JPH08278997A - Shelf dividing method by category management - Google Patents

Abstract

PURPOSE: To actualize the best assortment for a store by determining the assortment by making good use of standardized data of category data, PI values, constituent elements of articles, constitution factors, etc. CONSTITUTION: Hardware consists of a computer 11, a display part 14, a keyboard 12, a mouse 13, a printer 15, and a storage device 16, which stores a category constituent element file 23, a temporary assortment file 29, a new assortment file 30, a file 36 of PI values by category factors, etc. Then the new index concept of the categories and their constituent elements, article actual constitution factors, PI values, etc., is introduced and a large amount of POS data are efficiently compared and analyzed together with them to objectively evaluated the current assortment; and simulation including article data that are currently not handled can be performed by using actual constituent factors of articles, so the optimum plan for assortment can be generated.

Description

Detailed Description of the Invention

[0001]

[Industrial application] The present invention makes use of internal information including categories and PI values that have been set / registered in advance to provide clear judgment preparation in the shelving allocation work in stores and facilitate selection of alternative products. It relates to the shelving allocation method.

[0002]

2. Description of the Related Art Conventionally, in the shelving allocation system, the numerical basis is determined by the sales quantity of POS data, and the size is limited by the shelving area. Regarding the replacement of products and the introduction of new products, a method of picking up a poorly-sold product from POS data and simply replacing it with the product has been adopted.

[0003]

The POS data, which serves as a numerical basis in the above-mentioned prior art, is not corrected in the sales scale, and therefore the sales scale is corrected in the area scale, that is, in the shelf area. It was only done in. For this reason, in actual shelving allocation for stores with a particularly small area, the faces of hot-selling products are taken large, and the faces of products that should be lined up originally have been scrapped although the sales volume is small. When making revisions, the POS sales data is the only index data, so judgment on what is needed and what is not needed is largely empirical, inefficient, and can vary widely from person to person. There is. Similarly, regarding replacement of unsuccessful products, there were no clear indicators as to why unsuccessful products were unsuccessful and what was appropriate as an alternative product. It is an object of the present invention to provide a method and apparatus for correcting the above-mentioned problems in the prior art, providing a scale correction and a clear assortment standard, and efficiently implementing the standard.

[0004]

[Means for Solving the Problems] In order to achieve the above object, the present invention sets a product classification framework called a category, and also configures a component and a product (single product) that constitute the category. The premise is to decompose each into actual constituent factors and make them universal. Furthermore, while obtaining POS data and using the actual data for comparison,
In order to make POS data universal like the category data, PI values (purchasing points per 1,000 people) are introduced, and each data is made into a database. Various conditions are given to the index thus stored in the database and the actual data, and comparison and search are repeatedly executed on the computer to obtain an optimum product lineup (optimum solution).

[0005]

According to the present invention, categories and their components,
Introducing new index concepts such as actual product composition factors and PI values,
An efficient assortment plan is created because the current assortment can be objectively evaluated by efficiently comparing and analyzing a huge amount of POS data with them, and simulations can also be performed including the product data that is not currently handled by using the actual constituent factors of the product. it can. Furthermore, even within the limited condition of shelf space, the above category constituent elements and actual product constituent factors can be easily compared, referenced, and inquired, so that ideal shelving allocation can be easily performed.

[0006]

Embodiments of the present invention will be described in detail below with reference to screens.

FIG. 1 is a hardware configuration diagram. The hardware includes a computer 11, a display unit 14, a keyboard 12, a mouse 13, a printer 15, and a storage device 16. The storage device 16 has a product system file 17, a product code master file 18, and a product price master file 1
9, product cost master file 20, product attribute file 2
1, product sales cost estimate file 22, category component file 23, handled product file 24, unhandled product file 25, assorted product file 26, unassorted product file 27, factor matching value file 28, provisional product assortment file 29 , New assortment file 30, factor increase / decrease file 3
1, temporary registration product file 32, shelf condition file 32, shelf allocation file 34, product sales record file 35, category factor-specific PI value file 36, sales record factor-specific PI value file 37, category selection factor-specific PI value file 3
8. A PI value file 39 for each temporary product assembling factor is provided.

Next, an embodiment of this system will be described based on a case with reference to FIG. FIG. 2 is a flowchart of a series of processes of this system.

In step 1001, a category to be shelved is designated from the product system file shown in FIG. Here, milk is specified at the class level first, but any level from group to item may be specified.

When milk, which is a category, is designated, in step 1011 from the handling product file 24 shown in FIG.
The category-by-category assortment product file 26 of FIG. 2 is created, and similarly, by step 1012, the unhandled product file 25 of FIG.
Create 7.

