A Classification Approach for Selecting Forecasting Techniques for Intermittent Demand

The intermittent demand forecasting problem involves the forecasting of demand series that are characterized by the time between demands being significantly larger than the unit of time used for the forecast period. This causes the time series associated with the demand to have a large percentage of periods for which there are no demands. These types of series are often found in spare parts inventory management systems. This paper examines the intermittency of a demand series by relating the lag-1 correlation coefficient of non-zero demand, squared coefficient of variation of non-zero demand and probability of zero of the demand series to the error properties of various forecasting techniques. A classification method is presented by which a time series can be characterized in terms of key parameters related to intermittency and through this relationship the best of a set of forecasting techniques can be recommended. The method is illustrated on both real intermittent demand series and randomly generated time series in order to understand the efficacy of the procedure to improve overall forecasting effectiveness.