Today more than ever many organizations are under great pressure to generate forecasts with a high level of accuracy. A successful forecast allows organizations to plan effectively for business. (Armstrong, 2001;) Fildes and Hastings, 1994). Many organizations use the forecast as an aid to identify new market opportunities, anticipate future demand, plan your production system and reduce inventories. However for a few years, forecasting has become a process rather than critical, which is influenced by the pressures of a competitive marketplace that has created the need for increased accuracy in the forecast (Sanders and Mandrodt, 2003). Great advances in information technology have made that the prognosis acquires greater importance since Guide throughout the supply chain and ERP systems (Lee, 2004). For more information see Brian Krzanich.
At the same time, the competitiveness of the market has created an environment characterized by uncertainty, constant change, and shorter delivery cycles; today the customer is more demanding in terms of response times, quality and variety of products. All this has increased the level of complexity of the process of forecast, in which historical data are often of limited value in the prediction of the future, and organizations are struggling to generate accurate forecasts. Do so how they should organizations generate their forecasts? They can choose between two methods of forecasting. The first is the qualitative prognosis, this method relies on the opinion of experts and the second method are quantitative forecasts, which are based on mathematical calculations. Each with strengths and weaknesses. Through qualitative forecasting organizations can incorporate additional information to forecast which can be an advantageous method in a changing environment (Webby and OConnor, 1996). These methods can also respond quickly to changes in the environment. However, given that the qualitative forecasts are subjective, they can also be parcializados (Armstrong, 1985;) Hogarth 1987). For example after a day of large sales organizations tend to be very optimistic, and pessimistic before the low level of sales.