JP2003323479A - System, apparatus and method for distribution process - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To increase the sales efficiency of each store by determining the way merchandise is distributed to a plurality of stores. <P>SOLUTION: A distribution process computer 10 uses a transaction DB 36 to rank stores for the merchandise to be distributed. This ranking is effected using the sales performance of each store as to the group (merchandise attribute group) to which the merchandise belongs, to arrange the stores in ranks S, A, B and C. A process that is executed according to the rank of each store determines the stores to which the merchandise is distributed and the amounts of merchandise distributed. Based on the result of this distribution simulation, the merchandise is distributed to each store. Additional distributions are determined by a process that is executed based on the sales performance of each store for the merchandise. <P>COPYRIGHT: (C)2004,JPO

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a system, an apparatus and a method for deciding how to distribute a distribution object to a plurality of distribution destinations.
Particularly, when the same product is sold in a plurality of stores, it is for determining how to distribute the product to the plurality of stores.

[0002]

2. Description of the Related Art Conventionally, how to distribute a product to a plurality of stores depends on trial and error based on the experience of the distributor and the experience of the person in charge of each store. It was done. On the other hand,
Japanese Patent Publication No. 2-23464 discloses an assortment system of products in stores. This is because in order to realize a more optimal product lineup at each store, "features of the store (location conditions, business form, etc.), product features that should be available at each store" and "store features, each store" In consideration of the “sales record of individual products”, the products to be displayed at each store are automatically determined.

However, in such a system,
The following problems are occurring. First, when the product to be distributed is sold for the first time in a store (a new product, etc.), there is no sales record of the product in each store. Therefore, the above method cannot be used.

Secondly, when the product to be distributed is not sold in the whole store or is a rare product, the sales record of the product is small. Therefore, even if the above method is used, the sales record of each store makes little sense, and the sales efficiency at each store cannot be improved.

Thirdly, since it is difficult for the operator to grasp the data of the sales record itself at each of the plurality of stores,
It is difficult for the operator to decide the distribution method in consideration of the sales record. That is, the intention of the operator cannot be easily reflected in the distribution method.

The present invention solves such a problem by
An object of the present invention is to provide a distribution processing system, a device and a method capable of improving the handling efficiency at the distribution destination and easily reflecting the intention of the operator in the distribution method. For example, when trying to distribute products to multiple stores, for products that have no sales record or products with a small sales record, go to stores that sell better, and for products that have abundant sales records, sell them better. By allocating to existing stores, sales efficiency at each store can be improved.

[0007]

(1) The distribution processing system of the present invention comprises a plurality of distribution destination terminal devices, a distribution processing device, and a distribution execution device, and the distribution destination terminal device handles the handling of objects. The record information is recorded, and the distribution processing device receives and records the handling record information of the object at each distribution destination from each distribution destination terminal device, and based on the handling record information corresponding to the distribution target to be newly distributed. Then, each allocation target is categorized, and the total amount of the allocation target to be newly allocated is received, and the allocation target is allocated to each allocation target using the classification determined for each allocation target. Generates distribution data indicating how to distribute the objects and transmits the distribution data to the distribution execution device, and the distribution execution device receives the distribution data and distributes the distribution target object based on the distribution data. To execute the process to allocate first It has a feature.

(2) to (4) The distribution processing device of the present invention is a device for generating distribution data for determining how to distribute a distribution object to each distribution destination. In response to the handling record information of the object in, the distribution target is classified based on the handling record information corresponding to the distribution target to be newly distributed, and the total amount of the distribution target to be newly distributed is received, It is characterized in that the distribution data indicating how to distribute the object to be distributed to each of the allocation destinations is generated using the classification determined for each allocation destination.

Therefore, since the distribution method is determined by using the classification based on the handling record information at each distribution destination, it is possible to improve the handling efficiency of future distribution objects. For example, when trying to distribute a product to multiple stores, by using the classification based on the sales performance of the product in each store, the sales efficiency of the product in the entire store is improved, and as a result, the sales are increased. Can be stretched.

Further, the operator or the like can easily identify the intention of the operator or the like by specifying the classification in order to select the distribution destination or by setting the weight or the like for each classification in order to determine the distribution amount. It can be reflected in the allocation method. further,
By using the classification, the allocation method can be easily changed according to various circumstances.

(5) The distribution processing device according to the present invention is configured to indicate to which allocation destination the allocation target is allocated, or to which allocation destination the allocation target is allocated. It is characterized by generating distribution data. As a result, the distribution amount of the distribution target object for each store can be determined based on the classification.

(6) In the distribution processing device of the present invention, the handling record information corresponding to the distribution target is past handling record information of the distribution target. With this, when the handling record information of the distribution target is appropriately present, by using the handling record information,
The distribution target can be more optimally supplied to each distribution destination.

(7) In the distribution processing device of the present invention, the handling record information corresponding to the distribution target is past handling record information for the entire group to which the distribution target belongs.

Thus, even if the distribution record of the distribution target does not exist or is small, the distribution record of the distribution target can be optimally supplied by using the distribution record of the group to which the distribution target belongs. Can be done. For example, when distributing a new product or a rare product to a plurality of stores, it is possible to realize a more optimal product lineup at each store by using the sales record of the group to which the product belongs.

(8) The distribution processing device according to the present invention is such that the distribution indicating how much of a distribution object is distributed to the selected distribution destination selected by using the classification determined for the allocation destination. It is characterized by generating data. Accordingly, by selecting the classification, it is possible to determine to which distribution destination the distribution is to be made.

(9) The distribution processing device according to the present invention is a ratio of the handling record information corresponding to the distribution object of each selected distribution destination to the handling record information corresponding to the distribution object of all the selected distribution destinations. According to the above, the distribution data indicating how much of the distribution object is distributed to each selected distribution destination is generated. As a result, based on the ratio of the handling record information for the selected distribution destination only,
The distribution amount of the distribution object can be determined.

(10) The distribution processing device according to the present invention distributes a predetermined amount of the distribution object to each of the selection distribution destinations, and the remaining distribution objects after the distribution are distributed to all the selection distribution destinations. According to the ratio of the handling record information corresponding to the distribution target of each of the selected distribution destinations to the handling record information corresponding to the distribution target, the distribution indicating how much to allocate to each selected distribution destination It is characterized by generating data.

