Ordering support device and method

Abstract

We propose an ordering support device and method that can prevent stock shortages and price increases due to product shortages. [Solution] Using past and future manufacturing and transportation information for each product, the system predicts the daily number of units that can be received in the future for each product. Based on past sales performance for each product, it also predicts the daily demand for each product relative to its selling price. Based on these predictions, the system identifies products at risk of stockout, extracts alternative products from the available inventory, and determines a reduced selling price for alternative products that will not experience future stockouts, based on the daily number of units that can be received and the demand for each of the products at risk of stockout and their alternatives.

Description

Technical Field

[0001] The present invention relates to an order processing support apparatus and method, and is suitable for application to an order processing support apparatus that supports order processing of products in the distribution industry such as retail and wholesale.

Background Art

[0002] In recent years, the distribution industry such as retail and wholesale has been facing market fluctuations and rapid changes in consumer behavior, and along with this, inventory management and supply chain efficiency improvement have been urgently demanded. For example, regarding inventory management, excessive inventory leads to waste of capital, and inventory shortages lead to a decrease in customer satisfaction and opportunity loss, so it is required to balance excessive inventory and inventory shortages.

[0003] In addition, in recent years, due to the influence of SNS (Social Network Service) and the like, products instantly become popular and demand suddenly increases, while the supply of those products cannot keep up and out-of-stock situations occur, which may lead to a decrease in sales opportunities and customer loyalty.

[0004] Therefore, in the distribution industry, the demand for products is predicted with high accuracy, and by reflecting the prediction results in orders, a large amount of products with high demand are received to prevent out-of-stock situations, or the selling price of products with low inventory is increased to maximize profits using the limited inventory.

[0005] In Patent Document 1, an invention is disclosed that can provide an operator with order timing determination parameters and minimum order quantity determination parameters that do not cause out-of-stock situations or limit out-of-stock situations to a predetermined number of times while considering product demand prediction, incoming lead time, and inventory quantity.

[0006] Specifically, Patent Document 1 discloses an ordering support device that simulates the occurrence of stockouts in a predetermined period, assuming that, based on product data including product name or product code, time-series sales performance, and delivery date, the following are carried out economically: sales of a specified product in accordance with the time-series sales performance, orders placed for a specified product in accordance with the order multiplier calculated by multiplying the moving average value by the order multiplier when the inventory falls below the minimum inventory level calculated by multiplying the minimum inventory level by the minimum inventory level, and receipt of the ordered quantity obtained when the delivery date corresponding to the previously made order has passed. The device displays the combination of the order multiplier and minimum inventory level when no stockout occurs. [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Japanese Patent Application Publication No. 11-296611 [Overview of the Initiative] [Problems that the invention aims to solve]

[0008] In reality, even if you order the predicted demand, there are lead times for manufacturing and transportation, so the goods may not arrive immediately. Also, due to shortages of raw materials or supply chain problems, even if you order the predicted demand in advance, production and delivery may not be possible, resulting in shortages at the point of sale. Furthermore, raising prices for scarce goods carries the risk of creating a negative impression on customers. Conventional technology does not have a mechanism to avoid such shortages and price increases caused by supply shortages.

[0009] This invention was made in consideration of the above points, and aims to propose an ordering support device and method that can avoid shortages due to insufficient supply of goods and price increases due to scarcity. [Means for solving the problem]

[0010] To solve the above problems, the present invention provides an ordering support device for assisting in the ordering of goods, comprising: a manufacturing information acquisition unit that acquires past and future manufacturing information for each of the said goods; a transportation information acquisition unit that acquires past and future transportation information for each of the said goods; an incoming quantity prediction unit that uses the manufacturing information and transportation information for each of the said goods to predict the number of goods that can be received each day in the future; a demand prediction unit that predicts the number of goods that can be received each day in the future relative to the selling price based on the past sales performance of each of the said goods; an incoming quantity extraction unit that extracts an incoming product from among the said goods that has a risk of being out of stock, based on the prediction results of the incoming quantity prediction unit and the prediction results of the demand prediction unit; and a price determination unit that determines the selling price of the said goods, wherein the price determination unit determines a reduced selling price for the incoming product and the incoming product, and

[0011] Furthermore, in the present invention, an ordering support method is provided which is performed by an ordering support device that assists in the ordering of goods, and the method includes: a first step of acquiring past and future manufacturing information and transportation information for each of the said goods; a second step of using the acquired manufacturing information and transportation information for each of the said goods to predict the number of goods that can be received each day in the future and to predict the number of goods that will be in demand each day in the future relative to the sales price based on the past sales performance of each of the said goods; a third step of identifying goods that are at risk of being out of stock based on the predicted number of goods that can be received each day in the future and the number of goods that will be in demand each day in the future relative to the sales price predicted for each of the said goods, and extracting alternative goods from the said goods for which the identified goods are at risk of being out of stock; and a fourth step of determining the sales price of the goods that are at risk of being out of stock and the alternative goods for which the said goods are at risk of being out of stock.

