A Parametric Bootstrapping Approach to Forecast Intermittent Demand

Intermittent demand is characterized by demand data that has many time periods with zero demands. It is hard to model intermittent demand by conventional distributions. In previous research, an algorithm to generate intermittent demand was developed. The algorithm generates demand based on two stages: probabilistically generating whether or not a demand will occur and then generating non-zero demand if appropriate. This paper reports on efforts to utilize the demand generation procedures as an intermittent demand forecasting techniques called MC-ARTA-IDFPB based on a parametric bootstrapping approach. The parameters are probability of non-zero demand after zero demand, probability of non-zero demand after non-zero demand, mean of non-zero demand, non-zero demand variance and lag 1 correlation coefficient of non-zero demands respectively. This paper compares the effectiveness of MC-ARTA-IDF-PB with other relevant intermittent demand forecasting techniques and evaluates its performance in a small empirical study.