As a result, even when the number of objects to be distributed is small, at least a predetermined amount is distributed to each of the selective distribution destinations, so that the situation where any of the selective distribution destinations is not distributed at all does not occur.

(11) The distribution processing device according to the present invention generates the distribution data indicating how to distribute the distribution object based on the distribution amount or distribution ratio set for the classification. Is characterized by.

As a result, it is possible to evenly distribute to the distribution destinations of the same classification. Also, by the operator arbitrarily setting the distribution amount etc. for each classification,
The intention of the operator can be reflected.

(12) The distribution processing device according to the present invention is a group composition calculated from the handling record information corresponding to the distribution object of the group to which the distribution object belongs and the number of kinds of group composition belonging to the group. It is characterized in that the distribution data indicating how much the distribution object is distributed is generated based on the handling capacity per hit. Therefore, by considering the handling capacity per group constituent, the distribution target can be more optimally supplied at each distribution destination.

(13) The distribution processing device according to the present invention, based on the sales forecast value calculated from the handling record information corresponding to the distribution object, calculates the distribution data indicating how much the distribution object is distributed. It is characterized by generating. As a result, for example, increase sales efficiency at each distribution destination,
As a result, sales can be distributed so that sales can be increased.

(14) The distribution processing device according to the present invention is characterized in that it generates the distribution data indicating how much of the distribution object is distributed based on the sales forecast value in consideration of the weekend index. As a result, it is possible to preferentially perform concentrated distribution to the stores with a high weekend sales ratio.

(15) In the distribution processing device of this invention, the handling record information includes at least sales information.

(16) In the distribution processing device according to the present invention, the handling record information includes at least an operation rate calculated from sales information and inventory information. Thereby, for example, when distributing a product to be sold in each store to a plurality of stores, based on the sales record of the product in each store and the classification determined based on the inventory record , It is possible to determine how to distribute the product.

(17) According to the distribution processing method of the present invention, a computer is used to receive the handling record information of the object at each distribution destination, and based on the handling record information corresponding to the distribution target to be newly distributed, Evaluate each allocation destination, receive the total amount of the allocation object to be newly allocated, and use the evaluation determined for each allocation destination to determine the allocation object for each allocation destination. It is characterized by generating distribution data indicating how to distribute. Therefore,
Since the distribution method is determined by using the evaluation based on the handling record information at each distribution destination, it is possible to improve the handling efficiency of the distribution target in the future. For example, when trying to distribute a product to multiple stores, by using an evaluation based on the sales performance of the product in each store, the sales efficiency of the product in the entire store is increased, and as a result, the sales are increased. Can be stretched.

In the present invention, "distribution processing device"
Corresponds to the distribution processing computer 10 in the following embodiment. The “allocation execution device” corresponds to the distribution center computer 40 in the following embodiment. "Distribution destination terminal device"
In the embodiments below, store computers 50, 52, 54
Corresponds to.

The "distribution object" is an article to be distributed to distribution destinations, and in the following embodiment, the SKU to be distributed to each store by the head office corresponds. It should be noted that the concept also includes data to be distributed to distribution destinations through a telecommunication line or the like. "Group to which the distribution target belongs" is
They are grouped based on the characteristics of the objects to be distributed, and in the following embodiment, the product attribute group to which the SKU to be distributed by the headquarters to each store belongs. The "commodity attribute group" is a group based on the characteristics of the commodity. In the following embodiments, items grouped by item (eg, jacket, pullover, etc.) and size are relevant. In addition to items and sizes, items that are grouped using item subs (eg, knits, fabrics, jerseys, etc.) are also applicable.

The "handling record information" is the record information handled by the distribution destinations, and in the following embodiment, the sales amount, the number of sold sheets, and the SKU operating rate correspond. For example, when the distribution destination is a factory (such as when parts for production are distributed), the production amount and the like also apply. It is not limited to past ones, but future ones (eg sales forecast, planned production volume, etc.)
It is a concept that also includes.

“Distribution destination” and “scheduled distribution destination” are places where the objects to be distributed are distributed, and each store corresponds in the following embodiment. In addition, when the distribution target is data, which includes factories and business offices, the concept includes computers and stores in shopping malls. The “selected distribution destination” is a distribution destination selected from among allocatable distribution destinations. In the following embodiment, the distribution destination selected using the classification determined for the distribution destination,
The stores of ranks S and A selected by the operator correspond.
When all ranks (S to C) are selected, all stores are applicable.

The "remaining distribution target" is the remaining distribution target after a predetermined amount is distributed to each selected distribution destination, and in the following embodiment, each store of rank S and A in the 1: 100 distribution method. The distribution target (SKU) after being distributed one by one corresponds to.

The "group constituent" is an SKU belonging to a product attribute group or an SKU (operating SK) that has been sold in a predetermined amount or more.
It corresponds to U).

The "handling capacity per group constituent" is calculated from the handling record information of the group and the number of types of group constituents.
It corresponds to the number of SKUs and the number of sales / operating SKUs.

The "sales forecast value" is a concept including not only the forecast of sales of products etc. but also the forecast of demand such as electricity consumption.

In the following embodiment, "the past handling record information about the entire group to which the distribution target belongs" corresponds to the sales amount of the product attribute group to which the SKU to be distributed belongs.

"Receiving the total amount of the distribution target to be newly distributed" is not limited to the case where the total amount is acquired from the operator or another computer, but the information for calculating the total amount is acquired. This is a concept that includes cases.

[Evaluation determined for each allocation destination]
Is determined based on the handling record information at each allocation destination, and in the following embodiment, the ranks S to C set for the store and the reference value calculated for each store (the mathematical formula)
(7)), "Sales number / SKU number", "Sales number / operating SKU number", etc. calculated for each store are applicable.

The "recording medium recording a program" is a concept including a flexible disk, a CD-ROM, a hard disk, a memory card, a ROM, a punch card, a tape and the like. In addition to a recording medium recording a program that can be directly executed by a computer, a recording medium recording a program that can be executed once it is installed in another recording medium (such as a hard disk) or encrypted or , Is a concept that includes a recording medium that records a compressed program. What is a "program"?
It is a concept that includes not only a program that can be directly executed by a CPU but also a source format program, a compressed program, an encrypted program, and the like.

