Demand curve estimation device and ordering support system

Abstract

To provide a demand curve estimating device and an order support system with which a gross profit is maximized under a situation where the number of commercial materials sold for different sales prices is not sufficiently accumulated.SOLUTION: A demand curve estimating device 10 includes an estimation unit 111, an acquisition unit 112, and a correction unit 113. The estimation unit 111 estimates a demand curve of a commercial material on the basis of the sales result of the commercial material. The acquisition unit 112 acquires a first demand amount at a first sales price and a second demand amount at a second sales price from the demand curve. In a case where the second sales price is lower than the first sales price and the first demand amount is larger than the second demand amount, the correction unit 113 corrects the demand curve such that the second demand amount is equal to or larger than the first demand amount. In a case where the first sales price is lower than the second sales price and the second demand amount is larger than the first demand amount, the correction unit 113 corrects the demand curve such that the second demand amount is equal to or smaller than the first demand amount.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates to a demand curve estimation device and an ordering support system. [Background technology]

[0002] Dynamic pricing has been attracting attention in recent years. Dynamic pricing refers to determining a sales price that maximizes gross profit based on the sales price of a commodity, such as a product or service, and an estimated demand for that commodity. When dynamic pricing is implemented in a situation where the sales volume of a commodity at each of multiple different sales prices is not sufficiently accumulated, it is necessary to update prices while balancing "exploration" and "exploitation." "Exploration" refers to setting a price with an unclear gross profit and accumulating sales data. "Exploration" refers to setting a price that maximizes gross profit. Various techniques have been proposed to update prices while balancing "exploration" and "exploitation." For example, Patent Document 1 discloses a technology for estimating a demand curve using a contextual bandit algorithm. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-163047 Summary of the Invention [Problem to be solved by the invention]

[0004] The contextual bandit algorithm is an algorithm that has been used in the advertising field, such as estimating the most effective web articles based on the characteristics of the web article's viewers when placing web ads. When the contextual bandit algorithm is applied to estimating demand curves, it has been found to produce inappropriate demand curves. Specific examples of inappropriate demand curves include demand curves that show overall excessive or insufficient demand for each of multiple sales prices, or demand curves that are not downward sloping. An inappropriate demand curve makes it impossible to maximize gross profit.

[0005] The present disclosure has been made in consideration of the above circumstances, and aims to solve the problem of maximizing gross profit in a situation where the sales volume of commercial products at each of multiple different sales prices is not sufficiently accumulated. [Means for solving the problem]

[0006] A demand curve estimation device according to one aspect of the present disclosure includes an estimation unit, an acquisition unit, and a correction unit. The estimation unit estimates a demand curve indicating an expected demand quantity for a commercial product or service relative to a sales price of the commercial product, based on sales records indicating sales volumes of the commercial product at each of a plurality of different prices. The acquisition unit acquires a first demand quantity calculated from the demand curve for a first sales price and a second demand quantity calculated from the demand curve for a second sales price different from the first sales price. The correction unit corrects the demand curve to increase the second demand quantity to or above the first demand quantity when the second sales price is lower than the first sales price and the first demand quantity is greater than the second demand quantity. When the first sales price is lower than the second sales price and the second demand quantity is greater than the first demand quantity, the correction unit corrects the demand curve to decrease the second demand quantity to or below the first demand quantity.

[0007] An order support system according to one aspect of the present disclosure includes the demand curve estimation device and an order quantity determination device. The order quantity determination device acquires inventory quantities of the merchandise in a store. The order quantity determination device determines an order quantity for the merchandise based on the acquired inventory quantities and a demand curve corrected using the demand curve estimation device. The order support system of this aspect maximizes gross profit in a situation where the sales quantities of the merchandise at each of a plurality of different sales prices are not sufficiently accumulated. [Effects of the Invention]

[0008] According to the present disclosure, gross profit can be maximized in a situation where the sales volume of merchandise at each of a plurality of different sales prices is not sufficiently accumulated. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a diagram illustrating a configuration example of an order support system 1 according to an embodiment of the present disclosure. [Figure 2] FIG. 1 is a diagram illustrating an example of the configuration of a demand curve estimation device 10. [Figure 3] 10 is a diagram showing an example of a demand curve GY estimated by an estimation unit 111. FIG. [Figure 4A] 10 is a diagram showing an example of a demand curve GYA1 estimated by an estimation unit 111. FIG. [Figure 4B] 10 is a diagram showing an example of a demand curve GYA2 estimated by an estimation unit 111. FIG. [Figure 5] This is a diagram showing the relationship between the demand curve GY and the upper limit of the confidence interval for each selling price. [Figure 6A] 10 is a diagram for explaining a first correction process executed by a correction unit 114. FIG. [Figure 6B] 10 is a diagram for explaining a second correction process executed by a correction unit 114. FIG. [Figure 7] 10 is a diagram for explaining a third correction process executed by a correction unit 114. FIG. [Figure 8] 10 is a flowchart showing the flow of a demand curve estimation method executed by the processing device 110 in accordance with the program P. [Figure 9] It is a flowchart showing the flow of an order quantity determination method executed in the order support system 1.

