JP2002203021A - Shelving allocation proposition data generation system and terminal device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a shelving allocation proposition system capable of evaluat ing the result of shelving allocation. SOLUTION: For a center device 21 in a shelving allocation center, customer POS data and market POS data are inputted (S11, S12) from a sales office and a research firm respectively. Then, the center device performs database searching (S13), conducts a predetermined analysis (S14), and generates shelving allocation proposition data (S16). The shelving allocation proposition data are proposed (S17) to the customer, who executes (S18) the specific shelving allocation. The POS data based on the proposed shelving allocation is communicated to the shelving allocation center for feeding back (S11) the effect produced after the shelving allocation is executed.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

The present invention relates to a POS (Point of Point)
The present invention relates to a shelf allocation proposal system and a terminal device that use a sale), and more particularly to a shelf allocation proposal system and a terminal device that can evaluate the result of a shelf allocation proposal.

[0002]

2. Description of the Related Art Conventionally, a store computer POS data and a commercially available market PO have been programmed by a programmed computer.
2. Description of the Related Art There is known a system for comparing and analyzing S data to perform an assortment analysis. Further, there is also known a system in which data selected through human judgment based on the result of an assortment analysis is input to the system, and shelf programmed proposal data is calculated by a programmed computer.

[0003]

In these systems, commercially available market POS data must be installed each time the market POS data is updated. Further, various changes relating to the overall market and sales of each store can be read from the market and store POS data.
In the OS data, there are few input items of store attributes represented by store location conditions, customer classes, and the like, and it was not possible to analyze in detail the relationship between the analysis result of assortment and store attributes. In addition, it was not possible to update the know-how of the shelf layout from the results before and after the shelf layout.

[0004] A conventional shelf layout proposal system has been configured to use the above-mentioned commercially available market POS data. Because there is a time lag in obtaining market POS data,
There was a problem that it was not possible to propose a shelf layout reflecting the latest market POS data. Further, there is a problem that the know-how of the shelf split cannot be updated because the effect cannot be verified after the shelf split. Also, there are few input items for store characteristics,
There was a problem that it was difficult to propose a store layout specific to that store. Furthermore, it is time-consuming to calculate the optimal number of faces based on the results of the product assortment analysis based on experience and applying it to the shelf layout. there were.

SUMMARY OF THE INVENTION The present invention has been made to solve the above-mentioned problems. A shelf layout proposal system that automatically creates shelf layout proposal data after inputting shop POS data, and can evaluate the results of the shelf layout. An object of the present invention is to provide a shelf allocation proposal system and a terminal device.

Furthermore, the present invention provides a system for forming marketplace POS data for shelf split analysis by continuously inputting store POS data and store attribute data to a shelf layout providing system, and provides marketplace POS data for shelf split analysis. It is an object of the present invention to provide a shelf allocation proposal system that can do the above.

[0007]

According to the present invention, a shelf allocation proposal data creation system for creating shelf allocation proposal data based on actual shelf allocation data of a product by a computer system inputs POS data of a store. Means, means for inputting POS data in the market, and means for automatically creating shelf layout proposal data for the store based on the POS data of the store and the POS data in the market.

[0008] Since store shelf plan proposal data is automatically created based on store POS data and market POS data, it is possible to save time and to propose an optimal shelf plan even without experience. Become.

Preferably, the apparatus further includes a means for inputting store attributes. Since the shelf layout proposal data is created in consideration of the store attributes, a more detailed shelf layout can be proposed. here,
The store attributes include a customer class, a location, a sales floor area, a number of customer stores, a shelf space (a number of scales, a number of stages), a total number of faces, and the like. It is preferable that the item of the store attribute can be arbitrarily set at any time according to the cutting edge analysis.

Considering store attributes, POs of stores before and after shelf allocation
Since the S data is compared and the shelf plan proposal data is changed based on the accumulated shelf plan know-how, shelf plan data more suitable for the store can be provided.