[0039]

BEST MODE FOR CARRYING OUT THE INVENTION 1. System Configuration and Outline of Processing FIG. 1 shows, as an embodiment, the configuration of a distribution processing system for distributing apparel products of the same brand to a plurality of stores. . The distribution processing computer 10 and the aggregation processing computer 20 are installed in the headquarters responsible for product distribution, and the database server 30 stores a store master, a database (store DB32, product DB34) that stores the product master, and sales by store. A transaction DB 36 for accumulating data, inventory data, etc. is provided. The distribution center computer 40 is installed in a distribution center that receives and ships products and stores them. The store computers 50, 52, 54 are installed in each of a plurality of stores that sell the products distributed by the headquarters. Each computer can communicate via the Internet or the like. The outline of the processing of this system will be described below.

First, the distribution processing computer 10 uses the transaction DB 36 to rank each store with respect to the product to be distributed ("store ranking process") (see FIG.
2 steps S10). This ranking is performed by using the sales record of the product attribute group to which the product belongs in each store, and each store is divided into ranks S, A, B, and C. For example, if the product is “jacket (product number A, size A, color A)”, the product attribute group is “jacket, size A”.

In the following description, the product specified by the product number, size and color is called SKU, and the relationship between this SKU and product attribute group is shown in FIG. This product attribute group is grouped by item, which is the type of product, and size. This item is, for example, a jacket, a pullover, a cardigan, or the like.

Then, to which store and how much the SKU to be distributed is distributed is determined by a process executed by the distribution processing computer 10 based on the rank of each store (initial distribution). Time distribution simulation) (Fig. 2, step S12).

Based on the result of the distribution simulation, SKUs are distributed to each store (step S1 in FIG. 2).
4), SKUs distributed at each store are sold (Fig. 2
Step S16). Along with this, the store computer 50 and the like record sales data. In addition, the aggregation processing computer 20
Receives sales data and the like from the store computer 50 and the like, receipt / shipment data and the like from the distribution center computer 40, and stores them in the transaction DB 36 of the database server 30 (data totaling process) (step S18 in FIG. 2).

Further, when a predetermined amount of SKU is received at the distribution center, the head office determines which store the SKU is to be additionally distributed to and how much to be additionally distributed by the distribution processing computer 10 to the product to which the SKU belongs. It is determined by the process executed based on the sales results of the attribute group (allocation simulation at the time of additional allocation) (step S20 in FIG. 2). Based on the result of this distribution simulation,
SKUs are additionally allocated, and SKUs are additionally allocated at each store
Are sold (steps S22, S24, S26 in FIG. 2).

The distribution processing method by the distribution simulation at the initial distribution includes (1) normal distribution, (2) 1: 100 distribution, and (3) product rank distribution. Further, the distribution processing method by the distribution simulation at the time of additional distribution includes (4) sales forecast distribution and (5) distribution based on the composition ratio of the number of sold sheets.

2. Hardware Configuration of Each Computer FIG. 4A shows the hardware configuration of the distribution processing computer 10. The distribution processing computer 10 includes a CPU 102, a hard disk 104, a display 106, a communication unit 108, and a memory 11.
0, a keyboard / mouse 112, a CD-ROM drive 114, a flexible disk drive 116 and the like.

The communication unit 108 is for communicating with the database server 30, the distribution center computer 40, and the like. The hard disk 104 stores a distribution processing program, an operating system (OS), and the like for executing store ranking processing and distribution simulation. The distribution processing program is installed from the CD-ROM 118 or the like via the CD-ROM drive 114 or downloaded via a communication line.

FIG. 4B shows the hardware configuration of the database server 30. This database server 30 has CPU30
2, hard disk 304, communication unit 306, memory 308, CD-ROM
It has a drive 310, a flexible disk drive 312, and the like. The hard disk 304 contains the above-mentioned store DB3
2. Product DB34, transaction DB36, etc. are recorded. In the product DB 34 shown in FIG. 5A, for each SKU, a brand, a product number, an upper standard sales price at a store, a sales year, a season, etc. are recorded. In the transaction DB 36 shown in FIG. 5B, for each SKU in each store, the cumulative sales amount for the week (cumulative sales amount from Monday), the cumulative sales amount for the week, the purchase number, and the inventory number are recorded. Has been done.

The hardware configuration of the aggregation processing computer 20 is basically the same as that of the distribution processing computer 10 shown in FIG. 4A. However, the hard disk stores an aggregation processing program for receiving sales data from the store computer 50 and recording it in the transaction DB 36 of the database server 30.

3. Processing of Distribution Processing Computer 10 The processing by the distribution processing computer 10 will be described below for the first distribution (3.1) and the additional distribution (3.2).

3.1 Initial Distribution At the initial distribution, the distribution processing computer 10 will be used to explain (1) store ranking processing and (2) distribution simulation.

(1) Store Ranking Process FIG. 6 shows a flowchart of the store ranking process. CPU
102 displays a set period input screen (not shown) 1
It is displayed on 06 and prompts the operator to set "setting period, brand, sales year, season" (Fig. 6, step S5
0). This set period is the period of the sales amount fetched from the transaction DB 36.

The CPU 102 causes the operator to set the "200
When "0 year 37th week to 40th week" is input, the store master and the product master such as sales data for each product attribute group in each store are retrieved from the database server 30 and stored in the hard disk 104 (FIG. 6). Steps S51, S52). Then, a product attribute group setting screen (not shown) is displayed on the display 106 to prompt the operator to set "item, size" (step S53 in FIG. 6). Here, it is assumed that the operator has set "item (jacket), size (A)" (step S54 in FIG. 6).

The CPU 102 extracts the sales amount and the like of each store for the set product attribute group (jacket, size A) from the data acquired in step S52 in FIG. 6 (step S55 in FIG. 6). Then, the total sales amount of each store for the set period (hereinafter referred to as the period sales amount), the SKU operating rate shown in Formula (1), and the sales operating ratio shown in Formula (2) are calculated, and these are shown in FIG. Display in the actual data column of the store rank setting screen shown (Fig. 6 Step S
55, S56).