Embodiments for Carrying Out the Invention

[0010] <A. Embodiment> FIG. 1 is a diagram showing a configuration example of an order support system 1 according to an embodiment of the present disclosure. The order support system 1 is an information system for supporting the ordering of merchandise sold in stores 40A and 40B. As shown in FIG. 1, the order support system 1 includes a demand curve estimation device 10 and an order quantity determination device 20. As shown in FIG. 1, each of the demand curve estimation device 10 and the order quantity determination device 20 is connected to a communication network 30 such as the Internet.

[0011] Each of stores 40A and 40B in the present embodiment is a retail store such as a convenience store. In store 40A, merchandise X such as confectionery is sold. On the other hand, in store 40B, merchandise X and merchandise Y such as soft drinks are sold. As shown in FIG. 1, a terminal device 41A is installed in store 40A. A terminal device 41B is installed in store 40B. Each of the terminal device 41A and the terminal device 41B is connected to the communication network 30. Each of the terminal device 41A and the terminal device 41B has a communication function for communicating with the order quantity determination device 20 via the communication network 30.

[0012] In store 40B, a store employee (hereinafter referred to as employee B) inputs information indicating the type and quantity of the sold merchandise into the terminal device 41B upon the sale of the merchandise. The terminal device 41B transmits sales performance information indicating the type and sales quantity of the sold merchandise and a second identifier indicating store 40B to the order quantity determination device 20 in response to the input by employee B. The sales quantity is the number of the sold merchandise. Similarly, in store 40A, a store employee (hereinafter referred to as employee A) of store 40A inputs information indicating the type and quantity of the sold merchandise into the terminal device 41B upon the sale of the merchandise. The terminal device 41A transmits the sales performance information and a second identifier indicating store 40A to the order quantity determination device 20 in response to the input by employee A.

[0013] The order quantity determination device 20 stores the order quantity of each commodity (in other words, the amount of stock for each store) for each type of commodity in association with each of the stores 40A and 40B. The order quantity determination device 20 also stores sales performance information received from each of the stores 40A and 40B via the communication network 30. In this embodiment, the demand curve of each commodity is estimated by the demand curve estimation device 10 for each store based on the sales performance information stored in the order quantity determination device 20.

[0014] A demand curve is a curve that indicates the number of commodities (demand quantity) that can be expected to be sold for the selling price of the commodities. In this embodiment, the demand curve estimation device 10 estimates the demand curve at a predetermined time after the closing of business for the day at stores 40A and 40B, for example. The order quantity determination device 20 determines the purchase quantity and sales price that maximize gross profit based on the demand curve estimated by the demand curve estimation device 10 and the inventory quantity of the commodities. Gross profit is the value obtained by subtracting the sales cost, such as the purchase price, from the sales amount. The inventory quantity is the value obtained by subtracting the sales quantity indicated by the sales performance for one day from the previous purchase quantity.

[0015] As described above, product Y is not sold at store 40A. Therefore, when store 40A starts selling product Y, a demand curve is estimated using the sales record of product Y at store 40B. However, if the period between when store 40B starts selling product Y and when store 40A starts selling product Y is short, there may be cases where the sales record of product Y is not sufficiently accumulated. In this embodiment, by having the demand curve estimation device 10 execute a demand curve estimation method that prominently demonstrates the features of the present disclosure, an appropriate demand curve can be obtained even when the sales record is not sufficiently accumulated.

[0016] Fig. 2 is a diagram showing an example of the configuration of the demand curve estimation device 10. As shown in Fig. 2, the demand curve estimation device 10 includes a processing device 110, a communication device 120, and a storage device 130. The processing device 110 is, for example, a CPU (Central Processing Unit). The processing device 110 functions as a control center in the demand curve estimation device 10 by operating in accordance with a program stored in a non-volatile storage device 132. The communication device 120 is a device that communicates with the order quantity determination device 20 via a communication network 30.

