JP2009080685A - Apparatus for preparating forecast by product - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To obtain an apparatus for preparing forecast by product capable of efficiently preparing forecasts without omitting all products. <P>SOLUTION: A grouping processing part 131 recognizes the development order of specified categories and groups products belonging to respective categories on the basis of the specified categories, a category master 10 in which categories classified by the characteristics of respective products are registered and a product category master 11 in which categories to which products belong and the characteristics of these categories are registered. A forecast development processing part 133 calculates yearly forecasts of the categories and the products in the descending order of categories on the basis of the development order, the target total number of yearly sales and the sales rates of respective categories, calculates a monthly forecast of each product based on the calculated total numbers of yearly sales of each category and each product and monthly sales ratios of each category and each product and registers the calculated forecast in a table of monthly forecast by product 15. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a product-specific forecast creating apparatus for creating a product-specific forecast, and in particular, a product-specific forecast while classifying products into categories and expanding the forecast in the order from the upper category to the lower category. The present invention relates to a product-specific forecast making apparatus.

  Conventionally, a production plan for a product and a procurement plan for parts necessary for producing the product are determined based on forecast information of the product. As a conventional technique for creating a product forecast, for example, Patent Document 1 is available.

  Patent Document 1 discloses a demand collecting unit that stores the number of product orders from a plurality of customers in a demand database for each customer identification number, and the number of past orders from each customer for each customer over a predetermined period. Based on the historical demand database, the number of orders and the number of past orders, and the fluctuation analysis unit that calculates the demand forecast of the product by predetermined statistical processing based on the fluctuation And the demand gathering unit collects a plurality of customers having a predetermined order number or less in a plurality of customers, and processes a group of customers assigned with an identification number as one independent customer. Yes.

JP 2004-70633 A

  In the prior art described in Patent Document 1, a forecasting system is improved by collecting a plurality of customers having a predetermined order number or less into a group of customers to which an identification number is assigned. . However, at present, the product life cycle is short and frequent product changes such as model changes occur, and there is also a complex correlation between products. Depending on the number of products sold, other products are sold. There was a problem that it was not possible to cope with the situation where the number was affected.

  In addition, due to diversification of customer requests, the number of target products is increasing and demand fluctuations are intensifying, and a timely forecast review is required. In the product field where demand takes a long time and the demand fluctuates depending on the season, it takes time to expand from annual sales to monthly sales. There was also a problem that it was difficult to follow.

  Furthermore, since the conventional technology described in Patent Document 1 does not consider the switching between new and old products and the correlation between products, the work of stacking the old and new product switching and the correlation between products as a whole is manual and inconsistent. There was also a problem that it was highly likely to occur.

  The present invention has been made in view of the above, and provides a product-specific forecast creation apparatus that can be a creation target of a forecast without leaking all products and can efficiently create a forecast. The purpose is to obtain.

  In order to solve the above-described problems and achieve the object, the present invention provides a category master in which categories classified according to product characteristics are registered, a product category in which the categories to which products belong and characteristics of the categories are registered. A storage unit that stores a master, a monthly forecast table for each product in which monthly forecasts for each product are registered, a category designated from the outside, the category master, and the product category master. A grouping processing unit for grouping products belonging to the specified category, an expansion order recognized by the grouping processing unit, and an annual target total sales number specified from the outside. , And the sales ratio for that category. Calculate the annual forecast for the category and product in order from the lowest category to the product, and calculate the annual target sales for the category and product, and the monthly sales ratio for the category and product entered from the outside. And a forecast expansion processing unit that calculates monthly forecast for each product and registers the calculated monthly forecast for each product in the monthly forecast table for each product.

  According to the present invention, the grouping processing unit registers the category designated from the outside, the category master in which the category classified by the product characteristics is registered, the product to which the category to which the product belongs and the characteristics of the category are registered. Based on the category master, it recognizes the expansion order of the specified category, groups the products belonging to the specified category, and the forward expansion processing unit recognizes the expansion order recognized by the grouping processing unit and the external Based on the annual target total sales number specified from the outside and the sales ratio for that category, the category is expanded from the top category to the bottom category, and from the bottom category to the product. And annual forecast for the product, and for the calculated category and product Calculate monthly forecast for each product based on the annual target sales number and monthly sales ratio for the category and product input from the outside, and register the calculated monthly forecast for each product in the monthly forecast table 15 for each product. As a result, it is possible to obtain a product-specific forecast creation device that can be made into a forecast creation target without leaking all products, and can create forecasts efficiently. .

  Embodiments of a product-specific forecast creation apparatus according to the present invention will be described below in detail with reference to the drawings. Note that the present invention is not limited to the embodiments.

Embodiment 1 FIG.
A first embodiment of the present invention will be described with reference to FIGS. FIG. 1 is a diagram showing an example of a configuration of a forecast creation support system to which a product-specific forecast creation apparatus according to the present invention is applied. In FIG. 1, the forecast creation support system includes a product-specific forecast creation apparatus 1 and one to a plurality of (in this case, two) terminals 2 connected via a network 3 such as the Internet or an intranet. The user accesses the product-specific forecast creation apparatus 1 using the terminal 2, and the product-specific forecast creation apparatus 1 is based on information input by the user using the terminal 2 and information registered in the own apparatus. Create a forecast for the desired product.

  FIG. 2 is a block diagram showing a configuration of the forecast creating apparatus 1 shown in FIG. In FIG. 2, the forecast creation apparatus 1 includes a category master 10, a product category master 11, monthly sales results data 12 by product, a forecast expansion unit 13, a screen display information processing unit 14, and a monthly forecast table 15 by product. I have. The category master 10, the product category master 11, and the monthly forecast table 15 for each product are stored in a storage unit (not shown). This storage means may be provided in the product-specific forecast creation apparatus 1 or may be connected to a network 3 that is accessible to the product-specific forecast creation apparatus 1 or a network different from the network 3.

  FIG. 3 is a diagram showing an example of registration items registered in the category master 10 shown in FIG. In FIG. 3, as a registration item of the category master 10, a category code for registering a category code for identifying a product characteristic (category) and a category name indicated by the category code registered in the category code are registered. Name.

  FIG. 4 is a diagram showing an example of registration items registered in the product category master 11 shown in FIG. In FIG. 4, a product code in which a product code for identifying a product is registered, and a category to which the product indicated by the product code registered in the product code belongs (category registered in the category master 10 shown in FIG. 3). Category characteristic values in which characteristic values for are registered.

  FIG. 5 is a diagram showing an example of registration items registered in the monthly sales performance data 12 by product shown in FIG. In FIG. 5, as the registration items registered in the monthly sales results data 12 by product, the year in which the product code for identifying the product is registered and the year indicating the year of the data are registered. And the number of monthly sales in which the number of monthly sales in the year registered in the year is registered.

  Returning to FIG. 1, the forecast expansion unit 13 includes the category master 10, the product category master 11, the monthly sales performance data 12 by product, the designated category input from the outside, the annual target total number of sales, and the sales ratio. Based on the above, sales data of past products is aggregated and forecasted.

  The forecast development unit 13 includes a grouping processing unit 131, a performance data totaling processing unit 132, and a forecast development processing unit 133. The grouping processing unit 131 extracts and extracts the product codes belonging to each designated category based on the designated category, category master 10, and product category master 11 that perform the forecast expansion designated by the user (from the outside). Product codes are grouped by related category.

  The performance data totaling processing unit 132 totals the number of sales by month and year for each designated category and product code based on the product codes grouped by the grouping processing unit 131.

  The forecast development processing unit 133 calculates the annual forecast (annual forecast) from the highest category to the lowest category in order from the designated category based on the total annual target sales number and the annual sales ratio designated by the user. To do. Further, the forecast development processing unit 133 calculates monthly forecasts (monthly forecasts by product) of each product based on the calculated annual forecasts and monthly sales ratios designated by the user. The forecast expansion unit 13 registers the monthly forecast of each product (monthly forecast for each product) in the monthly forecast table 15 for each product.

  The screen display information processing unit 14 has an interface function for communicating with the terminal 3 via the network 3, and various data calculated by the result data totaling processing unit 132 and each calculated by the forecast expansion processing unit 133. Display information for displaying each forecast on the terminal 3 is generated and transmitted. Further, the screen display information processing unit 14 receives information from the terminal 3 and notifies the grouping processing unit 131 and the forecast expansion unit 13.

  Next, the operation of the forecast creation support system will be described with reference to the flowchart of FIG. In the category master 10, the product category master 11, and the monthly sales result data 12 for each product, various data are registered in advance by the administrator of the product-specific forecast creation apparatus 1.

