Demand forecast calculation device, demand forecast calculation method, and demand forecast calculation program

Abstract

To provide a demand prediction calculation device which can accurately and easily calculate a predictive demand quantity on the basis of past order reception records with respect to a product constituting a meal menu.SOLUTION: The demand prediction calculation device of the present embodiment extracts order reception records in a past prescribed period to generate predictive demand quantity work data including order reception days, menu days, menu days of week, meal divisions, menu pockets, a product, the number of order receptions, a total used quantity acquired from a product master with the product as a key, a used quantity per serving of past meal menus associated with the menu days, the menu days of week, the meal divisions, the menu pockets, and the product, the number of servings calculated by dividing the total used quantity by a used quantity per serving, and the number of meals calculated by multiplying the number of order receptions by the number of servings, aggregates the predictive demand quantity work data by menu day of week, meal division, and menu pocket, calculates the average number of meals including all of the products, and divides the calculated average number of meals by the number of servings in units of menu days of week, meal divisions, and menu pockets to calculate a predictive demand quantity with respect to a current meal menu.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a demand forecast calculation device, a demand forecast calculation method, and a demand forecast calculation program. [Background technology]

[0002] For example, in a company that provides boxed lunches based on menus to private homes and facilities, the menus are determined on a set cycle and food is ordered based on the determined menus. In this way, regular ordering has traditionally relied on the experience of the person in charge, which can result in excess or shortage of inventory. Patent Document 1, for example, is an example of a conventional food ordering system. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2013-250697 Summary of the Invention [Problem to be solved by the invention]

[0004] However, Patent Document 1 does not describe anything about calculating the demand forecast quantity for products that make up a menu with high accuracy based on past order records.

[0005] The present invention has been made in consideration of the above, and aims to provide a demand forecast calculation device, a demand forecast calculation method, and a demand forecast calculation program that are capable of calculating demand forecast quantities with high accuracy based on past order records for products that make up a menu. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the object, the present invention provides a demand forecast calculation device equipped with a control unit, the control unit being configured to be able to access a product master that associates and registers products, inventory management units, and total usage amounts, which are the contents contained in one unit of inventory; a menu master that registers menu menus, including menu dates, menu days, meal categories (morning / lunch / dinner), menu pockets, which are positions on the menu, products, and the amount of each product used per serving in the menu; and order records, including order dates, customers, products, order quantities, menu dates, menu days, meal categories, and menu pocket data; and (1) extracts order records for a predetermined period in the past and calculates order dates, menu dates, menu days, meal categories, and The system is characterized by (1) generating demand forecast number work data including menu pockets, products, order quantities, total usage obtained from the product master using the product as a key, menu date, menu day, meal category, menu pocket, and serving quantity per past menu linked to the product, the number of servings calculated by dividing the total usage quantity by the serving quantity per past menu, and the number of meals calculated by multiplying the number of orders by the number of servings, (2) aggregating the demand forecast number work data by menu day, meal category, and menu pocket, and calculating the average number of meals for all products, and (3) comprising a demand forecast number calculation means for calculating the demand forecast number for the current menu by dividing the calculated average number of meals by the number of meals for each menu day, meal category, and menu pocket.

[0007] According to one aspect of the present invention, the device is configured to be able to access current inventory data including products and current inventory quantities, and the demand forecast quantity calculation means may aggregate the demand forecast quantities on a product-by-product basis and calculate the required order quantity for each product by subtracting the current inventory quantity from the demand forecast quantity.

[0008] According to another aspect of the present invention, the required order quantity may be modifiable in response to an operation by a person in charge.

[0009] According to another aspect of the present invention, the control unit may further include an order processing means for creating order data including supplier, product, and order quantity based on the required order quantity for each product.

[0010] In order to solve the above-mentioned problems and achieve the object, the present invention provides a demand forecast calculation device executed by an information processing device having a control unit, wherein the control unit is configured to be able to access a product master in which products, inventory management units, and total usage amounts, which are the contents contained in one unit of inventory, are associated and registered; a menu master in which menu menus, including menu dates, menu days, meal categories (morning / lunch / dinner), menu pockets, which are positions on the menu, products, and the amount of each product used per serving in the menu, and order records, including data on order dates, customers, products, order quantities, menu dates, menu days, meal categories, and menu pockets, and the control unit executes the following operations: (1) extracting order records for a predetermined period in the past, and extracting order dates and menu pockets; The method includes a demand forecast number calculation step of: (1) generating demand forecast number work data including the date, menu day, meal category, menu pocket, product, order quantity, total usage obtained from the product master using the product as a key, the serving quantity used for past menus linked to the menu date, menu day, meal category, menu pocket, and product, the number of servings calculated by dividing the total usage quantity by the serving quantity used, and the number of meals calculated by multiplying the number of orders by the number of servings; (2) aggregating the demand forecast number work data by menu day, meal category, and menu pocket, and calculating the average number of meals for all products; and (3) calculating the demand forecast number for the current menu by dividing the calculated average number of meals by the number of servings for each menu day, meal category, and menu pocket.

