CROSSREFERENCES TO RELATED APPLICATIONS

[0001]
This application is a Divisional of U.S. patent application Ser. No. 10/407,201, filed on Apr. 7, 2003, which claims the benefit of U.S. Provisional Application No. 60/370,143, filed on Apr. 8, 2002, each of which is incorporated by reference in its entirety for all purposes.
FIELD OF THE INVENTION

[0002]
The invention is in the field of determining distribution policies for single period inventory systems.
GLOSSARY OF TERMS

[0003]
The following terms listed alphabetically together with their acronyms are employed in the description and claims of this application with respect to the present invention:

[0000]
Availability A_{ij}, and System Availability Percentage % SA

[0004]
Availability A_{ij }is an industry term referring to the probability of completely satisfying the demand for an i^{th }consumer item where i=1, 2, . . . , m at a j^{th }location where j=1, 2, . . . , n of a single period inventory system without an occurrence of a sellout due to insufficient draw at that location. In mathematical terms, A_{ij}=F(λ_{ij},D_{ij}) where F is the cumulative probability distribution function (cdf) of demand for the i^{th }consumer item at the j^{th }location, and λ_{ij }and D_{ij }are its mean demand and draw, respectively. The probability distribution function in the discrete case and the probability density function in the continuous case are both denoted by the letter “f”. The System Availability Percentage % SA for a single period inventory system is given by % SA=1000ΣΣA_{ij}/mn=100−% ESO.

[0000]
Demand X_{ij}, Mean Demand λ_{i}, and Mean Demand Matrix λ

[0005]
The demand process for a consumer item at a location has a random but nonstationary nature, and therefore cannot be subjected to ensemble inferences based on a single realization. Mean demands λ_{ij }for a consumer item at a location over time are presupposed to be the outcome of a stochastic process which can be simulated by a forecast model whilst the demand X_{ij }for an i^{th }consumer item at a j^{th }location of a single period inventory system at a future point in time is a random variable with a conditional probability distribution conditioned on its mean demand λ_{ij }at that point in time. A mean demand matrix λ is a matrix of mean demands λ_{ij}.

[0000]
Distribution Policy

[0006]
A distribution policy is the delivered quantities of each i^{th }consumer item where i=1, 2, . . . , m at each j^{th }location where j=1, 2, . . . , n of a single period inventory system in accordance with a predetermined business strategy.

[0000]
Draw D_{ij}, and Draw Matrix D

[0007]
Draw D_{ij }is an industry term referring to the delivered quantity of an i^{th }consumer item to a j^{th }location of a single period inventory system. A draw matrix D is a matrix of draws D_{ij}.

[0000]
Returns R(λ_{ij},D_{ij}), Total Expected Returns ER(λ,D), and Expected Returns Percentage % ER

[0008]
Returns R(λ_{ij},D_{ij}) is an industry term referring to the number of unsold items of an i^{th }consumer item at a j^{th }location of a single period inventory system, and is given by R(λ_{ij},D_{ij})=max(D_{ij}−λ_{ij},0) where D_{ij}, X_{ij}, and X_{ij }are the i^{th }consumer item's draw, demand and mean demand, respectively, at the j^{th }location. The total expected returns ER at all n locations of a single period inventory system is given by ER(λ,D)=ΣΣER(λ_{ij},D_{ij}) where ER(λ_{ij},D_{ij}) is the expected value of R(λ_{ij},D_{ij}). The expected returns percentage % ER of a distribution policy for a single period inventory system is given by % ER(λ,D)=100ER(λ,D)/ΣΣD_{ij}=100−% ES(λ,D).

[0000]
Safety Stock SS_{i }and Total Safety Stock Q

[0009]
For the purpose of the present invention, safety stock SS_{i }refers to the difference between an actual draw of an i^{th }consumer item at a j^{th }location of a single period inventory system and its demand forecast at that location, namely, SS_{ij}=D_{ij}−λ_{ij}, and therefore can assume positive or negative values. This is in contradistinction to the traditional industry definition of safety stock, namely, SS_{ij}=max{0, D_{ij}−λ_{ij}}. The total safety stock Q of all m consumer items at all n locations of a single period inventory system is given by Q=ΣΣSS_{ij}.

