As you can see, there are many techniques for estimating sales forecasts. Let's take each one and describe them, and talk about the kind of data that is used. The first four methods fall under one umbrella, surveys. The Executive Opinion method is where you solicit your manager's opinion regarding sales forecasts. Now, this assumes that a manager is in a good position to accurately forecast sales. The next method was originally developed by the RAND Corporation. The idea behind Delphi forecasts is to solicit a panel of experts and asked them to make a sales forecast. Everyone's forecast is shared with all the members, and then each member makes a new forecast. This process is repeated until there is consensus. That's called the "Delphi method." The last two survey methods are surveys conducted with either your sales force or your customers. In the case of sales forces, you were asking sales forces to estimate future sales, and these estimates might be overly optimistic or pessimistic. In the case of customers, you are asking customers to indicate their sales intentions. Generally, they tend to be exaggerated. The next three methods are mathematical in nature. Moving average methods predict sales for the future based upon sales from the past. However, instead of looking at the immediate past, say for the last year, you look at many years in the past and you average the results. The forecaster decides how many periods to unclothed, and in the following year, the oldest sales data is dropped and sales from the just past year is added. So, that's the idea of a rolling average. This is relatively easy to calculate, and it's a really good method as long as sales are relatively stable and there aren't big swings in sales. Exponential smoothing methods are somewhat like moving averages, except a forecaster can allow a certain time period to have more influence than others. The last mathematical method is a forecasting statistical technique called "Regression." In its simplest form, regression examines one factor which is called a "predictor variable," and that could be something like gross domestic product and a dependent variable such as sales. Through complex mathematics, regression establishes a mathematical relationship between the two variables that allows a forecaster to predict future sales based upon changes to the independent variable. This is considered way more complex and in some cases, a more accurate method of forecasting. Now, the last two methods are operational in nature. Break even forecasts are based upon calculations for the unit sales necessary for the firm to break even financially. Sometimes the goal isn't break-even, but some other type of goal, but the idea is the same. This kind of forecast, what is driving the forecast is some company driven factor, as opposed to a market driven factor. Finally, there are capacity forecasts. In rare cases of firm sells everything that it can make, and in such cases, the sales forecast is based upon the capacity of the firm's production capabilities.