[0011] More preferably, the shelf layout proposal system is P
An analysis means for analyzing the sales situation of the store based on the OS data is further included.

[0012] Since the sales situation of the store is analyzed based on the POS data and the shelf plan proposal data is created according to the analysis result, highly reliable shelf plan proposal data supported by the data can be provided.

[0013] Preferably, the shelf layout proposal system has an analysis means, and the analysis means compares and analyzes the shop POS data and the market POS data. The analysis means may analyze each product category.

Further, “face elasticity analysis” described later,
Means for analyzing a change rate such as “price elasticity analysis” and “shelf arrangement position elasticity analysis” may be included. Further, it may include “sales / profit simulation analysis” described later.

The product category indicates the specific contents of the product attributes such as the age group, efficacy, brand, etc. of the user. Specifically, in the case of eye drops, categories such as allergy, blurred vision, dry eye, antibacterial, hyperemia, refreshing sensation, cataract, and tired eye are considered. The categories may be appropriately combined.

As the analysis, a share analysis, a sales contribution analysis, a price range analysis, a growth rate, a sales force analysis, and an ABC analysis for each category may be performed. In this case, the analysis may be performed on a number basis, a money amount basis, a gross profit amount basis, or the like.

Not only store POS data, but also market POS
Since analysis is performed in consideration of data, more detailed analysis is possible.

Further, according to the present invention, the market POS data can be constructed by inputting and accumulating the individual store POS data and the store attribute data.
S data can be provided.

More preferably, means for comparing the POS data of the store after the shelf split based on the shelf allocation proposal data with the POS data of the store before the shelf split and / or inputting the shelf split data at the time of collecting the POS data of the store. Means.

In the present invention, the store POS before and after the shelf split is
The comparison of the data allows the evaluation of the results of the shelving. Also, if the storage is made in correspondence with the shelf allocation state and the store POS data, it becomes easier to predict the store POS data after the shelf allocation.

The POS data referred to here refers to sales information such as the number, amount, selling price, gross profit and the like. Here, the shelf layout proposal data refers to data serving as a basis for performing an actual shelf layout in a store. The shelf layout data refers to data reflecting an actual shelf layout state in a store. POS
Data may be input via a network, via a recording medium such as an FD, or by a human. Product name is JAN
(Japanese Article Number) codes may be used.

Preferably, the shelf allocation proposal data creation system is based on a comparative analysis of the store POS data and the POS data in the market and / or a comparative analysis of the store POS data and the shelf allocation data at the time of POS data collection. Means for updating the shelf plan proposal data is further included.

The POS data of the stores before and after the shelf split are comprehensively compared using the store attribute data and the shelf split status (or the shelf split data) using the analysis results described later and the like, and based on the accumulated shelf split know-how. Since the discount proposal data is changed, a shelf layout suitable for the actual situation can be proposed.

[0024] In another aspect of the present invention, the shelf layout proposal data creation system is connected to a store terminal device having POS data via a network, and
And a center device to which market POS data can be input. The center device is means for obtaining the POS data of the store from the terminal device of the store via the network, means for inputting the POS data in the market, and the shelf layout of the store based on the POS data of the market and the POS data of the store. It includes means for automatically creating shelf allocation proposal data serving as a basis of data, and means for outputting shop shelf allocation data from a center device to a store terminal device via a network.

Further, the center device may include a means for obtaining store attributes, and may be used for analysis. In still another aspect of the present invention, a terminal device connected to a center device capable of proposing data for performing store shelving via a network inputs POS data of a store where the terminal device is provided. Means, means for transmitting POS data input to the center device, and means for receiving shelf layout proposal data from the center device.

Further, the terminal device may include a means for acquiring the store attribute.

[0027]

Embodiments of the present invention will be described below with reference to the drawings.

