Abstract: POS data are entered at step S1. The input POS data are arranged and those data for a day on which there was no stock at the store opening time and for a day on which the goods are out of stock at the store closure time are discarded to formulate data sets of the daily sale amounts for individual goods. At step S3, non-routine goods are discarded. At step S4, basic statistic values of the goods, such as mean value, standard deviation, maximum value, minimum value, skewness value, kurtosis value, Geary value etc. of the daily sale amounts of the goods are calculated. At step S5, the goods are classified into one of preset plural types, such as Poisson type, normal type, causal type and other type. Besides, an optimum amount for restocking order is found on the basis of the class types.