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Abstract

An article residual value predicting device of the invention comprises an article residual value predicting computer, a first data memory device connected to the article residual value predicting computer to store, as basal record data, respective items such as article names, used article values for each article type, new article values for each article type, and year and month data to which the used article value is applied, a second data memory device connected to the article residual value predicting computer to store item category scores. The article residual value predicting computer comprises article residual rate proven-value calculating means for reading out the used article value and new article value for each article type stored in the first data memory device, calculating article residual rate proven-value from the ratio of the used article value to the new article value, and storing a calculated result thus obtained as an article residual rate proven-value in the first data memory device, category score calculating means for reading out the article name, article residual rate proven-value, year data to which the used article value is applied and month data to which the used article value is applied, which are stored in the first data memory device, and calculating an item category score by performing a regression analysis based on the qualification theory I using the readout article residual rate proven-value as an objective variable and the readout article name, the year to which the used article value is applied as an explanatory variable and the month to which the used article value is applied as an explanatory variable, and storing a calculated score thus obtained in the second data memory device, article residual rate predictive-value calculating means for reading out the score stored in the second data memory device with respect to a specified item category and adopting a year-classified score relative to the year at some future point to be predicted as the year-classified score to calculate an article residual rate predictive-value from an equation “(article residual rate predictive-value)=(item-classified score)+(year-classified score)+(month-classified score)+(constant value)”, and article residual rate calculating means for multiplying the article residual rate predictive-value by a new article value to calculate an article residual value. The first data memory device serves to store maker-classified new article sales quantity or article name-classified new article sales quantity before elapsed years. The article residual value predicting computer further comprises a first weight coefficient calculating means for reading out the maker-classified new article sales quantity or article name-classified new article sales quantity before elapsed years stored in the first data memory device, calculating a weight coefficient from an equation “(maker-classified new article sales quantity before elapsed years)/(maker-classified record number)” or “(article name-classified new article sales quantity before elapsed years)/(article name-classified record number)”, and storing the weight coefficient based on the calculated new article sales quantity in the first data memory device, and weighting means for reading out the weight coefficient based on the calculated new article sales quantity from the first data memory device and duplicating the number of relevant records stored in the first data memory device corresponding to the weight coefficient based on the readout new article sales quantity and storing the record numbers increased by duplicating. The category score calculating means serves to perform the aforementioned regression analysis using concurrently all the relevant records weighted by the weighting means collectively.

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First Claim