FIG. 1 is a schematic diagram showing the overall configuration of a shelf layout proposal system according to the present invention. Referring to FIG. 1, the shelf layout proposal system includes a terminal device 11 such as each of the stores 10a and 10b and a store PO from each of the stores 10a and 10b.
Center devices 21 of a shelf allocation center 20 that receives S data and creates shelf allocation proposal data based on actual shelf allocation data of a product are mutually connected via a network such as the Internet 40. Market POS data is input to the center device via a recording medium such as an FD. The terminal device of the research company 30 that can provide the market POS data may be connected to each other via a network such as the Internet 40. The store POS data may be input to the center device 21 via a recording medium such as an FD.

This network is not limited to the Internet 40, and may be a closed system or the like as long as data communication is possible.

When a sufficient number of stores transmit the store POS data to form the market POS database, the data communication with the research company 30 may be closed.

FIG. 2 is a block diagram showing the configuration of the terminal device 11 provided in the stores 10a, 10b and the like in FIG.

Referring to FIG. 2, a terminal device 11 includes a CPU 111 for controlling the entire terminal device 11 and a store PO connected to the CPU 111 via an interface 112.
POS device 113 for creating S data, CRT
, A storage device 115 such as a hard disk for storing predetermined programs and data, an input / output device 116 such as a keyboard and a mouse, an output device 117 such as a printer, and a terminal device 11 connected to the Internet 40.
And a communication device 118 such as a modem for connecting to the

FIG. 3 is a diagram showing the structure of the data file 121 stored in the POS device 113 of the terminal device 11. With reference to FIG. 3, the data file 121 stores each day P
The OS data and the number of sales, the price, the selling price, and the gross profit for each product are stored. It may be calculated on a monthly or weekly basis. If the store has POS data to be compared, it can also send it to the center device. POS
Comparison of data can be performed for an appropriate period, such as one week or three months.

The gross profit here refers to a profit obtained by selling a product. For example, in a retail store, the profit is represented by “(sales price−cost (delivery price)) × number of units sold”. is there.

FIG. 4A shows the center device 21 shown in FIG.
FIG. 3 is a block diagram showing a main part of FIG. The center device 21 includes a CPU 201 for controlling the entire center device 21 and a CPU 2.
Storage device 203, a display device 204 such as a CRT, an input / output device 205 such as a keyboard and a mouse, an output device 206 such as a printer, and a center device 21 connected to the Internet 40. And a communication device 207 such as a modem for connecting to a communication device.

As shown in FIG. 4B, POS data, store attribute data and shelf layout data, merchandise information (item master) data, company information data, analysis result data, Stores market POS data and analysis programs obtained from the research companies, shelf allocation creation programs, comparison processing programs, and the like.

Next, the basic processing contents of the center device 21 of the shelf allocation center 20 will be described. FIG.
5 is a flowchart showing a basic operation of the first embodiment.

Referring to FIG. 5, center device 21 is connected to store PO of customer 10 via Internet 40.
The S data and the market POS data are input from the research company 30 (steps S11 and S12; steps are abbreviated below).

Next, the database 210 is searched (S
13) Perform a predetermined analysis (S14).

Based on the analysis result, the number of faces of each product is trially calculated (S15), the shelf layout is determined from the product lineup based on the analysis result and the trial calculated number of faces, and shelf layout proposal data to be described later is created. (S16). Here, the face refers to a surface of a product to be shown to a customer at the time of shelf allocation. Next, a proposal is made to each customer shop via the Internet 40 (S17). Based on this, the store 10 carries out the shelving (S18).

In the store 10, the store POS data after the shelf allocation is transmitted again to the center device 21 of the shelf allocation center 20 via the terminal device 11.

Note that sales information such as store POS data is not limited to the Internet 40, but may be transferred on an electronic recording medium such as an FD or a paper medium. The same applies to the acquisition of market POS data.

FIG. 6 is a flowchart showing the processing contents of the terminal device 11 in the store 10.

