Both traditional forecasting and scenario planning are prediction methods that use analysis techniques on present and past data and trends. Each technique requires consideration of risk and uncertainty in their predictions, and also rely on accurate and up to date information to make viable predictions. These methods are commonly used to estimate variables or conditions at some point in the future, such as a variable, or the popularity or prevalence of a trend. However, the approaches differ in their methodology and approach.
Traditional forecasting relies heavily on rigid and structured methodologies. This form of forecasting relies upon previous data or judgements from experts to predict future conditions based solely upon past and known variables and conditions. Two common approaches are qualitative and quantitative (Kunštić & Spahija, 2011). In quantitative forecasting, the future is predicted as a function of past data – past numerical data is subjected to analysis, and the observed trends are used to predict future trends or occurrences. Common methods include N-period moving averages and seasonal indexes. Qualitative forecasting essentially takes the same approach, but uses the opinions of experts or judges to generate the past data for future analysis. Common methods include the Delphi method and market research. In either instance, the viability of the future prediction is only as reliable as the past data and the presumption that the future will be the same. One key advantage of traditional methods is that they can rely on mathematical or generally scientific data, helping to make the prediction independent of human influence. However, the rigid nature of traditional forecasting often does not account for even simple changes in the environment (Ord & Fildes, 2012).
By contrast, scenario planning is a much less rigid and structured approach. Rather than relying on past data and operating under the assumption that the past will predict the future, scenario planning involves the recognition that many factors combine in complex ways to create futures which may not be foreseeable using past data (Amer, Daim, & Jetter, 2013). This technique is known as systems thinking. The method involves combining known facts about the future along with key forces, namely social, technical, economic, environmental, and political factors and trends, to generate potential future scenarios, which are then considered and evaluated (Chermack, 2005). A common approach is a six step process: (1) deciding assumptions for change, (2) creating a viable framework using these assumptions, (3) producing 7 to 9 scenarios, (4) reducing to two to three viable scenarios, (5) drafting the scenarios, and (6) identifying the issues or trends emerging in each scenario (Meinert, 2014). After presentation and analysis of the scenarios, those that remain plausible provide a viable insight to what trend may emerge in the future. The process can be repeated multiple times with different assumptions to create additional scenarios. A key advantage of scenario planning is the flexibility allowed, as it does not require the assumption that past trends are indicative of future trends. Rather, it only requires consideration of these trends as possible assumptions. However, the method can be stressful, particularly when working with individuals who rely upon controllable and known factors. Additionally, while the method does rely somewhat on scientific tools, scenario planning is much more an art than a science, is subjective, and is exposed to human influence and fallibility (Mietzner & Reger, 2005).
Amer, M., Daim, T. U., & Jetter, A. (2013). A review of scenario planning. Futures, 46, 23-40.
Chermack, T. J. (2005). Studying scenario planning: Theory, research suggestions, and hypotheses. Technological Forecasting and Social Change,72(1), 59-73.
Kunštić, M., & Spahija, B. (2011, May). A comparison of traditional forecasting methods for short-term and long-term prediction of faults in the broadband networks. In MIPRO, 2011 Proceedings of the 34th International Convention (pp. 517-522). IEEE.
Meinert, S. (2014). Field Manual–Scenario Building.
Mietzner, D., & Reger, G. (2005). Advantages and disadvantages of scenario approaches for strategic foresight. International Journal of Technology Intelligence and Planning, 1(2), 220-239.
Ord, K., & Fildes, R. (2012). Principles of business forecasting. Cengage Learning.