[0012] According to the ordering support device and method of the present invention, by setting the sales price of a substitute product for a product at risk of being out of stock to the displayed sales price, it is possible to guide consumers to purchase substitute products instead of products at risk of being out of stock, while avoiding the possibility of both the product at risk of being out of stock and its substitute products becoming out of stock in the future. [Effects of the Invention]

[0013] According to the present invention, it is possible to realize an ordering support device and method that can avoid stock shortages due to insufficient supply of goods and price increases due to scarcity. [Brief explanation of the drawing]

[0014] [Figure 1] This is a block diagram showing the configuration of the order processing support device according to this embodiment. [Figure 2]It is a chart showing a configuration example of a manufacturing information table. [Figure 3] It is a chart showing a configuration example of a shipping information table. [Figure 4] It is a chart showing a configuration example of a product information table. [Figure 5] It is a chart showing a configuration example of a sales performance information table. [Figure 6] It is a diagram showing a screen configuration example of a price cut scenario simulation result screen. [Figure 7] It is a flowchart showing the processing procedure of an order business support process. [Figure 8] It is a diagram for explaining the processing content of a demand forecasting unit regarding the demand forecast of each product. [Figure 9] It is a diagram for explaining the processing content of a demand forecasting unit regarding the demand forecast of a target category. [Figure 10] It is a flowchart showing the processing procedure of a substitute product selling price determination process.

Embodiments for Carrying Out the Invention

[0015] The following describes in detail one embodiment of the present invention with reference to the drawings.

[0016] (1) Configuration of the Order Business Support Device According to this Embodiment In FIG. 1, reference numeral 1 indicates an order business support device according to this embodiment as a whole. This order business support device 1 is a computer device that supports order operations for products and the like, and includes a central processing unit 3, a main memory device 4, a secondary storage device 5, an input device 6, and an output device 7 that are interconnected via an internal bus 2.

[0017] The central processing unit 3 is a processor that controls the operation of the entire order processing support device 1, and is composed of a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or the like. The main memory device 4 is composed of a high-speed and volatile semiconductor memory such as a DRAM (Dynamic Random Access Memory), and is used as the working memory of the central processing unit 3.

[0018] The secondary storage device 5 is composed of a large-capacity non-volatile storage device such as a hard disk drive or an SSD (Solid State Drive), and stores various application programs and data that need to be stored for a long time.

[0019] When the application program held in the secondary storage device 5 is read from the secondary storage device 5 to the main memory device 4 by the central processing unit 3 at the startup of the order processing support device 1 or when necessary, and the central processing unit 3 executes the application program read into the main memory device 4, various processes of the entire order processing support device 1 as described below are executed.

[0020] The input device 6 is a user interface such as a keyboard or a mouse, and is used when a user inputs various operations and information to the order processing support device. The output device 7 is composed of a display device such as a liquid crystal display or an organic EL (Electro-Luminescence) display, and a user interface such as a printer, and is used to present various information to the user. In the following, it is assumed that the output device 7 is composed of a display device.

[0021] Alternatively, the ordering support device 1 may be connected to a network, and a communication device that communicates with external devices via the network may be provided on the ordering support device 1. Furthermore, the ordering support device 1 may not be equipped with an input device 6 and an output device 7, and instead, the ordering support device 1 may perform calculations and other processing according to instructions from a client terminal (not shown) connected via the network, and display the results on that client terminal.

[0022] (2) Ordering support function Next, the order processing support function according to this embodiment, which is installed in the order processing support device 1, will be described.

[0023] Some consumers' product needs can be met by similar products within the same product category. In this case, the product that consumers choose to purchase will depend on the selling price of the product. Therefore, if there is a product that is at risk of becoming out of stock in the future (hereinafter referred to as a "stockout-risk product"), it is believed that by lowering the price of a product that can serve as a substitute for that stockout-risk product (hereinafter referred to as a "substitute product"), it is possible to guide consumers to purchase the substitute product instead of the stockout-risk product, thereby preventing the stockout of the stockout-risk product.

[0024] Therefore, the ordering support device 1 is equipped with an ordering support function that determines whether there are any products at risk of being out of stock based on the sales performance of each product to date, guides consumers to purchase alternative products from the products at risk of being out of stock if such products exist, and determines a price reduction for the alternative products to ensure that neither the products at risk of being out of stock nor their alternatives are out of stock, and presents this information to the user.