SKU occupancy rate = number of SKUs for fixed price sale / number of SKUs for store inventory ... (1) Sales occupancy ratio = period sales amount (or period sales quantity) × SKU occupancy rate ... (2) , In the above formula, the number of SKUs sold at a fixed price (operating SKU
The number) refers to the number of types of SKUs sold at a fixed price in the store, and the number of SKUs in the store inventory refers to the number of types of SKUs in stock at the store. That is, S
The KU operation rate is an index showing the sales power at the store.

The operator selects the period attribute and the sales operation ratio in the attribute setting field 510. The pull-down menu of the period attribute is “period sales amount” or “period sales number” and determines which is considered in the ranking of the store. The pull-down menu for the sales utilization ratio is either "enabled" or "disabled", and the SKU
Decide whether to consider the operating rate.

Here, the operator selects the period attribute "period sales amount" and the sales operation ratio "invalid", and the analysis button 520
It is assumed that is clicked (step S58 in FIG. 6). CPU102
Ranks stores according to the period sales amount (Fig. 6
Steps S60, S62). In other words, the ranking of each store is determined by the period sales amount of each store, the top 40% or more is rank S, the top 40 to 70% is rank A, the top 70 to 90% is rank B, and the bottom 10.
Rank less than% as rank C and rank each store. The rank of each store is displayed in the rank column of the store rank setting screen shown in FIG. 7 (step S66 in FIG. 6).

The store rankings are "period sales number", "period sales amount and SKU operation rate", "period sales number and SKU".
The case where the operation rate is used will be described later.

(2) Allocation Simulation FIG. 8 shows the distribution simulation by the distribution processing computer 10 at the time of the first allocation, divided into distribution processing methods (a) normal distribution, (b) 1: 100 distribution, and (c) product rank distribution. Explain. It should be noted that the distribution method at the time of additional distribution described below may be used as the distribution method at the time of initial distribution.

(A) The normal distribution CPU 102 displays the distribution simulation screen shown in FIG. 9 on the display 106, and prompts the operator to input the SKU to be distributed (step S100 in FIG. 8). The operator is SK
In the U input field 600, enter the SKU to be distributed. The product attribute group to which this SKU belongs is the “jacket, size A” set above.

The CPU 102 has a transaction DB 36 and a product DB.
From 34, sales data of the product attribute group (jacket, size A) to which the SKU belongs, sales rank, store total, distributable total amount of the SKU (total allocated number), SKU detailed information, corresponding SKU Take out the item "JK (jacket)" from and display it (Fig. 8 Step S10
2, S104). SKU detailed information includes bean-picture display, "material", "upward price" that is the standard selling price at the store, number of items stored at the distribution center (PK), and number of orders excluding the number of items received at the distribution center (order remaining ) Etc. (SKU Bean picture display field 610, SK
See U detailed information column 620). The total number of distributions is "PK (30) + order backlog (60) = 90". The item “JK (jacket)” corresponding to the SKU is displayed in the item column 630.

The operator sets the total number of sheets to be distributed, the distribution target store, or the distribution processing method (step S106 in FIG. 8). The total distribution number can be set by the operator by clicking the distribution number change button 640 and inputting a numerical value in the total distribution number setting field 650. Stores to be distributed are the store selection fields
You can select from "S stores only, S / A stores, S / A / B stores, all store allocation" from the pull-down menu of 660. The distribution processing method can be changed to 1: 100 distribution or product rank distribution by clicking the 1: 100 distribution command button 670 or the product rank button 680. Here, it is assumed that the operator sets only the distribution target store “S / A store” without changing the total distribution number “90”.

When the distribution target store “S / A store” is selected, the CPU 102 shifts to the normal distribution process shown in FIG. 10 (steps S108 and S110 in FIG. 8). The 1: 100 distribution command button 670,
If the product rank button 680 is not clicked, it is determined as normal distribution.

The CPU 102 makes a ratio of the period sales amount of the product attribute group of each store of rank S and A to the period sales amount of the product attribute group “jacket, size A” of the entire store of rank S and A ( The composition ratio of the period sales amount) is calculated (step S200 in FIG. 10). According to the composition ratio of the amount of sales during this period, the total number of distribution “90” is distributed to determine the distribution number for each store, and
It is displayed in the distribution number column of each store shown in (Fig. 10, step S20.
2, S204). In other words, the distribution number of sheets for a certain store is a value obtained by rounding up the calculated value a of Expression (3) to an integer.

[0065] Total distribution number x composition ratio of period sales amount of the store = calculated value a (3) Note that the composition ratio of period sales amount of each store is all stores (ranks S to C). Instead, it is calculated from the period sales amount of only the distribution target stores (rank S, A). Further, the numerical value in the lower column of the total distribution number setting field 650 is the total value of the distribution number for each store.

The distribution number of sheets for each store is determined as described above. In the above description, when the given total distribution number and the total distribution number are different, the total distribution number is used. However, the calculation processing of the CPU 102 may adjust the number of distributions to each store so that the total number of distributions matches the total number of distributions (the same applies to the following examples).

(B) 1: 100 allocation FIG. 11 shows a flowchart of the 1: 100 allocation processing in step S112 of FIG. When the 1: 100 allocation command button 670 is clicked in the state where the total allocation number is “90” and the distribution target store is “S / A store”, the CPU 102 shifts to the 1: 100 allocation process shown in FIG. 11 ( (FIG. 8 Steps S106, S108, S111, S112).

The CPU 102, for each store of rank S and A,
Allocate one by one from the higher ranks (Fig. 11 step S30
0). It is determined whether or not the remaining distribution number (total distribution number-the number of stores of ranks S and A) is 0 (step S302 in FIG. 11). In this embodiment, assuming that the remaining distribution number is not 0, the product attribute group of each store of rank S, A for the period sales amount for the product attribute group “jacket, size A” of the entire store of rank S, A Is calculated (step S304 in FIG. 11). By allocating the remaining distribution number according to the composition ratio of the period sales amount, the distribution number for each store is determined and displayed in the distribution number column of each store shown in FIG. 9 (steps S306 and S308 in FIG. 11). In other words, the distribution number of sheets for a certain store is a value obtained by rounding up the calculated value b of the mathematical expression (4) to an integer.