  The user accesses the product-specific forecast creation apparatus 1 using the terminal 2. Upon receiving access from the terminal 2 (step S100), the screen display information processing unit 14 and the grouping processing unit 131 of the product-specific forecast creation apparatus 1 execute a product grouping process for a category in which the forecast is developed. (Step S101).

  With reference to the flowchart of FIG. 7, the detailed operation of the product grouping process for the category in which the forecast is developed will be described. The screen display information processing unit 14 generates category designation screen information for requesting the user to designate a category for expanding the forecast (step S200). The screen display information processing unit 14 transmits a category designation request including the generated category designation screen information to the terminal 2 (step S201).

  The terminal 2 displays a category designation screen for requesting category input (category designation) based on the category designation screen information included in the received category designation request. The user inputs the name of at least one category in the order of development using the input means (not shown) of the terminal 2 in accordance with the category designation screen.

  FIG. 8 is a diagram illustrating an example of a category designation screen in which a category is input by the user. In FIG. 8, the category name “series” to be expanded first is input, the category name “inverter” to be expanded second is input, and the category name “capability” to be expanded third is set. Have been entered.

  The user inputs to the terminal 2 that the forecast expansion is to be executed. In FIG. 8, the execution start button 5 is clicked. When the terminal 2 recognizes that the execution start button 5 has been operated, the terminal 2 generates category information in which the input categories (designated categories) are associated with each other in the category expansion order. Here, the terminal 2 associates the category designation information in which the name “series” is associated with the expansion order 1, the name “inverter” is associated with the expansion order 2, and the name “capability” is associated with the expansion order 3. Generate. The terminal 2 transmits a category designation request response including the generated category designation information to the product-specific forecast creation apparatus 1.

  When receiving the category designation request response (step S202), the screen display information processing unit 14 of the product-specific forecast creation apparatus 1 extracts the category information included in the category designation request response. The screen display information processing unit 14 notifies the grouping processing unit 131 of the extracted category designation information.

  The grouping processing unit 131 acquires the category code of the designated category based on the notified category designation information and the category master 10 (step S203). Specifically, the grouping processing unit 131 extracts a category name included in the category designation information. The grouping processing unit 131 searches the name of the category master 10 using the extracted name as a search key, and performs the category code registered in the category code of the category master 10 in association with the name that matches the search key. The grouping processing unit 131 associates the category codes acquired in the expansion order of the category information associated with the name as the search key.

  In the category master 10 shown in FIG. 3, the category code “category 1” is registered in association with the name “series”, the category code “category 2” is registered in association with the name “inverter”, and the name “capability” "Category 4" is registered in association with "". Therefore, the grouping processing unit 131 associates the category code “Category 1” with the expansion order No. 1, associates the category code “Category 2” with the expansion order No. 2, and associates the category code “Category 4” with the expansion order No. 3. .

  The grouping processing unit 131 groups product codes for each designated category code based on the acquired category code (designated category code) and product category master 11 (step S204).

  Specifically, the grouping processing unit 131 extracts the category characteristic value of the category code “category 1” associated with the expansion order No. 1 from the product category master 11, and associates the extracted category characteristic value with the expansion order No. 1. Associated with the category code “category 1”. In the product category master 11 shown in FIG. 4, category characteristic values “X-Series”, “Y-Series”, “Z-Series” are used as category characteristic values for the category code “category 1” of the product category master 11. ”And the category characteristic values“ X-Series ”,“ Y-Series ”, and“ Z-Series ”are associated with the category code“ category 1 ”.

  The grouping processing unit 131 extracts a product code registered in the product code of the product category master 11 in association with the extracted category characteristic value, and associates the extracted product code with the category characteristic value. In the product category master 11 illustrated in FIG. 4, the grouping processing unit 131 associates the product codes “A” to “G” with the category characteristic value “X-Series” and the products with the category characteristic value “Y-Series”. The codes “H” and “I” are associated with each other, and the product codes “J” and “K” are associated with the category characteristic value “Z-Series”.

  As a result, the category characteristic values “X-Series”, “Y-Series”, and “Z-Series” are associated with the category code “category 1” associated with the expansion order No. 1, and the category characteristic value “X-Series”. Are associated with product codes “A” to “G”, product characteristics “H” and “I” are associated with category characteristic value “Y-Series”, and product code “J” is associated with category characteristic value “Z-Series”. And “K” are associated with each other, and the grouping of product codes for the category code “category 1” in the development order No. 1 is completed.

  Next, the grouping processing unit 131 performs grouping for the category code “category 2” associated with the second expansion order. The grouping for the category code “category 1” associated with the expansion order No. 1 is that the category code “category 1” is the highest category in the expansion order No. 1, so that all the product codes registered in the product category master 11 are displayed. Grouped as subject. However, grouping for category codes other than the highest category is performed only for product codes grouped for one category higher than the category code. That is, the product codes having the same category characteristic value are grouped for the product codes grouped with respect to the next higher category.

  In the product category master 11 shown in FIG. 4, for example, product codes “A” to “G” are associated (grouped) with the category characteristic value “X-Series” of the category code “category 1”. Therefore, the grouping processing unit 131 extracts the category characteristic values “Inverter” and “non-Inverter” of “Category 2” of the product category master 11 associated with each of the product codes “A” to “G”. The product code “A”, “B”, “D”, “F”, “G” is associated with the category characteristic value “Inverter” associated with the category characteristic value “X-Series” and the category characteristic value “X-Series”. ”And product codes“ C ”and“ E ”are associated with the category characteristic value“ non-Inverter ”associated with the category characteristic value“ X-Series ”to perform grouping.

  Similarly, the grouping processing unit 131 converts the product codes “H” and “I” associated with the category characteristic value “Y-Series” of the category code “category 1” according to the category characteristic value of the category code “category 2”. The product codes “J” and “K” associated with “Z-Series” of the category code “category 1” are grouped by the category characteristic value of the category code “category 2”.

  As described above, the grouping processing unit 131 performs grouping on the category characteristic values from the highest category to the lowest category in order from the specified category code.

  FIG. 9 is a diagram illustrating an example of a processing result of the grouping process performed by the grouping processing unit 131. FIG. 9 shows the processing result of the grouping process when the designated category code is category code “category 1”, “category 2”, and “category 4”. The category characteristic value “X-Series” includes the category characteristic value “ Inverter "and" non-Inverter "belong to the category characteristic value" Inverter "of the category characteristic value" X-Series ", and the category characteristic values" 3 "," 5 ", and" 8 "belong to the category characteristic value" Inverter ". Product characteristics “A” and “B” belong to the category characteristic value “3” of the category characteristic value “Inverter” of the characteristic value “X-Series”, and the category characteristic value “X-Series” of the category characteristic value “X-Series” The product characteristic “D” belongs to the category characteristic value “5” of “Inverter”, and the category characteristic value “X-Series” Product codes “F” and “G” belong to the category characteristic value “8” of the category characteristic value “Inverter”, and the category characteristic value “non-Inverter” of the category characteristic value “X-Series” includes the category characteristic. The values “3” and “5” belong, the product characteristic “C” belongs to the category characteristic value “3” of the category characteristic value “non-Inverter” of the category characteristic value “X-Series”, and the category The category characteristic value “5” of the category characteristic value “non-Inverter” of the characteristic value “X-Series” is grouped as belonging to the product code “E”.

  The category characteristic value “Y-Series” includes the category characteristic values “Inverter” and “non-Inverter”. The category characteristic value “Inverter” of the category characteristic value “Y-Series” includes the category characteristic value “Inverter”. 3 ”, the product characteristic“ H ”belongs to the category characteristic value“ 3 ”of the category characteristic value“ Inverter ”of the category characteristic value“ X-Series ”, and the category characteristic value“ Y-Series ” The category characteristic value “5” belongs to the category characteristic value “non-Inverter”, and the category characteristic value “5” of the category characteristic value “non-Inverter” of the category characteristic value “Y-Series” includes the product code “5”. Grouped as “I” belongs.

  Further, the category characteristic value “Inverter” belongs to the category characteristic value “Z-Series”, and the category characteristic value “3” belongs to the category characteristic value “Inverter” of the category characteristic value “Z-Series”. The category characteristic value “3” of the category characteristic value “Inverter” of the category characteristic value “Z-Series” is grouped so that the product codes “J” and “K” belong to it.

  The grouping processing unit 131 uses the processing result (association of grouping of category characteristic values and product codes) by the grouping processing as shown in FIG. 9 as group information, and this group information is the actual data totaling processing unit 132. To end the grouping process (step S205).

  Returning to FIG. 6, when the group information by the grouping process is notified, the result data totaling processing unit 132 performs the result data totaling process for totaling the result data for the designated category and each product based on the notified group information. Execute (Step S102).