[0000]
Sales S(λ_{ij},D_{ij}), Total Expected Sales ES(λ,D), and Expected Sales Percentage % ES

[0010]
Sales S(λ_{ij},D_{ij}) refers to the number of sold items of an i^{th }consumer item at a j^{th }location of a single period inventory system as upper bounded by the draw D_{ij }at that location for that consumer item at each point in time, and is given by S(λ_{ij},D_{ij})=min(D_{ij},X_{ij})=D_{ij}−R(λ_{ij},D_{ij}) where D_{ij}, X_{ij}, and λ_{ij }are the i^{th }consumer item's draw, demand, and mean demand, respectively, at the j^{th }location. The total expected sales ES(λ,D) of all m consumer items at all n locations of a single period inventory system is given by ES(λ,D)=ΣΣES(λ_{ij},D_{ij}) where ES(λ_{ij},D_{ij}) is the expected value of S(λ_{ij},D_{ij}). The expected sales percentage % ES of a distribution policy for a single period inventory system is given by % ES(λ,D)=100ES(λ,D)/ΣΣD_{ij}=100−% ER(λ,D).

[0000]
Sellout SO(λ_{ij},D_{ij}), Expected Number of Sellouts ESO(λ_{ij},D_{ij}), Total Expected Number of Sellouts ESO(λ,D), and Expected Sellout Percentage % ESO

[0011]
Sellout SO(λ_{ij},D_{ij}) is an industry term referring to an occurrence of demand being greater than a delivered quantity of an i^{th }consumer item at a j^{th }location of a single period inventory system, namely, SO(λ_{ij},D_{ij})=δ(D_{ij}<X_{ij}) where δ is a binary indicator function:
$\delta \left(\mathrm{condition}\right)=\begin{array}{cc}1,& \mathrm{if}\text{\hspace{1em}}\mathrm{condition}\text{\hspace{1em}}\mathrm{is}\text{\hspace{1em}}\mathrm{true}\\ 0,& \mathrm{else}\end{array}$
where D_{ij}, X_{ij}, and λ_{ij }are the i^{th }consumer item's draw, demand, and mean demand, respectively, at that the j^{th }location. The expected number of sellouts ESO(λ_{ij},D_{ij}) for an i^{th }consumer item at a j^{th }location of a single period inventory system is given by ESO(λ_{ij},D_{ij})=P(X_{ij}>D_{ij})=1−F(λ_{ij},D_{ij}). The total expected number of sellouts of all m consumer items at all n locations of a single period inventory system is given by ESO(λ,D)=ΣΣESO(λ_{ij},D_{ij})=mn−ΣΣF(λ_{ij},D_{ij}). The expected sellout percentage (% ESO) of a distribution policy for a single period inventory system is given by % ESO(λ,D)=100ESO(λ,D)/mn=100−% SA.
Single Period Inventory Systems

[0012]
Single period inventory systems are largely concerned with consumer items having a limited shelf life at the end of which an item loses most, if not all, of its consumer value, and the stock of which is not replenished to prevent an occurrence of a sellout. Such consumer items can include perishable goods, for example, fruit, vegetables, flowers, and the like, and fixed lifetime goods, for example, printed media publications, namely, daily newspapers, weeklies, monthlies, and the like. Two common problems of single period inventory systems are known in the industry as the socalled “newsvendor” problem, i.e., the sale of the same item throughout a multilocation single period inventory system and the socalled “knapsack” problem, i.e., the sale of different items at the same location.

[0000]
Stockout ST(λ_{ij},D_{ij}), Expected Stockout EST(λ_{ij},D_{ij}), Total Expected Stockout EST(λ,D), and Expected Stockout Percentage % EST

[0013]
Stockout ST(λ_{ij},D_{ij}) is the quantity of unsatisfied demand for an i^{th }consumer item at a j^{th }location of a single period inventory system, and is given by ST(λ_{ij},D_{ij})=max(X_{ij}−D_{ij},0)=X_{ij}−S(λ_{ij},D_{ij}) where D_{ij}, X_{ij }and λ_{ij }are the i^{th }consumer item's draw, demand, and mean demand, respectively, at the j^{th }location. The total expected stockout EST(λ,D) of all m consumer items at all n locations of a single period inventory system is given by EST(λ,D)=ΣΣEST(λ_{ij},D_{ij}) where EST(λ_{ij},D_{ij}) is the expected value of ST(λ_{ij},D_{ij}). The expected stockout percentage % EST for a distribution policy is given by % EST(λ,D)=100EST(λ,D)/ΣΣD_{ij}.
BACKGROUND OF THE INVENTION

