Introduction the human component as an input to the statistical prognosis. The development of a statistical prognosis plays a leading role in the creation of a valid demand plan and should not routinely be treated as a black box. Christopher Koch, warns that by throwing a bunch of historical sales in a program and waiting for a magic number the basic concept behind demand software planning is not necessarily absolute truth. Many gliders have understood that, without the human component, the statistical forecasts by itself alone do not provide adequate estimates of demand. In an article for the CIO magazine, Ben Worthen said that the forecast demand sounds like an exact science, but if you look carefully people are half of the equation in the process. In the Special Edition of Forseight magazine in June 2005, several authors focused their views about how to integrate the qualitative and quantitative forecasts. Nigel Harvey (2005, p.18) thing He summarized: as Paul Goodwin and others, I think that the judgment of experts and statistical methods complement each other in the process of forecasting and that the problem for planners is to decide when to combine them and how to reach the best combination.
Many demand planners understand that the automatic prognosis does all the work. The statistical models used in the development of their forecasts are working with raw data. Models do not know whether the numbers represent French fries or USB memories, are not able to interpret a slope in sales as an excess in production, or know if a peak in demand is the result of additional advertising or sale of random type. Moreover, statistical models predict no unexpected circumstances. Ana Ku (2002), rightly mentioned in his note if the entries to your forecast model are poor, would be very difficult to achieve a good outcome no matter be so good your model..