  Here, the actual data aggregation processing calculates the monthly sales (monthly sales) and annual sales of the specified category and products belonging to the specified category for each year, and the monthly sales account for the annual sales. It is assumed that the ratio and the ratio of the annual sales number of the category that is one lower level belonging to this category to the annual sales number of the category are obtained.

  First, the record data totaling processing unit 132 extracts all product codes from the group information. The actual data totaling processing unit 132 searches the product code of the monthly sales result data 12 by product using the extracted product code as a search key, and acquires the year and the number of monthly sales associated with the product code that matches the search key. The result data totaling processing unit 132 adds the number of sales for each month acquired for each year to obtain the number of sales for the year. The performance data totaling processing unit 132 associates the acquired year and monthly sales quantity and the obtained annual sales quantity with the product code and adds them to the group information.

  Next, the actual data totaling processing unit 132 selects the target category characteristic value from the lowest category characteristic value of the group information, and determines the target category from the monthly sales number and the annual sales number of the product code belonging to the selected target category characteristic value. The number of monthly sales and the number of annual sales for the characteristic value are obtained. The actual data totaling processing unit 132 associates the obtained monthly sales number and annual sales number and the current year with the target category characteristic value and adds them to the group information.

  For example, it is assumed that the result data aggregation processing unit 132 selects the category characteristic value “3” belonging to the category characteristic value “Inverter” belonging to the category characteristic value “X-Series” as the target category characteristic value. The actual data totaling processing unit 132 recognizes the product codes “A” and “B” belonging to the target category characteristic value from the group information. The actual data totaling processing unit 132 adds the annual sales numbers of the recognized product codes “A” and “B” in the same year to obtain the annual sales number of the target category characteristic value in the current year.

  In the monthly sales results data 12 by product shown in FIG. 5, the product code “A” has registered monthly sales numbers from FY2004 to 2007, and the product code “B” has monthly sales numbers from FY2005 to 2007. Is registered. Therefore, there is no monthly sales number and annual sales number of the product code “B” in FY2004. In such a case, the monthly sales volume and the annual sales volume of the product code “A” in fiscal 2004 are directly the monthly sales volume and annual sales volume of the target category characteristic value, and the target category characteristic value of the fiscal year 2005 to 2007 is the same. Monthly sales and annual sales are the total of monthly sales for the same month for product code “A” and product code “B”, and the same year for product code “A” and product code “B”. The total number of annual sales.

  The actual data totaling processing unit 132 displays the monthly sales volume and the annual sales volume in the fiscal year 2004 to 2007 in the category characteristic value “Inverter” that belongs to the category characteristic value “Inverter” that belongs to the category characteristic value “X-Series”. 3 ”is added to the group information.

  The actual data totaling processing unit 132 obtains the monthly sales number and the annual sales number of each target category characteristic value for each fiscal year and adds them to the group information, and then the same level as the current target category characteristic value (in this case, the highest level in this case). It is determined whether there is an unprocessed category characteristic value among the lower-level category characteristic values. When there is an unprocessed category characteristic value among the category characteristic values at the same level as the current target category characteristic value (in this case, the lowest level), the actual data aggregation processing unit 132 is the same as the current target category characteristic value. From the category characteristic value of the level, select the open category characteristic value, select the new target category characteristic value, and the monthly sales number and the annual sales number of the product code belonging to the selected target category characteristic value by month for the target category characteristic value Repeat the process for obtaining the number of sales and the number of annual sales.

  When there is no unprocessed category characteristic value among the category characteristic values at the same level as the current target category characteristic value (in this case, the lowest level), the actual data totaling processing unit 132 sets 1 from the current target category characteristic value. Select the category characteristic value that is the highest (in this case, deployment number 2) as the target category characteristic value, and the number of monthly sales and the number of monthly sales of the category characteristic value that belong to the target category characteristic value. Then, the process for obtaining the annual sales number is repeated to obtain the monthly sales number and the annual sales number for all the category characteristic values from the lowest category characteristic value to the highest category characteristic value in order.

  After obtaining the monthly sales number and the annual sales number for all the category characteristic values and product codes, the actual data aggregation processing unit 132 determines the ratio of the monthly sales number to the annual sales number of all the category characteristic values and product codes, and the top The ratio of the annual sales number of the lower category characteristic value to the annual sales number of the category characteristic value of is determined. The actual data totaling processing unit 132 determines the ratio of the monthly sales to the obtained annual sales and the ratio of the annual sales of the lower category characteristic value to the annual sales of the upper category characteristic value. Associate with product code. Below, for all category characteristic values and product codes included in the group information, the number of monthly sales, annual sales, the ratio of monthly sales to the annual sales, and the lower category characteristic value for the annual sales number of the upper category characteristic value Information to which the ratio of the annual sales number is added is called group information with performance data.

  The result data totaling processing unit 132 notifies the group information with result data to the forecast expansion processing unit 133. When the group information with performance data is notified, the forecast development processing unit 133 executes the forecast development process for the designated category (step S103).

  The detailed operation of the forecast expansion process will be described with reference to the flowchart of FIG. The forecast development processing unit 133 notifies the screen display information processing unit 14 of the group information with sales number information in order to request the user for information necessary for creating the annual forecast.

  The screen display information processing unit 14 generates annual forecast expansion screen display information including actual data based on the group information with actual data (step S300). The screen display information processing unit 14 transmits an annual target total sales number / sales ratio information request including the generated annual forecast development screen display information to the terminal 2 (step S301).

  The terminal 2 displays the annual forecast development screen based on the annual forecast development screen display information included in the annual target total sales / sales ratio information request. FIG. 11 is a diagram showing an example of a display screen based on the annual forecast development screen display information, and is an annual forecast development screen for the highest category of the designated category. In FIG. 11, the annual forecast expansion screen shows a category type (Category Type in the figure) that displays the name of the designated category as a display item, past performance information that displays past annual performance data, and current year information. Is displayed as a forecast that displays information from the next fiscal year onwards. Here, “Series”, “Inverter”, and “Capability” are specified as the specified category, so “Series”, “Inverter”, “Capability”, and “Product code” are displayed as the category type. , The category sales value of the top-level category “X-Series”, “Y-Series” and “Z-Series” for the year 2004-2006 and the ratio of sales to the total annual sales for each series in the past It is displayed as performance information. In addition, the current year information displays actual information (number of sales results and sales ratio) up to the present year.

  The user determines the annual target total sales number and sales ratio of each series (designated category) with reference to the displayed actual data, and inputs the determined annual target total sales number and sales ratio of each series to the terminal 2. To do. For example, the total annual sales target for Total in the current year information on the display screen shown in FIG. 11 and the total annual sales target for FY2008 and 2009 are shown in the column for total annual sales for the current fiscal year. Enter the total sales ratio for the current fiscal year information corresponding to the category characteristic values “X-Series,” “Y-Series,” and “Z-Series”, and the sales ratios for FY2008 and 2009 for Forecast. Enter the sales ratio of the category characteristic value.

  FIG. 12 shows a display screen on which the annual target total number of sales and the sales ratio are input. In FIG. 12, “1500” is input as the annual target total sales number for this year (here, 2007), and “55.0 (%)” is input as the sales ratio of the category characteristic value “X-Series”. “35.0 (%)” is input as the sales ratio of the category characteristic value “Y-Series”, and “10.0 (%)” is input as the sales ratio of the category characteristic value “Z-Series”. Yes. In addition, “1800” is input as the annual target total number of sales in fiscal 2008, “60.0 (%)” is input as the sales ratio of the category characteristic value “X-Series”, and the category characteristic value “Y-Series”. "30.0 (%)" is input as the sales ratio of "10.0", and "10.0 (%)" is input as the sales ratio of the category characteristic value "Z-Series". Furthermore, “2000” is input as the annual target total sales number for fiscal 2009, “65.0 (%)” is input as the sales ratio of the category characteristic value “X-Series”, and the category characteristic value “Y-Series” is displayed. “30.0 (%)” is input as the sales ratio of “5”, and “5.0 (%)” is input as the sales ratio of the category characteristic value “Z-Series”.

  The terminal 2 associates the input sales ratio with the category characteristic value, and generates an annual target total sales number / sales ratio information request response including each sales ratio and the annual target sales number associated with the category characteristic value. The terminal 2 transmits the generated annual target total sales / sales ratio information request response to the product-specific forecast creation apparatus 1.

  When the annual target total sales / sales ratio information request response is received (step S302), the screen display information processing unit 14 of the product-specific forecast creation apparatus 1 receives the category characteristic value from the annual target total sales / sales ratio information request response. Extract each sales ratio and annual target sales number associated with. The screen display information processing unit 14 notifies the forecast development processing unit 133 of each sales ratio and the annual target number of sales associated with the extracted category characteristic value.

