JP7754732B2 - Order quantity determination device - Google Patents

Description

This disclosure relates to an order quantity determination device.

A phenomenon known as cannibalization can occur in stores that sell products. Cannibalization is a phenomenon in which, for example, one product and other products of the same type compete for demand with each other. To improve sales at a store, it is important to determine the order quantity for each product while taking cannibalization between products into account. One example of a technology that determines the order quantity for each product while taking cannibalization between products into account is the technology disclosed in Non-Patent Document 1.

A.Gurhan Kok, Marshall L.Fisher, “Demand Estimation and Assortment Optimization Under Substitution:Methodology and Application”, OPERATIONS RESEARCH, Vol.55, No.6, November-December 2007, pp.1001-1021

The technology disclosed in Non-Patent Document 1 is based on the premise that there is a store that handles all of the product groups handled by each store where cannibalization occurs. Hereinafter, a store that handles all of the product groups handled by each store where cannibalization occurs is referred to as a store with a complete product lineup. However, there are restrictions on the product lineup in real stores, and there are cases where a store with a complete product lineup does not exist among the multiple stores operated. In store operations where a store with a complete product lineup does not exist, the technology disclosed in Non-Patent Document 1 cannot be applied to determining order quantities that take cannibalization between products into account.

This disclosure has been made in light of the above circumstances, and aims to solve the problem of determining product order quantities while taking into account cannibalization between products in store operations where no store has a complete product lineup.

An order quantity determination device according to one aspect of the present disclosure includes an acquisition unit, an estimation unit, and a determination unit. The acquisition unit acquires a second sales quantity of a first product sold at a first store that does not sell the first product, between a first product and a second product that competes with the first product in demand. The acquisition unit also acquires a first sales quantity of the first product sold at a second store selected from a plurality of stores that sell the first product and the second product. The estimation unit estimates a substitution probability, a first raw demand rate, and a second raw demand rate based on the first sales quantity and the second sales quantity. The substitution probability indicates the probability that if one of the first product and the second product is unavailable, the other product will be purchased as a substitute. The first raw demand rate indicates the probability that the first product or the second product will be purchased at the first store when both the first product and the second product are available for purchase. The second raw demand rate indicates the probability that the first product or the second product will be purchased at the second store under the circumstances. The determination unit determines the order quantities of the first product and the second product at the first store based on the substitution probability, the first raw demand rate, and the second raw demand rate estimated by the estimation unit.

The order quantity determination device disclosed herein can determine the order quantity of a product by taking into account cannibalization between products in store operations where no store has a complete product lineup.

FIG. 1 is a diagram illustrating a configuration example of a product sales system according to an embodiment of the present disclosure. FIG. 4 is a diagram showing an example of the exterior of a store 40. 10 is a diagram showing an example of sales performance information D1 stored in management device 20. FIG. FIG. 2 illustrates an example of the configuration of a management device 20. 10 is a flowchart showing the flow of an order quantity determination method executed by a processing device 210 in a management device 20 according to a program AP.

<A. Embodiment>
FIG. 1 is a diagram illustrating an example configuration of a product sales system including a management device 20 according to an embodiment of the order quantity determination device of the present disclosure. The product sales system illustrated in FIG. 1 is an information system that performs unattended product sales at stores 40A, 40B, 40C, and 40D. Store 40A includes a communication device 410A connected to a communication network 30, such as the Internet. Store 40B includes a communication device 410B connected to the communication network 30. Store 40C includes a communication device 410C connected to the communication network 30. Store 40D includes a communication device 410D connected to the communication network 30. Hereinafter, when it is not necessary to distinguish between stores 40A, 40B, 40C, and 40D, stores 40A, 40B, 40C, and 40D will be referred to as stores 40. In the following, when there is no need to distinguish between communication device 410A, communication device 410B, communication device 410C, and communication device 410D, communication device 410A, communication device 410B, communication device 410C, and communication device 410D will be referred to as communication device 410.

In this embodiment, the store 40 is a micro-store. A micro-store is a device that sells products unattended. Figure 2 is a diagram showing the exterior of the store 40. Figure 2 illustrates the exterior of store 40A. As shown in Figure 2, the store 40 has a housing 430 with an opening. A display shelf 420 for displaying products is provided in the opening of the housing 430. The store 40 shown in Figure 2 has two tiers of display shelves 420, but the store 40 may have three or more tiers of display shelves 420, or may have only one tier of display shelf 420. The opening in the housing 430 is covered by a door 440. The door 440 is constructed by fitting a glass plate or the like into a frame made of metal or the like. The door 440 is attached to the housing 430 so that it can be opened and closed freely using three hinges 440b. Although detailed illustration is omitted in Figure 2, the store 40 has a locking mechanism that locks the door 440. At store 40, when a customer completes the product purchase procedure, the locking mechanism unlocks door 440. Once door 440 is unlocked, the customer of store 40 grasps handle 440a on door 440 to open it and removes the purchased product from store 40.