Referring to FIG. 6, terminal device 11 first inputs store attribute data (S111), and then inputs store POS data (S112). These data are input on a predetermined screen as described later. Thereafter, the input shop data is transmitted to the shelf allocation center 20 (S113).

Based on these data, the shelf allocation center 20 performs an analysis, which will be described in detail later, and transmits the analysis result together with a shelf allocation proposal.
114). If the analysis result suggests a change in the shelf layout, the shelf layout is changed (S115).

When the shelf layout is changed (YE in S115)
S), even if it has not been changed (NO in S115), a new store POS including store attribute data and subsequent sales information
A process for transmitting data to the shelf allocation center 20 is performed (S111, S112, S113).

FIG. 7 is a view showing an example of a screen display for inputting the shop data shown in S111 and S112 of FIG. Here, the store 10 may be, for example, a pharmacy, a drug store, a supermarket, a convenience store, a GMS (general supermarket), a home center, and the like.

With reference to FIG.
To request data analysis, POS data file, title, date of extracted data, comparison date before introduction,
Enter the store code, the number of stores, the monthly sales of pharmaceuticals, the sales floor area, the location conditions, the number of customers and the number of customers, the number of scales, the number of stages, the total number of faces, and the like. The item of the store attribute may be added or deleted according to the cutaway analysis.

The analysis items include overall analysis, category analysis, manufacturer analysis, sales contribution analysis, price range analysis, growth rate /
There are sales force analysis, top-selling product analysis, sales / profit simulation analysis, etc., and a desired analysis can be selected.

Note that the location conditions consider whether the shop is in front of the station, in a residential area, or the like.

It is assumed that store POS data is prepared in advance as a file in which necessary items are extracted.

Next, the processing contents in the shelf allocation center 20 will be described. FIG. 8 is a flowchart showing processing contents in the shelf allocation center 20.

Referring to FIG. 8, in shelf allocation center 20, center device 21 obtains data from each store created in FIG. 6 (S101). Next, research company 30
The market POS data is obtained from (S102). Store PO
If a certain number of store POS data are collected as a result of accumulating and adding S data to market POS data according to certain rules, all market POS data is
It may be constructed from OS data.

Here, in order to obtain data from each store, shelf allocation data at the time of POS data collection of the store may be input. This data is obtained by inputting each product JAN code (bar code) from the end of the products in the store in order from the end.
This is data in which product information (product name, size) is calculated from the N code, and from this information, it is possible to know which product is located at which position on the shelf.

Next, the POS data is analyzed according to a predetermined procedure (S103), and the number of faces is estimated (S10).
4) is performed, and if desired, “sales / profit simulation analysis” (S105) is performed to create shelf allocation proposal data (S106). The trial calculation of the number of faces and the “sales / profit simulation analysis” will be described later.

Thereafter, the created analysis result and shelf allocation proposal data are transmitted to the terminal device 11 of the store 10 (S10).
7). After transmitting the shelf allocation proposal, the shelf allocation center 20 waits for input of new store POS data including store attribute data and subsequent sales information as shown in FIG.

FIG. 9 is a flowchart showing the processing contents of the analysis processing shown in S103 of FIG. The analysis may include a comparative analysis process (S1031 to S1037) and a process of analyzing the change rate (S1038 to S1040). With respect to the analysis described in the flowchart, a desired analysis may be selectively performed without performing all the analysis, or the order of the analysis may be changed.

In this example, for example, a product “eye drops” will be described. Referring to FIG. 9, in the analysis processing, first, an overall analysis is performed (S1031). In the overall analysis, for example, “sales of eye drops in the entire store” is compared with “average sales amount of eye drops per store in the market”. That is, here, the store POS for the group of products to be analyzed
The sales amount and gross profit data of the data and the “sales amount per store” data of the market POS are used.