[0025] As a means to realize the order support function of this embodiment, as shown in Figure 1, the secondary storage device 5 of the order support device 1 stores a manufacturing information table 10, a transportation information table 11, a product information table 12, and a sales performance information table 13, while the main storage device 4 stores a manufacturing information acquisition unit 20, a transportation information acquisition unit 21, a receiving quantity forecasting unit 22, an alternative product extraction unit 23, a demand forecasting unit 24, a price determination unit 25, and a price reduction scenario display unit 26.

[0026] The manufacturing information table 10 is a table used to store and retain information on the past and future manufacturing plans for each product (hereinafter referred to as manufacturing information). As shown in Figure 2, the manufacturing information table 10 is composed of a date column 10A, an SKU column 10B, and a production quantity column 10C. In the manufacturing information table 10, one record (row) corresponds to one day's worth of past or future manufacturing information for a corresponding product.

[0027] The SKU column 10B stores the identifier of the Stock Keeping Unit (SKU) to which the corresponding product belongs (hereinafter referred to as the SKU identifier). The SKU identifier is an identifier assigned to each type of product, and here we assume that there is a one-to-one correspondence between the product and the value of the SKU identifier. The same applies below.

[0028] The date column 10A stores the past or future manufacturing date or planned manufacturing date of the product, and the quantity column 10C stores the number of units of the product manufactured on that manufacturing date or planned to be manufactured on that planned manufacturing date.

[0029] Therefore, in the example in Figure 2, it is shown that, for example, 100 units of a product with the SKU identifier "HS1001" were manufactured or are scheduled to be manufactured on "2024 / 03 / 01".

[0030] The transportation information table 11 is a table used to store and retain information on past and future transportation plans (hereinafter referred to as transportation information) for each product delivered to the target store, etc. As shown in Figure 3, the transportation information table 11 is composed of a shipping date column 11A, an arrival date column 11B, an SKU column 11C, and a quantity column 11D. In the transportation information table 11, one record corresponds to information on one past or future transportation plan (transportation information) for one product to the target store, etc.

[0031] The SKU field 11C stores the SKU identifier of the corresponding product. The shipping date field 11A stores the date on which the product was received from the requester, such as the manufacturer, and shipped or is scheduled to be shipped (hereinafter referred to as the shipping date). The arrival date field 11B stores the date on which the product arrived or is scheduled to arrive at the target store or other delivery destination (hereinafter referred to as the arrival date). The quantity field 11D stores the quantity of the corresponding product that was transported or is scheduled to be transported at that time.

[0032] Therefore, in the example shown in Figure 3, for example, it is indicated that a carrier received 40 units of a product with the SKU identifier "HS1001" on "2024 / 03 / 01" and delivered or is scheduled to deliver those products to the target store, etc., on "2024 / 03 / 02".

[0033] The product information table 12 is a table used to manage classification information for products assigned to each SKU identifier at the target store, etc., and is created in advance and provided to the order processing support device 1. As shown in Figure 4, this product information table 12 is composed of an SKU column 12A, a major classification code column 12B, a medium classification code column 12C, and a minor classification code column 12D. In the product information table 12, one record corresponds to the classification information of a product assigned to one SKU identifier.

[0034] The SKU field 12A stores the SKU identifier of the corresponding product. The major category code field 12B stores the code assigned to the major product category to which the product belongs (major category code), the subcategory code field 12C stores the code assigned to the subcategory product category to which the product belongs (subcategory code), and the minor category code field 12D stores the code assigned to the minor product category to which the product belongs (minor category code).

[0035] Furthermore, the major, medium, and minor categories of products can be freely defined. For example, the major category could be defined as "juice," the medium categories as "fruit juice," "carbonated juice," "lactic acid beverage," etc., and the minor categories as "orange juice," "apple juice," "grape juice," etc.

[0036] Therefore, in the example in Figure 4, it is shown that the major category code for the major product category to which the product with the SKU identifier "HS1001" belongs is "A001", the minor category code for the medium product category is "B001", and the minor category code for the minor product category is "C001".

[0037] The sales performance information table 13 is a table used to manage daily sales performance information for each product at target stores, etc. (hereinafter referred to as sales performance information). As shown in Figure 5, it is composed of a date column 13A, an SKU column 13B, a sales price column 13C, and a sales quantity column 13D. In the sales performance information table 13, one record corresponds to the daily sales performance information for one product.

