3.5 Categories of Forecasting Methods

Qualitative Forecasting

Qualitative forecasting techniques, unlike their quantitative counterparts, rely on subjective judgments and opinions rather than historical data. These methods are particularly valuable when historical data is scarce or inapplicable, such as in situations involving new products, emerging markets, or disruptive technologies. They are typically employed for intermediate-term or long-term forecasts that inform strategic decision-making.

Several established qualitative forecasting techniques exist, each offering unique advantages.

Executive Judgement (Top Down)

This approach leverages the expertise of high-level executives within an organization. These executives collaboratively analyze market data, identify future trends, and potentially utilize statistical models and market research to arrive at a consensus forecast.

Sales Force Opinions (Bottom-up)

The sales force, by virtue of their direct customer interaction, possesses valuable insights into customer behaviour and market trends. This approach solicits individual sales forecasts from sales personnel within their designated territories. These individual forecasts are then aggregated to form a comprehensive forecast for a district or region.

Delphi Method

Developed by the Rand Corporation, the Delphi Method provides a structured approach to gathering expert opinions. A panel of experts anonymously participates in a series of surveys, iteratively refining their forecasts based on the anonymized collective insights provided. This anonymity fosters open and unbiased consideration of all perspectives, ultimately leading to a consensus forecast.

Market Surveys

Market research firms can be employed to conduct surveys that gauge consumer sentiment toward products and future purchasing intentions. The resulting data provides valuable qualitative insights to inform forecasting models.

Quantitative Forecasting

Quantitative forecasting models are used to forecast future data as a function of past data. They are appropriate to use when past numerical data is available and when it is reasonable to assume that some of the patterns in the data are expected to continue into the future. These methods are usually applied to short- or intermediate-range decisions. Some examples of quantitative forecasting methods are causal (econometric) forecasting methods, last-period demand (naïve), simple and weighted N-Period moving averages and simple exponential smoothing, which are categorized as time-series methods. Quantitative forecasting models are often judged against each other by comparing their accuracy performance measures. Some of these measures include Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE).

The quantitative forecasting methods will be explained in detail in the following section.

“3 Forecasting” from Introduction to Operations Management Copyright © by Hamid Faramarzi and Mary Drane is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted. Modifications: Catagories of Forecasting section content expanded and rewritten.