Forecasts serve as decision support tools that allow leaders to plan for the future by performing “what-if” analyses to determine how changes in inputs affects outcomes. For example, forecasts help a business identify appropriate responses to changes in demand levels, price-cutting by the competition, economic ups and downs and more. To receive the greatest benefit from forecasts, leaders must understand the finer details of the different types of forecasting methods, recognize what a particular forecasting method type can and cannot do, and know what forecast type is best suited to a particular need.

Naive Forecasting Methods

The naïve forecasting methods base a projection for a future period on data recorded for a past period. For example, a naïve forecast might be equal to a prior period’s actuals, or the average of the actuals for certain prior periods. Naïve forecasting makes no adjustments to past periods for seasonal variations or cyclical trends to best estimate a future period’s forecast. The user of any naïve forecasting method is not concerned with causal factors, those factors that result in a change in actuals. For this reason, the naive forecasting method is typically used to create a forecast to check the results of more sophisticated forecasting methods.

Qualitative and Quantitative Forecasting Methods

Whereas personal opinions are the basis of qualitative forecasting methods, quantitative methods rely on past numerical data to predict the future. The Delphi method, informed opinions and the historical life-cycle analogy are qualitative forecasting methods. In turn, the simple exponential smoothing, multiplicative seasonal indexes, simple and weighted moving averages are quantitative forecasting methods.

Casual Forecasting Methods

Regression analysis and autoregressive moving average with exogenous inputs are causal forecasting methods that predict a variable using underlying factors. These methods assume that a mathematical function using known current variables can be used to forecast the future value of a variable. For example, using the factor of ticket sales, you might predict the variable sale of movie-related action figures, or you might use the factor number of football games won by a university team to predict the variable sale of team-related merchandise.

Judgmental Forecasting Methods

The Delphi method, scenario building, statistical surveys and composite forecasts each are judgmental forecasting methods based on intuition and subjective estimates. The methods produce a prediction based on a collection of opinions made by managers and panels of experts or represented in a survey.

Time Series Forecasting Methods

The time series type of forecasting methods, such as exponential smoothing, moving average and trend analysis, employ historical data to estimate future outcomes. A time series is a group of data that’s recorded over a specified period, such as a company’s sales by quarter since the year 2000 or the annual production of Coca Cola since 1975. Because past patterns often repeat in the future, you can use a time series to make a long-term forecast for 5, 10 or 20 years. Long term projections are used for a number of purposes, such as allowing a company’s purchasing, manufacturing, sales and finance departments to plan for new plants, new products or new production lines.