[0011] In order to solve the above-mentioned problems and achieve the object, the present invention provides a demand forecast calculation program to be executed by an information processing device having a control unit, the control unit being configured to be able to access a product master that associates and registers products, inventory management units, and total usage amounts, which are the contents contained in one unit of inventory, a menu master that registers menu menus including menu date, menu day, meal classification (morning / lunch / dinner), menu pocket, which is the position on the menu, products, and the amount of each product used per serving in the menu, and order history that includes data on order date, customer, product, order quantity, menu date, menu day, meal classification, and menu pocket, and the control unit (1) extracts order history for a predetermined period in the past and calculates order date, menu date, menu day, and menu pocket data. The program is characterized by the following: (1) generating demand forecast number work data including meal category, menu pocket, product, order quantity, total usage obtained from the product master using the product as a key, menu date, menu day, meal category, menu pocket, and serving quantity for past menus linked to the product, the number of servings calculated by dividing the total usage quantity by the serving quantity, and the number of meals calculated by multiplying the number of orders by the number of servings; (2) aggregating the demand forecast number work data by menu day, meal category, and menu pocket, and calculating the average number of meals for all products; and (3) calculating the demand forecast number for the current menu by dividing the calculated average number of meals by the number of servings for each menu day, meal category, and menu pocket. [Effects of the Invention]

[0012] According to the present invention, it is possible to calculate the demand forecast quantity for products that make up a menu with high accuracy based on past order records. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is a block diagram showing an example of the configuration of a demand forecast calculation device according to this embodiment. [Figure 2] FIG. 2 is a diagram showing an example of a schedule leading up to placing an order. [Figure 3]FIG. 3 is a diagram showing the order date, past order date, closing date, and day of the week of the menu date. [Figure 4] FIG. 4 is a diagram showing an example of a processing flow from calculation of a demand forecast to placing an order with a supplier. [Figure 5] FIG. 5 is a diagram for explaining the calculation logic of the demand forecast performed in the "demand forecast process T1" in the process flow of FIG. [Figure 6] FIG. 6 is a diagram showing sample data for explaining a specific example of the processing of the control unit of the demand forecast calculation device according to this embodiment. [Figure 7] FIG. 7 is a diagram showing sample data for explaining a specific example of the processing of the control unit of the demand forecast calculation device according to this embodiment. [Figure 8] FIG. 8 is a diagram showing sample data for explaining a specific example of the processing of the control unit of the demand forecast calculation device according to this embodiment. [Figure 9] FIG. 9 is a diagram showing sample data for explaining a specific example of the processing of the control unit of the demand forecast calculation device according to this embodiment. [Figure 10] FIG. 10 is a diagram showing sample data for explaining a specific example of the processing of the control unit of the demand forecast calculation device according to this embodiment. [Figure 11] FIG. 11 is a diagram showing sample data for explaining a specific example of the processing of the control unit of the demand forecast calculation device according to this embodiment. [Figure 12] FIG. 12 is a diagram showing sample data for explaining a specific example of the processing of the control unit of the demand forecast calculation device according to this embodiment. [Figure 13] FIG. 13 is a diagram showing sample data for explaining a specific example of the processing of the control unit of the demand forecast calculation device according to this embodiment. [Figure 14] FIG. 14 is a diagram showing sample data for explaining a specific example of the processing of the control unit of the demand forecast calculation device according to this embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0014] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention is not limited to this embodiment.

[0015] [1. Overview] For example, a company that provides boxed lunches based on menus to private homes and facilities determines the menus and orders food based on the determined menus on a set cycle.

[0016] In this case, it would be possible to predict the amount of inventory needed based on past order trends, but this would result in a complex system for aggregating actual results.

[0017] In the present invention, demand forecasts are calculated with high accuracy and ease by utilizing information held in the system, such as past order records and current inventory.