In step 1021, milk constituent elements are extracted from the category constituent element file 23 shown in FIG. 9 based on past experience values (priorities), in order from the most important elements, for example,
The components of the category that are also the components of the product are selected as the evaluation items for displaying the selling price, the processing method, and the brand in that order and replacing the product.

In step 1031, in order to determine the order of priority for selecting products by the category constituent elements, the priority reference value is input in order from the constituent elements which are the most important requirements in constructing the category. If the priorities are as they are displayed, enter the numbers as they are. If you want to change the priorities, enter only the components for which you want to select the numbers based on the priorities, and enter the components of the factor matching value file 28 in FIG. It is stored in the priority value of. Next, with respect to the constituent factors, the constituent factors of the constituent elements selected from the product attribute file 21 of FIG. 7 are displayed for each constituent element, and the range which is better to be divided into ranges such as the selling price is designated, and the constituent elements are designated. The most important factor (selling requirement) is prioritized and stored in the factor priority value column of the factor matching value file in the same manner as the component priority value.

In step 1041, the sales performance (POS data) is converted into PI values for each factor. The PI value is calculated from the sales record of the product sales record file 35 of FIG. 21 for each constituent factor of the selected component, and the PI by sales record factor of FIG. 23.
Store in the value file 37. Further, the PI value is recalculated for each factor selected from the category factor-specific PI value file 36 of FIG. 22 and stored in the category selection factor-specific PI value file of FIG.

In step 1051, sales performance factor-specific PI
The difference between the PI values stored in the value file 37 and the category selection factor-specific PI value file 38 is calculated and stored in the PI difference value column of the factor matching value file 28 in FIG. The calculation subtracts the category PI value from the sales performance PI value.

In step 1052, the component priority value, the component priority value, P of the factor matching value file 28 of FIG.
The factor difference value is calculated by multiplying the I difference value and stored in the factor factor value file 28.

From step 1121 to step 1123, a comparison table for each factor of the registered product (new product file or product file) PI value and category PI value is displayed in a graph as shown in FIG. If the PI value of the registered product is lower than the category PI value, the graph portion of the factor is picked on the graph. Since the input section is displayed on the graph screen, register plus. If the PI value of the registered product is protruding only for a certain factor (for example, if the number of products is too large) and you want to reduce the number of products that have that factor, still pick the graph with the mouse and display the input section. Let's register minus. This is done for all the constituent elements, and the data is shown in FIG.
It is stored in the factor increase / decrease file 31 of No. 7.

In step 1131, the factor increase / decrease file 3
Based on the data registered in 1, the products having the factors to be expanded are extracted from the unfinished product file 27 in FIG.
The products to be deleted are extracted from the assortment product file 26 of FIG. 12 or the new product product file 30 of FIG. 16 and displayed.

In step 1141, step 1131
18 is selected and registered in the temporary registration product file 32 of FIG. 18, and the corresponding product in the product lineup product file 26 or the new product lineup file 30 is replaced from the data classification, and the temporary product of FIG. 15 is again displayed. The alignment file 29 is created.

In step 1151, step 1091
In the same manner as above, the PI value of the temporary assortment product and the PI value of the assortment product or the new product as well as the category PI value are calculated for each factor, and the calculation result is displayed on the screen as the factor-specific PI value table for comparison of product as shown in FIG. At the same time, it is stored in the temporary product assortment factor-specific PI value file 39 of the same format as in FIG.

In step 1152, step 1092
Similarly, if the PI value of the provisional product lineup product is satisfactory, the factor matching value comparison with the product group of the product lineup product file 26 or the new product lineup file 30 is performed. If not satisfied, the process moves to step 1161.

Also in step 1153, step 1093
Perform the same process as. Step 1 is to calculate the factor matching value
The procedure of 051 and 1052 is performed, and the result is stored in the factor matching value file 28 of FIG. If the factor conformity value of the provisional product lineup is small, the process moves to step 1161 and step 1131.
Return to step 1 to select the product again or to cancel the selection of the product and proceed to step 1171. If so, step 1
Move to 171.

In step 1171, it is decided whether or not to replace the product, and in step 1181, a new product lineup file 30 similar to that shown in FIG. 16 is created.

In step 1191, step 1181
Based on the new product assembling file 30 created by the above process, various kinds of conditions are given from the product sales cost estimate file 22 of FIG. 8, the product cost master file 20 of FIG. 6, the shelf condition file 33 of FIG. Perform face simulation to maximize profit. This process is similar to that of a general shelving allocation system, and detailed description thereof will be omitted in this embodiment.