1 + remaining allocated number of sheets × compositional ratio of period sales amount of the store = calculated value b (4) (c) product rank allocation FIG. 12 shows a flowchart of the product rank allocation processing of step S114 in FIG. Show. The CPU 102 displays the product rank command button 68 with the total number of distribution “90” and the distribution target store “S / A store”.
When 0 is clicked, the process proceeds to the product rank allocation process shown in FIG. 12 (steps S106, S108, S114 in FIG. 8).

The CPU 102 displays the rank-specific distribution number setting screen shown in FIG. 13A, and prompts the operator to set the distribution number of each rank (step S400 in FIG. 12). Here, the operator
It is assumed that the number of distributions is set to rank S, "5", the number of distributions is "4" to rank A, the number of distributions is "0" to rank B, and the number of distributions is "0" to rank C.

When the operator clicks the total value calculation button (step S402 in FIG. 12), the CPU 102 uses the set number of distributions by rank to calculate the total number of distributions when distributing to each store in formula (5). Based on the calculation, the window shown in FIG. 13B is displayed (step S404 in FIG. 12).

Total number of distributed sheets = number of stores of S × 5 + number of stores of A × 4 + number of stores of B × 0 + number of stores of C × 0 (5) The CPU 102 clicks the distribution execution command “OK” by the operator. When this is done (step S406 in FIG. 12), the number of distributions for each store is displayed in the distribution number column for each store shown in FIG. 9 according to the set rank-specific distribution number (step S408 in FIG. 12). Thereby, it is possible to evenly distribute the stores to the same rank.

In the case of this distribution method, the operator agrees (or approximates) the total distribution number with the total distribution number.
Then, adjust the allocation number. It should be noted that a combination of the distribution numbers in each rank may be prepared in advance, and the CPU 102 may select and present the distribution number that most matches the total distribution number.

3.2 At the time of additional allocation The result (allocation data) of the above-mentioned allocation simulation at the time of initial allocation is transferred to the distribution center computer 40. The distribution center outputs distribution data using the distribution center computer 40, and performs shipping work for distributing SKUs to each store based on the output data. Each store sells the SKU distributed from the distribution center.

Accordingly, the aggregation processing computer 20
The sales data and the like are received from the store computer 50 and the like and accumulated in the transaction DB 36 of the database server 30 (data aggregation processing). Below, regarding the allocation simulation by the allocation processing computer 10 at the time of additional allocation,
(a) Sales forecast distribution, and (b) Distribution according to the composition ratio of the number of sold sheets. In addition, the distribution method at the time of the first distribution described above,
It may be used as a distribution method at the time of additional allocation.

(A) Sales forecast distribution FIG. 14 shows a flowchart of distribution simulation at the time of additional distribution. CPU 102 will be distributed by the operator
When the SKU is input (steps S500 and S502 in FIG. 14), the number of sold sheets in the predetermined period for the SKU is taken out from the transaction DB 36 and is shown in the formula (7) from the sales forecast value in the current week shown in the formula (6). A reference value is calculated, and this is displayed together with sales data and the like (see the reference value display column of the distribution simulation screen in FIG. 15) (steps S504 and S506 in FIG. 14). Further, the distribution number column of each store is displayed with the reference value rounded up to an integer. The numerical value displayed in this distribution number column is the distribution number for each store.

In this distribution method, the total number of distributions for each store is the total number of distributions. In addition, when the total distribution number is given, the total distribution number may be distributed to each store according to the ratio of the distribution number of each store calculated above.

Formula (6) Sales forecast value for the current week = {Pre1 × a + Pre2 × b + Pre3 × c + Pre4
× d} / (a ＋ b ＋ c ＋ d) Pre1: Weekly sales figures for one week ago a: Pre1 weight Pre2: Weeks two weeks before sales b: Pre2 weights Pre3: Weeks three weeks before sales c: Pre3 Weight of Pre4: Number of sales in the week before 4 weeks d: Weight of Pre4 Formula (7) Reference value = Sales forecast value of the current week / Digestion rate- (Stock number + Shipment number +
Sales quantity for the current week) Here, the consumption rate is a rate obtained by dividing the estimated cumulative sales quantity of the SKU at each store by the cumulative purchase quantity. The number of shipments is the number of ordered SKUs in each store but not yet received.

The sales forecast value for the current week is calculated in consideration of the weekend index (ratio of sales on Saturday and Sunday to the sales for one week) which is the sales ratio on Saturday and Sunday during the set period. Then, the reference value may be calculated (see the formula (8)). As a result, it is possible to increase the priority of a store with a high weekend sales ratio and preferentially distribute the store to the store.

Formula (8) Sales forecast value for the current week = {Pre1 × a + Pre2 × b + Pre3 × c + Pre4
× d} / (a ＋ b ＋ c ＋ d) × weekend index Pre1: Sales quantity for the week one week ago a: Weight for Pre1 Pre2: Sales quantity for the week two weeks ago b: Weight for Pre2 Pre3: Sales quantity for the week three weeks ago c: Weight of Pre3 Pre4: Number of sales in the week 4 weeks before d: Weight of Pre4 The weights a to d of Pre1 and the digestion rate may be arbitrarily set by the operator.

(B) Distribution by Sales Composition Ratio FIG. 16 shows a flowchart of the distribution processing by sales composition composition ratio in step S508 of FIG. CPU102, regarding the SKU in the entire store to which the SKU was distributed at the time of the initial distribution,
The ratio of the total number of sold sheets for each store for the SKU for the current week (including the current week) is calculated (composition ratio of the number of period sold sheets for each store) (FIG. 16, step S600). By allocating the additional distribution number according to the composition ratio of the number of sales in this period, the distribution number for each store is determined and displayed in the distribution number column of each store on the distribution simulation screen shown in FIG. 15 (FIG. 16). Steps S602, S604). In other words, the distribution number of sheets for a certain store is a numerical value obtained by rounding up the calculated value c of Expression (9) to an integer.