[0018] In this way, highly accurate demand forecasting allows for the proper order quantity to be placed, enabling proper inventory management. In addition, it prevents excessive food orders and prevents inventory loss due to excess inventory.

[0019] The demand forecast calculation device of the present invention can be applied to all industries where inventory equals loss, such as ready-made meals, home-cooked meals, and eating out.

[0020] [2. Configuration] An example of the configuration of a demand forecast calculation device according to this embodiment will be described with reference to Fig. 1. Fig. 1 is a block diagram showing an example of the configuration of a demand forecast calculation device according to this embodiment.

[0021] The demand forecast calculation device 100 is a commercially available desktop personal computer. Note that the demand forecast calculation device 100 is not limited to a stationary information processing device such as a desktop personal computer, and may be a portable information processing device such as a commercially available notebook personal computer, PDA (Personal Digital Assistant), smartphone, or tablet personal computer.

[0022] The demand forecast calculation device 100 includes a control unit 102, a communication interface unit 104, a storage unit 106, and an input / output interface unit 108. The units included in the demand forecast calculation device 100 are connected to each other so as to be able to communicate with each other via any communication path.

[0023] The communication interface unit 104 communicably connects the demand forecast calculation device 100 to the network 300 via a communication device such as a router and a wired or wireless communication line such as a dedicated line. The communication interface unit 104 has a function of communicating data with other devices via the communication line. Here, the network 300 has a function of communicably connecting the demand forecast calculation device 100 with the store terminals 400... and the server 200, and is, for example, the Internet or a LAN (Local Area Network). The store terminals 400... are terminals provided in each store and configured to be able to communicate with the demand forecast calculation device 100.

[0024] An input device 112 and an output device 114 are connected to the input / output interface unit 108. The output device 114 may be a monitor (including a home television), a speaker, or a printer. The input device 112 may be a keyboard, a mouse, a microphone, or a monitor that functions as a pointing device in cooperation with a mouse. In the following, the output device 114 may be referred to as the monitor 114, and the input device 112 may be referred to as the keyboard 112 or the mouse 112.

[0025] Various databases, tables, files, etc. are stored in the storage unit 106. Computer programs that work in conjunction with an OS (Operating System) to issue commands to a CPU (Central Processing Unit) to perform various processes are recorded in the storage unit 106. The storage unit 106 can be, for example, a memory device such as a RAM (Random Access Memory) or a ROM (Read Only Memory), a fixed disk device such as a hard disk, a flexible disk, an optical disk, etc.

[0026] The storage unit 106 also includes a product master 106a, a menu master 106b, a data file 106c, and a work table 106d.

[0027] The product master 106a can be configured as a table or the like in which products, inventory control units, total usage amounts, and suppliers are registered in association with each other (see FIG. 6).

[0028] The menu master 106b registers the menus decided in the past and the present. The menu may include the menu date, the menu day, the meal category, the menu pocket, the product, and the amount used per serving.

[0029] The data file 106c is a file for storing the order record, current inventory data, order data, and the like.

[0030] The order record may include data on the order date, customer, product, number of orders, menu date, menu day of the week, meal category, and menu pocket.

[0031] The current stock quantity data may include the product and the current stock quantity. The order data may include the supplier, the product, and the order quantity.

[0032] The work table 106d is a table to be used as a work area of ​​the control unit 102, in which work data (intermediate data) and the like are generated, and in which, for example, demand forecast number work data for calculating the demand forecast number and the like are developed.

[0033] The control unit 102 is a CPU or the like that performs overall control of the demand forecast calculation device 100. The control unit 102 has an internal memory for storing control programs such as an OS, programs that define various processing procedures, required data, etc., and executes various information processing operations based on these stored programs.

[0034] The control unit 102 is configured to be able to access the product master 106a, the menu master 106b, and the data file 106c stored in the storage unit 106. The product master 106a, the menu master 106b, and the data file 106c may be provided in another location (for example, the server 200) as long as the control unit 102 is able to access them.

[0035] The control unit 102 conceptually includes a master maintenance unit 102a, an order processing unit 102b, a demand forecasting unit 102c, an order processing unit 102d, and a screen display control unit 102e.

[0036] The master maintenance unit 102a performs settings such as inputting, adding, deleting, and editing data for the product master 106a and the menu master 106b in response to, for example, an operator's operation on a master maintenance screen displayed on the monitor 114.