A user of store 40 displays a screen for using store 40 on a terminal device, such as a smartphone, by executing a specific application program on the terminal device. A specific example of a screen for using store 40 is a screen that lists the names, selling prices, and images of products on sale at store 40 and accepts operations to specify the products desired for purchase and to pay for the specified products. When operations to specify products and to pay for the products are performed on the terminal device, the terminal device transmits purchase information indicating the type and number of products purchased by the user and the store from which the products were purchased to management device 20 via communication network 30. Upon receiving the purchase information, management device 20 transmits unlock information to the store 40 indicated in the purchase information, instructing the store 40 to unlock the door 440. At the store 40, the door 440 is unlocked in accordance with the unlock information received via communication device 410.

In this embodiment, as shown in FIG. 1, product X and product Z are on sale at store 40A. Product X is, for example, a snack or the like. Product Z is, for example, a soft drink. Cannibalization does not occur between product X and product Z. As mentioned above, cannibalization refers to the phenomenon in which a certain product competes with another product of the same type for demand. Product X, product Y, and product Z are on sale at store 40B. Product W and product Z are on sale at store 40C. Product Y and product Z are on sale at store 40D. Product Y and product W are, like product X, snacks or the like. In this embodiment, cannibalization occurs between product X, product Y, and product W. Cannibalization does not occur between product Z and each of product Y and product W. Product Y is an example of a first product in this disclosure. Product X is an example of a second product in this disclosure. Product Z is an example of a third product in this disclosure.

The management device 20 stores sales performance information D1 indicating the sales quantity of each product sold at each of stores 40A, 40B, 40C, and 40D. Figure 3 is a diagram showing an example of sales performance information D1. As shown in Figure 3, sales performance information D1 is a table that associates a store identifier, which uniquely identifies a store, with the sales quantity of each of products W, X, Y, and Z sold at the store identified by the store identifier. Identifier A in Figure 3 is the store identifier of store 40A. Identifier B in Figure 3 is the store identifier of store 40B. Identifier C in Figure 3 is the store identifier of store 40C. Identifier D in Figure 3 is the store identifier of store 40D. An example of a store identifier is a character string indicating the name of the store. M_t_s in Figure 3 indicates the sales quantity of the product at each store. The subscript "t" is any of A, B, C, and D. For example, when t = A, the sales quantity is the sales quantity at store 40A. The subscript "s" indicates the type of product. The subscript "s" is one of W, X, Y, and Z. For example, when s = X, the sales quantity is the sales quantity of product X. For example, when M_t_s = M_B_Y, M_B_Y is the sales quantity of product Y sold at store 40B. In this embodiment, product Y and product W are not sold at store 40A, so in the sales performance information D1 shown in FIG. 3, the sales quantities of product Y and product W at store 40A are each zero. Similarly, in this embodiment, product W is not sold at store 40B, so in the sales performance information D1 shown in FIG. 3, the sales quantity of product W at store 40B is zero. In this embodiment, product X and product Y are not sold at store 40C, so in the sales performance information D1 shown in FIG. 3, the sales quantities of product X and product Y at store 40C are each zero. Additionally, in this embodiment, product X and product W are not sold at store 40D, so in the sales performance information D1 shown in FIG. 3, the sales quantities of product X and product W at store 40D are each set to 0. Each time management device 20 receives purchase information via communication network 30, sales performance information D1 is updated in accordance with the received purchase information.

For example, once a week, management device 20 determines the order quantity for each product for each of stores 40A, 40B, 40C, and 40D based on the sales performance of the most recent week. After determining the order quantity for each product for each of stores 40A, 40B, 40C, and 40D, management device 20 initializes the sales quantity for each product in sales performance information D1 to 0. When store 40A begins selling new product Y, it is important to determine the order quantities for product X and product Y while taking into account cannibalization with product X currently on sale. If product X, product Y, and product W are currently on sale at any of stores 40B, 40C, and 40D, the product lineup at that store covers all the products that would cause cannibalization, and therefore the store has a complete product lineup. Therefore, if product X, product Y, and product W are on sale at any of stores 40B, 40C, and 40D, the technology disclosed in Non-Patent Document 1 can be applied to determine the order quantities of product X and product Y at store 40A. However, in this embodiment, none of stores 40B, 40C, and 40D have a complete product lineup, so the technology disclosed in Non-Patent Document 1 cannot be applied. The management device 20 in this embodiment is a device that solves this problem.

Figure 4 is a diagram showing an example configuration of the management device 20. As shown in Figure 4, the management device 20 includes a processing device 210, a communication device 220, and a storage device 230. The processing device 210 is, for example, a CPU (Central Processing Unit). The processing device 210 functions as the control center of the management device 20 by operating in accordance with a program stored in the non-volatile storage device 232. The communication device 220 is a device that communicates with the communication device 410 via the communication network 30.

