energy use, etc.) suddenly assumed increased importance. Qualitative forecasting methods offer a way to generate forecasts in such cases. Three important qualitative forecasting methods are: the Delphi technique, scenario writing, and the subject approach.
In the Delphi technique, an attempt is made to develop forecasts through "group consensus." Usually, a panel of experienced people are asked to respond to a series of questionnaires. These people, who should ideally come from a variety of backgrounds (marketing, production, management, finance, purchasing, etc.) are asked to respond to an initial questionnaire. Sometimes, a second questionnaire that incorporates information and opinions of the whole group is distributed for further discussion or study. Each expert is asked to reconsider and revise his or her initial response to the questions. This process is continued until some degree of consensus among experts is reached. It should be noted that the objective of the Delphi technique is not to produce a single answer at the end. Instead, it attempts to produce a relatively narrow spread of opinions—the range in which opinions of the majority of experts lie.
Under the scenario writing approach, the fore-caster starts with different sets of assumptions. For each set of assumptions, a likely scenario of the business outcome is charted out. Thus, the forecaster generates several different future scenarios (corresponding to the different sets of assumptions). The decision maker or business person is presented with the different scenarios, and has to decide which scenario is most likely to prevail.
The subjective approach allows individuals participating in the forecasting decision to arrive at a forecast based on their feelings, ideas, and personal experiences. Many corporations in the United States have started to increasingly use the subjective approach. Internally, these subjective approaches sometimes take the form of "brainstorming sessions," in which managers, executives, and employees work together to develop new ideas or to solve complex problems. At other times, the subjective approach may take the form of a survey of the company's sales people. This approach, which is known as the sales force composite or grass roots method, is relied on because, as Howard Weiss and Mark Gershon stated in Production and Operations Management, "presumably, because salespeople interact directly with purchasers, they have a good feel for which products will or will not sell and the quantity of sales for the various products…. The advantage of using thesalespeople's forecasts is that (in theory) salespeople are most qualified to explain the demand for products, especially in their own territories. The disadvantage is that salespeople may tend to be optimistic in their estimates if they believe that a low estimate might lead to the unemployment line." Moreover, the opinions of salespeople should not be relied on to the exclusion of all else because they may not be aware of impending changes in other areas, such as availability of raw materials, national economic developments, or the arrival of a formidable new competitor.
A final subjective approach that is also sometimes used is known as the "user expectations" approach. This method of forecasting is essentially an exercise in market research, for it involves extracting information from prospective buyers. "Essentially, user expectations provide better forecasts than the (optimistic) sales force composite," wrote Weiss and Gershon. "Unfortunately, typically it is easier and less costly to obtain the sales force composite than it is to obtain the user expectations."
QUANTITATIVE FORECASTING METHODS Quantitative forecasting methods are used when historical data on variables of interest are available—these methods are based on an analysis of historical data concerning the time series of the specific variable of interest. There are two major categories of quantitative forecasting methods. The first type uses the past trend of a particular variable in order to make a future forecast of the variable. In recognition of this method's reliance on time series of past data of the variable that is being forecasted, it is commonly called the "time series method." The second category of quantitative forecasting techniques also uses historical data. But in forecasting future values of a variable, the forecaster examines the cause-and-effect relationships of the variable with other relevant variables such as the level of consumer confidence, changes in consumers' disposable incomes, the interest rate at which consumers can finance their spending through borrowing, and the state of the economy represented by such variables as the unemployment rate. Thus, this category of forecasting techniques uses past time series on many relevant variables to produce the forecast for the variable of interest. Forecasting techniques falling under this category are called causal methods, since such forecasting is predicated on the cause-and-effect relationship between the variable forecasted and the other selected elements.
Anderson, David P., Dennis J. Sweeney, and Thomas A. Williams. An Introduction to Management Science: Quantitative Approaches to Decision Making . West Publishing, 1994.
Brealey, Richard A., and Stewart C. Myers. Principles of Corporate Finance. McGraw-Hill, 1991.
Chase, Charles W. Jr. "Composite Forecasting: Combining Forecasts for Improved Accuracy." Journal of Business Forecasting. Summer 2000.
Jones, Vernon Dale, Stuart Bretschneider, and Wilpen L. Gorr. "Organization Pressures on Forecast Evaluation: Managerial, Political, and Procedural Influences." Journal of Forecasting. July 1997.
McMaster, Mike. "Foresight: Exploring the Structure of the Future." Long Range Planning. April 1996.
O'Connor, Marcus, William Remus, and Ken Griggs. "Going Up—Going Down: How Good are People at Forecasting Trends and Changes in Trends?" Journal of Forecasting. May 1997.
Sanders, Nada R. "Measuring Forecast Accuracy: Some Practical Suggestions." Production and Inventory Management Journal. Winter 1997.
Waddell, Dianne, and Amrik S. Sohal. "Forecasting: The Key to Managerial Decision Making." Management Decision. January 1994.
Weiss, Howard J., and Mark E. Gershon. Production and Operations Management. Allyn and Bacon, 1989.