[0014]
One computer implemented approach for calculating a demand forecast involves defining a socalled demand forecast tree capable of being graphically represented by a single top level node with at least two branches directly emanating therefrom, each branch having at least one further node. The demand forecast is computed on the basis of historical sales data typically associated with bottom level nodes of a demand forecast tree by a forecast engine capable of determining a mathematical simulation model for a demand process. One such forecast engine employing statistical seasonal causal time series models of count data is commercially available from Demantra Ltd, Israel, under the name Demantra™ Demand Planner.

[0015]
One exemplary demand forecast application is the media distribution problem, namely, determining the number of copies of different daily newspapers to be delivered daily to different locations to minimize two mutually conflicting indices commonly quantified for evaluating the efficacy of a distribution policy for a newspaper: the frequency of sellouts, and the number of returns both typically expressed in percentage terms. It is common practice in the industry that a draw for a newspaper at a location for a given day is greater than its demand forecast at that location for that day so as to reduce the probability of a sellout but with the inherent penalty that returns will be greater. In the case of distribution policies for newspapers, safety stocks are allocated to locations to ensure a predetermined availability level for a given demand probability function to achieve a reasonable balance between expected returns and expected occurrences of sellouts. Moreover, it is common practice that locations are sorted into one of several classes depending on the average number of copies sold, each class being assigned a different availability level, say, 70%, 80%, and the like.
BRIEF SUMMARY OF THE INVENTION

[0016]
Broadly speaking, the present invention provides a novel computer implemented system for determining a distribution policy for a single period inventory system on the basis of performance metrics, for example, returns, sellout, and stockout other than the hitherto employed availability metric. In contradistinction to prevailing distribution policy practice which effectively regards each location of a single period inventory system as an isolated entity, the present invention is based on the notion that a distribution policy should allocate draw units on the basis of relative merit in accordance with an allocation decision criterion subject to one or more constraints rather than in some arbitrary absolute fashion. The choice of the most appropriate allocation decision criterion coupled with one or more constraints for a single period inventory system is highly dependent on characteristics of the single period inventory system in question, for example, the frequency distribution of the mean demands at its nodes, amongst others, and a business objective.

[0017]
The preferred allocation decision criteria of the present invention can be divided into two groups as follows:

[0018]
Group I consists of simple allocation decision criteria, including inter alia:

[0000]
(i) maximum incremental availability max_{i,j }{F(λ_{ij},D_{ij}+1)−F(λ_{ij},D_{ij})};

[0000]
(ii) minimum availability min_{i,j }{F(λ_{ij},D_{ij})};

[0000]
(iii) minimum incremental expected return min_{i,j }{ER(λ_{ij},D_{ij}+1)−ER(λ_{ij},D_{ij})}; and

[0000]
(iv) maximum decremental expected stockout max_{i,j }{EST(λ_{ij},D_{ij})−EST(λ_{ij},D_{ij}+1)};

[0019]
each being subject to one or more of the following constraints ΣΣSS_{ij}≦Q where Q is the total safety stock threshold for delivery of all m consumer items to all n locations, Σ_{j}SS_{ij}≦q_{1} ^{i }where q_{1} ^{i }is the safety stock of the i^{th }consumer item at all locations, Σ_{i}SS_{ij}<q_{2} ^{j }where q_{2} ^{j }is the safety stock of all the consumer items at a j^{th }location, % EST(λ,D)≦s where s is a predetermined expected stockout percentage threshold, % ER(λ,D)≦r where r is a predetermined expected return percentage threshold, % ESO(λ,D)≦e where e is a predetermined expected sellout percentage threshold, a_{ij}≦D_{ij}≦b_{ij }where a_{ij }and b_{ij }are respectively lower and upper boundaries for a draw of an i^{th }consumer item at a j^{th }location of a single period inventory system; A≦ΣΣD_{ij}≦B where A and B are respectively lower and upper boundaries for the draw of all m consumer items at all n locations of a single period inventory system, A_{1} ^{i}≦Σ_{1}D_{ij}≦B_{1} ^{i }where A_{1} ^{i }and B_{1} ^{j }are respectively lower and upper boundaries for the draw of all m consumer items at a j^{th }location of a single period inventory system, and A_{1} ^{i}≦E_{j}D_{ij}≦B_{1} ^{i }where A_{1} ^{i }and B_{1} ^{i }are respectively lower and upper boundaries for the draw of a i^{th }consumer items at all n locations of a single period inventory system.

