JP2007087025A - Method for making reservation order by predicting sales amount of commodity by day of week linked with sales or profit budget - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To make an optimum order to achieve a sales or profit budget by intentionally manipulating, a sales amount fluctuation index of each commodity. <P>SOLUTION: In this method for making a reservation order by predicting a sales amount by day of week of a commodity, using a computer which computes a difference between sales result and reservation order amount of a day preceding a date where the reservation order for a computed reservation order amount of a corresponding day of week can be corrected, outputs the difference as a corrected reservation order amount to be subtracted from or added to the reservation order amount of the corresponding day of week, and finally computes and outputs a final order amount, the sales amount fluctuation index of the commodity is used as a parameter containing a level of sales price, a level of attracting public attention and a level of PR activity, for achieving the sales or profit budget of the commodity for a predetermined period or for improving the sales or profit as variables of sales promotion measures. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention discloses a method for predicting the number of products sold by day of the week and making a reservation order using a computer, as disclosed in JP 2004-62795 A by the same applicant, in particular, various products in retail businesses such as supermarkets, Foods such as seasonings, canned bottles, confectionery, beverages, household items such as consumables, fresh foods such as meat, fish, and fruits, especially products called daily products such as milk, tofu, and natto Predicting the number of sales items by day of the week linked to sales or profit budget related to the application, development, and improvement of the method of forecasting the number of sales items by day of the week in the retail industry that is usefully applied Regarding the method.

  Therefore, the disclosure content of JP 2004-62795 A is incorporated as part of the disclosure content of the present invention in the following description.

  Japanese Patent Application Laid-Open No. 2004-62795 discloses a predetermined sampling period that is stored and updated in a memory for a product X subject to sales quantity prediction in a method for predicting the sales quantity of a product by day of the week using a computer and making a reservation order. The ratio between the maximum number of sales results per day and the average number of sales results is set as a first variable, and the number of days from the date of manufacture of the product to the expiration date is assigned as a second variable for each product and stored in the memory. A step of retrieving and outputting the out-of-stock safety index α for the product X for which the sales quantity is to be predicted from the out-of-stock safety index map, and storing the number of 1000 sold per day for a predetermined sampling period stored in the memory and updated for that product. The step of calculating and outputting the number of sales 1000 for each day of the week on average, the number of customers visiting each future day of the week, that is, the number of on-site forecasted customers A for each day of the week Step of multiplying and storing, the above-mentioned number of on-site forecast visitor numbers A for each day of the week multiplied by 1000 sales number B for each day of the week, and dividing by 1000 to calculate the standard sales number C for each day of the week; The step of inputting and storing the sales volume fluctuation index β of the product which mainly fluctuates depending on the strength of the selling price of the product, the above-mentioned sales volume fluctuation index β is multiplied by the above-mentioned standard sales volume by day of the week C × β The step of calculating and outputting the calculated number of sales days of the corresponding day of the product X, that is, the reserved order quantity C × β × α is calculated and output based on the above-mentioned standard sold number C × β and the out-of-stock safety index α. Step, the sales result of at least one day prior to the date and time when the reservation order for the reservation order quantity C × β × α of the corresponding day of the week calculated and output in the above step can be corrected and the reservation of the day A step of calculating a difference from the order quantity and outputting the difference as a reservation order correction quantity to be subtracted from or added to the reservation order quantity on the corresponding day of the week, and the reservation on or from the reservation order quantity on the corresponding day of the week A method is disclosed, comprising the step of adding and subtracting the order correction quantity and calculating and outputting the final order quantity for the product X on the corresponding day of the week.

  The focus is on predicting the number of sales by day of the week based on the sales performance of the product X by day of the week and optimizing the order quantity based on the prediction. The number fluctuation index β is treated as one index that varies qualitatively depending on the level of the selling price of the product when the sales volume is predicted. Therefore, the sales volume fluctuation index β has been treated as an index unrelated to the sales or profit budget of the product X for a predetermined period.

