Forecasting is the process of making statements about expected future events based upon evidence, research, and experience. For example, a business might estimate the exchange rate between the U.S. and the EU one year from now to determine the real financial cost of a project.
An important and often overlooked aspect of forecasting is the relationship it holds with planning. Forecasting can be described as predicting what the future will look like, whereas planning predicts what the future should look like. While both are managerial functions, forecasting is rife with external uncertainty while planning is hindered by internal uncertainty.
Forecasting Methods
Forecasting can be accomplished in a variety of different ways, some more statistically reliable than others. Following are a few critical points of differentiation and specific strategies to keep in mind when forecasting.
Quantitative vs. Qualitative
One of the simplest points of differentiation between methods is the reliance on numbers for accuracy. Quantitative forecasting generally uses statistical confidence intervals and historical data to project potential future trends that are based upon the criteria being analyzed. In this format, results are expressed in certainty intervals (i.e., how confident can we be that this will be the case?) and often rely on financial data (exchange rates, industry growth, etc.).
Qualitative approaches are the opposite; they rely on logical premises or past experience to generate estimates about future circumstances. The inherent problem with the qualitative approach is simple: subjectivity. While quantitative measure use data to express objective results, qualitative approaches do not have this luxury. Generally this type of forecast will include the opinions of experts, upper management, and market research.
Quantitative vs. qualitative forecasting
This flow chart compares quantitative and qualitative forecasting methods. Qualitative forecasting relies more on opinions than data and can employ market research, sales-force input, or a jury of executives. In contrast, quantitative forecasting relies more on objective, numerical data, and can look at chronological trends and statistical regression to infer cause-and-effect.
Causal Forecasting
Another method of forecasting, which is likely to be both quantitative and qualitative, is the causal/econometric approach. This strategy tasks managers with identifying cause and effect relationships of past instances by defining a series of if/then statements that express the likelihood of the outcome which follows. For example, if consumer spending is down in Q2, then it is likely that gross domestic product (GDP) growth will be down in Q3. Whether or not this is true would have to be supported with data, but the forecast is that Q2 consumer spending results could forecast Q3 GDP growth.
Implications of Forecasting
Keeping these methods in mind, it is important to understand how management uses these forecasts to draw conclusions. Forecasting plays a role in the implementation of policies and strategies. The practice helps businesses create plans for different situations, in addition to contingency plans for adapting if and when necessary.
Forecasting enables a manager to look at the current environment and identify likely scenarios, each of which may require a deviation from the overall strategy. As the management team implements the broader strategy, it must continuously monitor the current environment for deviations and use forecasting to adapt both the primary strategy and contingency plans for potential shifts.
To summarize, forecasts enable businesses to prepare new strategies or reinforce the existing strategy, based upon the projections made.