[0037] The order processing unit 102b inputs the order record in response to an operator's operation on an order input screen (not shown) displayed on the monitor 114, for example, and registers it in the data file 106c.

[0038] The demand forecasting unit 102c (1) extracts actual orders for a specified period in the past from the data file 106c, and generates demand forecast number work data including the order date, menu date, menu day, meal category, menu pocket, product, order quantity, total usage obtained from the product master 106a using the product as a key, the serving usage amount for past menus in the menu master 106b linked to the menu date, menu day, meal category, menu pocket, and product, the number of servings calculated by dividing the total usage amount by the serving usage amount, and the number of meals calculated by multiplying the number of orders by the number of servings, (2) aggregates the demand forecast number work data by menu day, meal category, and menu pocket, and calculates the average number of meals for all products, and (3) calculates the demand forecast number for the current (confirmed) menu in the menu master 106b by dividing the calculated average number of meals by the number of meals for each menu day, meal category, and menu pocket.

[0039] The demand forecasting unit 102c also calculates the required order quantity for each product by subtracting the current stock quantity from the demand forecast quantity. The current stock quantity data in the data file 106c is updated by the control unit 102.

[0040] The required order quantity may be modifiable in response to an operation by a person in charge. For example, the demand forecasting unit 102c may output a demand forecast file including the required order quantity for each product, and the person in charge may modify the required order quantity in the demand forecast file as necessary.

[0041] The order processing unit 102d creates order data including the supplier, product, and order quantity based on the required order quantity for each product, and registers the data in the data file 106c. In this case, the order data may be created by importing a demand forecast file (with or without corrections by the person in charge).

[0042] The screen display control unit 102e controls the display of various screens (for example, a master maintenance screen, an order entry screen, etc.) displayed on the monitor 114 and the input therefor.

[0043] [3. Specific Examples] A specific example of the processing of the demand forecast calculation device 100 in this embodiment will be described with reference to Fig. 1 to Fig. 14. Fig. 2 to Fig. 14 are diagrams for explaining a specific example of the processing of the demand forecast calculation device 100 in this embodiment.

[0044] (3-1. Overall processing) Figure 2 is a diagram showing an example of a schedule leading up to placing an order. If the date on which an order is placed with a food manufacturer is X day, this shows an example of a schedule starting from X day. Figure 3 is a diagram showing the order date, past order date, closing date, and day of the week for menu date. In Figure 3, the "order date" is every Friday, the "past order date" is "Monday of the week 12 weeks ago to Wednesday of the same week," the closing date is "Monday of the week 7 weeks from now," and the menu date is "Thursday of the week 8 weeks from now to Wednesday of the week 9 weeks from now."

[0045] In Figure 2, the menu to be forecasted is determined on X-4 (Monday) (Step S1). The target period for the menu is one week from Thursday 8 weeks after X to Wednesday 9 weeks after X.

[0046] Demand forecasting is performed on the X-1 day (step S2). The demand forecasting is performed based on, for example, order trends over the past 12 weeks. Here, the "order trends over the past 12 weeks" refers to the order trends from the order date "two days before the 12 weeks before the X-2 day" to "X-2 day."

[0047] On day X (Friday), an order is placed with the food manufacturer (step S3). When placing an order, a purchase order is created. The calculated demand forecast results are manually checked and changed, and the current order quantity is confirmed. The order date is set to day X (the starting point for everything). Below, the schedule is used as an example of a basic cycle.

[0048] The goods are delivered from the food manufacturer between Thursday, 5 weeks after the X date, and Wednesday, 6 weeks after the X date (step S4). The delivery destination is each warehouse.

[0049] The deadline for accepting orders for the menu items targeted for demand forecasting is set to be the Monday seven weeks after the X date (step S5).

[0050] Thereafter, the process proceeds from issuing shipping instructions to the warehouse to shipping to the customer (step S6).

[0051] FIG. 4 is a diagram showing an example of a processing flow from calculating a demand forecast to placing an order with a supplier. In FIG. 4, the demand forecasting unit 102c executes a demand forecasting process (step T1). In the demand forecasting process, the demand forecast is calculated by referring to the menu master 106b, actual orders, and product master 106a, and a file of the demand forecast results is output. Then, if necessary, the person in charge manually modifies (maintains) the demand forecast, and the demand forecast is finalized.

[0052] The order processing unit 102d executes the order processing (step T2), in which order data is created based on the demand forecast.