  However, this sales volume fluctuation index β is not only the level of the selling price of the product X, but also the visibility of the display shelf, gondola, wagon location, position, price tag (POP) shape, etc. of the product X. It varies depending on the level of strength and the level of PR activities for promoting motivation for purchase through distribution of insert leaflets for the product X, posting on the website, setting up a tasting corner, in-store broadcasting, etc. Yes.

  Based on the above findings, the present invention is based on the above findings, and for each product, the sales volume fluctuation index β and the level of sales price (a), the level of visibility (b), and PR activities as a function thereof. The relationship between the sales level and level (c) is mapped and databased, and the sales volume fluctuation index β is linked to the input sales or profit budget to change the sales volume necessary to achieve the input sales or profit budget. The index β is selected, and how many of the selling price level (a), visibility level (b), and PR activity level (c) to achieve the selected β are selected. For example, select the combination with the highest gross profit from the same week last year or the combination with the highest measure efficiency from the combinations that were found, and according to the selected combination, sell price and publicity And PR activities are changed. On the other hand, as disclosed in Japanese Patent Application Laid-Open No. 2004-62795, the order quantity of the product is calculated based on the sales quantity fluctuation index β selected for achieving the budget as described above. In order to maintain the optimum stock, the calculated number of sold items is corrected by the out-of-stock safety index α, the number of stocks the day before the last day, and the number of deliveries the day before the last day, and the final order quantity is calculated.

  According to the present invention, for each product, a sales volume fluctuation index β is selected in consideration of the sales or profit budget for a predetermined period, for example, week, month, quarter, and the selected sales volume fluctuation index β. Based on the order quantity to maintain the optimal stock based on, the level (a) of the selling price that realizes the selected sales volume fluctuation index β (a), the level of visibility (b) and the PR activity A combination of measures including the strength level (c) is displayed with each evaluation result.

As a result, according to the present invention, optimal ordering for the purpose of achieving sales or profit budget is realized for each product, a specific product group, or the entire product group. At the same time, various measures to realize optimal ordering become clear.

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

  FIG. 1 is a block diagram for explaining an embodiment of a method for predicting the number of items sold by day of the week for a commodity linked to the sales budget of the present invention, in this embodiment Korean kimchi, below the dashed line. The flow above is the method disclosed in Japanese Patent Application Laid-Open No. 2004-62795, in which the order quantity by day of the product is determined while maintaining the stock quantity of the product at the optimum number. Calculation and selection of sales quantity variation index β with the sales budget for a predetermined period in mind according to the present invention, selection and calculation thereof, and a method for determining a sales promotion measure with an evaluation result for realizing the selected sales quantity fluctuation index β Is shown.

  In the lower flow of FIG. 1, first, the number of sales B for 1000 people per day for the product is calculated in S1 based on the past sales database for the product and the past visitor number database for the store. Next, in S2, the forecasted week weather, the events around the store, the activity status of competing stores, etc., based on the sales volume B by 1,000 days of the week calculated in S1 and the number of customers visiting by day of the past. The number of sales by day C is calculated based on the number of predicted visits by day of the week entered with reference to. Next, in S3, calculation is made based on the database of the sales budget for the predetermined period and the sales quantity fluctuation index β of the product in the upper-side flow (described in detail below) and the sales volume by sales day C calculated in S2 earlier. Based on the selected sales volume variation index β, the day-by-day standard sales volume C × β is calculated. Next, at S4, based on the sold-out safety index α for the product from the database of the sold-by-day standard C × β and out-of-stock safety index α previously calculated in S3, the modified standard sold-by-day number C × β × α is calculated. Finally, in S5, the calculation is made in S6 based on the day-to-day corrected standard sales number C × β × α calculated in S4 or based on the number of sales of the product the day before and the number of orders placed the day before. The final order quantity for the product is calculated and confirmed by adding or subtracting the reserved correction quantity.

