[ 0065 ] fig1 shows a demand forecast tree 1 having a single top level node ( 00 ) with five branches a , b , c , d and e for correspondingly representing the sale of item i ( top level - 1 node ( 10 )) at locations 1 and 2 ( bottom level nodes ( 11 ) and ( 21 )), item ii ( top level - 1 node ( 20 )) at locations 1 and 3 ( bottom level nodes ( 21 ) and ( 23 )), item iii ( top level - 1 node ( 30 )) at location 1 , 2 and 3 ( bottom level nodes ( 31 ), ( 32 ) and ( 33 )), item iv ( top level - 1 node ( 40 )) also at locations 1 , 2 and 3 ( bottom level nodes ( 41 ), ( 42 ) and ( 43 )); and item v ( top level - 1 node ( 50 )) at location 1 ( bottom level node ( 51 )) only . fig2 shows an exemplary table 2 containing historical sales data of item i at the bottom level nodes ( 11 ) and ( 12 ). similar tables exist for the sale of the other items at their respective outlets . [ 0066 ] fig3 shows a computer implemented system 3 as illustrated and described in commonly assigned co - pending u . s . patent application ser . no . 10 / 058 , 830 entitled “ computer implemented method and system for demand forecast applications ”, the contents are which are incorporated herein by reference , capable of implementing the present invention . the computer implemented system 3 includes a database server 4 for storing time series of sales values , a forecast engine 6 including two or more computer servers 7 each independently capable of computing demand forecast information for an entire branch of the demand forecast tree 1 , and a computer manager 8 for allocating branches into tasks . the evaluation of the efficacy of a distribution policy for a perishable consumer item , say , a newspaper , at an outlet over an evaluation period , say , a month , in accordance with the present invention is now described with reference to fig4 and 5 . sellouts of the newspaper at the outlet over the evaluation period are identified by comparing the number of sold copies ( s ) against draw ( d ) respectively shown as the solid graph line and the dashed graph line in fig5 . the sales values excluding truncated sales values are input to a forecast engine , for example , the demantra ™ demand planner , commercially available from demantra ltd , israel , to estimate mean demand values typically rounded to the nearest integer for the newspaper at the sellouts over the month shown as squares in fig5 . the forecasted mean demand value at an occurrence of a sellout may be greater than , equal to or less than the sales at the sellout . for example , the forecasted mean demand value for day 5 is 29 copies which is less than the draw of 33 copies whilst the forecasted mean demand value for day 10 is 30 copies which is greater than the sales of 28 copies . based on the assumption that demand for the newspaper is a random variable x with a poisson conditional probability distribution conditioned on the forecasted mean demand value λ , the hidden demand h at each sellout is estimated using the following expression h = e  ( x - d | x ≥ d ) = λ  ( 1 + f  ( d ) 1 - f  ( d ) ) - d for example , the hidden demand for day 5 is 4 after having been rounded to the nearest integer based on the forecasted mean demand value λ = 29 and the draw d = 33 . the expected full demand efd for the newspaper that could be expected to be sold at the outlet are then calculated at each occurrence of a sellout as follows : efd = d + h ( see crosses in fig5 ). continuing the above example , the expected full demand for day 5 is 33 + 4 = 37 copies . the performance metrics for evaluating the efficacy of the distribution policy for the newspaper at the outlet over the evaluation period can include inter alia : the total hidden demand for the newspaper at the outlet over the evaluation period ; the ratio of total hidden demand to total sales over the evaluation period ; and the ratio of total hidden demand to draw over the evaluation period . in the present case , the total hidden demand for the newspaper is 19 copies which is equal to 2 . 12 % of sales and 1 . 90 % of draw . the adjusted sales data can be beneficially employed to improve demand forecasting as evidenced in fig6 in which an adjusted demand forecast derived from adjusted sales data yields a reduced mape = 8 . 79 % as opposed to the mape = 12 . 10 % of an unadjusted demand forecast derived from unadjusted sales data where : mape = ∑  actual   sales   values - demand   forecast   values  ∑  actual   sales   values  × 100  % with the summation being over all the time points over a given period . the evaluation of the efficacy of a recommended draw for a perishable consumer item , say , a newspaper , at an outlet to an actual draw over an evaluation period , say , a month , in accordance with the present invention is now described with reference to fig7 to 9 . the entries in the different columns of the table of fig7 are derived from the following relationships : the four bias columns of fig7 show the biases in four performance metrics es − bs , er − br , eso − bso and est − bst which inherently exist by failing to take into account hidden demand at occurrences of sellouts , as particularly applies to case 4 . the four δ columns of fig7 show the gains or losses in four performance metrics es − s , er − r , eso − so and est − st by virtue of delivery of a consumer item to an outlet in accordance with a recommended draw as opposed to an actual draw . turning now to fig8 and 9 , the recommended draw ( shown as a dashed line in fig8 ) is preferably arrived at by initially running a forecast engine on historical adjusted sales data to estimate a demand forecast for the newspaper at the outlet . the hidden demand at the occurrences of sellouts is preferably estimated as described hereinabove with reference to fig4 and 5 . thereafter , safety stock is added to the demand forecast at each time point over the evaluation period in accordance with a predetermined availability percentage , say , 80 %, typically rounded to the nearest integer to yield the recommended draw . the month evaluation period includes five occurrences of sellouts ( shown as circles in fig7 ) on days , 1 , 9 , 19 , 21 , and 28 , namely , 16 . 13 % sellouts , with total sales of 876 out of a total draw of 985 copies , namely , 11 . 07 % returns . the present invention estimates that the hidden demand at the occurrences of the sellouts on days , 1 , 9 , 19 , 21 , and 28 is as follows : 5 , 4 , 5 , 4 , and 4 , respectively , making for a total stockout of 22 copies which is the equivalent of 2 . 51 % total sales and 2 . 23 % total draw . the recommended draw would result in a lower draw of 960 copies as opposed to the actual draw of 985 copies but with only four occurrences of sellouts on days 2 , 8 , 21 , and 28 . not only is the recommended draw 2 . 54 % lower than the actual draw but the recommended draw on taking into consideration the hidden demand at occurrences of sellout would achieve 1 . 03 % higher expected sales of 885 copies as opposed to actual sales of 876 copies and as opposed to draw bounded sales of just 871 copies if the hidden demand at occurrences sellouts would not have been taken into account . the recommended draw on taking into account the hidden demand at the occurrences of sellout would be expected to render 12 . 90 % sellouts and 7 . 81 % returns with columns es - r and es - r in fig9 showing the gained sales and reduced returns achievable by the recommended draw in comparison to the actual draw . while the invention has been described with respect to a limited number of embodiments , it will be appreciated that many variations , modifications , and other applications of the invention can be made within the scope of the appended claims . other inferior approaches may be employed for arriving at mean demand values not requiring a run of a forecast engine , for example , assuming that a mean demand value for a perishable consumer item at an outlet at an occurrence of a sellout is the truncated sales value .