[0053] FIG. 5 is a diagram for explaining the calculation logic of the demand forecast performed in "demand forecast process T1" in the processing flow of FIG. 4. The information required for calculating the demand forecast and the data linkage are as shown in FIG. 4. When calculating the demand forecast quantity, the product master 106a, the confirmed menu items in the menu master 106b, current stock quantity data, and actual orders received over a predetermined period of time in the past (for example, the past 12 weeks) are used. The product master 106a and current stock quantity data use the latest data at the time of making the demand forecast.

[0054] (3-2. Sample data) 6 to 14 are diagrams showing sample data for explaining a specific example of the processing by the control unit 102 of the demand forecast calculation device 100 according to this embodiment. A specific example of the processing by the control unit 102 of the demand forecast calculation device 100 according to this embodiment will be explained with reference to FIGS.

[0055] (Prerequisite data for processing) Figure 6 shows an example of data in the product master 106a. The product master 106a has fields for product, inventory management unit, total usage, and supplier. "Total usage" is the amount of content contained in one unit of inventory. In the example shown in the figure, the first line contains the product "yogurt," inventory management unit "pack," total usage "300g," and supplier "SII0001."

[0056] Figure 7 shows an example of current stock quantity data. The current stock quantity data includes data on the product and the current stock quantity. In the example shown in the figure, the first line shows the product "yogurt" and the current stock quantity "40 packs."

[0057] Figure 8 shows an example of confirmed menu data. The confirmed menu is the menu that is the target of demand forecasting this time. The menu has the following items: menu date, menu day, meal category, menu pocket, product, and serving size. "Meal category" is the breakfast / lunch / dinner category. "Menu pocket" is the placement location on the menu. "Serving size" is the amount of one serving of the product used in the menu. In the example shown in the figure, the first line has the menu date "5 / 5 / 2022", menu day "Thursday", meal category "morning", menu pocket "1", product "yogurt", and serving size "20g".

[0058] Figure 9 is a diagram showing an example of past menu data. Past menus are menus linked to the order records to be compiled. In the example shown in the figure, the first line shows the menu date "2021 / 12 / 30", the menu day "Thursday", the meal category "morning", the menu pocket "1", the product "yogurt", and the amount used per serving "20g".

[0059] Figure 10 shows an example of order record data for the past 12 weeks. This order record is used to calculate the demand forecast, and is linked to information about the menu used. Order record and menu are linked by menu date, menu day of the week, meal category, menu pocket, and product, and when making a demand forecast, these are used as keys to obtain the "amount used per serving" from past menus.

[0060] The order record includes data on the order date, customer, product, order quantity, menu date, menu day, meal category, and menu pocket. In the example shown in the figure, the first line is the order date "2021 / 12 / 13", customer "TOK0001", product "yogurt", order quantity "10 packs", menu date "2021 / 12 / 30", menu day "Thursday", meal category "morning", and menu pocket "1".

[0061] (S1: Demand forecast processing) The demand forecasting process will be described in detail with reference to Figures 11 to 14. Figures 11 to 14 are diagrams for explaining the demand forecasting process in detail. The demand forecasting unit 102c (1) extracts actual orders for a specified period in the past from the data file 106c, and generates demand forecast number work data including the order date, menu date, menu day, meal category, menu pocket, product, order quantity, total usage obtained from the product master 106a using the product as a key, the serving usage amount for past menus in the menu master 106b linked to the menu date, menu day, meal category, menu pocket, and product, the number of servings calculated by dividing the total usage amount by the serving usage amount, and the number of meals calculated by multiplying the number of orders by the number of servings, (2) aggregates the demand forecast number work data by menu day, meal category, and menu pocket, and calculates the average number of meals for all products, and (3) calculates the demand forecast number for the current (confirmed) menu in the menu master 106b by dividing the calculated average number of meals by the number of meals for each menu day, meal category, and menu pocket. A specific example of the demand forecasting process will be described below.

[0062] (1) Past order records are extracted and stored in the demand forecast number work of the work table 106d. The data stored in the demand forecast number work is referred to as demand forecast work data.