[0038] The SKU column 13B stores the SKU identifier of the corresponding product, and the date column 13A stores the date of the corresponding product. The selling price column 13C stores the selling price of the product on that day, and the sales quantity column 13D stores the number of units of the product sold on that day. Therefore, in the example in Figure 5, it is shown that on "2024 / 03 / 01", a product with the SKU identifier "HS1001" was sold in quantities of "120" at a selling price of "120".

[0039] On the other hand, the manufacturing information acquisition unit 20 is a program that has the function of acquiring manufacturing information for each product and storing the acquired manufacturing information for each product in the manufacturing information table 10. As for the methods by which the manufacturing information acquisition unit 20 acquires manufacturing information for each product, it can be acquired by acquiring manufacturing information for each product that has been manually entered by the user via the input device 6 of the order support device 1, by acquiring manufacturing information that has been provided by the user stored in a portable storage medium such as a USB (Universal Serial Bus) memory and reading it from that portable storage medium, or by acquiring manufacturing information for each product manufactured at the manufacturing plant by communication via a network from a server device or the like at the manufacturing plant of each product.

[0040] The transportation information acquisition unit 21 is a program that acquires transportation information for each product and stores the acquired transportation information for each product in the transportation information table 11. Methods for the transportation information acquisition unit 21 to acquire transportation information for each product include acquiring transportation information manually entered by the user via the input device 6 of the order support device 1, acquiring transportation information for each product provided by the user stored in a portable storage medium such as a USB memory stick by reading it from that portable storage medium, and acquiring transportation information for each product entrusted to a transportation company via communication over a network from the server equipment of each transportation company.

[0041] The Inventory Availability Prediction Unit 22 is a program that predicts the maximum number of items that a target store can receive (hereinafter referred to as the maximum inventory availability) for each item and for each future day, based on the manufacturing and transportation information of each item. The Substitute Product Extraction Unit 23 is a program that extracts items similar to items at risk of being out of stock as substitute products and outputs a list of such items (hereinafter referred to as the substitute product list).

[0042] The demand forecasting unit 24 is a program that has the function of predicting the daily demand for each product in the future. The price determination unit 25 is a program that has the function of determining the reduced price of substitute products to prevent shortages of products at risk of stockout and their substitute products. Details of the demand forecasting unit 24 and the price determination unit 25 will be described later.

[0043] The price reduction scenario display unit 26 is a program that has the function of displaying the simulation results of the future demand for the stockout-risk product and its substitute product on the output device 7 when the price of the substitute product is reduced as described above.

[0044] In practice, the price reduction scenario display unit 26 displays a price reduction scenario simulation result screen 30, such as the one shown in Figure 6, on the output device 7 as a result of such simulation.

[0045] The price reduction scenario simulation results screen 30 includes a simulation results display area 31, within which a graph 32 displays the simulation results of the demand for the stockout-risk product and its substitute product up to date, and the future demand for the stockout-risk product and its substitute product if the price of the substitute product is reduced. In Figure 6, the vertical axis of the graph 32 represents the demand, and the horizontal axis represents the date.

[0046] Furthermore, the simulation result display area 31 displays a mark 33 indicating the timing of discounting the substitute product, and a string 34 indicating the discount rate. This allows users to easily recognize the change in demand for the product at risk of being out of stock and the substitute product due to the discounting of the substitute product. In this embodiment, the "timing of discounting the substitute product" is the day the simulation was performed.

[0047] Furthermore, on the price reduction scenario simulation results screen 30, by clicking the "Adopt" button 35 located in the lower right corner of the screen, it is possible to decide whether to adopt the conditions such as the timing and rate of the discount in the simulation whose results are displayed in the simulation results display area 31 at that time.

[0048] In this case, according to the simulation results, the quantity of the predicted substitute product and the product at risk of being out of stock that corresponds to the future daily demand for those products is determined as the daily order quantity for each product, and then each product is ordered according to the determined order quantity.

[0049] (3) Order processing support Next, we will explain the flow of a series of processes (hereinafter referred to as "order support processes") executed in the order support device 1 in relation to the order support function. In the following, the entity executing each process will be described as a program ("...part"), but it goes without saying that in practice, the central processing unit 3 (Figure 1) of the order support device 1 executes the process based on that program.

[0050] (3-1) Order processing support Figure 7 shows the flow of the order support process. This order support process is started when the user performs a predetermined operation on the order support device 1 via the input device 6 (Figure 1). First, the manufacturing information acquisition unit 20 acquires the manufacturing information for each product and stores the acquired manufacturing information in the manufacturing information table (S1). Then, the transportation information acquisition unit 21 acquires the transportation information for each product and stores the acquired transportation information in the transportation information table 11 (S2).

