Forecasting, whether quantitative or qualitative in nature, is used every day in all business to some level or degree. Accurate forecasting can make a company, and inaccurate forecasting can break a company. Forecasting is a very interesting and sometimes controversial subject in management science for a number of good reasons. First, the science of forecasting is not 100% accurate, and we can never compensate for the infinite number of variables that can affect a forecast. At times, forecasting seems to be no more than an educated guess.

Secondly, the accuracy of forecasting is determined by the quality of the data used to create the projections. Lastly and perhaps far more importantly, forecasts do not take into consideration technology advances or competitive moves in the market. This lack of consideration is why the term probability is often used in forecasting.

Consider the following questions: What variables can affect the accuracy of a forecasting model?

How do we compensate for these variables?

How does it correlate with Seasonal and/or Cyclical Patterns?