To determine which forecasting method is best suited for a particular scenario, it is important to consider the following factors (Bryman & Bell, 2015):

1. The nature of the data: Some forecasting methods are more suitable for certain types of data than others. For example, time series analysis is typically used for data that exhibits a consistent pattern over time, such as sales data, while qualitative methods are often used for data that is difficult to quantify or measure, such as consumer attitudes or opinions (Bryman & Bell, 2015).
2. The level of accuracy required: Different forecasting methods can produce varying levels of accuracy, depending on the complexity of the data and the assumptions made during the analysis (Bryman & Bell, 2015). It is important to choose a method that will produce forecasts that are accurate enough to meet the needs of the organization (Bryman & Bell, 2015).
3. The resources available: The choice of forecasting method may also be influenced by the resources available for conducting the analysis, such as time, expertise, and budget (Bryman & Bell, 2015). Some methods may require more resources to implement than others, so it is important to consider the organization’s constraints when choosing a method (Bryman & Bell, 2015).

In conclusion, the choice of forecasting method will depend on the specific context and requirements of the scenario (Bryman & Bell, 2015). It is important to consider the nature of the data, the level of accuracy required, and the resources available when selecting a method to ensure that it is well-suited to the needs of the organization (Bryman & Bell, 2015).

References: Bryman, A., & Bell, E. (2015). Business research methods (4th ed.). Oxford, UK: Oxford University Press.

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