KR102138831B1 - Integrated management apparatus and method for providing counseling service - Google Patents

Abstract

The method for calculating expected sales performed in the apparatus for calculating expected sales according to an embodiment of the present invention includes: determining a target category related to a product to be sold, calculating sales and average market share of the target category, and at a target store And calculating expected sales based on sales and the average market share.

Description

Expected sales calculation device and method {INTEGRATED MANAGEMENT APPARATUS AND METHOD FOR PROVIDING COUNSELING SERVICE}

The present invention relates to an apparatus and method for calculating expected sales. More specifically, the present invention relates to an apparatus and method for calculating expected sales that can predict in advance expected sales that can be sold in a store.

In order to increase the distribution and sales profits of various products, stores are distributed by region, or multiple stores are distributed within a specific region. In this way, it is possible to increase the sales profit of the product and to distribute the product smoothly by expanding the sales place through the distributed arrangement of stores. However, due to various factors acting on each store, there is a limit in increasing profits only by expanding the number of sales outlets because the sales rate for each product cannot be uniform.

In this way, not only the expansion of sales destinations for product sales, but also sales of actual products must be managed together. Such sales are one of the factors that need to be importantly managed in order to supply logistics or expand profits.

In connection with this, Korean Patent Registration No. 10-1895672 in the prior art document describes a payment manager that manages sales information of sales targets sold in stores to provide investment information in real time. The payment manager in the prior art literature receives real-time sales information and provides the received sales information to the registered terminals. As described in the prior art literature, existing technologies for managing sales collect information related to sales in real time and only provide the contents to users, managers, or investors, etc., in stores to plan sales strategies, etc. There was a problem in that it was impossible to predict in advance the details related to future sales.

Therefore, there is a need for a technique for solving the above-mentioned problems.

Meanwhile, the above-described background technology is technical information acquired by the inventor for the derivation of the present invention or acquired in the derivation process of the present invention, and is not necessarily a known technology disclosed to the general public before filing the present invention. .

The embodiments disclosed herein have an object to provide an apparatus and method for calculating expected sales that can calculate expected sales that can be sold in a store.

The embodiments disclosed herein have an object to provide an apparatus and method for calculating expected sales that can calculate expected sales so as to establish a sales strategy.

The embodiments disclosed herein have an object to provide an apparatus and method for calculating expected sales to determine a store or a product to be promoted for promotion of a product.

As a technical means for achieving the above-described technical problem, according to an embodiment, the expected sales calculation method performed by the expected sales calculation device comprises: determining a target category related to a product to be sold, and the average and sales of the target category Calculating a market share, and calculating expected sales based on sales at the target store and the average market share.

According to another embodiment, the apparatus for calculating expected sales determines a target category related to a product being sold in the plurality of stores, and a communication unit that receives information for calculating sales and market share for each of the plurality of stores, and the target category It includes a control unit for calculating the sales and average market share of, and calculates expected sales based on the sales in the target store and the average market share.

According to any one of the above-described problem solving means of the present invention, it is possible to present an apparatus and method for calculating expected sales that can calculate expected sales that can be sold in stores.

According to any one of the problem solving means of the present invention, it is possible to present an apparatus and method for calculating expected sales that can calculate expected sales so as to establish a sales strategy.

According to any one of the problem solving means of the present invention, it is possible to present an apparatus and method for calculating expected sales to determine a store or a product to promote a product promotion.

The effects obtainable in the present invention are not limited to the above-mentioned effects, and other effects not mentioned can be clearly understood by those skilled in the art from the following description. will be.

1 is a diagram illustrating a system for calculating expected sales according to an embodiment.
2 is a block diagram illustrating an apparatus for calculating expected sales according to an embodiment.
3 is a view for explaining expected sales calculation according to an embodiment.
4 is a diagram illustrating data related to calculation of expected sales in a database according to an embodiment.
5 is a graph showing the distribution of stores based on sales according to an embodiment.
FIG. 6 is a diagram for explaining a comparison section and a calculation section used for product selection for price promotion according to an embodiment.
7 is a flowchart illustrating an operation for calculating expected sales according to an embodiment.
8 is a flowchart illustrating an operation for a promotion for product promotion based on expected sales according to an embodiment.
9 is a flowchart illustrating a selection operation of a model store according to an embodiment.
10 is a flowchart illustrating a product decision for a promotion according to an embodiment.

Hereinafter, various embodiments will be described in detail with reference to the accompanying drawings. The embodiments described below may be embodied in various different forms. In order to more clearly describe the features of the embodiments, detailed descriptions of the matters well known to those skilled in the art to which the following embodiments pertain are omitted. In the drawings, parts irrelevant to the description of the embodiments are omitted, and like reference numerals are assigned to similar parts throughout the specification.