  In the upper flow of FIG. 1, the sales quantity fluctuation index β is first calculated and selected in S7 based on the sales budget and the sales quantity fluctuation index β database for a predetermined period inputted for the product. The calculated and selected sales volume fluctuation index β is reviewed at any time during the period, for example, while checking the sales budget achievement status every week.

FIG. 2 shows an example of a database of the sales volume fluctuation index β of the product. Here, three variables for realizing each sales volume fluctuation index β, the level a of the selling price of the product, the level b of the visibility level, and the level c of the PR activity are set to a ₁ to. is divided into five stages of _{a 5, b 1 ~b 5,} c 1 ~c 5. A reduction amount or a ratio of the selling price is associated with each of the sales price levels a _{1 to} a ₅ . Each of the levels b _{1 to} b ₅ of the visibility level is associated with the display shelf location, position, wagon, gondola location, and price tag (POP) shape. Each of the levels c _{1 to} c ₅ of the PR activity is associated with distribution of a leaflet for the product, posting on a homepage, installation of a tasting corner, in-store broadcasting, and a combination and frequency thereof. Yes.

  3A and 3B show specific examples of the database of the sales quantity fluctuation index β of the corresponding product conceptually shown in FIG. 2, and FIG. 3A shows the level a of the sales price level of the product and the visibility of the product. The level b of strength and the level c of PR activity are divided into 9 levels from 0.7 to 1.5, 4 levels from 1.0 to 1.3, and 4 levels from 1.0 to 1.3. FIG. 3B corresponds to eight examples of combinations of the level a of the selling price of the product, the level b of the visibility, and the level c of the PR activity. A specific example of the sales volume fluctuation index β is shown.

  Next, in S8, calculation is performed with the sales budget in mind in S7, the selected sales volume fluctuation index β, the predicted number of customers visiting the forecast week, and the slip cost, rebate, net cost, selling price, and net value of the product. Based on a database of price information such as rate (gross profit), evaluate each of the sales promotion measures proposed by the sales price, visibility, and PR activities to realize the selected sales volume fluctuation index β, Calculate.

  FIG. 4A shows the predicted sales volume of the product, Korean kimchi when the sales volume fluctuation index β is 1.0, and the predicted sales volume for the 15th week in FIG. 4B. FIG. An example of the forecasted number of sales for the forecasted week of the product, Korea Kimchi, when the sales volume fluctuation index β is set to 1.5 by operating only the slip cost and selling price without changing PR activities It is shown. These predicted sales numbers are allocated to each day of the week based on the predicted number of customers visiting each day of the week. In S9, each of the plurality of measure proposals that realize the same sales volume variation index β evaluated and calculated in S8 earlier is, for example, the amount of sales, the high margin, or the cost of executing the measure. The level is evaluated and displayed, and one measure plan is selected based on the display.

  4A shows that despite the sales volume fluctuation index β = 1.0, the number of customers visiting the store is expected to increase compared to the same week in the previous year. It is over 100%. On the other hand, in FIG. 4B, the sales volume fluctuation index β is set to 1.5 by changing only the voucher cost and selling price in order to increase sales, but it is predicted that the same week-on-year sales will certainly increase significantly. However, it is predicted that the same week of price increases will be 95.8%, lower than the same week in the previous year. Here, only one example of FIG. 4B is shown as a measure plan for realizing the sales volume fluctuation index β = 1.5, but actually, a plurality of measure plans are presented.

  In the above embodiment, the selection of the sales volume fluctuation index β targeted to achieve the sales budget for a specific product has been described. However, the method of the present invention uses the sales volume fluctuation index β targeted to achieve the profit budget. The same applies to the selection.