Next, a category analysis is performed (S103).
2). In the category analysis, the category share of eye drops at the store is compared with that of the market. The unit for comparison may be a “number” basis, a “money” basis, a “gross profit” basis, or the like. That is, as the data used here, among the store POS data, “sales quantity” data, “sales amount” data, and “gross profit amount” data, and “number”, “amount” data, “ Provisional gross profit "data. "Provisional gross profit"
Initially, the data is obtained by multiplying the sales amount of the market POS data by the gross profit rate of the dealer to be analyzed. Store PO
Due to the increase in the S data, gross profit data according to the actual situation will be obtained in the future.

As for the category, settings such as user age group and consumer needs can be made. Next, a maker analysis for analyzing the share of each maker is performed (S1033),
A sales contribution analysis is performed (S1034). In the sales contribution analysis, a matrix analysis of “number” × “gross profit amount” is performed for each category of eye drops in the store. The data used here are "sales quantity" data and "gross profit" data in the store POS data.

Next, price range analysis is performed (S103).
5). In the price range analysis, a matrix analysis of “selling price” × “number” is performed for each eye drop category in the shop. The data used here is “sales quantity” data and “sales price” data in the store POS data.

Next, a growth rate / sales force analysis is performed (S1).
036). In the growth rate / sales force analysis, a matrix analysis of “growth rate” × “number” is performed for each category of eye drops in the store, and the data is compared with corresponding data in the market. The data used here are “sales quantity” data of the store POS data, “quantity” data of the market POS data, and “item sales quantity per item dealer”.

Next, a selling item analysis is performed (S103).
7). Based on the marketable merchandise ranking in the market, the item sales of the store are compared. As a basis for comparison, a number base, a money amount base, a gross profit amount base, and the like are considered. The data used here include “number of sales” data, “sales amount” data, and “gross profit” data among the store POS data, and “quantity” among the market POS data.
Data, "amount" data, and "provisional gross margin" data.

Next, the trial calculation of the number of faces will be described.
The trial calculation of the number of faces is to estimate the optimum number of faces of the product so that the gross profit or sales is maximized.
The data used here is “gross profit amount” data in the store POS data, “number” data in the market POS data, and “total face number” in the store attribute data. Further, “face elasticity analysis”, “price elasticity analysis”, and “shelf arrangement position elasticity analysis” of each product (S1038 to S104)
0) etc., the seasonal fluctuation rate,
It is preferable to perform the calculation by reflecting the market volatility and the like. When the number of product faces is 1 or less, it is possible to propose a study as a product to be discontinued. The optimum number of faces for each product is calculated based on the current product, but when changing the item of the product, a change instruction is issued to calculate the optimum number of faces.

Further, after the trial calculation of the face, a “sales / profit simulation analysis” described later is performed to simulate the sales / profit when the shelves are divided based on the trial calculation of the face.

The “sales / profit simulation analysis” is for predicting sales and profits when the shelves are split based on the shelf split proposal data. This uses a trial calculation result of the “number”, “amount”, “gross profit amount”, and the number of faces of each product POS data. Further, by adding the market growth rate and the seasonal fluctuation rate, the “sales / profit simulation analysis” in the desired month for shelf allocation can be performed more accurately. Further, by adding the change rates obtained by the "face elasticity analysis", "price elasticity analysis", and "shelf arrangement position elasticity analysis", the "sales / profit simulation analysis" can be performed more accurately.

When it is desired to change the number of faces, sales, and the shelf arrangement position, the user can input the changed values and re-perform the “sales / profit simulation analysis”. Furthermore, by newly adding items of selling price and cost, the price can be set using a price more suitable for the actual sales site.

After the face is calculated, based on the result, the “development scale” and “development step” of the store attribute data are added.
In addition, the number of development scales and the number of
To automatically create shelf plan proposal data. further,
Preferably, the shelf allocation proposal data is created using the change rate obtained by the “shelf arrangement position elasticity analysis” of each product after the determination by the “sales / profit simulation analysis”.