Throughout the specification, when a component is "connected" to another component, this includes not only a case of'directly connecting', but also a case of'connecting with other components in between'. In addition, when it is said that a certain configuration "includes" a certain configuration, this means that other configurations may be included, but not excluded, unless otherwise specified.

Hereinafter, exemplary embodiments will be described in detail with reference to the accompanying drawings.

Before explaining this, however, the meanings of terms used below are defined first.

'Expected sales' means sales expected to be sold when certain conditions are met at the target store, or sales expected to be sold in comparison with other stores of the same conditions. Expected sales can be calculated based on the market share (MS) of the category containing the related product. Here, the category means a category for classification of items to which the product belongs, for example,'soybean paste', and the product is a single product (a specific product to which a manufacturer, capacity, product name, etc. are assigned) classified within the classified item, For example, it may mean a product such as Y company miso of 1kg capacity.

1 is a diagram illustrating a system for calculating expected sales according to an embodiment.

As shown in FIG. 1, the expected sales calculation system 10 may include a plurality of store terminals 11 and 12 and an expected sales calculation device 100.

The plurality of store terminals 11 and 12 may access the network and communicate with the expected sales calculation device 100, and may be connected to the network in one of wired or wireless methods.

The plurality of store terminals 11 and 12 are located in the store, and various information generated by the sale of products in the store input by the store manager, for example, product name, manufacturer, product classification (category), sales situation (stock , Sales). The plurality of store terminals 11 and 12 may provide the collected information to the expected sales calculation device 100.

The expected sales calculation device 100 may calculate expected sales using information received from a plurality of store terminals. Expected sales calculation device 100 uses the information such as product name, manufacturer, product classification (category), and sales status (inventory, sales) received from a plurality of store terminals, and average market share in each store and total store And calculate expected sales based on the calculated sales and market share.

The expected sales calculation device 100 may use the calculated expected sales to determine a target store or a promotion-promoted product to promote a product promotion. Through this, when the expected sales calculation apparatus 100 calculates expected sales at each store, various promotions such as price discount, additional gift (1+1), coupon, and cashback may be performed based on the expected sales.

2 is a block diagram illustrating an apparatus for calculating expected sales according to an embodiment.

As illustrated in FIG. 2, the apparatus 100 for calculating expected sales may include an input/output unit 110, a communication unit 120, a storage unit 130, and a control unit 140.

The input/output unit 110 may include an input unit for receiving input from a user, and an output unit for displaying information such as a result of performing a job or a state of the expected sales calculation device 100.

The input/output unit 110 may receive a user input for inquiring or retrieving information related to expected sales or promotion progress, or may output information related to expected sales or promotion progress using a display screen or the like.

The communication unit 120 may perform wired/wireless communication with other devices or networks. To this end, the communication unit 120 may include a communication module supporting at least one of various wired and wireless communication methods. For example, the communication module may be implemented in the form of a chipset.

The wireless communication supported by the communication unit 120 may be, for example, Wi-Fi (Wireless Fidelity), Wi-Fi Direct, Bluetooth (Bluetooth), UWB (Ultra Wide Band), or NFC (Near Field Communication). In addition, the wired communication supported by the communication unit 120 may be, for example, USB or High Definition Multimedia Interface (HDMI). The above-described communication is only an example, and various communication techniques enabling the expected sales calculation device 100 to perform communication are possible.

The communication unit 120 may communicate with a plurality of store terminals 11 and 12, receive information for calculating expected sales from a plurality of store terminals, and guide information related to the progress of the promotion when determining promotion progress. A plurality of store terminals 11 and 12 may also provide information related to the promotion.

Various types of data, such as files, applications, and programs, may be installed and stored in the storage unit 130. Data stored in the storage unit 130 may be accessed and used by the control unit 140 to be described later, or new data may be stored by the control unit 140. In addition, the storage unit 130 may store a program capable of performing an overall operation for managing the expected sales calculation device 100 by the control unit 140. In particular, the storage unit 130 may store a program capable of performing an overall operation for the promotion using the calculated expected sales and the calculated expected sales.

Meanwhile, the control unit 140 controls the overall operation of the apparatus for calculating expected sales 100 and may include a processor such as a CPU. The control unit 140 may control other components included in the expected sales calculation device 100 to perform an operation corresponding to a user input received through the input/output unit 110.