  The forecast development processing unit 133 calculates the category annual sales number of the target category characteristic value based on each sales ratio and the annual target total sales number associated with the category characteristic value (step S303).

Here, the category annual sales number for the highest category characteristic value is obtained. The category annual target sales for the highest category characteristic value is
Top-level category annual target sales =
Annual target total sales x sales ratio of the highest category ÷ 100 ... (Formula 1)
Can be obtained.

  Here, the forecast development processing unit 133 displays the annual target total sales number for each fiscal year and the sales ratios associated with the category characteristic values “X-Series,” “Y-Series,” and “Z-Series”. , Category characteristic values “X-Series”, “Y-Series”, and “Z-”, which are the highest-level category characteristic values (category characteristic values in order of expansion in the group information) according to the above (Formula 1). Calculate the sales target for the highest category of “Sereis”.

  For the current fiscal year, the future target number of sales and sales ratio are calculated based on the actual data up to now. For example, in FIG. 12, the annual target total sales number of the entire series is “1500”, and the actual sales number of the entire series this year is “225”. Number—the number of actual sales in the current year ”, which is“ 1275 ”. In addition, in the case of the category characteristic value “X-Series”, the annual target total sales number is “1500” and the sales ratio is “55.0%”. Therefore, from the above (Formula 1), the category annual sales number is “825”. Thus, since the actual number of sales of the category characteristic value “X-Series” for this fiscal year is “105”, the future sales number will be “720” from “category annual target number of sales—the actual number of sales for the current year”. . Furthermore, the sales ratio of the future target sales number of the category characteristic value to the future target sales number of the entire series is calculated.

  The forecast expansion processing unit 133 associates the calculated highest category annual sales number (including the future target sales number and sales ratio in the current fiscal year) with the category characteristic value and adds it to the group information with actual data. Output to the screen display information processing unit 14.

  The screen display information processing unit 14 generates annual forecast development screen display information including the annual sales number calculated by the forecast development processing unit 133, that is, the annual sales number added to the group information with performance data (step S304). . Here, annual forecast expansion screen display information for displaying a display screen as shown in FIG. 13 is generated. The screen display information processing unit 14 transmits the generated annual forecast development screen display information to the terminal 2 (step S305).

  The terminal 2 displays the display screen of the annual forecast development screen based on the received annual forecast development screen display information. Here, the display screen shown in FIG. 13 is displayed. The user confirms the annual forecast of the series on the display screen displayed by the terminal 2 and inputs the request for expanding the annual forecast of the category one level lower to the terminal 2. Here, the category characteristic value “inverter” is selected and input. The terminal 2 transmits a deployment request including information on the selected category characteristic value “inverter” to the product-specific forecast deployment device 1.

  When the expansion request is received (step S306), the screen display information processing unit 14 of the product-specific forecast expansion apparatus 1 extracts the category characteristic value information selected from the expansion request, and extracts the category characteristic value ( The forecast expansion display screen information for the category characteristic value requested for expansion is generated. Here, the screen display information processing unit 14 generates annual forecast expansion display screen information including performance data for the category characteristic value “inverter” (step S307).

  Here, for example, as shown in FIG. 14, category characteristic values “X-Series”, “Y-Series”, and “Z-Series”, which are category characteristic values higher than the extracted category characteristic value “inverter”. The forecast expansion display screen information for displaying the annual forecast expansion display screen including the actual data including the category annual target number of sales is generated.

  The screen display information processing unit 14 transmits a sales ratio information request including the generated annual forecast expansion screen display information to the terminal 2 (step S308).

  The terminal 2 displays the annual forecast development screen based on the annual forecast development screen display information included in the sales ratio information request. Here, the terminal 2 displays the annual forecast development screen shown in FIG.

  The user determines the sales ratio of the category characteristic values “Inverter” and “non-Inverter” of each series with reference to the displayed result data, and inputs the determined sales ratio to the terminal 2. For example, in the display screen shown in FIG. 14, “X-Series” and “Y-Series” in the current year information Total sales ratio, Forecast 2008 and 2009 sales ratio columns are the category characteristics. Enter the sales ratio for the value.

  The terminal 2 associates the input sales ratio with the category characteristic value, and generates a sales ratio information request response including each sales ratio associated with the category characteristic value. The terminal 2 transmits the generated sales ratio information request response to the product-specific forecast creation apparatus 1.

  When the sales ratio information request response is received (step S309), the screen display information processing unit 14 of the product-specific forecast creation apparatus 1 extracts each sales ratio associated with the category characteristic value from the sales ratio information request response. The screen display information processing unit 14 notifies the forecast expansion processing unit 133 of each sales ratio associated with the extracted category characteristic value.

  The forecast expansion processing unit 133 calculates the category annual target sales number of the target category characteristic value based on each sales ratio associated with the category characteristic value and the category annual target sales number of the category characteristic value to which the category characteristic value belongs. Calculate (step S310).

Category annual target sales for non-top category characteristic values is
Category annual target sales =
The number of target sales for the next higher category × sales ratio of the category ÷ 100… (Formula 2)
Can be obtained. The upper category annual target sales number is the category annual target sales number of the category characteristic value to which the category characteristic value belongs, and may be extracted from the group information with performance data.

  In addition, for the current fiscal year, the forecast development processing unit 133 calculates the future target sales number and the sales ratio based on the actual data up to the present time, in the same manner as when calculating the annual target sales number for the highest category. calculate.

  The forecast expansion processing unit 133 associates the calculated annual sales volume of the category (including the future target sales volume and sales ratio in the current fiscal year) with the category characteristic value and adds it to the group information with actual data to display the screen. The information is output to the information processing unit 14.

  The screen display information processing unit 14 generates annual forecast development screen display information including the annual sales number calculated by the forecast development processing unit 133, that is, the annual sales number added to the group information with performance data (step S304). . Here, annual forecast expansion screen display information for displaying a display screen as shown in FIG. 15 is generated. The screen display information processing unit 14 transmits the generated annual forecast development screen display information to the terminal 2 (step S305).

  Terminal 2 displays the display screen of the annual forecast expansion screen based on the received annual forecast expansion screen display information, and the user confirms the annual forecast of the category characteristic value by looking at the display screen displayed by terminal 2. Then, the terminal 2 inputs a request to expand the annual forecast of the next lower category. The terminal 2 transmits a deployment request including the input category characteristic value information to the product-specific forecast deployment device 1.

  The screen display information processing unit 14 of the product-specific forecast development apparatus 1 extracts the category characteristic value information included in the received development request, generates forecast expansion display screen information for the extracted category characteristic value, and generates The terminal 2 requests the sales ratio information request including the forecast expansion display screen information, and the forecast expansion processing unit 133 sets the target category based on the sales ratio information included in the sales ratio information request response received from the terminal 2. The annual sales target for the category is calculated and added to the group information with actual data. Based on the group information with actual data, the screen display information processing unit 14 displays the operation of displaying the raw cast expanded display screen information on the terminal 2, The product codes belonging to the lowest category characteristic value are repeated in order (steps S304 to S310).

  Thereby, when the forecast expansion display screen from the highest category characteristic value to the product code as shown in FIG. 16 is displayed on the terminal 2, the user inputs a request for monthly forecast to the terminal 2, 2 sends a monthly forecast expansion request to the product-specific forecast creation apparatus 1.

  When receiving the monthly forecast expansion request (step S311), the screen display information processing unit 14 of the product-specific forecast creating apparatus 1 receives the monthly sales ratio instruction screen display information including the actual data based on the group information with actual data. Generate (step S312). The screen display information processing unit 14 transmits a monthly sales ratio instruction request including the monthly sales ratio instruction screen display information including the generated actual data to the terminal 2 (step S313).

  The terminal 2 displays a monthly sales ratio instruction screen based on the monthly sales ratio instruction screen information of the received monthly sales ratio instruction request. FIG. 17 is a diagram illustrating an example of the monthly sales ratio instruction screen displayed by the terminal 2. FIG. 17 shows a monthly sales ratio instruction screen for the category characteristic value “X-Series”. As past performance data, the number of sales results and the sales ratio for each month from FY2004 to FY2006 and the current year 2007 are shown. Results information for the current year (sales results and sales ratio) is displayed, as well as future target information for each month (sales target and sales ratio) and yearly target information (sales target and sales ratio). A column is displayed.

  The user inputs the sales ratio in the column of the sales ratio for each month after the present in the year target information with reference to past performance data. The terminal 2 generates a monthly sales ratio instruction request response in which the input sales ratio is associated with the category characteristic value and the month information, and transmits it to the product-specific forecast creation apparatus 1.

