The major strengths and weaknesses of analytic and non-analytic forecasting.

In contrast, non-analytic forecasting relies heavily on human judgment using subjective evaluation techniques like interviews, focus groups, surveys etc., rather than relying solely on numerical evidence from the past. This method has several advantages when compared with analytical approaches due mainly to its flexibility and ability to address uncertain scenarios; since it does not rely exclusively on existing data trends it can be adapted quickly in response to changing conditions without having to develop different models each time. The main disadvantage of non-analytical forecasting is its lack of accuracy and reliability compared with quantitative analytic methods which have more rigorous protocols for verifying predictive outcomes. An example of analytic forecasting might include a retail store chain analyzing sales trends over the last five years in order anticipate potential profits during upcoming quarters; whereas an example of non-analytical forecasting could involve conducting customer surveys or focus groups designed to gain insights into consumer preferences around product offering decisions over the next year.

Sample Solution

Analytic and non-analytic forecasting are both methods used for predicting the future performance of a company or industry. Analytic forecasting relies on mathematical models, such as regression or time series, to generate forecasts based on historical data. Non-analytic forecasting typically employs subjective measures to make predictions about the future. The major strength of analytic forecasting is its accuracy and precision; it uses sophisticated techniques that can yield highly accurate projections in certain circumstances. Additionally, it allows users to develop forecasts based on fairly robust assumptions and a wide range of data points. On the other hand, one major weakness of this method is its inflexibility; changes in variables may require users to implement new models every time they need an updated forecast. Another limitation is that analytics tend to rely heavily on prior information which may not always be available or reliable enough for providing meaningful results.