According to this embodiment, the control unit 140 may select a category for calculating expected sales. At this time, the control unit 140 may select a specific product, not a category. When a category is selected, the controller 140 may calculate sales and market share for the corresponding category using information received from each of the entire stores to calculate expected sales.

In addition, the control unit 140 may calculate the average market share for the category (or product) by calculating the average of the market share in all stores. When calculating the average market share, the control unit 140 may check the difference (gap) from the market share in the target store to calculate expected sales. The control unit 140 may calculate expected sales by multiplying the value obtained by subtracting the market share of the target store from the average market share by sales at the target store. Here, the controller 140 may calculate expected sales using Equation 1 below.

In Equation 1, the market share for calculating expected sales is the market share for the corresponding category in the target store, and the total sales means the total sales in the target store.

When calculating the expected sales, the control unit 140 may use the expected sales as a reference indicator for promoting a promotion for a specific product. The control unit 140 may determine a store or a product to be promoted by using expected sales in a target category to promote the promotion.

The controller 140 may aggregate expected sales by region or store and group them based on a management agency located at the store's location to determine a management method for each category, product, and store for improving sales. For example, the control unit 140 may determine which category to prioritize when promoting a promotion for a promotion in a specific region. Through this, the control unit 140 may select target categories and stores based on expected sales aggregates or rankings by region or category. In addition, the control unit 140 may determine which region or store to prioritize when a promotion for promotion is performed in a specific category. Through this, the control unit 140 may select a region or a store based on the expected sales aggregate or ranking for each category.

The control unit 140 may extract a store with a market share lower than the average of the target category in order to determine a store for the promotion. For example, the controller 140 may extract stores having a market share lower than the average using expected sales, and may select stores with expected sales exceeding a predetermined reference value as stores for promotion. That is, the larger the expected sales value, the higher the value of expected sales can secure sales according to the progress of the promotion. Therefore, the expected store can be used to determine the target store for the promotion.

When the target store to be promoted is determined, the controller 140 may collect data on the target store. For example, the controller 140 may collect the number of stores by group classified by a predetermined criterion, current sales, current market share, and expected sales. Here, the group means a set of stores divided based on the market share and sales of the stores for each of the entire stores.

The controller 140 may divide the entire store into a plurality of groups based on market share and sales of each store. The controller 140 may classify a plurality of ratings for each of the entire stores based on market share and sales, respectively, and generate a group corresponding to each of the plurality of ratings. The control unit 140 classifies a group having a relatively higher value for each of the market share and sales into a higher rating, and a group having a relatively lower value for each of the market share and sales into a lower rating. can do. For example, the control unit 140 includes a store with a market share of 50% and a market share of 500,000 won in the rating of the first group that includes a store with a market share of 30% and a sales value of 350,000 won. It may be classified as a lower grade than the second group of grades. On the other hand, the control unit 140 includes a store with a market share of 10%, a market share of 10%, and a rating of the first group that includes a store with a market share of 30% and a sales value of 350,000 won. It can be classified as a higher level than the third group.

On the other hand, if at least one of the market share or sales is higher, the control unit 140 may give priority to either the market share or sales, and compare the values of the items with higher priority to set the group rating. For example, if only one of market share and sales is high, a higher rank may be assigned to a group having a higher priority.

When the target store to be promoted is determined, the controller 140 may select a best practice store. For example, the same products sold in stores A and B of the same size may have similar sales in both stores A and B. Therefore, if the sales volume at store B does not reach the sales volume at store A for a particular product, it can be determined that the sales opportunity at store B is missed. To this end, the controller 140 may select a store to be compared with a target store, that is, a model store to determine a specific product for promotion.

Therefore, the control unit 140 may select a store with a high market share as an excellent store within a group including a store to be promoted. The control unit 140 may perform extraction of an excellent store based on Equation 2 below.

Here, the excellent store MS represents the market share of the excellent store, and μ represents the average market share. In addition, σ denotes a standard deviation of market share, and n is a standard deviation variable and may be set to a rational number.

In this way, the control unit 140 may extract, as an excellent store, a store having a value greater than a value obtained by calculating a predetermined standard deviation variable (n) for the standard deviation of the market share and the average market share.

At this time, if the number of stores in the group for extraction of the excellent store is greater than a predetermined criterion, the control unit 140 may select the determined excellent store as an exemplary store. However, if the number of stores in the group for determining the good store is less than a predetermined criterion (predetermined number of samples), the control unit 140 re-extracts the good store by adjusting the value of the standard deviation variable n calculated for the standard deviation. However, if the value of the variable is adjusted so small that it is difficult to adjust, it can be moved to a group of higher grades to extract an excellent store. For example, the controller 140 may set the initial value of the standard deviation variable n to 2, and if the number of stores in the group is less than a predetermined sample number, the variable value may be subtracted by -0.5. At this time, if the value of the variable is less than 0.5, the control unit 140 may change the group for extracting a good store into a group of a higher level.

