Forecasting using partially known demands
Most forecasting models for building a master schedule do not use information from orders that have been received for future delivery. We propose two basic algorithms that forecast the total demand by making use of information on orders already received. We test these algorithms using actual demand data from a printing firm. The behavior of the algorithms under special conditions like price promotions and shocks is also illustrated. We conclude that the proposed algorithms perform relatively better than exponential smoothing when partially known demand data is available.