[0063] The demand forecast worksheet has the following fields: order date, menu date, menu day, meal category, menu pocket, product, order quantity, total usage, amount used per serving, number of servings, and number of meals. Order date, menu date, menu day, meal category, menu pocket, product, and order quantity are obtained from actual orders. Total usage, amount used per serving, number of servings, and number of meals are supplemented. "Total usage" is the amount of content contained in one unit of the ordered product. "Amount used per serving" is the amount of the target product used in one serving of the menu, stored in the menu linked to actual orders. "Number of servings" is the number of servings that can be made from one unit of the target product (calculated by dividing total usage by amount used per serving). "Number of meals" is the number of meals that can be made from the ordered quantity (calculated by multiplying order quantity by number of servings).

[0064] 11 and 12(A) are diagrams showing examples of demand forecast quantity work data. In the example shown in the figures, the first line contains the order date "2021 / 12 / 13", menu date "2021 / 12 / 30", menu day of the week "Thursday", meal category "morning", menu pocket "1", product "yogurt", order quantity "10 packs", total amount used "300g", amount used per serving "20g", number of servings "15 servings", and number of meals "150 servings".

[0065] (2) The demand forecast quantity work is aggregated by menu information (menu day, meal category, menu pocket) and the average number of meals for all items is calculated. This is to eliminate variations in the number of meals for each menu (for example, healthy fish meals being more common than meat meals) and enable average demand forecasting.

[0066] Figure 12(B) is a diagram showing the calculation results of the average number of meals. The calculation results of the average number of meals include the items of menu day, meal category, menu pocket, total number of meals, number of target items, and average number of meals. In the example shown in the figure, the first line shows the menu day "Thursday", meal category "morning", menu pocket "1", total number of meals "240 servings (= 150 servings + 90 servings)", number of target items "2 items", and average number of meals "120 servings (= 240 servings / 2 items)".

[0067] (3) Based on the average number of meals added up by menu element based on past orders (the number of meals that will be needed on average for each menu item), calculate how many orders are likely to be generated for the current menu in order to prepare that number of meals. In other words, divide the "average number of meals" by the "number of servings" to reverse-calculate the demand forecast (find the amount of inventory that may be needed for the target menu).

[0068] 13(A) is a diagram showing an example of work data for demand forecast quantity by menu. In the example shown in the figure, the first line contains the menu date "2022 / 5 / 5", menu day "Thursday", meal category "morning", menu pocket "1", average number of servings "120 servings", product "yogurt", total amount used "300g", amount used per serving "20g", number of servings "15 servings", and demand forecast quantity "8 packs (= 120 servings / 15 servings)".

[0069] (4) The demand forecast quantity calculated for each menu item is calculated for each product, and the required order quantity is calculated based on the difference with the current inventory quantity. Figure 13(B) shows the calculation result of the required order quantity. The calculation result of the required order quantity includes the product, current inventory quantity, demand forecast quantity, and required order quantity. The current inventory quantity is obtained from the current inventory data.

[0070] In the example shown in the figure, the first line shows the product "yogurt," the current inventory quantity "40 packs," the forecast demand quantity "91 packs (= 8 + 15 + 4 + 64)," and the required order quantity "51 packs (= 91 - 40)."

[0071] (5) Output an order file based on the calculation results. Figure 14(A) shows an example of data in an order file. The data in the order file includes the supplier, product, and order quantity. In the example shown in the figure, the first line contains the supplier "SII0001," the product "yogurt," and the order quantity "51 packs."

[0072] (6) The person in charge modifies the output file as necessary and records the order data as a confirmed order quantity. Figure 14(B) is a diagram showing an example of modified data in the order file. In the example shown in the figure, the first line contains supplier "SII0001," product "yogurt," and order quantity "55 packs," and the order quantity has been modified from "51" to "55." The order processing unit 102d imports the order file and registers it in the data file 106c as order data.

[0073] As described above, according to this embodiment, (1) actual orders received for a predetermined period in the past are extracted from the data file 106c, and a demand forecast number including the order date, menu date, menu day, meal category, menu pocket, product, order quantity, total consumption obtained from the product master 106a using the product as a key, the consumption per serving of the past menu items in the menu master 106b linked with the menu date, menu day, meal category, menu pocket, and product, the number of servings calculated by dividing the total consumption by the consumption per serving, and the number of meals calculated by multiplying the number of orders by the number of servings is calculated. (2) the demand forecast number work data is generated, (3) the demand forecast number is calculated by dividing the calculated average number of meals by the number of servings for the current (confirmed) menu in the menu master 106b by the menu day, meal category, and menu pocket, and the demand forecast number is calculated. This makes it possible to calculate the demand forecast number for the products that make up the menu with high accuracy and easily based on past order records.