The control unit 140 may generate history data for the progress of the price promotion at intervals of a predetermined period. The control unit 140 may extract a product with poor handling performance, for example, a product with a low sales volume, a product not sold, and the like.

The control unit 140 may search the recent price promotion history for the extracted product. For example, the promotion may refer to a form of sale in which a price is temporarily lowered for a specific period in order to increase a sales amount of a specific product at intervals of a predetermined period. The controller 140 may search for a price promotion history based on the sales data. In this way, the control unit 140 may search the history of price promotion based on the sales data because it is practically impossible to check the promotion for the entire store by the field representative.

Through this, the controller 140 may select products for promotion in the order of high price elasticity index. The control unit 140 sets a calculation period of a predetermined period (for example, a recent week period) in order to search for a price promotion history, and a predetermined period (for example, 6 months before the calculation period) at a previous time of the calculation period Products can be selected based on the average selling price for the period. The control unit 140 may be performed based on Equation 3 below to select a product.

As in Equation 3, the controller 140 may select a product whose average selling price for the calculation section is smaller than the difference between the average selling price for the comparison section and the standard deviation.

The controller 140 may calculate the price elasticity index based on Equation 4 below.

The controller 140 may select products for promotion in the order in which the price elasticity index of the sales quantity is high.

On the other hand, the control unit 140 may determine a store to be compared to search for a product that has missed the sales opportunity of a specific product through selection of a model store, and based on the sales in the model store, the sales of the sales resulting from the price promotion Can be calculated. For this reason, the control unit 140 may check the efficiency of the promotion in advance through selection of a model store.

According to the present embodiment, the apparatus 100 for calculating expected sales may calculate expected sales available for sale in a specific store. Expected sales calculation device 100 may establish a sales strategy using expected sales, and may determine a store or a product to promote a product promotion.

3 is a view for explaining expected sales calculation according to an embodiment.

As illustrated in FIG. 3, the apparatus 100 for calculating expected sales may receive a market share of a target category from each of all store terminals. Also, the apparatus 100 for calculating expected sales may receive information for calculating the market share of the target category from each of the entire store terminals, and may directly calculate the market share.

The expected sales calculation device 100 may calculate the average market share 201 by averaging the market share for all stores. In addition, the apparatus 100 for calculating expected sales may determine a target store for calculating expected sales. For example, the apparatus 100 for calculating expected sales may determine a target store for calculating expected sales as the fourth store 240.

The expected sales calculation device 100 subtracts 10%, which is the market share of the fourth store 240, from 25%, which is the average market share 201, and 15%, which is the difference value 241 from the average market share 201. Can be obtained. Expected sales calculation device 100 calculates the expected sales of the category by multiplying the total sales amount (for example, 1 million won) for the target category of the fourth store 240 by the difference value 241 from the average market share ( 1 million won*15%=150,000 won).

Also, the apparatus 100 for calculating expected sales may calculate the maximum expected sales by using the market share of the store (third store 230) having the maximum market share, that is, the maximum market share 202. Here, the maximum expected sales is a form of expected sales, which means the expected sales compared to the stores with the highest sales.

Expected sales calculation device 100 is the maximum expected sales achievable at the fourth store 240 based on the market share of the third store 230, that is, the store with the highest market share of the target category, that is, the maximum market share 202 You can also calculate Here, the expected sales calculation device 100 is obtained by subtracting the market share (10%) of the fourth store 240 from the market share (30%) of the third store 230 having the maximum market share 202 The difference (20%) from the maximum market share (202) can be obtained. Expected sales calculation device 100 calculates the maximum expected sales by multiplying the difference value 242 from the maximum market share 202 by the total sales amount (for example, 1 million won) for the target category of the fourth store 240 (100*20% = 200,000 won).

4 is a diagram illustrating data related to calculation of expected sales in a database according to an embodiment.

As illustrated in FIG. 4, the apparatus for calculating expected revenue 100 may construct a database 300 for calculating expected revenue inside or outside the apparatus for calculating expected revenue 100. In the database 300, the market share reference information 310 of the category and the expected sales calculation history of the store may be stored.

The expected sales calculation device 100 may collect the market share reference information 310 for calculating expected sales. The expected sales calculation device 100 may access and receive the market share reference information 310 by connecting to a store terminal or a manufacturer server.