Composition ratio of additional distribution number × period sales number of the store = calculated value c ... (9) The result (distribution data) of the distribution simulation at the time of this additional distribution is transferred to the distribution center computer 40. .
The distribution center outputs distribution data using the distribution center computer 40, and performs shipping work for additionally distributing SKUs to each store based on the output data. Each store sells additional SKUs from the distribution center.

4. Other Examples 4.1 Other Examples of Store Ranking Processing (1) In the above embodiment, the stores are ranked based on the “period sales amount” of the product attribute group to which SKU belongs. Although it is done, "Period sales quantity", "Period sales amount and SKU operation rate", "Period sales quantity and SKU" of the relevant product attribute group
It may be performed based on the "operation rate". "Period sales volume and SKU
A case where the stores are ranked based on the “occupancy rate” will be described with reference to FIG. 17A. The abscissa represents the number of units sold in each product attribute group in each store during the period, and the ordinate represents the SKU operation rate in each product attribute group in each store. The ranking of each store is determined by the sales occupancy ratio (period sales number x SKU occupancy rate) and ranks in the top 40% or more
S, top 40-70% rank A, top 70-90% rank B,
Rank less than 10% of the bottom rank and rank each store. Curves 800, 802, 804, and 806 represent sales operation ratios among ranks. For comparison with this, FIG. 17B shows a case where the stores are ranked based on the “period sales quantity”.

When the SKU utilization rate is taken into consideration in this way, a store with a large number of sales but a low SKU utilization has a lower ranking, and conversely, a store with a small number of sales but a high SKU utilization rate
The rank goes up. In other words, the latter store with high sales efficiency goes up in rank, so that the sales amount can be increased by increasing the distribution amount to the store.

The vertical axis "number of sales SKUs" in FIG. 17B is the number of types of SKUs for which sales have occurred during the set period. Further, instead of the SKU operation rate, the inventory turnover rate or the like may be used to rank the stores.

(2) In the above embodiment, the stores are ranked based on the sales amount of the product attribute group to which the SKU to be distributed belongs. However,
It is not limited to this. For example, the stores may be ranked based on the sales amount of the SKU or the like.
Also, based on the sales amount of SKU similar to the SKU,
You may rank the stores. In addition, the stores may be ranked based on the sales amount of a product attribute group similar to the product attribute group to which the SKU belongs.

(3) In the above embodiment, from the “S store only, S / A store, S / A / B store, all store distribution” pull-down menu in the store selection field 660 of the distribution simulation screen shown in FIG. The distribution target store can be selected.
However, it is not limited to this, and for example,
Only rank B stores may be selectable.

4.2 Other Examples of Allocation Simulation (At First Allocation, At Additional Allocation) (1) In the above embodiment, the product attribute to which the SKU to be allocated belongs in the normal allocation processing and the 1: 100 allocation processing. Based on the sales amount of the group (composition ratio of the period sales amount), the distribution number of sheets for each store is calculated (FIG. 10, step S202, FIG. 11 step S306). However, the distribution number for each store may be calculated based on the sales number (composition ratio of the period sales number) of the product attribute group to which the SKU to be distributed belongs.

Further, the number of distributed sheets for each store may be calculated based on the sales amount or the number of sold sheets of the SKU to be distributed. Further, the distribution number of sheets to each store may be calculated based on the sales amount or the number of sold sheets of the SKU similar to the SKU. Further, the distribution number for each store may be calculated based on the sales amount or the number of sold products of the product attribute group similar to or related to the product attribute group to which the SKU to be distributed belongs.

(2) In the above embodiment, the reference value based on the sales quantity of the SKU to be distributed or the composition ratio of the period sales quantity is used in the sales forecast distribution processing and the distribution processing based on the composition ratio of the sales quantity. , The distribution number for each store is calculated (step S506 in FIG. 14, step S60 in FIG. 16).
2). However, the number of distributed sheets for each store may be calculated based on the sales amount of the SKU to be distributed.

Further, the distribution number for each store may be calculated based on the sales number or the sales amount of the product attribute group to which the SKU to be distributed belongs. Further, the distribution number of sheets for each store may be calculated based on the sales number or sales amount of the product attribute group similar to or related to the product attribute group to which the SKU to be distributed belongs.

(3) In the above embodiment, in the 1: 100 distribution process, one store is allocated to each store of ranks S and A selected by the operator from the higher rank (FIG. 11).
Step S300) may be distributed by a predetermined amount. Also,
Instead of allocating from the higher ranks, the sales amount or the number of sold sheets may be allocated in descending order.

(4) In the above embodiment, in the product rank distribution process, the operator is made to input the rank distribution quantity into the rank distribution quantity setting screen shown in FIG. 13A (step S400 in FIG. 12). . However, it may be the distribution number setting screen for each rank shown in FIG. This is because the number of distributions by rank is set in advance, and for the operator,
A desired item is selected. Further, the rank-based distribution ratio may be used instead of the rank-based distribution number.

(5) In the above embodiment, as the distribution processing method at the first time and at the time of addition, “normal distribution, 1: 100 distribution,
“Product rank distribution, sales forecast distribution, and distribution based on the composition ratio of the number of sold products” are described as an example. However, it is not limited to these. For example, the period sales quantity of the product attribute group to which the SKU to be distributed belongs and the number of types (SKU number) of SKUs belonging to the product attribute group (Figure
It is also possible to calculate the number of sales per type of SKU (the number of sales / the number of SKUs) for each store using (see 3), and distribute based on this calculated value.

Further, using the number of types (number of operating SKUs) of SKUs that belong to the product attribute group and have been sold by a predetermined amount or more (for example, one or more have been sold), the number of sales per one type of SKU (sales The number of sheets / the number of operating SKUs) may be calculated for each store and distributed based on this calculated value. As a result, it is possible to realize distribution processing that considers the sales power for each SKU. In addition, without ranking the stores,
An allocation simulation using “sales number / SKU number” and “sales number / operating SKU number” may be executed.

(6) In the above embodiment, the case where the operator himself has not set the total distribution number has been described. However, on the distribution simulation screen shown in FIG. 9, click the distribution number change button 640,
It may be set by inputting a numerical value in the total distribution number setting field 650.