[0051] Next, the incoming quantity prediction unit 22 predicts the maximum daily incoming quantity for each product from today onward, based on the manufacturing information for each product stored in the manufacturing information table 10 and the transportation information for each product stored in the transportation information table 11 (S3).

[0052] In practice, the incoming quantity forecasting unit 22 calculates, for each product, the average percentage of the total number of units manufactured in a day in the past that were actually delivered to the target store, etc., based on the manufacturing information for each product stored in the manufacturing information table 10 and the transportation information for each product stored in the transportation information table 11.

[0053] For example, the incoming quantity prediction unit 22 obtains the number of units of a certain product manufactured on a given day (hereinafter referred to as the total number of units manufactured) from the manufacturing information table 10, and the number of units of that product shipped to the target store, etc. on that day (hereinafter referred to as the total number of units shipped) from the transportation information table 11.

[0054] The incoming quantity forecasting unit 22 then divides the total number of products to be shipped by the total number of products manufactured, and multiplies the result of that division by 100 to calculate the percentage of products manufactured on that day that were shipped to the target stores, etc.

[0055] The incoming quantity prediction unit 22 similarly calculates these percentages for multiple days and then calculates the average of each percentage obtained. This average is the average percentage of the number of units of the product that were received by the target store, etc., out of the total number of units manufactured in a day in the past.

[0056] The incoming quantity forecasting unit 22 then calculates the maximum number of incoming quantities of the product for each day within a predetermined period from today onward by multiplying the planned production quantities for that product for each day from today onward, which are stored in the production information table 10, by this average value.

[0057] Furthermore, the incoming quantity prediction unit 22 performs the same calculation for all products, thereby calculating the maximum number of products that can be received each day from today onward for a predetermined period.

[0058] Next, the demand forecasting unit 24 forecasts the daily and individual demand figures for a predetermined period starting from today (S4).

[0059] In practice, as shown in Figure 8, the demand forecasting unit 24 creates a known price elasticity model (an elastic model, hereafter referred to as the demand forecasting model) 42 for each product, based on the product information 40 for each product stored in the product information table 12 and the sales performance information 41 for each product stored in the sales performance information table 13, using statistical machine learning techniques to output a predicted value for the number of units in demand, with the sales price as input (S4-1). As such a statistical machine learning technique, for example, deep learning using a neural network can be applied.

[0060] The demand forecasting unit 24 then calculates the predicted daily demand for each product from today onward (hereinafter referred to as the predicted demand) 44 by inputting the planned selling price 43 of the product into the corresponding demand forecasting model 42 for that product (S4-2).

[0061] Returning to Figure 7, the demand forecasting unit 24 then determines whether or not there are any products at risk of stockout based on the maximum daily stock available for each product calculated in step S3 and the daily forecast demand for each product 44 obtained in step S4 (Figure 8) (S5).

[0062] Specifically, the demand forecasting unit 24 identifies all products as stockout risk products if, for even one day in step S4, the demand predicted by the demand forecasting unit 24 is greater than the maximum possible stockout quantity predicted by the stockout quantity forecasting unit 22 in step S3. Therefore, in step S5, the demand forecasting unit 24 determines whether or not there is even one such stockout risk product.

[0063] If this judgment yields a negative result (S5: NO), the price determination unit 25 determines the daily predicted demand for each product from today onward, as predicted by the demand forecasting unit 24, as the order quantity for that product for that day. The price reduction scenario display unit 26 also displays on the output device 7 (Figure 1) that there are no products at risk of being out of stock (S12). With this, the order support process is completed.

[0064] In response to this, if a positive result is obtained in the judgment in step S5 (S5: YES), the processing from step S6 onwards is executed for each product at risk of being out of stock.

[0065] Specifically, first, the substitute product extraction unit 23 sets the category hierarchy for extracting substitute products for products at risk of being out of stock to "subcategory" (S6), and extracts all products in the same "subcategory" as the product at risk of being out of stock as substitute products for the product at risk of being out of stock (S7).

[0066] In practice, the alternative product extraction unit 23 obtains the subcategory code from the subcategory code column 12D (Figure 4) of the record corresponding to the product at risk of being out of stock in the product information table 12. The alternative product extraction unit then identifies all records from the product information table 12 that do not correspond to the product at risk of being out of stock, and whose subcategory code column 12D contains the same subcategory code as the product at risk of being out of stock. The unit then obtains the SKU identifier stored in the SKU column 12A of each of these records. The alternative product extraction unit 23 then outputs the above-mentioned alternative product list, which lists each product with the SKU identifier obtained in this way as an alternative product to the product at risk of being out of stock, to the demand forecasting unit 24.