  When the monthly sales ratio instruction request response is received (step S314), the screen display information processing unit 14 of the product-specific forecast creation apparatus 1 determines the sales ratio associated with the category characteristic value and the month information from the monthly sales ratio instruction request response. Extracted and output to the forecast expansion processing unit 133.

  The forecast expansion processing unit 133 calculates the target category characteristic value and monthly forecast of the product based on the sales ratio associated with the category characteristic value and the month information, and the group information with actual data (step S315). ).

Here, the target category characteristic value and monthly forecast (monthly target sales) of the product are as follows:
Monthly target sales =
Annual target number of sales for the category or product × sales ratio for the month ÷ 100… (Formula 3)
Can be obtained.

  The forecast development processing unit 133 uses the above (Equation 3) to calculate the target category characteristic value and monthly forecast of the product (monthly target sales number). Is obtained by (Equation 1) or (Equation 2) above and added to the group information with performance data, so that value may be used.

  The forecast expansion processing unit 133 associates each category characteristic value and the monthly target sales number of the product with the category characteristic value and the product, adds them to the group information with performance data, and outputs them to the screen display information processing unit 14.

  The screen display information processing unit 14 generates group information with performance data, that is, monthly forecast screen display information including the monthly forecast calculated by the forecast expansion processing unit 133 (step S316). The screen display information processing unit 14 transmits monthly forecast screen display information to the terminal 2 (step S317).

  The terminal 2 displays a monthly forecast screen based on the received monthly forecast screen display information. 18 and 19 are diagrams illustrating an example of a display screen of a monthly forecast screen displayed by the terminal 2. FIG. FIG. 18 is a format similar to the monthly sales ratio instruction screen of the category characteristic value “X-Series” shown in FIG. 17, and the monthly sales ratio specified by the user and the target sales number by month based on this sales ratio are as follows. It is displayed. FIG. 19 expands from “X-Series”, which is the highest category, to product codes “A” to “E”, and displays the target sales numbers for each month.

  The user confirms the monthly forecast screen display information displayed on the terminal 2 and inputs a termination request to the terminal 2, and the terminal 2 transmits the termination request to the product-specific forecast creation apparatus 1.

  When the termination request is received (step S318), the screen display information processing unit 14 of the product-specific forecast creation apparatus 1 notifies the forecast expansion processing unit 133 that the termination request has been received. The forecast development processing unit 133 registers the monthly forecast in the monthly forecast table 15 for each product (step S319).

  Here, the monthly forecast registered in the monthly forecast table 15 for each product is at least the category characteristic value of the group information with actual data and the target number of sales per month associated with the product code. In addition, group information of product codes may be included, or group information with performance data itself may be included.

  As described above, in the first embodiment, the grouping processing unit 131 registers the category designated from the outside, the category master 10 in which the category classified by the product characteristics is registered, the category to which the product belongs, and the category concerned. Based on the product category master 11 in which the characteristics are registered, the expansion order of the designated category is recognized, the products belonging to the designated category are grouped, and the forecast development processing unit 133 performs grouping. Based on the expansion order recognized by the processing unit 131, the annual target sales number input from the outside, the annual target total sales number specified from the outside, and the sales ratio with respect to the category, the lower order category from the higher order category , Annual focus on the category and product, from lowest category to product The monthly forecast for each product is calculated based on the calculated annual target sales for the category and product and the monthly sales ratio for the category and product input from the outside, and the monthly forecast for each product is calculated. Since the cast is registered in the monthly forecast table 15 for each product, it is possible to create the forecast without leaking all the products and efficiently create the forecast.

  Further, in the first embodiment, the result data aggregation processing unit 132 uses the grouping processing unit 131 from the monthly sales result data 12 by product in which the number of past sales results of the product in association with the product is registered. The number of monthly sales of products belonging to the group divided by the group is acquired, and the category display based on the acquired monthly sales and the monthly sales and annual sales of products belonging to the category are aggregated, and the screen display information Since the processing unit 14 displays the monthly sales volume and the annual sales volume aggregated by the actual data aggregation processing unit 132 as actual data, the past actual data at any stage when the sales number is developed by category. Trend analysis can be easily performed, and convenience can be improved.

  Further, in the first embodiment, the actual data totaling processing unit 132 is registered when the monthly sales number of the year for calculating the annual forecast is registered in the monthly sales result data 12 by product. Since the actual data is aggregated using the number of monthly sales up to the month, when reviewing forecasts after the processing month during the period, the monthly forecasts after the processing month will be based on the sales actual value until the processing month. Cast can be calculated.

Embodiment 2. FIG.
A second embodiment of the present invention will be described with reference to FIGS. The structure of a forecast creation support system to which the forecast creation apparatus for each product of the second embodiment is applied is the forecast creation apparatus 1 for each product of the forecast creation support system of the first embodiment shown in FIG. Is provided with a product-specific forecast making apparatus 1a.

  FIG. 20 is a block diagram showing a configuration of the product-specific forecast creation apparatus 1a according to the second embodiment. The product-specific forecast creation apparatus 1a of the second embodiment shown in FIG. 20 has a product group master 16 added to the product-specific forecast creation apparatus 1 of the first embodiment shown in FIG. Instead of the performance data totaling processing unit 132 of the cast developing unit 13, a performance data totaling processing unit 132a is provided. Components having the same functions as those of the product-specific forecast creation apparatus 1 of the first embodiment shown in FIG. 2 are given the same reference numerals, and redundant descriptions are omitted.

  In the product group master 16, information relating to a product code that is used in place of the product code is registered in association with the product code. FIG. 21 is a diagram illustrating an example of registration information of the product group master 16. In FIG. 21, as registration information of the product group master 16, a product code in which a product code for identifying a product is registered and a succeeding product that substitutes for the product indicated by the product code registered in the product code are identified. The subsequent product code in which the product code is registered, and the subsequent ratio in which the ratio of the product indicated by the product code registered in the product code to the product indicated by the subsequent product code is registered. That is, the product registered in the product code is an old product, and the new product replaced with the old product is registered in the subsequent product code.

  When calculating the actual data of the product codes grouped by the grouping processing unit 131, the actual data aggregation processing unit 132a does not aggregate only the product codes, but the product code is based on the product group master 16. Actual data is calculated in consideration of the performance of the old product in the actual data of the product that has been replaced as a new product.

  Next, the operation of the forecast creation support system of the second embodiment will be described. The operation of the forecast creation support system according to the second embodiment and that of the previous forecast creation support system according to the first embodiment are substantially the same, and the difference is the implementation described with reference to the flowchart of FIG. Since only the result data totaling process in step S102 of the operation of the forecast creation support system of aspect 1 is described, only the result data totaling process will be described here.

  When group information by grouping processing is notified from the grouping processing unit 131, the result data totaling processing unit 132a totals the specified category and the result data for each product based on the notified group information and the product group master 16. Execute actual data aggregation processing.

  Here, the actual data aggregation processing calculates the monthly sales (monthly sales) and annual sales of the specified category and products belonging to the specified category for each year, and the monthly sales account for the annual sales. It is assumed that the ratio and the ratio of the annual sales number of the category that is one lower level belonging to this category to the annual sales number of the category are obtained.

  First, the result data totaling processing unit 132a extracts all product codes from the group information. The actual data totaling processing unit 132a selects a processing target product code from the extracted product codes, and executes an association actual data totaling process for the processing target product code.

  The result data totaling processing unit 132a searches the product code of the monthly sales result data 12 by product using the processing target product code as a search key, and acquires the year and the number of monthly sales associated with the product code that matches the search key.

  For example, when the product code “A-3” is selected as the processing target product code, the performance data totaling processing unit 132a is associated with the product code “A-3” of the monthly sales performance data 12 by product shown in FIG. The number of sales by month for fiscal year 2006 and the current fiscal year is acquired.

  Next, the result data totaling processing unit 132a searches the continuation product code of the product group master 16 using the processing target product code as a search key, and the product code of the product group master 16 associated with the continuation product code that matches the search key. And get the continuation ratio.

  Here, since the processing target product code is the product code “A-3”, the result data totaling processing unit 132a searches for the continuation product code of the product group master 16 shown in FIG. A set of “A-1” and continuation ratio “1.0” and a set of product code “A-2” and continuation ratio “0.8” are acquired.

  The actual data totaling processing unit 132a searches the product code of the monthly sales result data 12 by product using the product code acquired from the product group master 16 as a search key, and sells by product monthly related to the product code that matches the search key. The number of sales by year and month of the record data 12 is acquired.