(7) In the above embodiment, in the sales forecast distribution at the time of additional allocation and the distribution based on the composition ratio of the number of sold sheets,
As an example, the case of using the number of sales for 5 weeks including the current week is explained. However, the period is not limited to this, and the operator may arbitrarily set the period. Alternatively, the cumulative number of sold sheets may be accumulated without setting the period.

(8) In the above embodiment, the reference value calculated by the equation (7) is used to calculate the number of sheets to be distributed to each store. Instead of the reference value, the current week's allocation predicted value ( = Sales forecast value for the current week / digestion rate) may be used to calculate the distribution number of sheets for each store.

(9) In the above-described embodiment, the distribution simulation using the reference value is executed without ranking the stores during the additional distribution. However, the distribution simulation may be executed for the ranked stores by using the reference value.

(10) In the above embodiment, equations (3) and (4)
The value obtained by rounding up the calculated value of is rounded up to an integer is the distribution number of sheets for each store. However, a numerical value obtained by rounding down the calculated value to an integer, or an integer value closest to the calculated value may be used as the distribution number for each store. Similarly, instead of rounding up the reference value calculated by the mathematical expression (7) to an integer, a number rounded down to an integer or the like may be used as the distribution number for each store.

4.3 Other Examples (1) In the above embodiment, the product attribute groups are grouped by item (eg, jacket, pullover, cardigan, etc.) and size. However, the present invention is not limited to this, and other elements (for example, color) may be used for grouping.
Also, item subs (eg, knits, fabrics, jerseys, etc.) may be used for grouping together with items and sizes. By using this item sub, it is possible to form a product attribute group having characteristics closer to the SKU to be distributed.

Also, the product attribute group includes only items,
It may be formed by only item subs or only sizes. For example, when trying to allocate a “jersey” type SKU, the item attribute group formed by the item sub “jersey” is used without setting the item.

A plurality of product attribute groups may be set. For example, by setting a plurality of items or item subs, there may be cases where the sales data of the product attribute group increases and it is useful.

(2) In the above embodiment, the SK which is the product specified by the product number, size and color as the distribution object.
The sales amount of the product attribute group (jacket, size A) is explained in association with U as the handling record information corresponding to the object to be distributed. However, the handling record information corresponding to the distribution target may be handling record information of a product having a characteristic related to or similar to other characteristics such as the type, property, and function of the distribution target. FIG. 19 shows an example of the distribution target and the handling record information corresponding to the distribution target.

(3) In the above-described embodiment, the case of deciding how many products should be distributed to which store in the distribution simulation has been described. However, the present invention can be applied to a case where only the store to be distributed (allocation destination) is determined or a case where only the product to be distributed (allocation amount) is determined.

(4) In the above embodiment, the case where the merchandise is physically distributed to each of the plurality of stores has been described as an example. However, by using an electric communication line, data and The present invention can also be applied to the case of delivering a program. The present invention can also be applied to the case where products are distributed to a plurality of product arrangement areas in the same store or the like. For example, in the same store,
This is a case where the same product is distributed to the first to fifth floors.

(5) In the above-described embodiment, the data totaling process is performed using the computer (totalization computer 20) separate from the distribution processing computer 10. However, the distribution processing computer 10 performs the data totaling process. May be.

(6) In the above embodiment, the case where apparel products are distributed to each store has been described as an example. However, the present invention can be applied to general products (food, electric products, etc.). it can. The items to be distributed are not limited to the items to be sold at each store. It can also be applied to products that are traded between companies.

(7) In the above embodiment, a store is explained as an example of a distribution destination, but the distribution destination is not limited to this. For example, it may be a factory, a sales office, a distribution center, or the like.

(8) In the above embodiment, the distribution simulation result (distribution data) is transferred to the distribution center computer 40. However, it may be transferred to the store computers 50, 52, 54. In this case, each store
A product shipping instruction may be issued to a distribution center or the like based on the distribution data. Further, each store may issue a shipping instruction for a product within the range of the number of distributions for each store indicated in the distribution data.

(9) In the above embodiment, the distribution center computer 40 sends receipt / shipment data to the aggregation processing computer 20 in the headquarters, but sends a document with the receipt / shipment data output to the headquarters. Good. Similarly, the store computers 50, 52, 54 are the central processing computers 20
Although the sales data is sent to, the document that outputs the sales data may be sent to the headquarters.

(10) In the above embodiment, the distribution processing computer 10 is used together with the totalization processing computer 20, the distribution center computer 40, the store computer 50, etc. to execute the above-mentioned distribution simulation and store ranking processing. The case is described as an example. However, the distribution processing computer 10 alone may execute the distribution simulation and the store ranking processing.

(11) In the above embodiment, the case where apparel products of the same brand are distributed is explained as an example. However, it is not limited to the case where the brands are the same.

4.4 Processing of Aggregation Processing Computer 20 FIG. 20 shows a flowchart of data aggregation processing by the aggregation processing computer 20 shown in FIG. The CPU of the aggregation processing computer 20 is the store computers 50, 52, 54 of each store.
From sales data (how many SKUs were sold),
Receives loading and unloading data from the distribution center computer 40 (how many SKUs have been received and how many have been collected), and has received ordering data (which SKUs have been ordered) from the operator. And stores it in the transaction DB 36 of the database server 30 (step S800 in FIG. 20). This process is repeated until a predetermined time is reached (step S802 in FIG. 20).

At a predetermined time, the sales data of each store is displayed.
It is totaled for each SKU and is recorded in the transaction DB 36 of the database server 30 as the number of sales on the day (step S804 in FIG. 20). Similarly, the receipt data and the issue data are totaled for each SKU and recorded in the transaction DB 36. As a result, daily sales data and the like are accumulated.

Furthermore, for each SKU in each store, the cumulative sales amount for the week (cumulative sales amount from Monday),
The cumulative number of sold sheets, the number of purchased sheets, the number of stocks, etc. for the week are also calculated (step S806 in FIG. 20) and recorded in the transaction DB36 (see FIG. 5B) of the database server 30 (step S808 in FIG. 20).

[Brief description of drawings]

FIG. 1 is a diagram showing a configuration of a distribution processing system.

FIG. 2 is a flowchart showing an outline of processing of a distribution processing system.