As shown in FIG. 4, the storage device 230 includes a volatile storage device 231 and a non-volatile storage device 232. The volatile storage device 231 is, for example, RAM (Random Access Memory). The volatile storage device 231 is used by the processing device 210 as a work area for executing programs. The non-volatile storage device 232 is, for example, flash ROM (Read Only Memory). The non-volatile storage device 232 stores the aforementioned sales performance information D1. In addition to the sales performance information D1, the non-volatile storage device 232 also stores product-related information D2 and store information D3.

Product relationship information D2 is a table that stores, in association with each other, identifiers (hereinafter referred to as product identifiers) that uniquely identify multiple products that are in a cannibalization relationship with each other. A specific example of a product identifier is a character string that indicates the name of a product. In this embodiment, product relationship information D2 stores, in association with each other, the product identifier of product X and the product identifier of product Y. In addition, in this embodiment, product relationship information D2 stores, in association with each other, the product identifier of product W and the product identifier of product Z.

Store information D3 is a table that associates the store identifier of store 40 with the product identifier of the product being sold at that store 40. In this embodiment, store information D3 stores the product identifiers of products X and Z in association with the store identifier of store 40A. In addition, store information D3 in this embodiment stores the product identifiers of products X, Y, and Z in association with the store identifier of store 40B. In addition, store information D3 in this embodiment stores the product identifiers of products W, Y, and Z in association with the store identifier of store 40C. In addition, store information D3 in this embodiment stores the product identifiers of products W and Z in association with the store identifier of store 40D.

In addition, various programs are stored in the non-volatile storage device 232. Specific examples of programs stored in the non-volatile storage device 232 include a kernel program and a program AP. The kernel program is not shown in Figure 4. The kernel program is a program that causes the processing device 210 to implement an OS (Operating System). The program AP is a program that causes the processing device 210 to implement an order quantity determination method that prominently demonstrates the features of the present disclosure.

When the management device 20 is powered on (not shown in FIG. 4), the processing device 210 reads the kernel program from the non-volatile storage device 232 to the volatile storage device 231. The processing device 210 begins executing the kernel program read into the volatile storage device 231. The processing device 210, operating in accordance with the kernel program, implements the OS. When the processing device 210, operating in accordance with the OS, receives an instruction to execute the program AP via an input device (not shown), it reads the program AP from the non-volatile storage device 232 to the volatile storage device 231. The processing device 210 then begins executing the program AP read into the volatile storage device 231. As shown in FIG. 4, the processing device 210, operating in accordance with the program AP, functions as a selection unit 211, an acquisition unit 212, an estimation unit 213, and a determination unit 214. The functions of the selection unit 211, acquisition unit 212, estimation unit 213, and determination unit 214 are as follows:

The selection unit 211 refers to store information D3 to determine whether or not each product designated as a target for determining order quantity is currently on sale for a store designated as a target for determining order quantity (hereinafter referred to as a target store). If a product that is not currently on sale is included in the target for determining order quantity, the selection unit 211 selects a store other than the target store that meets predetermined conditions as a reference store to use as reference when determining the order quantity for that product. The predetermined conditions in this embodiment are conditions related to the sales performance of products designated as a target for determining order quantity and currently on sale at the target store. The target store is an example of a first store in this disclosure. The reference store is an example of a second store in this disclosure.

For example, suppose the target store is store 40A, and the products for which the order quantity is to be determined are product X, product Y, and product Z. In this case, product Y is not yet sold at store 40A, so the predetermined condition for selecting a reference store is a condition related to the sales history of product X and product Z. More specifically, the predetermined condition is that the ratio of the sales quantity of product X to the sales quantity of product Z is closest to the ratio of the sales quantity of product X to the sales quantity of product Z at store 40A. As mentioned above, in this embodiment, product X and product Z are on sale at store 40B, while product X is not yet sold at stores 40C and 40D. For this reason, in this embodiment, store 40B is selected as the reference store.

The acquisition unit 212 acquires, from the sales performance information D1, the sales quantity V12 of products currently on sale at the target store (hereinafter, "on-sale products") among products that cannibalize each other. In this embodiment, the acquisition unit 212 acquires the sales quantity M_A_X of product X at store 40A as the sales quantity V12. The sales quantity V12 is an example of a second sales quantity in this disclosure. The acquisition unit 212 also acquires, from the sales performance information D1, the sales quantity V21 of a product (hereinafter, "new product") that cannibalizes with the on-sale product at the reference store. For example, if store 40B is selected by the selection unit 211 as the reference store, the acquisition unit 212 acquires the sales quantity M_B_Y of product Y at store 40A as the sales quantity V21. The sales quantity V21 is an example of a first sales quantity in this disclosure.