[0067] Next, the demand forecasting unit 24 groups the stockout risk products extracted in step S5 and each of the alternative products listed in the alternative product list provided by the alternative product extraction unit 23 in step S7 into a single category (hereinafter referred to as the target category), and forecasts the daily demand (sales) for the entire target category from today onward (S8).

[0068] Specifically, as shown in Figure 9, the demand forecasting unit 24 aggregates the daily sales performance data for the entire target category to date based on the alternative product list 50 provided by the alternative product extraction unit 23 and the sales performance information 41 for each alternative product stored in the sales performance information table 13 (S8-1). Based on the aggregated sales performance data 51 for the entire target category, it creates a product elasticity model (an elastic model, hereafter referred to as the target category demand forecasting model) 52, which is a demand forecasting model for the entire target category, using statistical machine learning techniques (S8-2). As a statistical machine learning technique, methods such as deep learning using neural networks can be applied.

[0069] Furthermore, the demand forecasting unit 24 uses the created target category demand forecasting model 52 to forecast the daily demand for the entire target category from today onward (hereinafter referred to as the forecast target category demand) 53 (S8-3).

[0070] Returning to the explanation in Figure 7, the price determination unit 25 then uses the demand forecasting model for the substitute product created in step S4 and the demand forecasting model for the target category created in step S8 to perform a substitute product sales price determination process (S9) to determine the discounted selling price of the substitute product. Details of the substitute product sales price determination process will be described later.

[0071] Next, the price determination unit 25 determines whether or not it was possible to determine the selling price of the substitute product in this substitute product selling price determination process (S10).

[0072] Then, if the price determination unit 25 obtains a positive result in the judgment in step S10 (S10: YES), it determines the predicted daily demand for each product from today onward, as predicted by the demand forecasting unit 24 in step S8, as the order quantity for that product for that day. The price reduction scenario display unit 26 also displays the price reduction scenario simulation result screen 30 described above for Figure 6 on the output device 7 (Figure 1) (S12). With this, the order support process is completed.

[0073] In contrast, if the judgment in step S10 yields a negative result (S10: NO), the price determination unit 25 raises the hierarchy of the product category from which to extract alternative products by one level (S11). Specifically, the price determination unit 25 raises the current product category hierarchy from "minor category" to "medium category," and raises it from "medium category" to "major category." Thus, the processing from step S7 onward is executed in the same manner as described above.

[0074] Then, when the price of the substitute product can be determined in the substitute product sales price determination process, and a positive result is obtained in step S10, the price determination unit 25 determines the predicted daily demand for each product from today onward, as predicted by the demand forecasting unit 24 in step S8, as the order quantity for that product for that day. The price reduction scenario display unit 26 also displays the price reduction scenario simulation result screen 30 described above for Figure 6 on the output device 7 (Figure 1) (S12). With this, the order support process is completed.

[0075] Furthermore, if the "Adopt" button 35 on the price reduction scenario simulation results screen 30 displayed on the output device 7 is clicked after the completion of the order support processing described above, the price determination unit 25 will determine the quantity corresponding to the predicted future daily demand for the substitute product and the product at risk of stockout as the daily order quantity for that substitute product and the product at risk of stockout, according to the simulation results displayed on the price reduction scenario simulation results screen 30.

[0076] (3-2) Process for determining the selling price of substitute products Figure 10 shows the specific processing details of the substitute product sales price determination process executed by the price determination unit 25 in step S9 of the order support process (Figure 7) described above. The price determination unit 25 determines the sales price of the substitute product according to the processing procedure shown in Figure 10.

[0077] In practice, when the above-mentioned order support process proceeds to step S9, the price determination unit 25 starts the alternative product sales price determination process shown in Figure 10, and first provisionally determines the planned sales price of all alternative products to a price obtained by discounting 5% from the planned sales price (hereinafter referred to as the discounted price) (S20).

[0078] Furthermore, the price determination unit 25 inputs the discounted price of each provisionally determined substitute product into the demand forecast model for that substitute product created by the demand forecasting unit 24 in step S4 of the order support processing, thereby simulating and calculating the daily demand for each substitute product over a predetermined future period, assuming that the selling price of each substitute product is set to the discounted price (S21).

[0079] Next, the price determination unit 25 determines whether, for all days within a predetermined future period, the number of demands for all substitute products predicted in step S21 is less than or equal to the maximum number of substitute products that can be received, as predicted in step S3 of the order support process (S22).

[0080] Obtaining a negative result in this judgment means that, for at least one substitute product, the number of units of demand for that substitute product predicted in step S21 is greater than the maximum number of units of that substitute product that can be received predicted in step S3 of the order support process, and therefore, there is a risk that the substitute product will be out of stock on some future day. Thus, at this point, the price determination unit 25 terminates the substitute product sales price determination process and proceeds to step S11 of the order support process described above for Figure 7.

