A chain of upscale deli stores in California, Nevada and Arizona sells Parmalat ice cream. The basic ingredients of this high-end ice cream are processed in Italy and then shipped to a small production facility in Maine (USA). There, the ingredients are mixed and fruit blends and/or other ingredients are added and the finished products are then shipped to the grocery chains' distribution centers (DC) in California by refrigerated trucks. Given that the replenishment lead time averages about five weeks, the replenishment managers at the DCs must place replenishment orders well in advance. The DC replenishment manager is responsible for forecasting demand for Parmalat ice cream. Demand for ice cream typically peaks several times during the spring and summer seasons as well as during the Thanksgiving and Christmas holiday season. The replenishment manager uses a "straight line" (i.e. simple) regression forecast model (typically fitted over a sales history of about two to three years) to predict future demand. Of the options listed below, what would be the best forecasting technique to use here? Simple average Simple exponential smoothing, Four-period moving average. Holt-Winter's forecasting method. Last period demand (naive)