The estimation unit 213 sets the substitution probability q, original demand rate p1, and original demand rate p2 as variables. The estimation unit 213 then estimates the values of the substitution probability q, original demand rate p1, and original demand rate p2 based on the sales quantity V21 and the sales quantity V12. The substitution probability q indicates the probability that if a new product or an on-sale product is unavailable, the other will be purchased as a substitute. The original demand rate p1 indicates the probability that a new product or an on-sale product will be purchased in a situation where both the new product and the on-sale product are available at the target store. The original demand rate p2 indicates the probability that a new product or an on-sale product will be purchased in a situation where both the new product and the on-sale product are available at the reference store. In this way, a feature of the present disclosure lies in setting an original demand rate in addition to the substitution probability and estimating the original demand rate in conjunction with the substitution probability.

As shown in FIG. 4, the estimation unit 213 includes a first setting unit 2131, a second setting unit 2132, a third setting unit 2133, and a calculation unit 2134. The first setting unit 2131 sets the substitution probability q, the original demand rate p1, and the original demand rate p2 as variables.

The second setting unit 2132 sets the first demand quantity X1 as the sum of the value obtained by multiplying the sales quantity V12 by the substitution probability q and the original demand rate p1 (i.e., V12 x q x p1) and the value obtained by multiplying the sales quantity V21 by the original demand rate p2 (V21 x p2). That is, X1 = V12 x q x p1 + V21 x p2. The first demand quantity X1 represents the quantity of the new product estimated to be sold at the target store if the new product is sold at the target store. Similarly, the second setting unit 2132 sets the third demand quantity X3, which indicates the demand quantity for the new product at the reference store, based on the substitution probability q, the original demand rate p1, and the original demand rate p2. Specifically, the second setting unit 2132 sets the third demand quantity X3 as the sum of the value obtained by multiplying the sales quantity of the new product in the reference store by the original demand rate p2 and the value obtained by multiplying the sales quantity of the on-sale product in the target store by the substitution probability q and the original demand rate p1.

The third setting unit 2133 sets the second demand quantity X2 as the sum of the value obtained by multiplying the sales quantity V12 by the original demand rate p1 (i.e., V12 x p1) and the value obtained by multiplying the sales quantity V21 by the original demand rate p2 and the substitution probability q (i.e., V21 x p2 x q). That is, X2 = V12 x p1 + V21 x p2 x q. The second demand quantity X2 indicates the quantity of on-sale products estimated to be sold at the target store if a new product is sold at the target store. Similarly, the third setting unit 2133 sets the fourth demand quantity X4, which indicates the quantity of the on-sale products estimated to be sold at the reference store, based on the substitution probability q, original demand rate p1, and original demand rate p2. Specifically, the third setting unit 2133 sets the fourth demand quantity X4 as the sum of the value obtained by multiplying the sales quantity of the above-mentioned on-sale product in the target store by the original demand rate p1 and the value obtained by multiplying the sales quantity of the above-mentioned new product in the reference store by the original demand rate p2 and the substitution probability q.

The calculation unit 2134 calculates the sum S1 of the first demand quantity X1 and the second demand quantity X2. Next, the calculation unit 2134 calculates the sum S2 of the third demand quantity X3 and the fourth demand quantity X4 for the reference store. Next, the calculation unit 2134 calculates the absolute value Δ1 of the difference between the sales quantity of the on-sale product at the target store and the value S1. Next, the calculation unit 2134 calculates the absolute value Δ2 of the difference between the sales quantity of the new product and the on-sale product at the reference store and the value S2. The calculation unit 2134 then calculates the values of the substitution probability q, original demand rate p1, and original demand rate p2 that minimize the value Δ1 + Δ2 according to a predetermined mathematical optimization algorithm. Note that a specific example of a mathematical optimization algorithm for calculating the values of the substitution probability q, original demand rate p1, and original demand rate p2 is quadratic programming. Furthermore, in order to reduce the processing load required to calculate the substitution probability q, original demand rate p1, and original demand rate p2, the calculation unit 2134 may discontinue calculation of the substitution probability q, original demand rate p1, and original demand rate p2 when the value of Δ1 + Δ2 becomes equal to or less than a predetermined threshold th. An appropriate value for the threshold th may be set through simulation or the like.

The determination unit 214 determines the order quantities of each of the new product and the currently on-sale product in the target store based on the substitution probability q, original demand rate p1, and original demand rate p2 estimated by the estimation unit 213. For example, assume that the estimation unit 213 calculates Q as the value of the substitution probability q, P1 as the value of the original demand rate p1, and P2 as the value of the original demand rate p2. In this case, the determination unit 214 determines the order quantity N1 of the new product to be V12 x Q x P1 + V21 x P2. The determination unit 214 also determines the order quantity N2 of the currently on-sale product to be V12 x P1 + V21 x Q x P2 minus the inventory quantity of the currently on-sale product. Note that with regard to a product (product Z in this embodiment) that will not be cannibalized with the new product among the products currently on sale in the target store, the determination unit 214 determines the sales quantity of that product in the target store as the order quantity of that product.