  Here, since a set of the product code “A-1” and the continuation ratio “1.0” and a set of the product code “A-2” and the continuation ratio “0.8” are acquired, the result data is aggregated. The processing unit 132a obtains the monthly sales numbers of the product code “A-1” in the fiscal years 2004 and 2005, and the product codes “A-2” in the fiscal years 2005 and 2006 from the monthly sales result data 12 by product shown in FIG. Get the number of monthly sales for the year.

  The actual data totaling processing unit 132a uses the product code acquired from the product group master 16 as a search key and adds the continuation ratio acquired from the product group master 16 together with the product code to the monthly sales number acquired from the monthly sales result data 12 by product. Multiplying and the value obtained by this multiplication is the number of monthly sales of the product code to be processed. Here, when there are a plurality of monthly sales numbers in the same year, the performance data totaling processing unit 132a adds the sales numbers in the same month in the same year, and the monthly value of the processing target product code is added. It is a number.

  Specifically, the result data totaling processing unit 132a acquires the monthly sales number of the product code “A-1” as the monthly sales volume in fiscal 2004, and the product code “A-1” as the monthly sales volume in the 2005 fiscal year. The monthly sales number and the monthly sales number of the product code “A-2” are acquired, and the monthly sales number of the product code “A-2” and the monthly sales number of the product code “A-3” are obtained as the monthly sales numbers in 2006. Have acquired.

  Since only the monthly sales volume of the product code “A-1” is acquired as the monthly sales volume in the fiscal year 2004, the result data totaling processing unit 132a reads “the product code“ A-1 ”in the fiscal year 2004”. The number of monthly sales of the product code “A-3” to be processed in fiscal 2004 is obtained by “the number of sales in the month × the sales ratio of the product code“ A-1 ””.

  Since the number of monthly sales of the product code “A-1” and the number of monthly sales of the product code “A-2” are acquired as the monthly sales numbers in 2005, the actual data totaling processing unit 132a (Number of sales of product code “A-1” in the month of 2005 × sales ratio of product code “A-1”) + (Number of sales of product code “A-1” in the month of 2005 × product code) The monthly sales number of the product code “A-3” to be processed in 2005 is obtained from “Sales ratio of“ A-1 ”).

  Since the number of monthly sales of the product code “A-2” and the number of monthly sales of the product code “A-3” are acquired as the monthly sales numbers in fiscal 2006, the actual data totaling processing unit 132a (Number of sales of product code “A-2” in the current month in fiscal 2005 × Sales ratio of product code “A-2”) + Number of sales of the product code “A-3” in the current month in fiscal 2005 ” The monthly sales number of the product code “A-3” to be processed is obtained.

  The result data totaling processing unit 132a adds the number of sales for each month of the calculated processing target product code for each year to obtain the number of annual sales for that year. The performance data totaling processing unit 132a associates the acquired year and monthly sales quantity and the obtained annual sales quantity with the product code indicated by the processing target product code and adds them to the group information.

  The actual data totaling processing unit 132a selects an unprocessed product code from among the product codes extracted from the group information as a processing target product code, and performs the above-described association actual data totaling processing operation for the processing target product code as group information. Repeat until there is no unprocessed product code among the product codes extracted from.

  When the association result data totaling process for the processing target product code is completed, the result data totaling processing unit 132a selects the target category characteristic value from the lowest category characteristic value of the group information, and the product codes belonging to the selected target category characteristic value The monthly sales and the annual sales for the target category characteristic value are obtained from the monthly sales and the annual sales. The result data totaling processing unit 132a associates the obtained monthly sales number and annual sales number and the current year with the target category characteristic value and adds them to the group information.

  The actual data totaling processing unit 132a obtains the monthly sales number and the annual sales number of each target category characteristic value for each fiscal year and adds them to the group information, and then the same level as the current target category characteristic value (in this case, the highest level in this case). It is determined whether there is an unprocessed category characteristic value among the lower-level category characteristic values. When there is an unprocessed category characteristic value among the category characteristic values at the same level (in this case, the lowest level) as the current target category characteristic value, the actual data totaling processing unit 132a is the same as the current target category characteristic value. From the category characteristic value of the level, select the open category characteristic value, select the new target category characteristic value, and the monthly sales number and the annual sales number of the product code belonging to the selected target category characteristic value by month for the target category characteristic value Repeat the process for obtaining the number of sales and the number of annual sales.

  If there is no unprocessed category characteristic value among the category characteristic values at the same level (in this case, the lowest level) as the current target category characteristic value, the actual data totaling processing unit 132a is 1 less than the current target category characteristic value. Select the category characteristic value that is the highest (in this case, deployment number 2) as the target category characteristic value, and the number of monthly sales and the number of monthly sales of the category characteristic value that belong to the target category characteristic value. Then, the process for obtaining the annual sales number is repeated to obtain the monthly sales number and the annual sales number for all the category characteristic values from the lowest category characteristic value to the highest category characteristic value in order.

  After obtaining the monthly sales number and the annual sales number for all the category characteristic values and product codes, the actual data totaling processing unit 132a displays the ratio of the monthly sales number to the annual sales number of all the category characteristic values and product codes, and the top The ratio of the annual sales number of the lower category characteristic value to the annual sales number of the category characteristic value of is determined. The actual data totaling processing unit 132a calculates the ratio of the monthly sales to the obtained annual sales and the ratio of the annual sales of the lower category characteristic value to the annual sales of the upper category characteristic value as the category characteristic value of the group information or the Associate with product code.

  The performance data totaling processing unit 132a notifies the group information with performance data to the forecast expansion processing unit 133.

  23 shows the product code “A-3” and the product code “A-3” aggregated by the actual data totaling processing unit 132a based on the product group master 16 shown in FIG. 21 and the monthly sales actual data 12 shown in FIG. The example of a display example of the performance data of "AB-1" is shown. In FIG. 23, the annual actual sales number of each year of the product code “A-3” is “(annual sales number of the product code“ A-1 ”× 1.0) + (product code“ A-2 ”” ”in FIG. Annual sales number of the relevant year x 0.8) + (annual sales number of the product code “A-3” x 1.0) ”, and the actual number of sales of the product code“ AB-1 ”in each fiscal year. Is "(annual sales number of product code“ A-2 ”× 0.2) + (annual sales number of product code“ B-1 ”× 1.0) + (product code“ AB-1 ”” ”) "Number of annual sales of the current year x 1.0)".

  As described above, in the second embodiment, the old product that was sold before the new product in association with the product (new product), and the subsequent ratio that indicates the ratio of replacing the old product with the new product, The product data master processing unit 132a includes a product group master 16 for registering the number of sales of the old product registered in the product sales data by month 12 according to the subsequent ratio of the new product to the old product. Since it is used as the number of monthly sales of products, it can immediately respond to switching between new and old products due to model changes, etc., enabling more accurate trend analysis and improving convenience.

Embodiment 3 FIG.
A third embodiment of the present invention will be described with reference to FIGS. Some products are sold in pairs with another product in relation to one product. For example, in the case of an air conditioner, an indoor unit and an outdoor unit are different products, but accessories such as an indoor unit, an outdoor unit, a rack in which the outdoor unit is installed, and a remote controller are related. In the third embodiment, a product-specific forecast creation apparatus for creating a forecast for products that are related in this way will be described.

  The structure of a forecast creation support system to which the forecast creation apparatus for each product of the third embodiment is applied is the forecast creation apparatus 1 for each product of the forecast creation support system of the first embodiment shown in FIG. Is provided with a product-specific forecast creation apparatus 1b.

  FIG. 24 is a block diagram showing a configuration of the product-specific forecast creation apparatus 1b according to the third embodiment. The product-specific forecast creation apparatus 1b according to the third embodiment shown in FIG. 24 is different from the product-specific forecast creation apparatus 1a according to the second embodiment shown in FIG. A calculation unit 18 is added. Components having the same functions as those of the product-specific forecast creation apparatus 1a of the second embodiment shown in FIG. 20 are given the same reference numerals, and redundant descriptions are omitted.

  The product connection master 17 registers connection relations of related products. FIG. 25 is a diagram illustrating an example of the configuration of the product connection master 17. In FIG. 25, the affected source product code (product code for which annual forecast and monthly forecast are obtained by the forecast expansion processing unit 133) is registered as the affected source product code. The affected product code related to one of the registered products is registered as the affected product code, and the product code registered in the affected product code in the column where the horizontal axis and vertical axis intersect And a correlation coefficient between the product code registered in the affected product code.

  For example, the product code “A” registered in the affected product code and the product codes “a”, “b”, “c” registered in the affected product code are related, and the correlation coefficient is “ 1 ”.