In step 1201, if the sales and profits are higher than the current results and are satisfactory, the face is determined. If it is not satisfied, the process moves to step 1211, and the factor resetting is selected to repeat the work, and the process moves to step 1121 or to step 1131.

In step 1221, the shelving allocation table in the format as shown in FIG. 27 is output and the data is stored in the shelving allocation file 34 in FIG.

At step 1231, the newly treated product is deleted from the unhandled product file 25 in FIG. 11 and registered in the handled product file 24 in FIG. Delete and register in the unhandled product file to end the process.

[0027]

According to the present invention, category data, P
Since standardized data such as I-values, product components, and component factors are used to determine the product lineup, the space for hot-selling products, which has been a problem with conventional shelving allocation systems, becomes larger than necessary. Such problems and the problem that the space for new products is not available can be improved, and the optimal product lineup for the store can be realized.

[Brief description of drawings]

FIG. 1 is a hardware configuration diagram of a shelving allocation system based on a category management system.

FIG. 2 is a flowchart of processing of the shelving allocation system by the category management system.

FIG. 3 is a data configuration diagram of a product system file for setting a category.

FIG. 4 is a data configuration diagram of a product code master file for managing a company product code.

FIG. 5 is a data configuration diagram of a product price master file for managing the selling price of the company.

FIG. 6 is a data structure diagram of a product price master file for managing the purchase cost of the company.

FIG. 7 is a data configuration diagram of a product attribute file for selecting product component factor information.

FIG. 8 is a data configuration diagram of a product sales cost estimation file for calculating the cost for each product during the shelving allocation simulation.

FIG. 9 is a data configuration diagram of a category component file for selecting a component of a category.

FIG. 10 is a data structure diagram of a handling product file for managing the handling products of the company.

FIG. 11 is a data structure diagram of an unhandled product file for managing the unhandled product of the company.

FIG. 12 is a data configuration diagram of an assortment product file for temporarily storing the products handled by category for dividing into shelves.

FIG. 13 is a data configuration diagram of an unsorted merchandise file for temporarily storing unhandled merchandise by category for dividing into shelves.

FIG. 14 is a data configuration diagram of a factor matching value file which is a condition setting table for setting a new product.

FIG. 15 is a data configuration diagram of a temporary product lineup product file for temporarily storing temporary product lineup products for comparing product lineups.

FIG. 16 is a data configuration diagram of a new assortment product file for temporarily storing a new assortment product for performing a shelving allocation simulation.

FIG. 17 is a data configuration diagram of a factor increase / decrease file for temporarily storing conditions for exchanging products.

FIG. 18 is a data configuration diagram of a temporary registration product file for temporarily storing products for changing the product lineup.

FIG. 19 is a data configuration diagram of a shelving condition setting file for shelving allocation.

FIG. 20 is a data configuration diagram of a shelving allocation file for storing shelved data.

FIG. 21 is a data configuration diagram of a file of product sales records.

FIG. 22 is a data configuration diagram of a category PI value reference data file for comparing sales results and PI values.

FIG. 23 is a data configuration diagram of a file for temporarily storing PI values for sales factors by factor.

FIG. 24 is a data configuration diagram of a file in which category PI value reference data is sorted by factors of sales results.

FIG. 25 is an example of a screen for comparing the corrected PI value of the assortment with sales performance data and the like.

FIG. 26 is an example of a screen for setting factors that are conditions for product selection.

FIG. 27 is an example of a shelving allocation table after a shelving allocation simulation is performed.

[Explanation of symbols]

11 ... CPU, 12 ... Keyboard, 13 ... Mouse, 14 ... Display, 15 ... Printer, 16 ... Storage device.

Claims (1)

[Claims] 1. A product assortment product file and an assortment product file for each category are created, and the components and components of the category are weighted and the product (single item) PI value and the category PI value are compared for each component of the category. The products with a large weighting factor are selected from the unsorted product file by weighting each factor that has a difference, and a new product file is created by replacing the product with a low PI value and a new product file is created. If the PI values of the new product lineup are satisfied by comparing the factor conformity values, face simulation is performed as it is, and if the PI values of the new product lineup are not satisfied, the condition of replacement is manually input again for each factor of the component and the product is selected. Compare the PI values and factor matching values with the product groups in the product lineup and repeat this until the PI value of the new product lineup is satisfied. Shelf allocation system to create a shelf allocation, such as sales carried out and Deployment, profit maximizing.