Furthermore, the processing device 210, operating in accordance with the program AP, executes the order quantity determination method shown in the flowchart of FIG. 5 for each store and each product. As shown in FIG. 5, the order quantity determination method in this embodiment includes the processes of steps SC110 to SC170.

In step SC110, the processing device 210 functions as the selection unit 211. In step SC110, the processing device 210 selects a reference store based on the sales performance information D1.

In step SC120, the processing device 210 functions as the acquisition unit 212. In step SC120, the processing device 210 acquires the sales quantity V12 and the sales quantity V21.

In steps SC130, SC140, SC150, and SC160, the processing device 210 functions as the estimation unit 213. More specifically, in step SC130, the processing device 210 functions as the first setting unit 2131. In step SC130, the processing device 210 sets the substitution probability q, the original demand rate p1, and the original demand rate p2 as variables.

In step SC140, the processing device 210 functions as the second setting unit 2132. In step SC140, the processing device 210 sets the first demand quantity X1 to the sum of the value obtained by multiplying the sales quantity V12 by the substitution probability q and the original demand rate p1 (i.e., V12 x q x p1) and the value obtained by multiplying the sales quantity V21 by the original demand rate p2 (V21 x p2). The processing device 210 also sets the aforementioned third demand quantity X3 based on the substitution probability q, the original demand rate p1, and the original demand rate p2.

In step SC150, the processing device 210 functions as the third setting unit 2133. In step SC150, the processing device 210 sets the second demand quantity X2 to the sum of the value obtained by multiplying the sales quantity V12 by the original demand rate p1 (i.e., V12 x p1) and the value obtained by multiplying the sales quantity V21 by the substitution probability q and the original demand rate p2 (i.e., V21 x q x p2). The processing device 210 also sets the aforementioned fourth demand quantity X4 based on the substitution probability q, the original demand rate p1, and the original demand rate p2.

In step SC160, the processing device 210 functions as the calculation unit 2134. In step SC160, the processing device 210 calculates the values of the substitution probability q, original demand rate p1, and original demand rate p2 that minimize the aforementioned Δ1 + Δ2 according to a mathematical optimization algorithm.

In step SC170, the processing device 210 functions as the determination unit 214. In step SC170, the processing device 210 determines the order quantity for each of the new product and the currently on-sale product at the target store based on the substitution probability q, original demand rate p1, and original demand rate p2 calculated in step SC160. Also, in step SC170, for products currently on sale at the target store that will not be cannibalized with the new product, the processing device 210 determines the sales quantity of the product as the order quantity for the product.

As explained above, when a new product that cannibalizes existing products begins sales, the management device 20 of this embodiment can determine the order quantity for each product, taking into account cannibalization between products, even if the multiple stores operated do not include a store with a complete product lineup.

<B. Transformation>
One embodiment of the present disclosure has been described above, but this embodiment may be modified as follows.
(1) The determination unit 214 may determine the order quantities of new products and on-sale products by further taking into account the size S1 of the new products, the size S2 of the on-sale products, and the space SS that can be allocated to displaying the new products and on-sale products on the display shelf 420. Specifically, the determination unit 214 sets the upper limit of the order quantity m1 of new products to the aforementioned N1, and sets the upper limit of the order quantity m2 of on-sale products to the aforementioned N2. Then, the determination unit 214 may select a combination of m1 and m2 that maximizes m1 + m2 under the condition m1 × S1 + m2 × S2 ≦ SS.

(2) In the above embodiment, the predetermined condition for selecting a reference store from among multiple stores other than the target store was a condition related to product sales performance. However, the predetermined condition for selecting a reference store may include at least one of a condition related to the store's location and a condition related to the store's customer demographic, in addition to a condition related to product sales performance. Specifically, if store information D3 includes at least one of location information indicating the store's location (commercial area, residential area, etc.) and customer demographic information indicating the customer demographic (information indicating the proportion of each age group), it is sufficient that the predetermined condition includes at least one of a location similar to that of the target store and a customer demographic similar to that of the target store. Furthermore, the predetermined condition for selecting a reference store may be at least one of a condition related to the store's location and a condition related to the store's customer demographic. Furthermore, a user of management device 20 may specify a reference store; in this case, the processing of selection unit 211 and step SC110 can be omitted.

(3) In the above embodiment, the store 40 was a micro-store, but it may also be a retail store where products are sold by staff, such as a convenience store. If the store 40 is a retail store, a terminal device operated by a store clerk may serve as the communication device 410.

