Showing papers by "Kaoru Tone published in 1992"

Method for classifying sale amount characteristics, method for predicting sale volume, method for ordering for restocking, system for classifying sale amount characteristics and system for ordering for restocking

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.

Sale quantity characteristics classification system and supplementary ordering system

Abstract: POS data are inputted at a step S1. At a step S2, the inputted POS data are put in order. For example, a daily sale quantity data set for each commodity is prepared by removing the day(s) in which no stock exists at the time of opening and the days(s) in which commodity(ies) is out of stock at opening. At a step S3, unsteady commodities are removed. At a step S4, basic statistical quantities of the daily sales quantity of each commodity, such as a mean value, standard deviation, maximum value, minimum value, distortion value, peak value, Geary value, etc., are calculated. At a step S5, the data are classified into a plurality of types determined in advance such as a Poisson type, a normal type, a Causal type, and other types. An optimum supplementary ordering quantity is determined on the basis of the classification described above.