Method, system, and program for determining the quantity of goods to be ordered

Abstract

To provide a technique which determines an appropriate order number of a commodity for both distribution centers and stores.SOLUTION: A method for determining an order number of a commodity includes: calculating a predicted total order number for all stores to be ordered at a first time point (July 15) based on demand prediction by past data up to a second time point (July 8); calculating a second order number of the commodity to be ordered at the first time point in each of a plurality of stores based on demand prediction by past data up to the first time point; determining whether or not adjustment in the order number is necessary based on a total value of the second order number for all the stores and the predicted total order number for all the stores; obtaining an index showing ease of selling the commodity in each of the plurality of stores when it is determined that the adjustment in the order number is necessary; and selecting at least one store from the plurality of stores according to the ease of selling shown by the index and adjusting the second order number for the selected store by increasing or decreasing so that the total value of the adjusted second order number is equal to or less than the predicted total order number for all the stores.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present disclosure relates to a method, system, and program for determining the order quantity of a product. [Background technology]

[0002] Currently, point of sales (POS) systems are widespread in many retail stores, such as supermarkets and convenience stores, and the collected sales data of products is used in a variety of ways. Among these systems, there are those that predict product demand based on sales data and use the data for ordering. For example, there is a system described in Patent Document 1. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6275078 Summary of the Invention [Problem to be solved by the invention]

[0004] Products are delivered from manufacturers to distribution centers such as wholesalers, and then delivered from the distribution centers to each store. Distribution centers tend to hold excess inventory in order to handle the total orders from each store. This is thought to be based on the conventional thinking that if a distribution center cannot deliver 100% of the orders placed by each store, there will be a shortage and it will be a problem. However, we believe that the problem should not be the occurrence of stockouts at stores from the distribution center, but rather the occurrence of stockouts at stores. Therefore, we believe that this is where improvements can be made to the combined ordering between distribution centers and stores.

[0005] The present disclosure provides a method, system, and program for determining the appropriate number of items to order for a distribution center and a store combined. [Means for solving the problem]

[0006] The method for determining the order quantity of a product disclosed herein is a method executed by one or more processors, and includes: calculating a first order quantity of the product to be ordered at a first time point for each of a plurality of stores based on a demand forecast using past data up to a second time point that is several days before the first time point; calculating a predicted total order quantity for all stores to be ordered at the first time point based on the total value of the first order quantities for all stores; calculating a second order quantity of the product to be ordered at the first time point for each of the plurality of stores based on a demand forecast using past data up to the first time point; determining whether an order quantity adjustment is necessary based on the total value of the second order quantities for all stores and the predicted total order quantity for all stores; if it is determined that the order quantity adjustment is necessary, obtaining an index that represents the sellability of the product for each of the plurality of stores; and selecting at least one store from the plurality of stores in accordance with the sellability represented by the index, and adjusting the second order quantity for the selected store by increasing or decreasing it so that the total value of the second order quantities after adjustment is less than or equal to the predicted total order quantity for all stores. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a block diagram showing a system according to a first embodiment. [Figure 2] FIG. 10 is an explanatory diagram relating to a first order quantity at a second time point in the first embodiment. [Figure 3] FIG. 10 is an explanatory diagram relating to a second order quantity at a first time point and an adjustment of the second order quantity in the first embodiment. [Figure 4] FIG. 10 is an explanatory diagram relating to a second order quantity at a first time point and an adjustment of the second order quantity in the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0008] [First embodiment] A system 3 for determining the order quantity of a product according to the first embodiment will be described with reference to the drawings. First, an ordering system 1 and a POS system 2, which are the prerequisites, will be described.

[0009] <Ordering System 1 and POS System 2> As shown in FIG. 1, the ordering system 1 has a store terminal 10 installed in a store and an ordering server 11 installed at the headquarters. The store terminal 10, together with a POS server 20 installed at the headquarters, constitutes a POS system 2. POS information (barcodes, RFID, etc.) that can be read by the store terminal 10 is attached to products in the store. When the POS information is read by the store terminal 10 at the time of sale, sales data related to the sales of the products is transmitted to the POS server 20 at the headquarters and stored as sales performance data in a POS database 21 installed in the POS server 20. In addition to the sales performance data, the POS database 21 stores data such as a product master and order history data related to the products. The POS system 2 is a common system, so a detailed description will be omitted.