[0017] As shown in FIG. 2, the storage device 130 includes a volatile storage device 131 and a nonvolatile storage device 132. The volatile storage device 131 is, for example, a random access memory (RAM). The processing device 110 uses the volatile storage device 131 as a work area for executing programs. The nonvolatile storage device 132 is, for example, a flash read-only memory (ROM). Various programs are stored in the nonvolatile storage device 132. Specific examples of the programs stored in the nonvolatile storage device 132 include a kernel program and a program P. The kernel program is a program that causes the processing device 110 to implement an operating system (OS). The kernel program is not shown in FIG. 2. The program P is a program that causes the processing device 110 to implement a demand curve estimation method that prominently exhibits the features of the present disclosure.

[0018] When the power supply (not shown in FIG. 2 ) of the demand curve estimation device 10 is turned on, the processing device 110 reads the kernel program from the nonvolatile storage device 132 to the volatile storage device 131. The processing device 110 starts executing the kernel program read into the volatile storage device 131. The processing device 110 operating in accordance with the kernel program implements an OS. The processing device 110 implementing the OS reads a program P from the nonvolatile storage device 132 to the volatile storage device 131 when the predetermined time described above is reached. Then, the processing device 110 starts executing the program P read into the volatile storage device 131. The processing device 110 operating in accordance with the program P functions as an estimation unit 111, a determination unit 112, an acquisition unit 113, and a correction unit 114, as shown in FIG. 2 . The functions of the estimation unit 111, the determination unit 112, the acquisition unit 113, and the correction unit 114 are as follows.

[0019] The estimation unit 111 estimates a demand curve for each store and each product based on the sales record of the product. More specifically, the estimation unit 111 estimates a demand curve for product X in store 40A based on the sales record of product X in store 40A. The estimation unit 111 estimates a demand curve for product X in store 40B based on the sales record of product X in store 40B. The estimation unit 111 estimates a demand curve for product Y in store 40B based on the sales record of product Y in store 40B. Furthermore, the estimation unit 111 estimates a demand curve for product Y in store 40A based on the sales record of product Y in store 40B.

[0020] If the estimation unit 111 can acquire the sales record of the product Y at each of the multiple stores, the estimation unit 111 may estimate the demand curve for the product Y at the store 40A based on the sales record of each of the multiple stores. Furthermore, the estimation unit 111 may estimate the demand curve for the product Y at the store 40A based on the sales record of the product Y at a store among the multiple stores that is the same as or similar to the store 40A in at least one of the store size, location, and customer demographic. This estimation improves the accuracy of the demand curve.

[0021] In this embodiment, the estimation unit 111 estimates a demand curve using a contextual bandit algorithm. As a specific method for estimating a demand curve using a contextual bandit algorithm, an existing technique such as the technique disclosed in Patent Document 1 may be used as appropriate.

[0022] A demand curve indicates the quantity demanded of a product relative to its selling price. Therefore, a fundamental principle in economics is that demand curves, like the demand curve GY shown in Figure 3, slope downward. A downward-sloping demand curve means that the quantity demanded for a certain selling price is greater than the quantity demanded for a higher selling price, as shown in Figure 3. In the example shown in Figure 3, the demand quantity D1 for selling price P1 is greater than the demand quantity D2 for selling price P2 (selling price P1 < selling price P2). Also, in the example shown in Figure 3, the demand quantity D2 for selling price P2 is greater than the demand quantity D3 for selling price P3 (selling price P2 < selling price P3). In Figure 3, the selling price Pmin is the lower limit of the selling price that can be set for the product (e.g., the purchase price), and the selling price Pmax is the upper limit of the selling price that can be set for the product.

[0023] As described above, because the contextual bandit algorithm is an algorithm for estimating advertising effectiveness, the demand curve estimated by the estimation unit 111 may not slope downward. For example, the demand curve GYA1 shown in FIG. 4A or the demand curve GYA2 shown in FIG. 4B may be estimated by the estimation unit 111. As shown in FIG. 4A, the demand quantity D1 indicated by the demand curve GYA1 at the selling price P1 is smaller than both the demand quantities D2 and D3 indicated by the demand curve GY at the selling price P2 and the selling price P3, respectively, and the demand quantity D2 is smaller than the demand quantity D3. In other words, the demand curve GYA1 slopes upward. The demand curve GYA1 is inappropriate in that it slopes upward. The demand curve GYA2 shown in FIG. 4B slopes downward in the relationship between the selling price Pmin and the selling price Pmax, but is inappropriate in that the demand quantity D2 is greater than the demand quantity D1.