[0020]
Group II consists of weighted composite allocation decision criteria each having two components oppositely acting upon the draw matrix D required to yield a predetermined business objective expressed in terms of an expected returns percentage (% ER) or an expected percentage of a parameter associated with occurrences of sellouts of all m consumer items at all n locations of a single period inventory system. The parameter associated with occurrences of sellouts may be either the number of sellouts of all m consumer items at all n locations of a single period inventory system in which case the allocation decision criterion is as follows:

[0000]
(v) w_{1}(% ER(λ,D)−% ER(λ,D^{0}))+w_{2}(% ESO(λ,D^{0})−% ESO(λ,D)) or

[0000]
the number of stockouts at all n locations of a single period inventory system in which case the allocation decision criterion is as follows:

[0000]
(vi) w_{1}(% ER(λ,D)−% ER(λ,D^{0}))+w_{2}(% EST(λ,D^{0})−% EST(λ,D))

[0021]
where w_{1 }and w_{2 }are weights, and D^{0 }is an initial draw matrix. The weighted composite allocation decision criteria can be subject to one or more of the above mentioned constraints, and also % ER(λ,D)=% ESO(λ,D) in the case of criterion (v), and also % ER(λ,D)=% EST(λ,D) in the case of criterion (vi). Typically D^{0}=λ. In point of fact, the latter criterion is conceptually more valid than the former criterion since the two parameters “returns” and “stockouts” have the same dimensions, namely, units of consumer items, which is not the dimension of sellouts. But this notwithstanding, it is envisaged that the former sellout criterion will gain more acceptance than the latter stockout criterion since expected sellout percentages rather than expected stockout percentages are more traditional in the art of single period inventory systems.

[0022]
To reach an optimal allocation of draw units, the simplest approach is to allocate additional draw units one by one starting from an initial draw allocation, say, equal to the mean demand matrix. But in the case of allocating a predetermined total draw quantity ΣΣD_{ij }or total predetermined safety stock quantity Q, it may be allocated with less iterations if it is initially allocated between the locations of a single period inventory system, say, in accordance with a predetermined availability at each location, and thereafter the initial draw allocation is finetuned to optimal allocations at each location in accordance with a selected allocation decision criterion by socalled pairwise switching.

[0023]
In connection with the weighted composite allocation decision criteria (v) and (vi), the present invention also provides a computer implemented Decision Support Tool for graphically displaying the expected returns percentages % ER for a multitude of expected returns percentages against their corresponding minimal expected sellout percentages % ESO, or vice versa. Alternatively, the Decision Support Tool can preferably graphically display expected returns percentages % ER for a multitude of expected returns percentages against their corresponding minimal expected stockout percentages % EST, or vice versa.
BRIEF DESCRIPTION OF THE DRAWINGS

[0024]
In order to better understand the invention and to see how it can be carried out in practice, preferred embodiments will now be described, by way of nonlimiting examples only, with reference to the accompanying drawings in which:

[0025]
FIG. 1 is a pictorial representation showing a demand forecast tree for computing demand forecast information for five different perishable consumer items;

[0026]
FIG. 2 is a table showing historical sales data associated with the demand forecast tree of FIG. 1;

[0027]
FIG. 3 is a block diagram of a computer implemented system for determining a distribution policy for a single period inventory system, and including a Decision Support Tool for facilitating user determination of a distribution policy for a single period inventory system;

[0028]
FIG. 4 is a pictorial representation of a simple single period inventory system having three locations for draw allocation in accordance with the present invention;

[0029]
FIG. 5 is a flow chart of a method for determining a distribution policy for a single period inventory system in accordance with the present invention;

[0030]
FIG. 6 is a flow chart showing the steps of a method for reallocating a predetermined draw to the locations of a single period inventory system based on maximal incremental availability in accordance with a first preferred embodiment of the method of FIG. 5;

[0031]
FIG. 7 is a table summarizing the results of the iterations for reallocating the combined total draw of the demand forecast and 15 safety stock units between the locations of the single period inventory system of FIG. 4 in accordance with the method of FIG. 6;