The block diagram explaining one Example of the method of estimating the sales quantity according to the day of the goods linked with the sales or profit budget of this invention, and performing a reservation order. The table | surface which shows the outline | summary of an example of the content of the database of the sales volume fluctuation | variation index (beta) of a certain product used by one Example of this invention of FIG. The level classification of the level a of the selling price of the product, the level b of the visibility level and the level c of the PR activity in the database of the sales volume fluctuation index β of the product shown in FIG. This is a specific example of selling price, visibility, and PR activities corresponding to. 8 combinations of the sales price level a, the visibility level b, and the PR activity level c in the database of the sales volume fluctuation index β of the product shown in FIG. It is a specific example of the sales unit variation index β corresponding to each example. 1000 people, when the sales volume fluctuation index β is set to 1, based on the estimated number of customers visiting a given week of slip cost, rebate, net cost, selling price, net value entry rate, sales volume of 1000 people The figure shows the sales volume, sales forecast volume, sales forecast, sales of the same week in the previous year, sales year-on-year comparison, net value input and price increase year-on-year comparison. For the above products, Korean Kimchi, reviewing only the voucher cost, rebate, net cost, and selling price, and changing the sales volume fluctuation index β to 1.5, sales volume of 1000 people, sales forecast volume, sales for the same week It shows the forecast, sales compared to the same week last year, net value input and price increase compared to the same week last year.

Claims (2)

  The first variable is the ratio of the maximum number of actual sales and the average number of actual sales for a predetermined sampling period that is stored and updated in the memory for the product X subject to sales number prediction, and the expiration date from the date of manufacture of the product The out-of-stock safety index α for the product X subject to sales quantity prediction is retrieved from the out-of-stock safety index map assigned to each product as the second variable and stored in the memory. A step of calculating and outputting the number of sold 1000 persons B by day of the week for a predetermined sampling period stored and updated in a memory; a step of inputting and storing the number of customers visiting each future day of the week; The above-mentioned step of multiplying the estimated number of customers A on the day of the week by the number of sales B per 1000 by the day of the week and dividing by 1000 to calculate the standard sales C per day of the week. The step of inputting and storing the sales quantity fluctuation index β of the product which mainly fluctuates depending on the sales price of the sales target X is corrected by multiplying the sales quantity fluctuation index β by the day-by-day standard sales quantity C. The step of calculating and outputting the number of sales per day C × β, the predicted number of sales on the corresponding day of the product X based on the above-mentioned standard sales number C × β for each modified day and the out-of-stock safety index α, ie, the reserved order number C × The step of calculating and outputting β × α, the sales result of at least one day preceding the date and time on which the reservation order can be corrected for the reservation order quantity C × β × α of the corresponding day of the week calculated and output in the above step and that day A step of calculating a difference from the number of reserved orders and outputting the difference as a reserved order correction number to be subtracted or added from the number of reserved orders on the corresponding day of the week, and the corresponding day of the week A computer comprising a step of calculating and outputting the final order quantity for the product X on the corresponding day of the week by adding or subtracting the reservation order correction quantity for the corresponding day of the week to or from the reservation order quantity. In the method of forecasting the sales volume by day of the week and making a reservation order, the sales volume fluctuation index β of the product is set to at least the strength of the sales price to achieve the sales or profit budget of the product for the predetermined period or increase the sales or profit. A sales or profit budget characterized by using as a parameter, including level (a), level of visibility (b) and level of public relations activity (c) as variables in sales promotion measures; A method for predicting the number of linked products sold by day of the week and placing a reservation order.   The method further includes a combination of the sales volume fluctuation index β of the product and the level (a) of the selling price of the product, the level (b) of visibility and the level (c) of PR activity. Based on the data from the database of the sales volume fluctuation index β of the product describing the relationship between the sales price and the sales or profit budget of the product for the predetermined period input from the terminal, the sales volume fluctuation index β of the product is calculated. According to a combination measure of the step of calculating, the level (a) of the selling price leading to the calculated sales volume fluctuation index β, the level (b) of visibility and the level (c) of PR activity A step of evaluating the profit based on the data of the price information database including the cost of the product and the calculated number of sold 1000 B per day, and the measure evaluation calculating step The method includes the steps of: displaying the evaluation result of each combination measure evaluated and calculated in step 1; and selecting, through the terminal, one of the combination measures with the respective evaluation results displayed in the display step. The method described.

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