[0024] One example of a conventional technique for estimating a demand curve using a contextual bandit algorithm is to set the upper limit of a confidence interval for the demand quantity at each selling price as the demand quantity at each selling price. The confidence interval for the demand quantity refers to a range of demand quantities within which the average actual demand quantity can be reliably included with a predetermined probability, such as 90%. FIG. 5 illustrates an example in which the upper limit of a confidence interval for the demand quantity at each selling price is set as the demand quantity at each selling price. In FIG. 5, the confidence interval for the demand quantity at each selling price is indicated by an arrow. Another example of estimating a demand curve using a contextual bandit algorithm is to use Thompson sampling to obtain the expected demand quantity at each selling price. The present embodiment employs the former example, i.e., to set the upper limit of a confidence interval for the demand quantity at each selling price as the demand quantity at each selling price. That is, the estimation unit 111 sets the upper limit of a confidence interval for the demand quantity at each selling price as the demand quantity at each selling price. In a configuration in which the upper limit of the confidence interval for the demand quantity at each selling price is used as the demand quantity at each selling price, the demand quantity represented by the estimated demand curve may be excessively large at any selling price. Note that a configuration in which the lower limit of the confidence interval for the demand quantity at each selling price is used as the demand quantity at each selling price may also be considered. In a configuration in which the lower limit of the confidence interval for the demand quantity at each selling price is used as the demand quantity at each selling price, the demand quantity represented by the estimated demand curve may be excessively small at any selling price.

[0025] The determining unit 112 , the acquiring unit 113 , and the correcting unit 114 play a role in correcting the demand curve estimated by the estimating unit 111 .

[0026] The determination unit 112 determines a selling price that serves as a reference when correcting the demand curve estimated by the estimation unit 111. Hereinafter, the selling price determined by the determination unit 112 is referred to as a "reference price." In this embodiment, the determination unit 112 determines the reference price based on the width of the confidence interval for the demand quantity calculated for each selling price by the estimation unit 111. More specifically, the determination unit 112 determines the selling price with the narrowest confidence interval as the reference price. Furthermore, the determination unit 112 sets each selling price other than the reference price among the prices from the lower limit of the selling price to the upper limit of the selling price as an unprocessed price. The reference price is an example of a first selling price in the present disclosure. Each unprocessed price is an example of a second selling price in the present disclosure.

[0027] The acquisition unit 113 acquires the demand quantity at the base price by referring to the demand curve estimated by the estimation unit 111. The demand quantity at the base price is an example of a first demand quantity in the present disclosure. Furthermore, the acquisition unit 113 acquires the demand quantity at the raw price by referring to the demand curve estimated by the estimation unit 111. The demand quantity at the raw price is an example of a second demand quantity in the present disclosure.

[0028] The correction unit 114 executes a first correction process, a second correction process, and a third correction process. The first correction process and the second correction process are processes for correcting a demand curve that is not downward sloping to a demand curve that is downward sloping, as will be described in detail later. The third correction process is a process for correcting a demand curve that shows an excessive demand quantity for each selling price.

[0029] The first correction process is a process executed for an unprocessed price that is higher than the reference price. In the first correction process, when the demand volume for the unprocessed price is greater than the demand volume for the reference price, the correction unit 114 corrects the demand curve estimated by the estimation unit 111 to reduce the latter demand volume to less than or equal to the former demand volume. In the first correction process, when the demand volume for the unprocessed price is less than or equal to the demand volume for the reference price, the correction unit 114 does not correct the demand volume for the unprocessed price. The correction unit 114 changes the unprocessed price for which the first correction process has been executed to the processed price.

[0030] The reduction in demand volume in the first correction process is a function that uses the difference between the base price and the raw price as a variable. The function exhibits a larger value as the difference between the base price and the raw price increases. For example, the increase in demand volume W1 at a first raw price whose difference from the base price is Δ1 is smaller than the decrease in demand volume W2 at a second raw price whose difference from the base price is Δ2 (Δ2 > Δ1). For example, if the demand curve GYA1 shown in FIG. 4A is the target of correction and the selling price P1 is the base price PB, the first correction process is performed on the selling price of the demand curve GYA1 that is higher than the base price PB. As a result of performing the first correction process on the portion of the demand curve GYA1 that is higher than the base price PB, the portion of the demand curve GYA1 that is higher than the base price PB is corrected to the demand curve GYB1 shown in FIG. 6A.

[0031] The second correction process is a process executed for unprocessed prices that are lower than the base price. In the second correction process, when the demand volume for the base price is greater than the demand volume for the unprocessed price, the correction unit 114 corrects the demand curve estimated by the estimation unit 111 to raise the demand volume for the former to equal or exceed the demand volume for the latter. In the second correction process, when the demand volume for the unprocessed price is equal to or greater than the demand volume for the base price, the correction unit 114 does not correct the demand volume for the unprocessed price. The correction unit 114 changes the unprocessed price for which the second correction process has been executed to the processed price.