[0032]
FIG. 8 is a flow chart similar to the flow chart of FIG. 6 but for the onebyone allocation of a predetermined draw to the locations of a single period inventory system in accordance with a second preferred embodiment of the method of FIG. 5;

[0033]
FIG. 9 is a table similar to the table of FIG. 7 except in accordance with the method of FIG. 8 for the onebyone allocation of up to 20 safety stock units;

[0034]
FIG. 10 is a flow chart showing the steps of a method in accordance with the present invention for determining a distribution policy for a single period inventory system using a weighted composite allocation decision criterion;

[0035]
FIG. 11 is a table summarizing the minimal expected sellout percentages (% ESO) for a multitude of expected returns percentages (% ER) for allocating draw units to the locations of the single period inventory system of FIG. 4 in accordance with the method of FIG. 10 together with their corresponding draw vectors D; and

[0036]
FIG. 12 is a graph showing the results of the table of FIG. 11 for facilitating user determination of the distribution policy for a single period inventory system.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

[0037]
FIG. 1 shows an exemplary 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 level1 node (10)) at Locations 1 and 2 (bottom level nodes (11) and (21)), Item II (top level1 node (20)) at Locations 1 and 3 (bottom level nodes (21) and (23)), Item III (top level1 node (30)) at Locations 1, 2 and 3 (bottom level nodes (31), (32) and (33)), Item IV (top level1 node (40)) also at Locations 1, 2 and 3 (bottom level nodes (41), (42) and (43)); and Item V (top level1 node (50)) at Location 1 (bottom level node (51)) only. FIG. 2 shows an exemplary table 2 containing historical sales data for Item I at the bottom level nodes (11) and (12). Similar tables exist for the sale of the other items at their respective locations.

[0038]
FIG. 3 shows a computer implemented system 3 with a processor 4, memory 6, a user interface 7, including suitable input devices, for example, a keypad, a mouse, and the like, and output means, for example, a screen, a printer, and the like, with other computer components for enabling operation of the system including result analysis. The computer implemented system 3 includes a database 8 for storing historical time series of sales information of items at locations, a forecast engine 9 for forecasting the mean demand λ_{ij }for each i^{th }perishable consumer item at each j^{th }location on the basis of the historical sales data, and an optimization application 11 for determining the distribution policy for a single period inventory system subject to one or more constraints. The computer implemented system 3 also includes a Decision Support Tool (DST) 12 for facilitating user determination of a distribution policy for a single period inventory system. The computer implemented system 3 may be implemented as illustrated and described in commonly assigned copending U.S. patent application Ser. No. 10/058,830 entitled “Computer Implemented Method and System for Demand Forecast Applications”, the contents of which are incorporated herein by reference. Whilst the present invention is being described in the context of a fully functional computer implemented system, it is capable of being distributed in as a program product in a variety of forms, and the present invention applies equally regardless of the particular type of signal bearing media used to carry out distribution. Examples of such media include recordable type media, e.g., CDROM and transmission type media, e.g., digital communication links.

[0039]
The present invention will now be exemplified for an exemplary “newsvendor” problem for determining the distribution policy for a single period inventory system 13 for delivering a single newspaper title between three locations, namely, j=1, 2 and 3 (see FIG. 4). For the sake of the example below, demand at Locations 1, 2 and 3 are assumed to have a Poisson probability distribution, and the single period inventory system has a mean demand vector λ=(10, 40, 100) but the present invention can be equally applied to other probability distributions of demand. Based on this assumption, the expressions for calculating expected return (ER) and expected stockout (EST) for Locations 1, 2, and 3 are as follows:
ER(λ_{ij} ,D _{j})=D _{j} f(λ_{j} ,D _{j}−1)+(D _{j}−λ_{j})F(λ_{j},D_{j}−2)
EST(λ_{j} ,D _{j})=D _{j} f(λ_{j} ,D _{j})+(λ_{j} −D _{j})(1−F(λ_{j} ,D _{j}−1))
where f(•) is the Poisson probability distribution function (pdf) and F(•) is the Poisson cumulative probability distribution function (cdf) for the demand for the consumer item at the j^{th }location, and λ_{j }and D_{j }are respectively the mean demand value and the draw at that location.