The market share standard information 310 includes the product manufacturer code or manufacturer name, the date on which the market share was calculated, the reference period (month or week) according to the calculation of the market share, product classification (category), store average market share, and store minimum It may include information such as market share and total sales.

The expected sales calculation device 100 may calculate expected sales of a store using category market share reference information. The expected sales calculation device 100 may calculate expected sales of a store using category market share reference information.

Expected sales calculation details 320 are store code or store name, market share calculation date, market share standard period (month or week), manufacturer code or manufacturer name, product classification (category), market share of manufacturer-category, market share Gaps (versus average market share or maximum market share) and expected sales (average market share or maximum market share).

As described above, the apparatus 100 for calculating expected sales may use the market share reference information 310 to calculate expected sales for each store, and generate expected sales calculation information 320 according to the calculation of expected sales.

The expected sales calculation device 100 may utilize the expected sales calculation history 320 to select a store or product for promotion.

5 is a graph showing the distribution of stores based on sales according to an embodiment.

5, the horizontal axis of the graph 400 represents sales, and the vertical axis represents market share. Each of the points distributed on the graph 400 represents a store.

Expected sales calculation device 100 may use the calculated expected sales to promote the promotion. At this time, the expected sales calculation device 100 may use the graph 400 to select a target store and a model store for the promotion.

The expected sales calculation device 100 may divide the entire store into at least two or more groups based on sales and market share. For example, the apparatus 100 for calculating expected sales may set a first market share reference line 421 for dividing a class based on a market share of a store. At this time, the first market share reference line 421 may be set based on a market share of about 62%. The expected sales calculation device 100 may move the first market share reference line 421 to the position of the second market share reference line 422 or may additionally set a new second market share reference line 422. The second market share baseline 422 may be set based on a market share of about 48%.

Also, the apparatus 100 for calculating expected sales may set the sales reference lines 411 and 412 based on sales. The expected sales calculation device 100 may set the first sales amount reference line 411 based on 380,000 won and the second sales reference line 412 based on 650,000 won.

For example, the apparatus 100 for calculating expected sales is divided into two stages (above average, below average) for each of market share and sales, or three stages (top 11%, top 40%, below, etc.) ).

For example, the expected sales calculation device 100 is divided into six grades using the first market share baseline 421, the first sales baseline 411, and the second sales baseline 421, and divided into six grades. Separated stores can be managed by grouping stores in one class. At this time, the numbers corresponding to each of the six groups are displayed in the graph.

For example, in the case of the fifth group 5, the market share is less than or equal to the first market share baseline 421, and is a set of stores located between the first sales baseline 411 and the second sales baseline 412.

The expected sales calculation device 100 may give a higher rating as the sales and market share of the store are higher, and a lower rating as the sales and market share are lower.

The apparatus for calculating expected sales 100 may set a group for each class for subdividing a store based on a baseline, and may determine a strategy for improving sales in the corresponding store group. Each point representing a store on the graph 400 may be classified and displayed in different colors according to characteristics.

For example, the apparatus 100 for calculating expected sales may classify and display the color of a store based on a level according to, for example, price competitiveness or unit price reduction. The expected sales calculation device 100 may display the colors of the stores by distinguishing them based on characteristics of the surrounding area of the store, for example, a residential dense area, a station area, a commercial area, a tourist resort, and a shopping mall. In addition, the expected sales calculation device 100 is based on the pie of the market for sales, such as population characteristics (eg, income level (apartment size), number of households per person, population age distribution, etc.) around the store. You can also display the colors of the stores separately.

Expected sales calculation device 100 may provide a color notation to visually check various attributes of a store by an administrator to assist in decision making for sales and promotion of the store.

Expected sales calculation device 100 may be displayed on the graph 400 based on the difference from the selling price of the competitor's products for each store, by dividing the price competitiveness into predetermined criteria (for example, upper, middle, lower, etc.). . In addition, the apparatus 100 for calculating expected sales may classify and display price competitiveness on a graph 400 based on a difference between an average price of a company's product price and a price of a competitor's product. Through this, the apparatus for calculating expected sales 100 may utilize the divided price competitiveness to be used as a criterion for determining whether there is room for lowering the unit price.

FIG. 6 is a diagram for explaining a comparison section and a calculation section used for product selection for price promotion according to an embodiment.

As shown in FIG. 6, the apparatus 100 for calculating expected sales may calculate the average selling price based on the comparison section 510 and the calculation section 520.