  The correlation forecast calculation unit 18 calculates a forecast of related products based on the product connection master 17, the monthly sales performance data 12 by product, and the monthly forecast table 15 by product.

  Next, the operation of the forecast creation support system of the third embodiment will be described. The difference between the product-specific forecast creation apparatus 1b of the third embodiment and the product-specific forecast creation apparatus 1a of the second embodiment is that the correlation forecast calculation unit 18 includes the product connection master 17, This is the presence / absence of processing for calculating the forecast of related products based on the monthly sales results data 12 by product and the monthly forecast table 15 by product. Therefore, only the operation related to the correlation forecast calculation unit 18 will be described here.

  After the forecast expansion processing unit 133 registers the monthly forecast in the monthly forecast table 15 for each product, the correlation forecast calculation unit 18 obtains information on the product code from the monthly forecast table 15 for each product, that is, forecast. The product code for which annual forecast and monthly forecast are calculated by the development processing unit 133, and the annual target sales number and monthly target sales number of the product code are extracted. Hereinafter, the product code extracted here may be referred to as an influence source product code.

  The correlation forecast calculation unit 18 extracts from the product connection master 17 a product code (affected product code) related to the affected product code and a related coefficient between the affected product code and the affected product code. Specifically, the influence source product code of the product connection master 17 is searched using the product code (influence source product code) extracted from the monthly forecast table 15 for each product as a search key, and is associated with the product code that matches the search key. The product code registered in the affected product code of the product connection master 17 and its correlation function are extracted. The correlation forecast calculation unit 18 sets a set of an affected product code and an affected product code having the same correlation coefficient associated with the affected product code as one correlation group.

  For example, in the case of the product connection master 17 shown in FIG. 25, the correlation between the product code “A” and the product codes “a” to “c” is “1”. The group product codes are “a” to “c”, and the product code “B” and the product codes “d” to “f” have a correlation coefficient “1”. The product codes that are the relationship groups are “d” to “f”, the product code “C” and the product codes “a” to “c” have a correlation coefficient “1”, and the product codes “d” to “d” Since “f” is the correlation coefficient “2”, the correlation groups for the product code “C” are two groups of product codes “a” to “c” and product codes “d” to “f”. The product code “D” and the product codes “d” to “f” have a correlation coefficient of “2”, and the product code “g” Is the correlation coefficient “3”, the correlation groups for the product code “C” are two groups of product codes “d” to “f” and the product code “g”, and the product code “E” and the product Since codes “a” to “c” have a correlation coefficient “1” and product code “g” has a correlation coefficient “2”, the correlation group for product code “E” is the product code “ There are two groups of “a” to “c” and the product code “g”.

  The correlation forecast calculation unit 18 totals the actual data of the affected product code based on the monthly sales actual data 12 by product. Since this process is the same as the actual data totaling process for the product code of the actual data totaling process executed by the actual data totaling processing unit 132a of the second embodiment, the description thereof is omitted here. The actual result data aggregation processing for the affected product code is not performed by the correlation forecast calculation unit 18, but the correlation forecast calculation unit 18 notifies the actual result data aggregation processing unit 132a of the affected product code, The result data totaling processing unit 132a may total the result data and notify the correlation forecast calculation unit 18 of the result of the totaling.

  The correlation forecast calculation unit 18 includes, for each affected product code, the annual target sales number and monthly target number of sales of the affected product code, the affected product code of the affected product code correlation group, and the affected product code. And correlation group information with performance data that associates the performance data with respect to the affected product code. The correlation forecast calculation unit 18 notifies the screen display information processing unit 14 of the generated correlation group information.

  The screen display information processing unit 14, based on the correlation group information notified from the correlation forecast calculation unit 18, the annual target sales number of the affected product code, the monthly target sales number, and the correlation group of the affected product code. The affected product code, the coefficient related to the affected product code, and the actual data for the affected product code are displayed, and the display screen information for the display screen requesting the input of the sales ratio of the correlation group is generated. . The screen display information processing unit 14 transmits a correlation group sales ratio request including the generated display screen information to the terminal 2.

  The terminal 2 generates and displays a display screen based on the received display screen information of the correlation coefficient group sales ratio request. The user inputs a sales ratio for the correlation coefficient group (correlation coefficient group sales ratio) to the terminal 2. The terminal 2 transmits a correlation group sales ratio request response including information relating the input correlation group sales ratio to the correlation coefficient group to the product-specific forecast creation apparatus 1b.

  The screen display information processing unit 14 of the product-specific forecast creation apparatus 1b sends the correlation group sales ratio associated with the correlation unit loop included in the received correlation group sales ratio request response to the correlation forecast calculation unit 18. Notice.

The correlation forecast calculation unit 18 relates to the affected product code based on the correlation group sales ratio associated with the notified correlation group, the target number of sales of the affected product code, and the correlation coefficient. Correlation coefficient group annual target number of sales, which is the sum of all affected product codes, is calculated. The correlation coefficient group annual target number of sales can be obtained by the following (formula 4).
Correlation coefficient group annual sales target =
(Effective source annual target number of sales x correlation group sales ratio ÷ 100) x correlation coefficient (Equation 4)
Here, the impact target annual sales number is the annual target sales number of the affected product code, and the correlation group sales ratio is the correlation group sales ratio of the affected product code related to the affected product code. The correlation coefficient is a correlation coefficient between the affected product code and the affected product code.

  The correlation forecast calculation unit 18 associates the correlation group annual target number of sales calculated by the above (Equation 4) with the affected product code and adds it to the correlation group information with performance data, and displays it on the screen display information processing unit 14. Notify

  The screen display information processing unit 14 generates an affected product code sales ratio request for requesting the sales ratio of the affected product code based on the notified correlation group information with performance data, and transmits it to the terminal 2. The screen display information processing unit 14 also includes information indicating the actual data and the annual target sales quantity of each correlation coefficient group in the affected product code sales ratio request.

  The terminal 2 displays a display screen for requesting the sales ratio of the affected product code based on the affected product code sales ratio request. FIG. 26 is a diagram illustrating an example of a screen displayed by the terminal 2. In FIG. 26, since the annual target sales number of the affected product code “A” is “0”, the total target sales volume of the affected target phase is also “0”, and the annual target sales number of the affected product code “B”. Is “96”, and since the correlation group has only the correlation coefficient “1”, “100”% is designated as the correlation group sales ratio with respect to the correlation coefficient “1”, and the annual number of sales from the influence source is “96”. Since the yearly target number of sales of the affected product code “C” is “81” and the correlation groups are two groups of correlation coefficients “2” and “3”, the correlation with the correlation coefficient “2” Correlation group annual target of “70”% for the related group sales ratio and “20”% for the correlation group sales ratio for the correlation coefficient of “3” and the target annual sales target for the correlation target is “2” Phase of sales number “113” and correlation coefficient “3” The total value of the related group annual target sales number “73” is “186”, the annual target sales number of the affected product code “D” is “184”, and the correlation group has the correlation coefficients “2” and “3”. ”Is specified as“ 60 ”% for the correlation group sales ratio for correlation coefficient“ 2 ”and“ 40 ”% for the correlation group sales ratio for correlation coefficient“ 3 ”. The sales number is the total value “442” of the correlation group annual target sales number “221” with the correlation coefficient “2” and the correlation group annual target sales number “221” with the correlation coefficient “3”. The annual target sales number of the code “E” is “150”, and the correlation group is two groups of correlation coefficients “1” and “2”, so the correlation group sales ratio for the correlation coefficient “1” is “ 80 ”%, correlation coefficient Correlation between the correlation group sales target “120” and the correlation coefficient “2” with a correlation coefficient “1” and “20”% specified as the correlation group sales ratio for “2” The total value is “150” with the related group annual target number of sales “60”.

  The user inputs the sales ratio in the sales ratio column corresponding to the affected product code on the display screen displayed on the terminal 2. The terminal 2 generates the affected product code sales ratio information in which the input sales ratio is associated with the affected product code and the affected product code, and the generated affected product code sales ratio request response is forecast for each product. It transmits to the creation apparatus 1.

  The screen display information processing unit 14 of the product-specific forecast creation apparatus 1 extracts the affected product code sales ratio information from the affected product code sales ratio request response, and outputs it to the correlation forecast calculation unit 18.

The correlation forecast calculation unit 18 calculates a product-specific annual forecast for the affected product code based on the affected product code sales ratio information and the correlation group annual target number of sales calculated by the above (Formula 4). The annual forecast for each product can be obtained by the following (Formula 5).
Annual forecast by product =
(Correlation group annual target sales x sales ratio of affected product code ÷ 100 ... (Formula 5)

  The correlation forecast calculation unit 18 associates the calculated annual forecast for each product with the affected product code and adds it to the correlation group information with performance data.

