  Demand functions for goods are generally cyclical in nature with
characteristics such as trend or stochasticity. Most existing demand
forecasting techniques in literature are designed to manage and forecast this
type of demand functions. However, if the demand function is lumpy in nature,
then the general demand forecasting techniques may fail given the unusual
characteristics of the function. Proper identification of the underlying demand
function and using the most appropriate forecasting technique becomes critical.
In this paper, we will attempt to explore the key characteristics of the
different types of demand function and relate them to known statistical
distributions. By fitting statistical distributions to actual past demand data,
we are then able to identify the correct demand functions, so that the the most
appropriate forecasting technique can be applied to obtain improved forecasting
results. We applied the methodology to a real case study to show the reduction
in forecasting errors obtained.