[0032] The reduction in demand quantity relative to the unprocessed price in the second correction process is also a function whose variable is the difference between the base price and the unprocessed price. This function also exhibits a larger value as the difference between the base price and the unprocessed price becomes larger. For example, if the demand curve GYA1 shown in FIG. 4A is the target of correction and the selling price P1 is the base price PB, the second correction process is performed on the selling price of the demand curve GYA1 that is lower than the base price PB. As a result of performing the second correction process on the portion lower than the base price PB, the portion of the demand curve GYA1 that is lower than the base price PB is corrected to the demand curve GYB2 shown in FIG. 6B.

[0033] The third correction process corrects the demand curve based on the statistical quantity of the confidence interval width calculated for each selling price by the estimation unit 111. In this embodiment, the statistical quantity is the average value of each confidence interval width, but it may also be the maximum value, minimum value, median, most frequently occurring value, or standard deviation of the confidence interval width. In the third correction process, the correction unit 114 further corrects the demand curve by subtracting a value WM corresponding to the statistical quantity from the demand quantity at each selling price represented by the demand curve corrected by the first and second correction processes. In this embodiment, the value WM is the average value of the confidence interval width × 1 / 2. However, the value WM may be any value calculated by a function using the statistical quantity of the confidence interval width as a variable, such as the square root of the statistical quantity. For example, if the demand curve GYB shown in FIG. 7 is obtained by the first and second correction processes, the demand curve GYB is corrected to the demand curve GYC shown in FIG. 7 by the third correction process.

[0034] Furthermore, the processing device 110 operating in accordance with the program P executes the demand curve estimation method shown in Fig. 8 for each store and each product. As shown in Fig. 8, the demand curve estimation method in this embodiment includes the processes of steps SA110 to SA170.

[0035] In step SA110, the processing device 110 functions as the estimation unit 111. In step SA110, the processing device 110 acquires sales performance information from the order quantity determination device 20. Next, the processing device 110 estimates a demand curve based on the acquired sales performance information. In addition, in the process of estimating the demand curve, the processing device 110 calculates the width of the confidence interval for each selling price.

[0036] In step SA120 following step SA110, the processing device 110 functions as the determination unit 112. In step SA120, the processing device 110 determines the base price and the unprocessed price based on the width of the confidence interval of the demand volume at each selling price calculated in step SA110.

[0037] In step SA130 following step SA120, the processing device 110 determines whether there is an unprocessed price higher than the reference price. If the determination result of step SA130 is "Yes," the processing device 110 executes the processing of step SA140 and then executes the processing of step SA150. In step SA140, the processing device 110 functions as the acquisition unit 113 and the correction unit 114. In step SA140, the processing device 110 executes a first correction process for unprocessed prices higher than the reference price. If the determination result of step SA130 is "No," the processing device 110 executes the processing of step SA150 without executing the processing of step SA140.

[0038] For example, suppose that the demand curve GYA1 shown in FIG. 4A is estimated in step SA110. Then, in step SA120, the selling price P1 in FIG. 4A is determined as the base price PB. As shown in FIG. 4A, the selling prices P2, P3, and Pmax are all unprocessed prices higher than the selling price P1, so the result of the determination in step SA130 is "Yes." Therefore, the first correction process is performed on the unprocessed prices higher than the selling price P1 on the demand curve GYA1. As a result, the portion of the demand curve GYA1 higher than the selling price P1 is corrected to the curve GYB1 shown in FIG. 6A.

[0039] In step SA150, the processing device 110 determines whether there is an unprocessed price that is lower than the base price. If the determination result in step SA150 is "Yes," the processing device 110 executes the processing of step SA160 and then executes the processing of step SA170. In step SA160, the processing device 110 functions as the acquisition unit 113 and the correction unit 114. In step SA160, the processing device 110 executes the second correction processing for unprocessed prices that are lower than the base price PB. If the determination result in step SA150 is "No," the processing device 110 executes the processing of step SA170 without executing the processing of step SA160.

[0040] When the demand curve GYA1 shown in FIG. 4A is estimated in step SA110 and the selling price P1 is the base price PB, there is a selling price Pmin as an unprocessed price lower than the base price PB. Therefore, the determination result in step SA150 is "Yes," and processing in step SA160 is executed. In step SA160, a second correction process is executed for the portion of the demand curve GYA1 that is lower than the base price PB. As a result, the portion of the demand curve GYA1 that is lower than the base price PB is corrected to the curve GYB2 shown in FIG. 6B.