The expected sales calculation apparatus 100 may calculate the average selling price in the calculation section 520 and the average selling price in the comparison section 510 based on the time point 500 for calculating expected sales. For example, in the case of the calculation section 520 for calculating the average selling price, the expected sales calculation device 100 is assumed to be about 1 week, and the comparison section 510 is relatively relative to the previous time point of the calculation section 520 It can be a long section, about six months. Here, the comparison section 510 may be set as a relatively long section compared to the calculated section to secure the reliability of the average selling price.

Through this, the apparatus 100 for calculating expected sales may determine a product for promotion promotion based on Equation 3 described above. The apparatus 100 for calculating expected sales may determine a product whose average selling price of the calculation section 520 is smaller than the difference between the average selling price of the comparison section 510 and the standard deviation of the comparison section as a product to be promoted.

7 is a flowchart illustrating an operation for calculating expected sales according to an embodiment.

As illustrated in FIG. 7, the apparatus 100 for calculating expected sales may determine a target category for calculating expected sales (S610 ).

The expected sales calculation device 100 may check sales of a target category for calculating expected sales and calculate an average market share (S620). The expected sales calculation device 100 may receive information related to sales and market share of the target category from each of the entire stores and calculate the average market share of all stores.

When the target store for calculating expected sales is determined, the expected sales calculation device 100 may calculate expected sales for the target store (S630). Expected sales calculation device 100 may calculate the expected sales by calculating the difference between the average market share and the market share of the corresponding store to the sales of the target store.

8 is a flowchart illustrating an operation for a promotion for product promotion based on expected sales according to an embodiment.

As illustrated in FIG. 8, the apparatus 100 for calculating expected sales may select a target store for the promotion (S710). The expected sales calculation device 100 may select a store having a market share of a specific category that is lower than the average, that is, a store having an expected sales of a predetermined amount or more as a target store for the promotion.

The expected sales calculation device 100 may select a model store (S720). Expected sales calculation device 100 can increase the efficiency of the promotion by predicting the expected sales during the promotion in the target store through the selection of a model store.

The expected sales calculation device 100 may determine a product for the promotion (S730). For example, a promotion may be marketing that cuts and sells a product for a specific period of time. Expected sales calculation device 100 may determine a product to proceed with the price promotion based on the sales data.

9 is a flowchart illustrating a selection operation of a model store according to an embodiment.

As illustrated in FIG. 9, the apparatus 100 for calculating expected sales may identify a group including a store to be promoted (S721 ). The expected sales calculation device 100 may select a model store within a group divided into a plurality of classes as shown in FIG. 5.

The expected sales calculation device 100 may extract an excellent store (S722). The apparatus 100 for calculating expected sales may extract a store having a market share of the corresponding store that is greater than the sum of the standard deviation in which the standard deviation variable (n) is calculated in the average market share as an excellent store. For example, the apparatus 100 for calculating expected sales may set the initial value of the standard deviation variable n to 2.

The expected sales calculation device 100 checks whether the number of stores in the group selecting the excellent store satisfies a predetermined sample number (S723). The expected sales calculation device 100 can confirm whether a predetermined sample number is satisfied because the reliability of the model store selection can be increased when at least a certain number of samples for selecting an excellent store are secured.

As a result of checking in step S723, when the expected sales calculation device 100 satisfies the predetermined number of samples, the process proceeds to step S724.

The expected sales calculation device 100 may select the extracted excellent store as a model store and proceed to step S730 (S724).

On the other hand, as a result of checking in step S723, when the expected sales calculation device 100 does not satisfy the predetermined number of samples, the process proceeds to step S725.

The expected sales calculation device 100 may adjust the value of the standard deviation variable n (S725). For example, the apparatus 100 for calculating expected sales may subtract the value of the standard deviation variable n by 0.5.

The expected sales calculation device 100 determines whether the standard deviation variable n is less than the reference value (about 0.5) (S726).

As a result of the determination in step S726, if the standard deviation variable n is greater than or equal to the reference value, the expected sales calculation device 100 proceeds to step S722 to extract an excellent store satisfying Equation 2 again (S727).

As a result of the determination in step S726, if the standard deviation variable n is less than the reference value, the expected sales calculation device 100 may proceed to step S727.

Expected sales calculation device 100 may increase the rating of the group for extracting excellent stores, and proceed to step S721. For example, the apparatus 100 for calculating expected sales may adjust the class of the group for extracting the excellent store in the group of the fourth class to be upgraded by one step when the current group's class for extracting the excellent store is the fifth class. .

10 is a flowchart illustrating a product decision for a promotion according to an embodiment.