(4) In the above embodiment, the selection unit 211 selected one reference store, but may select multiple reference stores. When the selection unit 211 selects multiple reference stores, the first setting unit 2131 may set the original demand rate p2 as a variable for each reference store. In this case, the second setting unit 2132 may set the first demand amount X1 to the sum of the arithmetic average of the values obtained by multiplying the original demand rate p2 at each reference store by the sales quantity of the new product and the value obtained by multiplying the sales quantity V12 by the substitution probability q and the original demand rate p1. Furthermore, in the above embodiment, cannibalization occurred between one type of product on sale and the new product. However, the present disclosure is also applicable to cases where cannibalization occurs between multiple types of products on sale and the new product. In this case, the substitution probability q may be set as a variable for each of the multiple types of products on sale, and the original demand rate may be set as a variable corresponding to a uniform value for each store, and the above order quantity determination method may be executed.

(5) In the above embodiment, a maximum of three types of products were on sale at store 40. However, four or more types of products may be on sale. When four or more types of products are on sale at store 40, the selection unit 211 may select a reference store based on the sales quantity of each product on sale at the target store and the sales quantity of each product at each of multiple stores other than the target store. Specifically, the selection unit 211 generates a first vector whose components are the sales quantity of each product on sale at the target store. Next, the selection unit 211 generates a second vector whose components are the sales quantity of each product for each of the multiple stores other than the target store. Then, for each of the multiple stores other than the target store, the selection unit 211 calculates the value obtained by dividing the dot product of the first vector and the second vector by the norm of the first vector and the norm of the second vector. This value represents the cosine of the angle between the first vector and the second vector. The selection unit 211 then selects one or more stores as reference stores in order of decreasing order of the value closest to 1.

<C: Other>
(1) In the above embodiment and variations, the non-volatile storage device 232 may include a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory device (e.g., a card, a stick, a key drive), a CD-ROM (Compact Disc-ROM), a register, a removable disk, a hard disk, a floppy (registered trademark) disk, a magnetic strip, a database, a server, or other suitable storage medium. The program may also be transmitted from a network via a telecommunications line.

(2) Each of the above embodiments and variations may be applied to systems using LTE (Long Term Evolution), LTE-A (LTE-Advanced), SUPER 3G, IMT-Advanced, 4G, 5G, FRA (Future Radio Access), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, UWB (Ultra-Wideband), Bluetooth (registered trademark), or other suitable systems, and/or next-generation systems based on and enhanced by these.

(3) The information described in each of the above embodiments and modifications may be represented using any of a variety of different technologies. For example, data, information, etc. that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields, magnetic particles, optical fields, photons, or any combination thereof.
It should be noted that terms explained in this specification and/or terms necessary for understanding this specification may be replaced with terms having the same or similar meanings.

(4) In each of the above embodiments and variations, input and output information may be stored in a specific location (e.g., the volatile storage device 231) or managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be sent to another device.

(5) In each of the above embodiments and variations, the determination may be based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a comparison of numerical values (e.g., a comparison with a predetermined value).

(6) The order of the process procedures, sequences, flowcharts, etc. illustrated in each of the above embodiments and variations may be changed as long as there is no contradiction. For example, the methods described herein present various step elements in an exemplary order and are not limited to the specific order presented.

(7) Each function illustrated in FIG. 4 may be realized by any combination of hardware and software. Furthermore, the method for realizing each function is not particularly limited. That is, each function may be realized using a single device that is physically or logically coupled, or may be realized using two or more devices that are physically or logically separated and connected directly or indirectly (e.g., via wire, wirelessly, etc.). The functional blocks may be realized by combining software with the single device or the multiple devices.
Functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, regard, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how each is implemented.

(8) The programs exemplified in each of the above embodiments and variants should be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, or functions, etc., regardless of whether they are called software, firmware, middleware, microcode, hardware description language, or by other names.
Software, instructions, etc. may also be transmitted or received over a transmission medium. For example, if the software is transmitted from a website, server, or other remote source using wired technologies such as coaxial cable, fiber optic cable, twisted pair, and Digital Subscriber Line (DSL), and/or wireless technologies such as infrared, radio, and microwave, these wired and/or wireless technologies are included within the definition of transmission media.

(9) The management device 20 may be a mobile station. A mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable terminology.

(10) In each of the above embodiments and variations, the phrase "based on" does not mean "based only on," unless otherwise specified. In other words, the phrase "based on" means both "based only on" and "based at least on."

(11) In each of the above embodiments and variations, the terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access." As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using at least one of one or more wires, cables, and printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

(12) In each of the above embodiments and variations, to the extent that the terms "including," "comprising," and variations thereof are used in this specification or the claims, these terms are intended to be inclusive, similar to the term "comprise." Furthermore, the term "or" used in this specification or the claims is not intended to be an exclusive or.

(13) Throughout this application, where articles have been added by translation, such as a, an, and the in English, these articles include the plural unless the context clearly indicates otherwise.