[0041] At the time of executing step SA170, the demand curve estimated at step SA110 has been corrected to a downward-sloping demand curve. At step SA170, the processing device 110 functions as the correction unit 114. At step SA170, the processing device 110 performs a third correction process on the demand curve corrected to a downward-sloping demand curve. By performing the process of step SA170, the demand quantity at each selling price represented by the demand curve is reduced by an amount corresponding to the statistical quantity of the width of the confidence interval. For example, if the demand curve at the time of executing step SA170 is the demand curve GYB in FIG. 7, the demand curve GYB is corrected to the demand curve GYC shown in FIG. 7 by the third correction process.

[0042] According to the demand curve estimating device 10 of this embodiment, a downward sloping demand curve is generated. In addition, the demand quantity at each selling price represented by the demand curve generated by the demand curve estimating device 10 is not excessively large.

[0043] FIG. 9 is a flowchart showing the flow of the order quantity determination method executed in the order support system 1. As shown in FIG. 9, the order quantity determination method includes the processes of step SB110 and step SB120. Step SB110 is a process executed by the demand curve estimation device 10. In step SB110, the demand curve estimation device 10 generates a demand curve for each store and each commodity by executing the demand curve estimation method shown in FIG. 8 for each commodity in each of the stores 40A and 40B. Step SB120 is a process executed by the order quantity determination device 20. In step SB120, the order quantity determination device 20 determines, for each store, the order quantity and selling price of each commodity that maximizes the gross profit based on the inventory quantity of the commodity in the store and the demand curve generated in step SB120.

[0044] As described above, according to this embodiment, since the demand curve is estimated using the contextual bandit algorithm, there is no obstacle to the estimation of the demand curve even in a situation where the sales results of the commodity are not sufficiently accumulated. Further, even if the demand curve estimated using the contextual bandit algorithm is an inappropriate demand curve, it is corrected to an appropriate demand curve in the demand curve estimation device 10. In the order support system 1, since the order quantity of the commodity is determined based on the appropriate demand curve, the occurrence of excess inventory is avoided. Since the occurrence of excess inventory is avoided, the gross profit can be maximized.

[0045] <B. Variation> Although one embodiment of the present disclosure has been described above, this embodiment may be modified as follows. (1) In the above embodiment, the selling price with the narrowest confidence interval was determined as the base price, but the base price may be the lower limit or upper limit of the selling price of the product. In an aspect in which the lower limit or upper limit of the selling price of the product is set as the base price, the determination unit 112 may be omitted. Furthermore, in an aspect in which the determination unit 112 is omitted, the first correction process may be performed by sequentially selecting base prices and unprocessed prices in ascending order from the lower limit of the selling price until the upper limit of the selling price is selected as the unprocessed selling price. In this aspect, the second correction process may be omitted. Similarly, the second correction process may be performed by sequentially selecting base prices and unprocessed prices in descending order from the upper limit of the selling price until the lower limit of the selling price is selected as the unprocessed selling price. In this aspect, the first correction process may be omitted.

[0046] (2) In the above embodiment, the estimation unit 111 estimates the demand curve using a contextual bandit algorithm. However, the demand curve may be estimated using other algorithms. In the above embodiment, the correction unit 114 executes the first correction process, the second correction process, and the third correction process. However, the correction unit 114 may execute only the first correction process and the second correction process without executing the third correction process. Alternatively, the correction unit 114 may execute only the third correction process. In an aspect in which the correction unit 114 executes only the third correction process, the determination unit 112 and the acquisition unit 113 may be omitted, and the correction unit 114 may execute the third correction process on the demand curve estimated by the estimation unit 111.

[0047] (3) The third correction process in the above embodiment was a process of subtracting a value corresponding to the statistic of the width of the confidence interval calculated for each selling price by the estimation unit 111 from the demand quantity at each selling price represented by the demand curve corrected by the first correction process and the second correction process. However, the demand curve may be generated based on the lower limit value of the confidence interval at each selling price in the estimation unit 111. The third correction process in this aspect may be a process of adding a value corresponding to the statistic of the width of the confidence interval to the demand quantity at each selling price represented by the demand curve corrected by the first correction process and the second correction process. According to this aspect, an inappropriate demand curve representing an underestimated demand quantity for each selling price is corrected to a demand curve representing an appropriate demand quantity, and the occurrence of opportunity loss due to out-of-stock is avoided.

[0048] (4) The store 40A and the store 40B in the above embodiment were retail stores such as convenience stores, but may be micro stores that conduct unmanned sales of merchandise. A micro store is a device including a shelf on which merchandise is displayed, a detection device, a communication device, and a settlement device. The detection device in the micro store detects the type and quantity of merchandise taken out from the shelf by the user. The communication device is connected to the communication network 30. The communication device transmits sales performance information corresponding to the detection result by the detection device to the order quantity determination device 20. The settlement device is a device that performs a settlement process according to the detection result by the detection device. Also, the merchandise in the above embodiment was a commodity, but it may be a service.

