Scenario Planning and Traditional Forecasting

 

The differences between scenario planning and traditional forecasting are numerous, as shown in the accompanying table 1.

Scenario Planning	Traditional Forecasting
Plausible futures	Probable futures
Based on uncertainty	Based on greater levels of certainty
Will make different trends visible	Based on different trends but complicated model with increasing number of trends
Illustrates uncertainty	Hides risks and uncertainties
Qualitative or quantitative	Quantitative
Used rarely	Used daily
Strong for a medium – to long- term perspective and when there are uncertainties	Strong for a short-term perspective and when there is a low degree of uncertainty

Table 1. Scenario Planning vs. Traditional Forecasting (Edgar, Abouzeedan, & Hedner, 2010, p. 5).

Scenario planning is made up of numerous scenarios, which potential substitute prospects may occur based on today’s decisions.  Typically, two to five scenarios containing adequate detail to determine the probability of success or failure of distinct strategic choices will suffice.  The scenarios aim is an important aspect that must be considered, along with customization to a specific context.  The scenario planning process is a systematic eight-step process consisting of:  1. Issue focus, 2. Key factors, 3. Consideration of external forces, 4. Critical uncertainties are discussed, 5. Generation of scenario logics, 6. Scenarios are generated with their narratives, 7. Appraise implications of each scenario and strategic options that go with them, 8. Discover early indicators which differentiate scenarios from each other (Ogilvy, 2015). 

Forecasts are estimations derived from a given model or based on the accumulated logic of an individual or a group.  It is the identification of patterns from utilizing a logical and analytical approach.  Traditional forecasting employs quantitative methods such as moving averages (time series), exponential smoothing (time series), and regression (causal) (Gentry, Wiliamowski, & Weatherford, 1995).  The use of moving average models for forecasting is based on the premise that the average performance of the recent past is a good indicator of future performance.  Causal forecasting is performed through use of regression techniques such as linear, quadratic, and cubic.  When performing forecasting, goodness of fit or how well the forecasting model is able to replicate data that is already known is the forecasting error (Gentry et al., 1995).

 

 

 

 

 

 

References

 

Edgar, B., Abouzeedan, A., & Hedner, T. (2010). Scenario Planning as a Tool to Promote Innovation in Regional Development Context.

Gentry, T. W., Wiliamowski, B. M., & Weatherford, L. R. (1995). A comparison of traditional forecasting techniques and neural networks. Intelligent engineering systems through artificial neural networks, 5, 765-770.

 Ogilvy, J. (2015, January 8). Scenario Planning and Strategic Forecasting. Retrieved from Forbes: https://www.forbes.com/sites/stratfor/2015/01/08/scenario-planning-and-strategic-forecasting/?sh=5e817e1b411a