As illustrated in FIG. 10, the expected sales calculation device 100 may extract a product whose handling performance is below a predetermined standard (S731). For example, the expected sales calculation device 100 may extract a product whose sales amount is less than a predetermined sales amount. In addition, the expected sales calculation device 100 may extract a product having a poor handling performance, such as a product whose inventory amount is equal to or greater than a predetermined inventory amount, and a product whose sales volume has plunged.

To this end, the apparatus 100 for calculating expected sales may generate history data for the progress of the promotion at predetermined intervals (eg, about 1 week).

Expected sales calculation device 100 may search the recent promotion history of the product (S732). The expected sales calculation device 100 may search for a promotion history of a target product for promotion, and select a product having a lower average selling price than the average selling price of the comparison section.

Expected sales calculation device 100 may determine and end a single product for the promotion process in the order of high price elasticity index among the selected products (S733). The expected sales calculation device 100 may calculate the price elasticity index based on Equation (4). That is, the expected sales calculation device 100 may determine the price elasticity index based on the ratio of the amount of change in the amount of sales to the amount of change in the price of sales or the rate of increase or decrease in the amount of sales compared to the rate of reduction of the price of sales.

The term'~unit' used in this embodiment means software or a hardware component such as a field programmable gate array (FPGA) or ASIC, and'~unit' performs certain roles. However,'~ wealth' is not limited to software or hardware. The'~ unit' may be configured to be in an addressable storage medium or may be configured to reproduce one or more processors. Thus, as an example,'~ unit' refers to components such as software components, object-oriented software components, class components, and task components, processes, functions, attributes, and procedures. , Subroutines, segments of program patent code, drivers, firmware, microcode, circuitry, data, database, data structures, tables, arrays, and variables.

The functionality provided within components and'~units' may be combined into a smaller number of components and'~units', or separated from additional components and'~units'.

In addition, components and'~ units' may be implemented to play one or more CPUs in a device or a secure multimedia card.

In addition, the integrated management method according to an embodiment of the present invention may be implemented as a computer program (or computer program product) including instructions executable by a computer. The computer program includes programmable machine instructions processed by a processor and may be implemented in a high-level programming language, object-oriented programming language, assembly language, or machine language. . The computer program can also be recorded on a tangible computer-readable recording medium (eg, memory, hard disk, magnetic/optical medium, or solid-state drive (SSD)).

Therefore, the integrated management method according to an embodiment of the present invention may be implemented by executing the computer program as described above by a computing device. The computing device may include at least some of a processor, a memory, a storage device, a high-speed interface connected to the memory and a high-speed expansion port, and a low-speed interface connected to the low-speed bus and the storage device. Each of these components is connected to each other using various buses, and can be mounted on a common motherboard or mounted in other suitable ways.

Here, the processor is capable of processing instructions within the computing device, such as to display graphical information for providing a graphical user interface (GUI) on an external input or output device, such as a display connected to a high-speed interface. Examples are commands stored in memory or storage devices. In other embodiments, multiple processors and/or multiple buses may be used in conjunction with multiple memories and memory types as appropriate. In addition, the processor may be implemented as a chipset formed by chips including a plurality of independent analog and/or digital processors.

Memory also stores information within computing devices. In one example, the memory may be comprised of volatile memory units or a collection thereof. As another example, the memory may be composed of non-volatile memory units or a collection thereof. The memory may also be other types of computer readable media, such as magnetic or optical disks.

And the storage device can provide a large storage space for the computing device. The storage device may be a computer readable medium or a configuration including such a medium, and may include, for example, devices within a storage area network (SAN) or other configurations, and may include floppy disk devices, hard disk devices, optical disk devices, Or a tape device, flash memory, or other similar semiconductor memory device or device array.

The above description of the present invention is for illustration only, and those skilled in the art to which the present invention pertains can understand that it can be easily modified to other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form.

The scope of the present invention is indicated by the following claims rather than the above detailed description, and it should be interpreted that all changes or modified forms derived from the meaning and scope of the claims and equivalent concepts thereof are included in the scope of the present invention. do.