(14) In this specification, the term "section" may be replaced with other terms such as circuit, device, or unit. Similarly, the term "apparatus" may be replaced with other terms such as circuit, device, or unit.

(15) As used in this disclosure, the terms "system" and "network" are used interchangeably.

(16) As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching a table, database, or other data structure), and ascertaining something that is considered to be a "determining." Also, "determining" and "determining" may include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and so on. Furthermore, "judgment" and "decision" can include regarding actions such as resolving, selecting, choosing, establishing, and comparing as having been "judgment" or "decision." In other words, "judgment" and "decision" can include regarding some action as having been "judgment" or "decision." Furthermore, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.

(17) It will be apparent to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the descriptions in this specification are for illustrative purposes only and do not have any limiting meaning on the present disclosure. Furthermore, multiple aspects selected from the aspects exemplified in this specification may be combined.

<D: Aspects understood from the above-described embodiments or modifications>
The following aspects can be understood from at least one of the above-described embodiments or modifications.
The order quantity determination device according to a first aspect includes an acquisition unit, an estimation unit, and a determination unit. The acquisition unit acquires a second sales quantity of a first product sold at a first store that does not sell the first product, among a first product and a second product that competes with the first product in demand. The acquisition unit also acquires a first sales quantity of the first product sold at a second store selected from a plurality of stores that sell the first product and the second product. The estimation unit estimates a substitution probability, a first raw demand rate, and a second raw demand rate based on the first sales quantity and the second sales quantity. The substitution probability indicates the probability that if one of the first product and the second product is unavailable, the other product will be purchased as a substitute. The first raw demand rate indicates the probability that the first product or the second product will be purchased at the first store when both the first product and the second product are available for purchase. The second raw material demand rate indicates the probability that the second product or the second product will be purchased at the second store under the circumstances. The determination unit determines order quantities of the first product and the second product at the first store based on the substitution probability estimated by the estimation unit, the first raw material demand rate, and the second raw material demand rate. When a first store that currently sells the second product starts selling a first product that competes with the second product in demand, the order quantity determination device according to the first aspect can determine the order quantities of the first product and the second product in consideration of the competition in demand for both products.

In the order quantity determination device according to the first embodiment (second embodiment), the estimation unit may include a first setting unit, a second setting unit, a third setting unit, and a calculation unit. The first setting unit sets the substitution probability, the first raw material demand rate, and the second raw material demand rate as variables. The second setting unit sets the first demand amount as the sum of a value obtained by multiplying the sales quantity of the second product at the first store by the substitution probability and the first raw material demand rate and a value obtained by multiplying the sales quantity of the first product at the second store by the second raw material demand rate. The first demand amount indicates the quantity of the first product estimated to be sold at the first store if the first product were sold at the first store. Furthermore, the second setting unit sets, as a third demand quantity, the sum of a value obtained by multiplying the sales quantity of the first commodity at the second store by the second raw demand rate and a value obtained by multiplying the sales quantity of the second commodity at the first store by the substitution probability and the first raw demand rate. The third demand quantity indicates the quantity of the first commodity estimated to be sold at the second store. The third setting unit sets, as a second demand quantity, the sum of a value obtained by multiplying the sales quantity of the second commodity at the first store by the first raw demand rate and a value obtained by multiplying the sales quantity of the first commodity at the second store by the substitution probability and the second raw demand rate. The second demand quantity indicates the quantity of the second commodity estimated to be sold at the first store if the first commodity were sold at the first store. The third setting unit sets the sum of the value obtained by multiplying the sales quantity of the second product at the first store by the first raw demand rate and the value obtained by multiplying the sales quantity of the first product at the second store by the substitution probability and the second raw demand rate as a fourth demand quantity. The fourth demand quantity indicates the quantity of the second product estimated to be sold at the second store. The calculation unit calculates a first value that is the absolute value of the difference between the sum of the first demand quantity and the second demand quantity and the sales quantity of the second product at the first store. The calculation unit also calculates a second value that is the absolute value of the difference between the sum of the third demand quantity and the fourth demand quantity and the sales quantities of the first product and the second product at the second store. The calculation unit then calculates the values of the substitution probability, the first raw demand rate, and the second raw demand rate so that the sum of the first value and the second value is minimum or equal to or less than a threshold.

The order quantity determination device in an example of either the first or second aspect (third aspect) may further include a selection unit. The selection unit selects a store from the plurality of stores that satisfies predetermined conditions as the second store. The order quantity determination device according to the third aspect can select a store from the plurality of stores that satisfies predetermined conditions as the second store.

In an example of the third aspect (fourth aspect), the first store may sell a third product that is different from the first product and different from the second product. Also, in the fourth aspect, the third product may be sold in at least one of the multiple stores. The predetermined conditions in the fourth aspect may include conditions related to the sales record of the second product and the sales record of the third product. The order quantity determination device according to the fourth aspect can select the second store from among the multiple stores based on the sales record of the second product and the sales record of the third product.

