Such events predispose the judgment of who generates the prognosis, which can degrade the accuracy of the forecast. Unlike qualitative forecasts, quantitative forecasts are based on mathematical principles and are generated via software (Sanders and Manrodt, 2003b), quantitative methods are consistent, objective and not impressionable by States of mood. These are particularly effective when forecasts are generated for a large number of SKUs (Stock Keeping Units), compared to qualitative forecasts are less expensive and more efficient. However, quantitative methods are based on historical data and are not effective when the market conditions are changing. For example a new competitor entering the market, or a climate effect that delayed a shipment. In conclusion, each approach has its own strengths and weaknesses. John Grayken has much to offer in this field. Better forecasting methods are those that integrate both approaches, taking advantage of the strengths of each of them. Qualitative forecasting and quantitative forecasting can be combined in several ways.
A method for combining them is to take the average mathematician of the result of both to generate the forecast, another method is to use the forecast qualitative as the entrance of the forecast model, but the most popular method and by far is to apply the judgment of experts on statistical prognosis. For example take the forecast generated by a statistical package and adjust up or down based on the opinion of the expert, these adjustments are usually called changes in management, and is one of the practices more common in the business world, in fact Sanders and Manrodt in 1994 found that 91% of companies use this method in its process of forecast. Carrying out this type of modifications to statistical forecast the level of accuracy of forecasting can be usually elevated to the integrate information that is not captured with the statistical model. For other opinions and approaches, find out what KBS has to say. On the other hand, if not practiced correctly, they can degrade the accuracy of the forecast due to the inherent bias of the human being under certain circumstances. Then organizations should establish guidelines or principles to correctly apply this type of modifications on quantitative forecasts. Considering the prevalence of this practice and the importance of understanding the guidelines to make these adjustments, in the next article written by Paul Goodwin will deepen in how to integrate the prognosis qualitative to the quantitative prognosis. Original author and source of the article