[0074] [4. Contribution to the United Nations-led Sustainable Development Goals (SDGs)] This embodiment can contribute to improving business efficiency and promoting appropriate management decisions by companies, thereby contributing to the achievement of SDGs Goals 8 and 9.

[0075] Furthermore, this embodiment can contribute to reducing waste and promoting paperless and electronic systems, thereby contributing to the achievement of SDGs Goals 12, 13, and 15.

[0076] Furthermore, this embodiment can contribute to strengthening control and governance, which can contribute to the achievement of Goal 16 of the SDGs.

[0077] 5. Other Embodiments The present invention may be implemented in various different embodiments other than those described above within the scope of the technical concept set forth in the claims.

[0078] For example, among the processes described in the embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods.

[0079] Furthermore, the processing procedures, control procedures, specific names, information including parameters such as registered data and search conditions for each process, screen examples, and database configurations shown in this specification and drawings can be changed as desired unless otherwise specified.

[0080] Furthermore, with regard to the demand forecast calculation device 100, the components shown in the figure are functional concepts, and do not necessarily have to be physically configured as shown in the figure.

[0081] For example, all or any part of the processing functions of the demand forecast calculation device 100, particularly the processing functions performed by the control unit, may be implemented by a CPU and a program interpreted and executed by the CPU, or may be implemented as hardware using wired logic. The program is recorded on a non-transitory computer-readable recording medium containing programmed instructions for causing the information processing device to execute the processes described in this embodiment, and is mechanically read by the demand forecast calculation device 100 as needed. That is, a computer program for providing instructions to the CPU in cooperation with the OS and performing various processes is recorded in a storage unit such as a ROM or HDD (Hard Disk Drive). The computer program is executed by being loaded into RAM and cooperates with the CPU to form the control unit.

[0082] This computer program may be stored in an application program server connected to the demand forecast calculation device 100 via any network, and all or part of it may be downloaded as needed.

[0083] Furthermore, the program for executing the processes described in this embodiment may be stored in a non-transitory computer-readable recording medium or configured as a program product. Here, the term "recording medium" includes any "portable physical medium" such as a memory card, a Universal Serial Bus (USB) memory, a Secure Digital (SD) card, a flexible disk, a magneto-optical disk, a ROM, an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable and Programmable Read Only Memory (EEPROM (registered trademark)), a Compact Disk Read Only Memory (CD-ROM), a Magneto-Optical disk (MO), a Digital Versatile Disk (DVD), and a Blu-ray (registered trademark) disc.

[0084] Furthermore, a "program" is a data processing method written in any language or description method, regardless of the format, such as source code or binary code. Note that a "program" is not necessarily limited to a single structure, but also includes a structure that is distributed as multiple modules or libraries, or a structure that achieves its function by cooperating with a separate program, such as an OS. Note that the specific configuration and reading procedure for reading a recording medium in each device shown in the embodiments, as well as the installation procedure after reading, can use well-known configurations and procedures.

[0085] The various databases stored in the memory unit are storage means such as memory devices such as RAM and ROM, fixed disk devices such as hard disks, flexible disks, and optical disks, and store various programs, tables, databases, and web page files used for various processes and providing websites.

[0086] The demand forecast calculation device 100 may be configured as an information processing device such as a known personal computer or workstation, or may be configured as the information processing device connected to any peripheral device. The demand forecast calculation device 100 may be realized by installing software (including programs, data, etc.) that causes the device to perform the processing described in this embodiment.

[0087] Furthermore, the specific form of distribution and integration of the devices is not limited to that shown in the drawings, and all or part of them can be configured by functionally or physically distributing and integrating them in any unit depending on various additions or functional loads. In other words, the above-described embodiments can be implemented in any combination, or embodiments can be implemented selectively. [Explanation of symbols]

[0088] 100 Demand forecast calculation device 102 Control section 102a Master Maintenance Department 102b Order Processing Department 102c Demand Forecasting Department 102d Order Processing Unit 102e Screen display control unit 104 Communication interface unit 106 Storage section 106a Product Master 106b Menu Master 106c Data File 106d Work Table 108 Input / Output Interface Section 112 Input Device 114 Output Device 200 servers 300 Network