[0049] <C: Others> (1) In the above embodiment and the modification example, the non-volatile storage device 132 may include a flexible disk, a magneto-optical disk (e.g., compact disk, digital versatile disk, Blu-ray (registered trademark) disk), a smart card, a flash memory device (e.g., card, stick, 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, and other appropriate storage media. Also, the program may be transmitted from a network via a telecommunication line.

[0050] (2) Each of the above embodiments and variations may be applied to systems using LTE (Long Term Evolution), LTE-Advanced (LTE-A), 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 enhanced based on these.

[0051] (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.

[0052] (4) In each of the above embodiments and modifications, input and output information may be stored in a specific location (e.g., the volatile storage device 131) or managed by 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.

[0053] (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 (true or false), or a comparison of numerical values ​​(e.g., a comparison with a predetermined value).

[0054] (6) The order of the illustrated processing procedures, sequences, or flowcharts 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 elements of various steps in an exemplary order, and are not limited to the specific order presented.

[0055] (7) Each function illustrated in FIG. 2 is realized by any combination of hardware and software. Furthermore, there are no particular limitations on how each function is realized. That is, each function may be realized by using a single device that is physically or logically coupled, or by using two or more devices that are physically or logically separated and connected directly or indirectly (for example, by wire, wirelessly, etc.). A functional block 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, election, establishment, comparison, assumption, expectation, consideration, 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.

[0056] (8) The programs exemplified in each of the above embodiments and variations should be broadly construed 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, 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.

[0057] (9) The demand curve estimator 10 may be a mobile station, which 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.

[0058] (10) In each of the above embodiments and modifications, unless otherwise specified, the phrase "based on" does not mean "based only on." In other words, the phrase "based on" means both "based only on" and "based at least on."

[0059] (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.

[0060] (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.

[0061] (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.

[0062] (14) In this specification, the term "part" 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.

[0063] (15) As used in this disclosure, the terms "system" and "network" are used interchangeably.

[0064] (16) The terms "determining" and "deciding" used in this disclosure may include a variety of operations. "Determining" and "deciding" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or another data structure), and considering something as having been "determined" or "decided" based on what has been ascertained. Also, "determining" and "deciding" may include considering something as having been "determined" or "decided" based on receiving (e.g., receiving information), transmitting (e.g., transmitting information), inputting, outputting, accessing (e.g., accessing data in a memory), etc. Further, "determining" and "deciding" may include considering something as having been "determined" or "decided" based on resolving, selecting, choosing, establishing, comparing, etc. That is, "determining" and "deciding" may include considering something as having been "determined" or "decided" based on performing some operation. Also, "determining (deciding)" may be read as "assuming", "expecting", "considering", etc.

[0065] (17) It is clear to those skilled in the art that this disclosure is not limited to the embodiments described in this specification. This disclosure can be implemented in modified and changed forms without departing from the spirit and scope of the disclosure as determined based on the description in the claims. Therefore, the description in this specification is for illustrative purposes and has no restrictive meaning for this disclosure. Also, a plurality of aspects selected from the aspects illustrated in this specification may be combined.

[0066] <D: Aspects understood from the above-described forms or variations> The following aspects can be understood from at least one of the above-described embodiments or modifications. A demand curve estimation device according to a first aspect includes an estimation unit, a first acquisition unit, and a correction unit. The estimation unit estimates a demand curve indicating an expected demand volume for a commercial product or service relative to a sales price of the commercial product, based on sales records indicating sales volumes of the commercial product at each of a plurality of different prices. The first acquisition unit acquires a first demand volume calculated from the demand curve for a first sales price and a second demand volume calculated from the demand curve for a second sales price different from the first sales price. When the second sales price is lower than the first sales price and the first demand volume is greater than the second demand volume, the correction unit corrects the demand curve to increase the second demand volume to or above the first demand volume. When the first sales price is lower than the second sales price and the second demand volume is greater than the first demand volume, the correction unit corrects the demand curve to decrease the second demand volume to or below the first demand volume. According to the demand curve estimation device of the first aspect, the demand curve estimated from the sales record is corrected to a demand curve indicating that the higher the sales price, the smaller the demand quantity, that is, a downward sloping demand curve.

[0067] In an example of the first aspect (second aspect), the estimation unit may estimate the demand curve using a contextual bandit algorithm. According to the demand curve estimation device of the second aspect, the demand curve estimated using the contextual bandit algorithm is corrected to a downward sloping demand curve.