FIG. 3 is a diagram showing a relationship between SKUs and product attribute groups.

[Fig. 4] Distribution processing computer 10, database server
It is a figure which shows the hardware constitutions of 30.

FIG. 5 is a diagram showing data of a product DB 34 and a transaction DB 36.

FIG. 6 is a flowchart showing a store ranking process.

FIG. 7 is a diagram showing a store rank setting screen.

FIG. 8 is a flowchart showing a distribution simulation at the time of initial distribution.

FIG. 9 is a diagram showing a distribution simulation screen.

FIG. 10 is a flowchart showing normal distribution processing.

FIG. 11 is a flowchart showing a 1: 100 distribution process.

FIG. 12 is a flowchart showing a product rank distribution process.

FIG. 13 is a diagram showing a distribution number setting screen for each rank.

FIG. 14 is a flowchart showing a distribution simulation at the time of additional distribution.

FIG. 15 is a diagram showing a distribution simulation screen.

FIG. 16 is a flowchart showing a distribution process based on the composition ratio of the number of sold sheets.

FIG. 17 is a graph showing the relationship between the number of sold sheets and the SKU operating rate at each store, and the relationship between the number of sold sheets and the sales amount.

FIG. 18 is a diagram showing a distribution number setting screen for each rank.

FIG. 19 is a table showing an example of distribution target objects and handling record information corresponding thereto.

FIG. 20 is a flowchart showing a data totaling process.

[Explanation of symbols]

10 ··· Distribution processing computer 20 .. · Aggregation processing computer 30 ... Database server 32 ... Transaction DB 40 ... Logistics center computer 50, 52, 54 ... ・ Store computers

Claims (17)

[Claims] 1. A distribution processing system comprising a plurality of distribution destination terminal devices, a distribution processing device, and a distribution execution device, wherein the distribution destination terminal device records handling record information of an object, and the distribution processing device From the distribution destination terminal device, receive and record the handling record information of the object at each distribution destination, and classify each distribution destination based on the handling record information corresponding to the distribution target to be newly distributed. Allocation data indicating how to distribute the distribution target to each of the distribution destinations by using the classification determined for each distribution destination after receiving the total amount of the distribution target to be distributed. And transmits to the distribution execution device, the distribution execution device receives the distribution data, and executes a process for distributing the distribution object to the distribution destination based on the distribution data, Distribution processing system characterized by Tem. 2. A distribution processing device for generating distribution data for deciding how to distribute a distribution object to each distribution destination, the distribution processing device receiving the handling record information of the object at each distribution destination, Based on the handling result information corresponding to the distribution target to be distributed to each distribution destination, classify each allocation target, receive the total amount of the distribution target to be newly allocated, and determine the classification determined for each allocation target Is used to generate distribution data indicating how to distribute the object to be distributed to each of the planned distribution destinations. 3. Receiving information on the handling results of the object at each distribution destination, and allocating a new allocation based on the handling result information corresponding to the distribution object to be newly allocated. A process of receiving the total amount of the objects and using the classification determined for each of the allocation destinations to generate distribution data indicating how to distribute the allocation object to each of the allocation destinations. A program that causes a computer to execute. 4. A recording medium on which the program according to claim 3 is recorded. 5. In the system, apparatus, program or recording medium according to any one of claims 1 to 4, to which allocation destination the allocation object is allocated, or to which allocation destination The distribution data indicating whether or not the distribution target is distributed is generated. 6. The system, apparatus, program or recording medium according to any one of claims 1 to 5, wherein the handling record information corresponding to the distribution target is past handling record information of the distribution target. Characterized by 7. The system, apparatus, program or recording medium according to any one of claims 1 to 5, wherein the handling record information corresponding to the distribution object is the past for the entire group to which the distribution object belongs. It is characterized by being handling record information. 8. The system, device, program or recording medium according to any one of claims 1 to 7, which is selected for a distribution destination selected using a classification determined for a distribution destination. The distribution data indicating whether or not to distribute the distribution target is generated. 9. The system, apparatus, program or recording medium according to claim 8, which corresponds to the distribution object of each selected distribution destination with respect to the handling record information corresponding to the distribution object of all the selected distribution destinations. According to the ratio of the handling record information, the distribution data indicating how much the distribution object is distributed to each selected distribution destination is generated. 10. The system, device, program or recording medium according to claim 8, wherein a predetermined amount of the distribution object is distributed to each of the selective distribution destinations, and the remaining distribution object after the distribution is According to the ratio of the handling record information corresponding to the distribution target of each selected distribution destination to the handling record information corresponding to the distribution target of all the selected distribution destinations, how much is distributed to each selected distribution destination. The above-mentioned distribution data indicating that is generated. 11. A system, an apparatus, a program or a recording medium according to any one of claims 1 to 7, wherein a distribution object is processed based on a distribution amount or a distribution ratio set for the classification. What is characterized by generating the distribution data indicating whether to distribute. 12. The system, device, program or recording medium according to claim 1, wherein the handling record information corresponding to the distribution target of the group to which the distribution target belongs and the group belonging to the group. What is characterized is that the distribution data indicating how much a distribution object is distributed is generated based on the handling capacity per group composition calculated from the number of types of composition. 13. The system, device, program or recording medium according to any one of claims 1 to 7, wherein how much is distributed based on a sales forecast value calculated from handling record information corresponding to the distribution object. What is characterized by generating the distribution data indicating whether to distribute an object. 14. The system, apparatus, program or recording medium according to claim 13, wherein the distribution data indicating how much the distribution object is distributed is generated based on the sales forecast value in consideration of a weekend index. Characterized by. 15. The system, apparatus, program or recording medium according to any one of claims 1 to 14, wherein the handling record information includes at least sales information. 16. The system, device, program or recording medium according to claim 1, wherein the handling record information includes at least an operating rate calculated from sales information and inventory information. What to do. 17. A computer is used to receive the handling record information of the object at each distribution destination, and to evaluate each distribution destination based on the handling record information corresponding to the distribution target to be newly distributed, A distribution indicating how to distribute the distribution target to each of the distribution destinations by using the evaluation determined for each distribution destination after receiving the total amount of the distribution target. A distribution processing method characterized by generating data.