DECISION MODELING WITH MICROSOFT EXCEL Chapter 13 Copyright 2001 Prentice Hall Part 3

Historical data play a critical role in the construction and testing of forecasting models. Whether a causal model or time-series quantitative model is used, the parameters of the model must be selected. For example, 1. In a causal model using a linear forecasting function, y = a + bx, the values of a and b must be specified. 2. In a time series model using a weighted n-period moving average, y t+1 =  0 y t +  1 y t-1 + … +  n-1 y t-n+1, the number of terms, n, and the values for the weights,  0,  1, …,  n-1, must be specified. ^

3. In a time-series model using exponential smoothing, y t+1 =  y t + (1-  )y t, the value of must be  specified. ^^ In order to specify the parameter values for any of these models, one typically must make use of historical data. It is often a useful practice to use part of the data to estimate the parameters and the rest of the data to test the model. With real data, it is also important to “clean” the data. In other words, examine the data for irregularities, missing information, or special circumstances, and adjust them accordingly.

For example, suppose a firm has weekly sales data on a particular product for the last two years (104 observations) and plans to use an exponential smoothing model to forecast sales for this product. The firm might use the following procedure: 1. Pick a particular value of , and compare the values of y t+1 to y t+1 for t = 25 and 75. ^ The first 24 values are not compared, so as to negate any initial or “startup” effect (so as to nullify the influence of the initial guess, y 1 ). ^

The manager would continue to select different values of  until the model produces a satisfactory fit during the period t = 25 to Test the model derived in step 1 on the remaining 29 pieces of data. If the model does a good job of forecasting values for the last part of the historical data, there is some reason to believe that it will also do a good job with the future. That is, using the best value of  from step 1, compare the values of y t+1 to y t+1 for t = 76 to 104. ^

On the other hand, if by using the data from weeks , the model cannot perform well in predicting the demand in weeks , then another forecasting technique might be applied. The same type of divide-and-conquer strategy can be used with any of the forecasting techniques presented in this chapter. This popular approach amounts to stimulating the model’s performance on past data. It should be stressed, however, that this procedure represents what is termed a null test. If the model fails on historical data, the model probably is not appropriate. If the model succeeds on historical data, one cannot be sure that it will work in the future.

Many important forecasts are not based on formal models. EXPERT JUDGMENT For example, during the high-interest-rate period of 1980 and 1981, the most influential forecasters of interest rates were Henry Kaufman of Salomon Brothers and Albert Wojnilower of First Boston. These gentlemen combined relevant factors such as the money supply and unemployment, as well as results from quantitative models, in their own intuitive way to produce forecasts that had widespread credibility and impact on the financial community.

Qualitative forecasts can be an important source of information. Managers must consider a wide variety of sources of data before coming to a decision. Expert opinion should not be ignored. A sobering and useful measure of all forecasts is a record of past performance. Managers should listen to experts cautiously and hold them to a standard of performance. There is, however, more to qualitative forecasting than selecting “the right” expert.

The Delphi Method confronts the problem of obtaining a combined forecast from a group of experts. THE DELPHI METHOD AND CONSENSUS PANEL The consensus panel approach is to bring the experts together in a room and let them discuss an event until a consensus emerges. However, due to group dynamics, one person with a strong personality can have an enormous effect on the forecast. The Delphi Method was developed by the Rand Corporation to retain the strength of a joint forecast, while removing the effects of group dynamics.

The method uses a coordinated set of experts. No expert knows who else is in the group. All communication is through the coordinator. Coordinator requests forecasts Coordinator receives Individual forecasts Coordinator determines (a)Median response (b)Range of middle 50% of answers Coordinator requests explanations from any expert whose estimate is not in the middle 50% Coordinator sends to all experts (a)Median response (b)Range of middle 50% (c)Explanations The process is as follows:

After three or four passes through this process, a consensus forecast typically emerges. The forecast may be near the original median, but if a forecast that is an outlier in round 1 is supported by strong analysis, the extreme forecast in round 1 may be the group forecast after three or four rounds. GRASSROOTS FORECASTING AND MARKET RESEARCH Other qualitative techniques are based on the concept of asking either those who are close to the eventual consumer, such as salespeople, or consumers themselves, about a product or their purchasing plans.

Consulting Salesmen In grassroots forecasting, salespeople are asked to forecast demand in their districts. In the simplest situations, these forecasts are added together to get a total demand forecast. In more sophisticated systems individual forecasts or the total may be adjusted on the basis of the historical correlation between the salesperson’s forecasts and the actual sales. Such a procedure makes it possible to adjust for an actual occurrence of the stereotyped salesperson’s optimism.

With Grassroots forecasts, the individual salesperson should be able to provide better forecasts than more aggregate models. There are, however, several problems: 1. High cost: The time salespeople spend forecasting is not spent selling. Some view this opportunity cost of grassroots forecasting as its major disadvantage. 2. Potential conflict of interest: Sales forecasts may well turn into marketing goals that can affect a salesperson’s compensation in an important way. Such considerations exert a downward bias in individual forecasts.

1. Product schizophrenia (i.e., stereotyped salesperson’s optimism): It is important for salespeople to be enthusiastic about their product and its potential uses. It is not clear that this enthusiasm is consistent with a cold-eyed appraisal of its market potential. In summary, grassroots forecasting may not fit well with other organization objectives and thus may not be effective in an overall sense. Consulting Consumers Market research is a large and important topic which includes a variety of techniques, from consumer panels through consumer surveys and on to test marketing.

The goal of market research is to make predictions about the size and structure of the market for specific goods and/or services. These predictions (forecasts) are usually based on small samples and are qualitative in the sense that the original data typically consist of subjective evaluations of consumers. A large menu of quantitative techniques exist to aid in determining how to gather the data and how to analyze them. Market research is an important activity in most consumer product firms. It also plays an increasingly important role in the political and electoral process.