Authors: Mohammad Anwar Rahman; Bhaba R. Sarker
Addresses: Department of Industrial Engineering Technology, The University of Southern Mississippi, 118 College Drive, Hattiesburg, MS 39406, USA ' Department of Industrial Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
Abstract: This paper investigates the forecasting of a large fluctuating seasonal demand prior to peak sale season using a practical time series, collected from the US Census Bureau. Due to the extreme natural events (e.g. excessive snow fall and calamities), sales may not occur, inventory may not replenish and demand may set off unrecorded during the peak sale season. This characterises a seasonal time series to an intermittent category. A seasonal autoregressive integrated moving average (SARIMA), a multiplicative exponential smoothing (M-ES) and an effective modelling approach using Bayesian computational process are analysed in the context of seasonal and intermittent forecast. Several forecast error indicators and a cost factor are used to compare the models. In cost factor analysis, cost is measured optimally using dynamic programming model under periodic review policy. Experimental results demonstrate that Bayesian model performance is much superior to SARIMA and M-ES models, and efficient to forecast seasonal and intermittent demand.
Keywords: intermittent time series; SARIMA; seasonal autoregressive integrated moving average; peak demand; dynamic programming; Bayesian model; intermittent demand; seasonal products; forecasting.
International Journal of Industrial and Systems Engineering, 2012 Vol.11 No.1/2, pp.137 - 153
Available online: 17 Apr 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article