FIG. 19 shows an example of the analysis result of the sales / profit simulation analysis. The number of faces, the number, the amount, the selling price, the gross profit, the cost, and the gross profit rate can be displayed for each product, each category, and the total sum in comparison with the analysis result. In addition, the number of faces, the number, the amount, the selling price, the gross profit, the cost, and the gross profit rate of each product can be displayed in parallel with the increase / decrease rate calculated by comparing them with the current numerical values.

Here, "face elasticity analysis" (S103)
8) analyzes how much sales can be expected when the number of faces of a specific product is increased, based on the number, amount, gross profit, and number of faces. The rate of change obtained by this analysis can be calculated in advance based on experimental results at an experimental store or using store POS data and shelf allocation data.

The "price elasticity analysis" (S1039) analyzes how much sales can be expected by reducing the price of a specific product by, for example, 50 yen.
Analyze based on quantity, gross profit, amount, and selling price. The rate of change obtained by this analysis can be calculated in advance based on experimental results at an experimental store, based on market POS data, or using store POS data and shelf allocation data.

"Shelves Position Elasticity Analysis" (S1040)
Is to analyze how much sales can be expected if the shelf arrangement location of a specific product is changed, and uses the number, the amount of money, the gross profit, and the number of faces. The rate of change obtained by this analysis can be calculated in advance based on experimental results at an experimental store or using store POS data and shelf allocation data.

Next, the specific contents of each analysis will be described. FIG. 10 is a diagram showing a data display example in the overall analysis. Referring to FIG. 10, the vertical axis indicates the monthly sales amount of “eye drops”, and the horizontal axis indicates the monthly sales amount per store for each pharmaceutical quotient obtained from the market POS data and the market POS data. The sales amount and the gross profit to be targeted by the store that has performed the analysis and the sales amount and the gross profit of the retail store that analyzed the POS data are displayed. Since the analysis result is displayed in this manner, the retailer can know his / her target amount (indicated by “index” in the figure).

Next, the result of the category analysis shown in S1032 of FIG. 9 will be described. FIG. 11 is a diagram illustrating an example of a category analysis result. The vertical axis shows the share, and the horizontal axis shows the purpose of eye drops. Market POS data and retail P
The share with the OS data is displayed. The difference between the market and the retail store can be recognized at a glance.

FIG. 11 shows, together with the graph, the difference from the market data for each eye drop. That is, the difference from the market data of each eye drop category is displayed in the lower column of the graph. When the market share is lower than the market, the color may be changed and displayed as shown in the figure.

Based on these data, it is possible to formulate a strategy according to the level of the market share compared to the market.

Next, the manufacturer analysis shown in S1033 of FIG. 9 will be described. This maker analysis is a type of category analysis, and uses a graph in which the horizontal axis indicates the maker name and the vertical axis takes a share. FIG. 12 is a diagram showing analysis data of the manufacturer analysis. The share of each maker is compared with the store POS data and the market POS data, and the merchandise of each maker is compared with the retail store and the market. In this case, as shown in FIG. 12, the display colors of those which are superior to the market and those which are inferior to the market may be changed so that the analysis result can be easily understood at a glance.

Next, the sales contribution analysis shown in S1034 of FIG. 9 will be described. In this analysis, eye drops corresponding to “category M” as a specific product field are analyzed.

FIG. 13 is a diagram showing the result of the sales contribution analysis. In the figure, the ordinate represents the gross profit, and the abscissa represents the number of units sold. Based on the market POS data, a line a of the gross profit amount with respect to the average number of sales is obtained. A coordinate axis b of a thick line crossing the center point of the average line a is created and the whole is divided into four zones A, B, C and D in the figure.

That is, the zone A is a group of products that need to be intensively promoted in the future because the number of sales and the gross profit are large. Since zone B is a product having a large gross profit despite a small number of units sold, it is necessary to develop products for this and to increase future sales. Zone C is a zone in which the number of sales is small and the gross profit is low, and therefore, it is a zone that requires consideration including suspension of sales. Zone D is a zone in which it is necessary to establish a sales strategy to maintain the current development, because the gross profit is low but the number of sales is large and the customer has the support.