Claims

1. A demand forecast calculation device including a control unit, The control unit A product master that associates and registers the product, inventory management unit, and total usage amount, which is the amount of content contained in one unit of inventory, and Menu date, menu day, breakfast / lunch / dinner meal classification, each item in the menu, a menu pocket showing the location of the item, and the amount of one serving of the item in the menu. Regarding the menu and the menu that defines the menu, the menu master that registers the menus that have been decided in the past and this time, Order history including order date, customer, each item in the menu, number of orders, menu date, menu day, meal category, and menu pocket data, It is configured to be accessible to (1) Extract the order record for a specified period in the past, and generate demand forecast quantity work data including the order date, menu date, menu day, meal category, product, menu pocket, order quantity, total usage obtained from the product master using the product as a key, the menu date, menu day, meal category, menu pocket, and the amount of usage per serving of the past menu menu in the menu master linked to the product, the number of servings calculated by dividing the total usage amount by the amount of usage per serving, and the number of meals calculated by multiplying the number of orders by the number of servings; (2) The demand forecast number work data is aggregated by menu day, meal category, and menu pocket unit, and the average number of meals for all products is calculated; and (3) a demand forecast number calculation means is provided for calculating the demand forecast number by dividing the calculated average number of meals by the number of servings for the current menu in the menu master by the menu day, meal category, and menu pocket unit.

2. The control unit It is configured to allow access to product and current inventory data, including current stock quantities. The demand forecast calculation device according to claim 1, characterized in that the demand forecast quantity calculation means aggregates the demand forecast quantities by product unit and calculates the required order quantity for each product by subtracting the current inventory quantity from the demand forecast quantity.

3. 3. The demand forecast calculation device according to claim 2, wherein the required order quantity can be modified in response to an operation by a person in charge.

4. The control unit 4. The demand forecast calculation device according to claim 2, further comprising an order processing means for creating order data including supplier, product, and order quantity based on the required order quantity for each product.

5. A demand forecast calculation method executed by an information processing device having a control unit, The control unit A product master that associates and registers the product, inventory management unit, and total usage amount, which is the amount of content contained in one unit of inventory, and Menu date, menu day, breakfast / lunch / dinner meal classification, each item in the menu, a menu pocket showing the location of the item, and the amount of one serving of the item in the menu. Regarding the menu and the menu that defines the menu, the menu master that registers the menus that have been decided in the past and this time, Order history including order date, customer, each item in the menu, number of orders, menu date, menu day, meal category, and menu pocket data, It is configured to be accessible to The control unit executes (1) Extract the order record for a specified period in the past, and generate demand forecast quantity work data including the order date, menu date, menu day, meal category, product, menu pocket, order quantity, total usage obtained from the product master using the product as a key, the menu date, menu day, meal category, menu pocket, and the amount of usage per serving of the past menu menu in the menu master linked to the product, the number of servings calculated by dividing the total usage amount by the amount of usage per serving, and the number of meals calculated by multiplying the number of orders by the number of servings; (2) The demand forecast number work data is aggregated by menu day, meal category, and menu pocket unit, and the average number of meals for all products is calculated; and (3) for the current menu menu in the menu master, the calculated average number of meals is divided by the number of servings to calculate the demand forecast number.

6. A demand forecast calculation program to be executed by an information processing device having a control unit, The control unit A product master that associates and registers the product, inventory management unit, and total usage amount, which is the amount of content contained in one unit of inventory, and Menu date, menu day, breakfast / lunch / dinner meal classification, each item in the menu, a menu pocket showing the location of the item, and the amount of one serving of the item in the menu. Regarding the menu and the menu that defines the menu, the menu master that registers the menus that have been decided in the past and this time, Order history including order date, customer, each item in the menu, number of orders, menu date, menu day, meal category, and menu pocket data, It is configured to be accessible to The control unit (1) Extract the order record for a specified period in the past, and generate demand forecast quantity work data including the order date, menu date, menu day, meal category, product, menu pocket, order quantity, total usage obtained from the product master using the product as a key, the menu date, menu day, meal category, menu pocket, and the amount of usage per serving of the past menu menu in the menu master linked to the product, the number of servings calculated by dividing the total usage amount by the amount of usage per serving, and the number of meals calculated by multiplying the number of orders by the number of servings; (2) A demand forecast calculation program for executing a demand forecast number calculation process that aggregates the demand forecast number work data by menu day, meal category, and menu pocket unit, and calculates the average number of meals for all products combined, and (3) calculates the demand forecast number by dividing the calculated average number of meals by the number of servings for the current menu in the menu master by menu day, meal category, and menu pocket unit.

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