[0040]
The use of the present invention for determining the distribution policy for the single period inventory system 13 is now described with reference to FIGS. 69 in connection with the first simple allocation decision criterion, namely, maximum incremental availability as given by max_{j }{F(λ_{j},D_{j}+1)−F(λ_{j},D_{j})} subject to one or more of the following constraints: ΣSS_{j}≦Q % ER(λ,D)≦r; % ESO(λ,D)≦e; % EST(λ,D)≦s; a_{j}≦D_{j}≦b_{j}; and A≦ΣD_{j}≦B. The use of the present invention as exemplified in FIGS. 610 can be equally extended to the other simple allocation decision criterion (ii) to (iv) by substitution of their corresponding expressions into the blocks entitled Criterion and Objective in the flow diagrams of FIGS. 6 and 8.

[0041]
To better exemplify the potential of the present invention for more advantageously allocating draw, the following performance metrics % SA, % ESO, % ER, % ES, and % EST are employed for comparing the allocation of the same safety stock quantity in accordance with the conventional approach of the same availability at each location and maximum incremental availability. In accordance with a conventional 80% availability at each location, this imposes a safety stock allocation of 2, 5 and 8 units to Locations 1, 2 and 3, respectively, which yields the following results: % SA=80.2%, % ESO=19.8%, % ER=10.7%, % ES=89.3%, and % EST=11.6%. FIG. 7 shows how the same safety stock allocation of Q=15 units using pairwise switching can arrive at a safety stock allocation of 5, 6 and 4 units to Locations 1, 2 and 3, respectively, which yields the following results: % SA=82.6%, % ESO=−17.4%, % ER=10.9%, % ES=89.1%, and % EST=1.8%, namely, adramatically increased % SA from 80.2% to 82.6% whilst paying only marginal penalties in terms of increased expected returns percentage (% ER) from 10.7% to 10.9% and increased expected stockout percentage (% EST) from 1.6% to 1.8%. The results exemplify that since locations with different mean demands contribute differently to overall system availability % SA, this performance metric can be improved substantially by allocating draw units to the locations which contribute most at a prevailing draw allocation D^{c }at the expense of other locations. In the present case, the initial safety stock allocation of providing an about 80% availability at each of the Locations 1, 2 and 3 is morphed to availabilities of 95%, 85%, and 68%, respectively.

[0042]
As mentioned earlier, pairwise switching can only be employed in the case of reallocation of a predetermined draw. The table of FIG. 9 shows the incremental effect of onebyone allocation of safety stock units to the Locations 1, 2 and 3, the column entitled “winning location” indicating which Location 1, 2 or 3 receives the next additional safety stock unit on the basis of its incremental availability being the greatest at any given prevailing draw allocation D^{c}. The table of FIG. 9 enables determining the results of the performance metrics % SA, % ESO, % ER, % ES, and % EST for termination conditions other than a predetermined safety stock quantity, say, % ESO≦15% which in this case imposes a safety stock allocation of 5, 7 and 6 units to the Locations 1, 2 and 3, respectively, which yields % SA=75.9%, % ESO=14.1%, % ER=12.1%, % ES=87.9%, and % EST=1.4%.

[0043]
The use of the present invention for allocating draw to the Locations 1, 2 and 3 is now described with reference to FIGS. 1012 in connection with the first weighted composite allocation decision criterion, namely, w_{1}(% ER(λ,D)−% ER(λ,D^{0}))+w_{2}(% ESO(λ,D^{0})−% ESO(λ,D)) subject to one or more of the following constraints: ΣSS_{i}≦Q, % ER(λ,D)≦r; % ESO(λ,D)≦e; % EST(λ,D)≦s; a_{i}≦D_{i}≦b_{i}; and A≦ΣD_{i}≦B. FIG. 11 shows the results for repetitions of the method set out in the flow diagram of FIG. 10 for different expected returns percentage constraints % ER(λ,D)≦r at intervals of about 2% to calculate their corresponding minimal expected sellout percentages % ESO. The DST 12 graphically shows these results (see FIG. 12) for enabling a user to select a draw vector D, thereby determining the draw allocation between the Locations 1, 2 and 3. This approach can be repeated for a multitude of different expected sellout percentage constraints % ESO(λ,D)≦e, say, at intervals of 5%. Also, this approach may be repeated using the second weighted composite allocation decision criterion based on stockouts rather than sellouts.

[0044]
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.