Next, the price range analysis shown in S1035 of FIG. 9 will be described. FIG. 14 is a diagram showing an example of data display in price range analysis. Referring to FIG. 14, the vertical axis indicates 1
The number per store is shown, and the selling price is shown on the horizontal axis.

Referring to FIG. 14, whether or not each product has an appropriate price range distribution, whether or not there is a hot selling product in each price range, and which price range has become the best selling product Analysis can be performed.

Next, the growth rate / sales force analysis shown in S1036 of FIG. 9 will be described. FIG. 15 is a diagram showing an example of the growth rate / sales force analysis. In the figure, the vertical axis shows the market size,
The growth rate is plotted on the horizontal axis. In the figure, a is an average line of the market obtained from the market POS data.

The coordinate axes of the bold line shown by b in the figure are zoning axes for the sales strategy, whereby the whole is divided into four zones as shown in A, B, C and D in the figure as in FIG. Separate. Zone A is a group of products having a high growth rate and a large market size, and is a field where it is necessary to promote sales as it is.

Zone B is a field where the market scale is large but the growth rate is small, and zone C is a field where both the growth rate and the market size are small. For these zones B and C, it is necessary to have a sales strategy that takes into account sales reduction and the like.

The zone D is a commodity zone having a small market size despite a high growth rate. Therefore, it is necessary to take special measures for sales.

Next, the analysis of the hot-selling merchandise shown in S1037 of FIG. 9 will be described. FIG. 16 is a diagram showing a hot-selling product analysis. The number of products sold, the market share, the corresponding data in the market, the difference from the market data, the store ABC analysis, and the like are described for each product. By comparing the market share of an item with the retailer's share of a product, sales chance loss products (products that are sold in the market but not sold in the store, products that are sold in the market but not sold in the store) ) Etc. can be found.

When creating the shelf allocation proposal data, the optimum zoning obtained from the analysis of the experimental results at the experimental shop or the market POS data may be considered. Further, zoning based on store attributes such as customer class and location may be considered. The zoning here refers to a shelf arrangement position for each category.

Next, evaluation before and after the shelf layout proposal will be described. FIG. 17 is a diagram for judging the effects before and after the shelf split. The vertical axis indicates the number of units sold per store, and the horizontal axis indicates the growth rate.

In the figure, □ indicates store POS data, and ● indicates market POS data. For example, for the product C, the growth rate and the number per store are low at the store POS,
It can be seen that both the growth rate and the number per store are high in the market. It can be seen that the growth rate of the product B is almost the same, but the number per store is more than doubled in the market. On the other hand, although the number of merchandise A remains the same per store, it can be seen that the growth rate is significantly different from the market.

From these facts, it is understood that it is particularly necessary to take measures for selling the product B. Further, the growth rate for each category and the growth rate for a certain period can be compared with the market.

Next, a specific example of shelf allocation based on shelf allocation proposal data will be described. FIG. 18 is a diagram illustrating an example of a specific shelf layout. Based on the above analysis, a sales strategy is set, and the shelf layout is performed so that strategically important products are arranged in a location that is easily visible to consumers. As shown in FIG. 18, in this example, the shelves are divided by category. Thus, by deciding the steps of each shelf according to the category of eye drops, it becomes easy for the customer to select according to the purpose of use, and the appearance is improved.

For a specific product such as eye drops for a personal computer, for example, a personal computer corner or the like may be newly provided to attract the user's eye. Recommended products,
It is also possible to make proposals with comments such as upbringing products and profit products.

The embodiments disclosed this time are to be considered in all respects as illustrative and not restrictive. The scope of the present invention is defined by the terms of the claims, rather than the description above, and is intended to include any modifications within the scope and meaning equivalent to the terms of the claims.