In an example (fifth aspect) of any one of the first, second, third, and fourth aspects, the first store may have a display shelf on which the first product and the second product are displayed. In the order quantity determination device of the fifth aspect, the determination unit may determine the order quantities of the first product and the second product in the first store by further taking into account the respective sizes of the first product and the second product and the area of the display shelf that can be allocated to displaying the first product and the second product. When there are restrictions on the display of products in the first store, the order quantity determination device according to the fifth aspect can determine the order quantities by taking into account the display restrictions.

20...management device, 30...communication network, 40A, 40B, 40C, 40D...store, 210...processing device, 211...selection unit, 212...acquisition unit, 213...estimation unit, 2131...first setting unit, 2132...second setting unit, 2133...third setting unit, 2134...calculation unit, 214...determination unit, 220...communication device, 230...storage device, 231...volatile storage device, 232...non-volatile storage device, AP...program.

Claims (4)

an acquisition unit that acquires, from a table that manages the sales quantities of products at each of the first store and the second store, a second sales quantity of a first product and a second product that competes with the first product in demand at a first store that does not sell the first product, a first sales quantity of the first product at a second store selected from a plurality of stores that sell the first product and the second product, and a sales quantity of the second product at the second store;
an estimation unit that estimates, based on the first sales volume and the second sales volume, a substitution probability indicating the probability that when one of the first product and the second product cannot be purchased, the other will be purchased as a substitute product, a first original demand rate indicating the probability that the first product or the second product will be purchased at the first store under circumstances in which both the first product and the second product are available for purchase, and a second original demand rate indicating the probability that the first product or the second product will be purchased at the second store under the circumstances;
a determination unit that determines order quantities of the first commodity and the second commodity in the first store based on the substitution probability, the first raw material demand rate, and the second raw material demand rate estimated by the estimation unit ; and
and
The estimation unit
a first setting unit that sets the substitution probability, the first raw material demand rate, and the second raw material demand rate as variables;
a second setting unit that sets a first demand indicating the quantity of the first commodity estimated to be sold at the first store if the first commodity is sold at the first store as the sum of a value obtained by multiplying the second sales quantity by the substitution probability and the first raw material demand rate and a value obtained by multiplying the first sales quantity by the second raw material demand rate, and sets a third demand indicating the quantity of the first commodity estimated to be sold at the second store as the sum of a value obtained by multiplying the first sales quantity by the second raw material demand rate and a value obtained by multiplying the second sales quantity by the substitution probability and the first raw material demand rate;
a third setting unit that sets a sum of a value obtained by multiplying the second sales quantity by the first raw material demand rate and a value obtained by multiplying the first sales quantity by the substitution probability and the second raw material demand rate as a second demand amount indicating the quantity of the second commodity estimated to be sold at the first store if the first commodity is sold at the first store, and that sets a sum of a value obtained by multiplying the second sales quantity by the first raw material demand rate and a value obtained by multiplying the first sales quantity by the substitution probability and the second raw material demand rate as a fourth demand amount indicating the quantity of the second commodity estimated to be sold at the second store;
a calculation unit that calculates values of the substitution probability, the first original demand rate, and the second original demand rate so that the sum of an absolute value of a difference between the sum of the first demand amount and the second demand amount and the sales quantity of the second commodity at the first store, and an absolute value of a difference between the sum of the third demand amount and the fourth demand amount and the sales quantity of the first commodity and the second commodity at the second store, is minimized or is equal to or less than a threshold value;
The determination unit
determining the order quantity of the first commodity in the first store as the sum of a value obtained by multiplying the second sales quantity by the substitution probability and the first raw material demand rate and a value obtained by multiplying the first sales quantity by the second raw material demand rate, and determining the order quantity of the second commodity in the first store as the sum of a value obtained by multiplying the second sales quantity by the first raw material demand rate and a value obtained by multiplying the first sales quantity by the substitution probability and the second raw material demand rate, minus an inventory quantity of the second commodity in the first store;
Order quantity determination device. a selection unit that selects a store that satisfies a predetermined condition from among the plurality of stores as the second store;
Equipped with
The order quantity determination device according to claim 1. a third product that is different from the first product and different from the second product is sold at the first store;
the third product is sold in at least one store among the plurality of stores;
The predetermined condition is the second Sales performance of the product and the third including terms and conditions regarding sales performance of the product.
The order quantity determination device according to claim 2. the first store has a display shelf on which the first product and the second product are displayed;
the determination unit determines the order quantities of the first product and the second product in the first store by further taking into consideration the sizes of the first product and the second product and the area of the display shelf that can be allocated to display the first product and the second product.
The order quantity determination device according to any one of claims 1 to 3.