The correlation forecast calculation unit 18 calculates the monthly forecast for each product based on the annual forecast for each product obtained by the above (Formula 5) and the monthly sales ratio of the affected product code. The monthly forecast by product can be obtained by the following (formula 6).
Monthly forecast by product =
Correlation group annual target number of sales × sales ratio of affected product for the month ÷ 100… (Formula 6)
The sales ratio of the affected product in the current month uses the value notified by the monthly sales ratio instruction request response, that is, the sales ratio of the current month used when the monthly forecast of the affected product is obtained.

  For example, as shown in FIG. 28, for the affected product code “c” correlated with the affected product code “A”, the annual target sales number of the affected product code “A” is “0”. The monthly forecast is “0” regardless of the sales ratio, and the affected product code “c” correlated with the affected product code “C” is affected by the affected product code “C”. Forecast by product is required based on the annual target number of sales of “c” and the monthly sales ratio of the affected product code “C”.

  The correlation forecast calculation unit 18 associates the calculated monthly forecast for each product with the affected product code and adds it to the correlation group information with performance data. The correlation forecast calculation unit 18 outputs the correlation group information with performance data to the screen display information processing unit 14.

  The screen display information processing unit 14 generates display screen information for displaying the annual forecast and the monthly forecast for the affected product code based on the performance data monthly correlation group information. The screen display information processing unit 14 transmits the generated display screen information to the terminal 2.

  The terminal 2 displays annual forecast and monthly forecast based on the display screen information. FIG. 27 shows an example of an annual forecast display screen for the affected product code displayed by the terminal 2. In FIG. 27, the number of sales targets in fiscal 2007 for the affected product codes “a” to “g” in the correlation group with the affected product codes “A” to “E” is displayed in association with the affected product code. is doing.

  FIG. 29 shows an example of a monthly forecast display screen for the affected product code displayed by the terminal 2. 29, the affected product code “b” correlated with the affected product code “A”, the affected product code “b” correlated with the affected product code “C”, and the affected product code. Monthly forecasts for each affected product code “b” correlated with “E”, and their sum, ie, the annual sales target number and the monthly sales target number for the entire affected product code “b”. The target sales number with respect to the affected product code and the target sales number as the affected product code are displayed.

  As described above, in the third embodiment, the product connection master 17 for associating related products as an affected product and an affected product and registering a related coefficient between the affected product and the affected product is provided. The correlation forecast calculation unit 18 includes the same correlation coefficient for the monthly forecast of the affected product registered in the monthly forecast table 15 for each product and the affected product registered in the product connection master 17. The monthly forecast for each correlation group is calculated based on the correlation group sales ratio for each correlation group that is the affected product group, and the impact of the monthly forecast and correlation group for each calculated correlation group Calculate the monthly forecast of the affected product based on the sales ratio to the original product, and produce the calculated monthly forecast. Because you have to register with another month forecast table 15, it is possible to create maintained polymerizable overall, more accurate product specific forecast.

  As described above, the forecast creation apparatus for each product according to the present invention is useful for creating forecasts for each product, and in particular, creating forecasts for products that have a large number of products and are related to each other. Suitable for

It is a figure which shows an example of a structure of the forecast preparation assistance system to which the forecast preparation apparatus classified by product in this invention is applied. It is a block diagram which shows the structure of the forecast production apparatus 1 shown in FIG. It is a figure which shows an example of the registration item registered into the category master shown in FIG. It is a figure which shows an example of the registration item registered into the product category master shown in FIG. It is a figure which shows an example of the registration item registered into the monthly sales performance data according to product shown in FIG. It is a flowchart for demonstrating operation | movement of the forecast preparation assistance system of this Embodiment 1. FIG. It is a flowchart for demonstrating the detailed operation | movement of the grouping process of the product with respect to the category which expand | deploys a forecast. It is a figure which shows an example of the category designation | designated screen into which the category was input by the user. It is a figure which shows an example of the process result of the grouping process which the grouping process part performed. It is a flowchart for demonstrating the detailed operation | movement of a forecast expansion process. It is a figure which shows an example of the display screen based on annual forecast expansion | deployment screen display information. It is a figure of the display screen into which the annual target total sales number and the sales ratio were input. It is a figure which shows an example of an annual forecast expansion | deployment screen. It is a figure which shows an example of an annual forecast expansion | deployment display screen containing performance data. It is a figure which shows an example of an annual forecast expansion | deployment screen. It is a figure which shows an example of the forecast expansion | deployment display screen from the highest category characteristic value to a product code. It is a figure which shows an example of a monthly sales ratio instruction | indication screen. It is a figure which shows an example of the display screen of a monthly forecast screen. It is a figure which shows an example of the display screen of a monthly forecast screen. It is a block diagram which shows the structure of the forecast preparation apparatus 1a classified by product of this Embodiment 2. FIG. It is a figure which shows an example of the registration information of a product group master. It is a figure which shows an example of the monthly sales performance data according to product. It is a figure which shows an example of the example of a display of the performance data of a product code. FIG. 10 is a block diagram illustrating a configuration of a product-specific forecast creation apparatus according to a third embodiment. It is a figure which shows an example of a structure of a product connection master. It is a figure which shows an example of the display screen which requests | requires an influence target product code sales ratio. It is a figure which shows an example of the display screen of an annual forecast with respect to the affected product code which a terminal displays. It is a figure for demonstrating calculation of the monthly forecast with respect to an influence target product code. It is a figure which shows an example of the display screen of the monthly forecast with respect to the influence destination product code which a terminal displays.

Explanation of symbols

1, 1a, 1b Product-specific forecast creation device 2 Terminal 3 Network 10 Category master 11 Product category master 12 Monthly sales result data by product 13 Forecast development unit 14 Screen display information processing unit 15 Monthly forecast table by product 16 Product group Master 17 Product Connection Master 18 Correlation Forecast Calculation Unit 131 Grouping Processing Unit 132 Performance Data Total Processing Unit 133 Forecast Deployment Processing Unit

Claims (5)

Category master in which categories classified according to product characteristics are registered, product category master in which the category to which the product belongs and characteristics of the category are registered, and monthly forecast for each product in which monthly forecast for each product is registered A storage unit for storing the table;
A grouping processing unit for recognizing an expansion order of the designated category based on an externally designated category, the category master, and the product category master, and grouping products belonging to the designated category; ,
Based on the expansion order recognized by the grouping processing unit, the annual target total sales number specified from the outside and the sales ratio for the category, products from the highest category to the lower category and the lowest category to the product Monthly forecast for each category based on the calculated annual target sales for the category and product and the monthly sales ratio for the category and product entered from the outside. A forecast expansion processing unit for registering the calculated monthly forecast for each product in the monthly forecast table for each product;
A product-specific forecast making apparatus. The storage unit
Monthly sales results data by product, where the number of past sales by month of the product is registered in association with the product,
Remember more,
The monthly sales number of the products belonging to the group grouped by the grouping processing unit is acquired from the monthly sales result data by product, and the designated category and the products belonging to the category based on the acquired monthly sales number An actual data aggregation processing unit that aggregates monthly sales and monthly sales of
A screen display information processing unit for displaying the monthly sales number and the annual sales number calculated by the actual data totaling processing unit as actual data;
The product-specific forecast making apparatus according to claim 1, further comprising: The actual data aggregation processing unit
When the monthly sales number of the year for calculating the annual forecast is registered in the monthly sales result data by product, the actual data is aggregated using the monthly sales number up to the registered month,
The product-specific forecast creation apparatus according to claim 2. The storage unit
A product group master in which an old product sold before the new product in association with the product (new product) and a subsequent ratio indicating a ratio of replacing the old product with the new product are registered;
Remember more,
The actual data aggregation processing unit
Using the number of monthly sales of the old product registered in the monthly sales performance data by product as the number of monthly sales of the new product according to the subsequent ratio of the new product to the old product;
The product-specific forecast making apparatus according to claim 2 or 3. The storage unit
A product connection master for associating related products as an affected product and an affected product, and registering a related coefficient between the affected product and the affected product,
Remember more,
Corresponding to a monthly forecast of the affected product registered in the monthly forecast table for each product and a group of affected products having the same correlation coefficient for the affected product registered in the product connection master The monthly forecast for each correlation group is calculated based on the correlation group sales ratio for each relationship group, and the impact is based on the calculated monthly forecast for each correlation group and the sales ratio of the correlation group to the affected product. A correlation forecast calculation unit that calculates monthly forecasts for products and registers the calculated monthly forecasts in the monthly forecast table for each product;
The product-specific forecast creation apparatus according to any one of claims 2 to 4, further comprising:

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