[0081] In contrast, obtaining a positive result in the judgment of step S22 means that the number of demands for all substitute products predicted in step S21 is less than the maximum number of substitute products that can be received predicted in step S3 of the order support process, and therefore there is no risk of shortages of these substitute products in the future.

[0082] Thus, at this time, the price determination unit 25 calculates the future daily demand for each product at risk of being out of stock, assuming that the total demand for the target category does not change significantly, and taking into account the impact of price reductions on each substitute product (S23).

[0083] Specifically, the price determination unit 25 calculates the future daily demand for products at risk of stockout by subtracting the future daily demand for each substitute product after the price reduction, calculated in step S21, from the future daily demand (sales) for the entire target category predicted in step S8 of the order support processing.

[0084] Subsequently, the price determination unit 25 determines whether, for all days within a predetermined future period, the number of demands for the stockout-risk product calculated in step S23 is less than or equal to the maximum number of stockout-risk products that can be received, calculated in step S3 of the order support processing (S24).

[0085] Obtaining a negative result in this judgment means that the demand for the product at risk of being out of stock, calculated in step S23, is greater than the maximum number of units of that product that can be received, as predicted in step S3 of the order support process, and therefore, there is a possibility that the product at risk of being out of stock will be out of stock on some future day.

[0086] Thus, at this point, the price determination unit 25 returns to step S20 and provisionally sets the price reduction rate of the substitute product to a predetermined percentage lower than the price reduction rate of the substitute product provisionally determined in step S20 (S20). After this, the process of steps S21 to S25-S20 is repeated until a positive result is obtained in step S24.

[0087] Then, the price determination unit 25 obtains a positive result in step S24 because the demand quantity calculated in the most recent step S23 is less than or equal to the maximum number of available stock items with a risk of stockout calculated in step S3 of the order support process. At this point, it determines the price reduction rate for each substitute product by multiplying the original planned selling price by the price reduction rate provisionally determined in the previous step S20 (S25). After this, it terminates the substitute product selling price determination process and returns to the order support process.

[0088] (4) Effects of this embodiment As described above, the order support device 1 of this embodiment determines a reduced selling price for the substitute product so that neither the product at risk of being out of stock nor its substitute product will be out of stock in the future.

[0089] Therefore, according to this order processing support device 1, by setting the sales price of the substitute product for the product at risk of being out of stock to the displayed sales price, it is possible to guide consumers to purchase the substitute product instead of the product at risk of being out of stock, while avoiding future stock shortages of both the product at risk of being out of stock and its substitute product. This makes it possible to avoid stock shortages due to insufficient supply and price increases due to scarcity.

[0090] Furthermore, with this ordering support device 1, the reduced selling price of the substitute product (the reduction rate in Figure 6) and the simulation results of the demand for the subsequent stockout-risk product and the substitute product are displayed on the price reduction scenario simulation results screen 30. Therefore, the user can easily determine the future daily order quantities for the stockout-risk product and its substitute product when the selling price of the substitute product is set to the displayed selling price. This simplifies the ordering process for products.

[0091] (5) Other embodiments In the above-described embodiment, the order processing support device 1 was described in the case where it is configured with a single computer device. However, the present invention is not limited to this, and for example, it may be configured with a distributed computing system consisting of multiple computer devices.

[0092] Furthermore, in the above-described embodiment, the case in which the price reduction scenario simulation result screen 30 (Figure 6) displays the price reduction rate of the alternative product was mentioned, but the present invention is not limited to this, and information other than the price reduction rate, such as the selling price, may be displayed instead.

[0093] Furthermore, in the above-described embodiment, when the "Adopt" button 35 on the price reduction scenario simulation results screen 30 displayed on the output device 7 is clicked, the price determination unit 25 performs a process to determine the daily order quantity of the predicted substitute product and the product at risk of stockout, according to the simulation results displayed on the price reduction scenario simulation results screen 30, based on the future daily demand for those substitute products and the product at risk of stockout. However, the present invention is not limited to this, and a program other than the price determination unit 25, or any other program, may perform such a process. [Industrial applicability]

[0094] This invention can be widely applied to ordering support devices of various configurations that assist in product ordering operations. [Explanation of Symbols]

[0095] 1... Order processing support device, 3... Central processing unit, 4... Main memory, 5... Secondary memory, 6... Input device, 7... Output device, 10... Manufacturing information table, 11... Transportation information table, 12... Product information table, 13... Sales performance information table, 20... Manufacturing information acquisition unit, 21... Transportation information acquisition unit, 22... Inventory quantity forecasting unit, 23... Substitute product extraction unit, 24... Demand forecasting unit, 25... Price determination unit, 26... Price reduction scenario display unit, 30... Price reduction scenario simulation result screen.