[0068] The demand curve estimation device in the second example (third example) may include a determination unit that determines the first selling price based on the width of a confidence interval for the demand quantity of the commodity calculated by the contextual bandit algorithm. According to the demand curve estimation device in the third example, the first selling price that serves as a reference point when correcting the demand curve estimated according to the contextual bandit algorithm based on sales performance is selected based on the width of the confidence interval.

[0069] In an example of the third aspect (fourth aspect), the determination unit may determine the selling price with the narrowest confidence interval as the first selling price. According to the demand curve estimation device of the fourth aspect, the price with the narrowest confidence interval, i.e., the price estimated to have the highest accuracy in the demand quantity, is selected as the first selling price, thereby improving the accuracy of the demand quantity at each selling price represented by the corrected demand curve.

[0070] A demand curve estimation device according to a fifth aspect includes an estimation unit and a correction unit. The estimation unit estimates a demand curve showing the expected demand volume of a commodity (good or service) relative to its sales price using a contextual bandit algorithm based on sales records showing the sales volume of the commodity at each of a plurality of different prices. The correction unit corrects the demand volume at each sales price shown by the demand curve in accordance with statistics of the width of a confidence interval for the demand volume of the commodity calculated for each sales price by the contextual bandit algorithm. The demand curve estimation device according to the fifth aspect can reduce the risk of the demand volume at each sales price shown by the demand curve estimated using the contextual bandit algorithm being too high or too low.

[0071] In an example of the fifth aspect (sixth aspect), the correction unit may add a value according to the statistical amount to the demand quantity at each selling price indicated by the demand curve, or subtract the value from the demand quantity at each selling price indicated by the demand curve. According to the demand curve estimation device of the sixth aspect, the demand curve estimated using the contextual bandit algorithm is translated along a coordinate axis indicating the demand quantity.

[0072] An order support system according to a seventh aspect includes the demand curve estimation device according to any one of the first to sixth aspects and an order quantity estimation device. The order quantity estimation device acquires an inventory quantity of the commodity in a store, and determines an order quantity of the commodity based on the acquired inventory quantity and a demand curve corrected using the demand curve estimation device. [Explanation of symbols]

[0073] 1...order support system, 10...demand curve estimation device, 20...order quantity determination device, 30...communication network, 40A, 40B...store, 110...processing device, 111...estimation unit, 112...determination unit, 113...acquisition unit, 114...correction unit, 120...communication device, 130...storage device, 131...volatile storage device, 132...non-volatile storage device, P...program.

Claims

1. an estimation unit that estimates a demand curve showing the expected demand for a commodity, which is a product or service, relative to the sales price of the commodity, using a contextual bandit algorithm based on sales records showing the sales quantities of the commodity at each of a plurality of different prices; a determination unit that determines a selling price with the narrowest confidence interval for the demand quantity of the commodity calculated by the contextual bandit algorithm as a first selling price; an acquisition unit that acquires a first demand amount calculated from the demand curve for the first selling price and a second demand amount calculated from the demand curve for a second selling price different from the first selling price; a correction unit that corrects the demand curve to raise the second demand amount to or above the first demand amount when the second selling price is lower than the first selling price and the first demand amount is greater than the second demand amount, and corrects the demand curve to lower the second demand amount to or below the first demand amount when the first selling price is lower than the second selling price and the second demand amount is greater than the first demand amount; A demand curve estimating device having the above.

2. An estimation unit that estimates a demand curve showing the expected demand for a commodity, which is a good or a service, relative to the sales price of the commodity using a contextual bandit algorithm based on sales records showing the sales volume of the commodity at each of a plurality of different prices; a correction unit that corrects the demand quantity at each selling price indicated by the demand curve in accordance with statistics of the width of a confidence interval for the demand quantity of the commodity calculated for each selling price of the commodity by the contextual bandit algorithm; The correction unit When the demand quantity calculated by the contextual bandit algorithm is the lower limit of the confidence interval, a value corresponding to the statistical quantity is added to the demand quantity at each selling price indicated by the demand curve, and when the demand quantity calculated by the contextual bandit algorithm is the upper limit of the confidence interval, a value corresponding to the statistical quantity is subtracted from the demand quantity at each selling price indicated by the demand curve. Demand curve estimator.

3. A demand curve estimation device according to any one of claims 1 and 2; an order quantity determination device that acquires an inventory amount of the merchandise in the store and determines an order quantity of the merchandise based on the acquired inventory amount and the demand curve corrected using the demand curve estimation device; An ordering support system equipped with:

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