10: Expected sales management system 11, 12: Store terminal
100: expected sales calculation device 110: input and output unit
120: communication unit 130: storage unit
140: control unit

Claims (20)

In the method for calculating expected sales performed by the apparatus for calculating expected sales,
Determining a target category related to the product to be sold;
Calculating sales and average market share of the target category;
Calculating expected sales based on sales at the target store and the average market share;
Selecting the target store for a promotion based on the expected sales;
Selecting a model store related to the target store; And
And determining a single product for promotion in the target category,
The step of selecting the target store,
Further comprising the step of classifying into at least two or more grades based on market share and sales, and selecting the target store within a group corresponding to the classified grade,
The group, the higher the market share and the sales, the higher the rating, the lower the market share and the sales, the lower the rating,
The step of selecting the above model stores is
Within the group in which the target store is selected, stores with a higher market share than the average are selected as excellent stores based on the following equation, and if the excellent stores satisfy the number of samples above a predetermined criterion, it is determined as a best store.
If the number of samples above the predetermined criteria is not satisfied, adjusting the number of samples to re-determine the excellent store,
[Mathematics]

Here, μ is the average market share, σ is the standard deviation of the market share, and n is the standard deviation variable. According to claim 1,
The step of selecting the above model stores is
If the standard deviation variable is less than the reference value, the method for calculating expected sales further comprising the step of extracting the excellent store by increasing the grade for extracting a good store. In the method for calculating expected sales performed by the apparatus for calculating expected sales,
Determining a target category related to the product to be sold;
Calculating sales and average market share of the target category;
Calculating expected sales based on sales at the target store and the average market share;
Selecting the target store for a promotion based on the expected sales;
Selecting a model store related to the target store; And
Further comprising the step of determining a single product for the promotion in the target category,
Determining the single product,
Extracting a product whose sales volume is less than a predetermined sales volume;
Searching for a recent promotion history of the extracted product and selecting a product that is less than the average sales volume for a predetermined period; And
A method of calculating expected sales, comprising determining the promotional products in the order of the highest price elasticity index among the selected products. The method of claim 3,
The price elasticity index,
A method for calculating expected sales that is determined based on the ratio of the amount of change in sales price to the amount of change in sales quantity or the ratio of the increase or decrease in sales quantity to the rate of reduction in sales price. According to any one of claims 1 and 3,
The step of calculating the expected sales,
And calculating the expected sales by multiplying the average market share by subtracting the market share at the target store from the sales at the target store. According to any one of claims 1 and 3,
The step of selecting the target store,
And selecting a store in which the expected sales of the target category of the target store are greater than or equal to a predetermined amount. delete delete delete A communication unit that receives information for calculating sales and market share for each of a plurality of stores; And
Determine target categories related to products being sold in the plurality of stores, calculate sales and average market share of the target categories, calculate expected sales based on sales in the target stores and the average market share, and the expectations And a control unit for selecting the target store for promotion promotion based on sales, selecting a model store related to the target store, and determining a single product for promotion promotion in the target category,
The control unit,
Classify into at least two or more grades based on market share and sales, and select the target store within the group corresponding to the classified grade,
The group, the higher the market share and the sales, the higher the rating, the lower the market share and the sales, the lower the rating,
The control unit,
In the group where the target store is selected, stores with a higher market share than the average are selected as excellent stores based on the following equation, and if the excellent stores satisfy the sample number of a predetermined standard or more, it is determined as a best store.
If the number of samples above the predetermined standard is not satisfied, the number of samples is adjusted to re-determine the excellent store,
[Mathematics]

Here, μ is the average market share, σ is the standard deviation of the market share, n is the standard deviation variable expected sales calculation device. The method of claim 10,
The control unit,
When the standard deviation variable is less than a reference value, an apparatus for calculating expected sales that extracts the excellent store by raising a grade for extracting a good store. A communication unit that receives information for calculating sales and market share for each of a plurality of stores; And
Determine target categories related to products being sold in the plurality of stores, calculate sales and average market share of the target categories, calculate expected sales based on sales in the target stores and the average market share, and the expectations And a control unit for selecting the target store for promotion promotion based on sales, selecting a model store related to the target store, and determining a single product for promotion promotion in the target category,
The control unit,
Expected sales to extract products whose sales volume is less than a predetermined sales volume, search for recent promotion history of the extracted products, and select products that are below the average sales volume during a predetermined section to determine promotional products in the order of highest price elasticity index among the selected products. Output device. The method of claim 12,
The control unit
An apparatus for calculating expected sales that determines the price elasticity index based on a ratio of a change in sales price to a change in sales quantity or a ratio of an increase or decrease in sales quantity to a reduction rate of sales price. The method of any one of claims 10 and 12,
The control unit,
Expected sales calculation device for calculating the expected sales by multiplying the average market share by subtracting the market share at the target store from the sales at the target store. The method of any one of claims 10 and 12,
The control unit,
Expected sales calculation device for selecting a store in which the expected sales of the target category of the target store is greater than or equal to a predetermined amount. delete delete delete A computer-readable recording medium on which a program for performing the method according to any one of claims 1 and 3 is recorded. A computer program stored in a medium to perform the method according to any one of claims 1 and 3, performed by the expected sales management device.

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