[Brief description of the drawings]

FIG. 1 is a schematic diagram showing an overall configuration of a shelf allocation proposal system.

FIG. 2 is a block diagram illustrating a configuration of a retail store device.

FIG. 3 is a diagram showing the contents of POS data.

FIG. 4 is a block diagram illustrating a configuration of a center device.

FIG. 5 is a flowchart showing processing contents of a shelf allocation proposal system.

FIG. 6 is a flowchart showing processing contents of the retail store apparatus.

FIG. 7 is a diagram illustrating a screen display example of the retail store apparatus.

FIG. 8 is a flowchart showing a processing procedure of the center device.

FIG. 9 is a flowchart illustrating a procedure of an analysis process.

FIG. 10 is a view showing a graph for whole analysis.

FIG. 11 is a graph showing a category analysis.

FIG. 12 is a graph for manufacturer analysis.

FIG. 13 is a diagram showing a sales contribution analysis graph.

FIG. 14 is a graph for price range analysis.

FIG. 15 is a graph for growth rate / sales force analysis.

FIG. 16 is a diagram for analyzing a hot selling product.

FIG. 17 is a graph for comparing growth rates before and after introduction.

FIG. 18 is a diagram showing a proposal example of shelf allocation.

FIG. 19 is a diagram showing a sales / profit simulation analysis result.

[Explanation of symbols]

10 dealers, 11 terminal devices, 20 shelf allocation centers, 2
1 center device, 30 research companies, 40 Internet, 111 CPU, 112 interface, 113
POS device, 114 display device, 115 storage device,
116 input device, 117 output device, 118 communication device, 201 CPU, 202 interface, 203
Storage device, 204 display device, 205 input device, 2
06 output device, 207 communication device, 210 database.

 ──────────────────────────────────────────────────続 き Continued on the front page (72) Inventor Toshiyuki Tanaka 1-8-1, Tatsuminishi, Ikuno-ku, Osaka, Osaka Prefecture Inside Rohto Pharmaceutical Co., Ltd. (72) Inventor Kaoru Narumoto 1-8-1, Tatsuminishi, Ikuno-ku, Osaka, Osaka No. 1 Rohto Pharmaceutical Co., Ltd. F-term (reference) 3E042 BA08 CC10 CD02 CD08 EA01

Claims (6)

[Claims] 1. A shelf allocation proposal data creation system for creating shelf allocation proposal data for a product by a computer system, comprising: means for inputting POS data of a store; means for inputting POS data in a market; Means for automatically creating shelf layout proposal data for the store based on POS data and POS data in the market. 2. The system according to claim 1, further comprising means for inputting the store attributes. 3. A means for comparing the POS data of the store after the shelf allocation based on the shelf allocation proposal data with the POS data of the store before the shelf allocation and / or inputting the shelf allocation data at the time of collecting the POS data of the store. The shelf allocation proposal data creation system according to claim 1 or 2, further comprising means. 4. The POS data of the shop and the P in the market
4. The shelf plan proposal according to claim 3, further comprising: means for updating the shelf plan proposal data based on a comparative analysis of OS data and / or a comparative analysis of POS data of the shop and shelf plan data at the time of POS data collection. Data creation system. 5. A store terminal device having POS data,
A shelf allocation proposal data creating system comprising a center device connected to a terminal device of the store via a network and capable of inputting POS data of a market, wherein Means for obtaining POS data via the network; means for inputting POS data in the market; shelves that are the basis of the shop split data based on the POS data in the market and the POS data in the shop. A shelf allocation proposal data generating system, comprising: means for automatically generating allocation proposal data; and means for outputting the store allocation proposal data from the center device to the store terminal device via the network. . 6. A terminal device connected to a center device capable of proposing data for performing store shelving via a network, comprising: means for inputting POS data of a store in which the terminal device is provided. A terminal device, comprising: means for transmitting the input POS data to the center device; and means for receiving shelf layout proposal data from the center device.