Claims

1. In an ordering support device that assists in the ordering of goods, A manufacturing information acquisition unit that acquires past and future manufacturing information for each of the aforementioned products, A transportation information acquisition unit that acquires past and future transportation information for each of the aforementioned products, A supply quantity prediction unit that uses the manufacturing information and transportation information of each of the aforementioned products to predict the future daily supply quantity of each of the aforementioned products, A demand forecasting unit predicts the future daily demand for each product relative to its selling price, based on the past sales performance of each of the aforementioned products. A substitute product extraction unit extracts substitute products from the products that have identified a risk of stockout based on the prediction results of the available quantity prediction unit and the prediction results of the demand prediction unit, A price determination unit that determines the selling price of the aforementioned product and Equipped with, The price determination unit, Based on the future daily availability and demand for the product with the risk of being out of stock and the substitute product for that product, the reduced selling price of the substitute product will be determined, as neither the product with the risk of being out of stock nor the substitute product for that product will be out of stock in the future. An ordering support device characterized by the following features.

2. The aforementioned unit for predicting the number of items that can be received is: Based on the manufacturing information and transportation information for each of the aforementioned products, the ratio of the number of products received to the number manufactured is calculated for each of the aforementioned products. For each product, the calculated percentage is multiplied by the future daily production quantity of that product to predict the future daily supply quantity of each product. The ordering support device according to feature 1.

3. The aforementioned alternative product extraction unit is: Based on the product category for each category hierarchy to which each of the aforementioned products belongs, the alternative products for the products with a risk of being out of stock are extracted. The ordering support device according to feature 1.

4. The system further comprises a price reduction scenario simulation display unit that displays the simulation results of a simulation of the future demand for the product with a risk of stockout and the substitute product of the product with a risk of stockout, when the substitute product is sold at the sales price determined by the price determination unit. The ordering support device according to feature 1.

5. The system further includes an order quantity determination unit that determines the future order quantities for the product with the risk of being out of stock and the substitute product for the product with the risk of being out of stock, based on the future demand quantities for the product with the risk of being out of stock and the substitute product for the product with the risk of being out of stock, as predicted by the simulation. The ordering support device according to feature 4.

6. In an ordering support method executed by an ordering support device that assists in the ordering of goods, The first step is to obtain past and future manufacturing and transportation information for each of the aforementioned products, A second step involves using the acquired manufacturing information and transportation information for each of the aforementioned products to predict the future daily number of items that can be received for each of the aforementioned products, and, based on the past sales performance of each of the aforementioned products, predicting the future daily demand for each of the aforementioned products relative to the selling price. A third step involves identifying products at risk of stockout based on the predicted future daily supply of each of the aforementioned products and the predicted future daily demand for each product relative to its selling price, and then extracting alternative products from the aforementioned products for the identified products at risk of stockout. The fourth step is to determine the selling price of the product with the aforementioned stockout risk and the aforementioned substitute product for the product with the aforementioned stockout risk. Equipped with, In the fourth step described above, the ordering support device, Based on the future daily availability and demand for the product with the risk of being out of stock and the substitute product for that product, the reduced selling price of the substitute product will be determined, as neither the product with the risk of being out of stock nor the substitute product for that product will be out of stock in the future. A method for supporting ordering operations, characterized by the following features.

7. In the second step described above, the ordering support device, Based on the manufacturing information and transportation information for each of the aforementioned products, the ratio of the number of products received to the number manufactured is calculated for each of the aforementioned products. For each product, the calculated percentage is multiplied by the future daily production quantity of that product to predict the future daily supply quantity of each product. The ordering support method described in feature 6.

8. In the third step described above, the ordering support device, Based on the product category for each category hierarchy to which each of the aforementioned products belongs, the alternative products for the products with a risk of being out of stock are extracted. The ordering support method described in feature 6.

9. The system further comprises a fifth step of displaying the simulation results of a simulation of the future demand for the product with the risk of stockout and the substitute product for the product with the risk of stockout, assuming the substitute product is sold at the determined selling price. The ordering support method described in feature 6.

10. The sixth step further comprises determining the future order quantities for the product with the risk of being out of stock and the substitute product for the product with the risk of being out of stock, based on the future demand numbers for the product with the risk of being out of stock and the substitute product for the product with the risk of being out of stock, as predicted by the simulation. The ordering support method according to feature 9.

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