[0010] In this specification, the store terminal 10 refers to an information processing device installed in a store, and is not limited to a mobile terminal such as an EOB (Electronic Order Book), a cash register installed at a cash register, an office computer installed in a store, or a general-purpose tablet, as long as it is capable of communicating with a server (information processing device) installed at headquarters.

[0011] The order server 11 transmits the order data received from the store terminal 10 to the supplier's logistics center 4 at a predetermined order execution timing using EDI (Electronic Data Interchange) or the like. Upon receiving the order, the supplier's logistics center 4 delivers the product to the store, accompanied by POS information that can be read by the store terminal 10. The POS information includes, for example, a product code, which is information that identifies the product. The product code can be considered order-specific information that can identify the order in which the product was delivered to the store by checking against databases such as a product master, order history data, and inventory data stored at the main store, and can also be considered information that can identify sales information required at the time of sale, such as the product name, price, and sales deadline.

[0012] As in the present embodiment, the number of products to be ordered is preferably a value determined by the system 3 (described later) that is displayed by default, and is the number approved by the staff (owner, etc.). If the number can be changed by the staff, it is preferable that only corrections to decrease the value determined by the system 3 are allowed.

[0013] <System 3 for determining order quantity> As shown in FIG. 1, a system 3 for determining the number of products to be ordered includes one or more processors 3a and determines the number of products to be ordered. The determined number of products to be ordered is displayed as a recommended value on the store terminal 10. There are various patterns for the timing of ordering from a store to a logistics center 4 and the timing of delivery to the store. For example, a pattern in which orders are placed three times a day and delivered to the store several hours after the order is placed, or a pattern in which orders are placed once a day and delivered to the store the next day or the day after, etc. In this embodiment, for ease of understanding, it is assumed that an order is placed once a day and delivered from the logistics center 4 to the store the next day. The logistics center 4 delivers inventory to the store in accordance with the order received from the store. Here, the logistics center 4 places an order with a supplier such as a product manufacturer and receives the product. However, the time from ordering to receiving the delivery at the logistics center 4 is longer than the time from ordering to receiving the delivery at the store. For example, in this embodiment, for the sake of explanation, it is assumed that the logistics center 4 must place an order seven days before the store's order date. The logistics center 4 needs to place an order ahead of time to avoid excess inventory.

[0014] Specifically, the system 3 has a forecast data acquisition unit 31, a first order quantity calculation unit 32, a forecast total order quantity calculation unit 33, a second order quantity calculation unit 34, a judgment unit 35, and an adjustment unit 36.

[0015] The predicted data acquisition unit 31 acquires predicted data. The predicted data is past data based on past sales results and includes data for calculating the daily demand quantity for each product. Examples of predicted data include the predicted number of customers and a purchase index (PI) for each product, as shown in FIG. 2. The purchase index is also called a PI (Purchase Index) value, and is the number of purchases per predetermined number of customers, and can also be considered an index of popularity that indicates what percentage of customers support a product. In this embodiment, the PI indicates the number of purchases per 1,000 customers who visit the store in a day, but it may also be the number of purchases per 100 customers who visit the store in a day.

[0016] The first order quantity calculation unit 32 calculates the first order quantity of products to be ordered at a first time point for each of a plurality of stores based on a demand forecast using past data up to a second time point several days before the first time point. The order quantity calculated by the first order quantity calculation unit 32 is referred to as the "first order quantity" in this specification to distinguish it from the order quantity calculated by the second order quantity calculation unit 34. The first time point is the timing when the store places an order. The second time point is the timing when the logistics center 4 places an order with a manufacturer or the like to secure the products ordered by the store at the first time point. In this embodiment, the second time point is seven days before the first time point, but is not limited to this. The number of days between the first time point and the second time point can be changed as appropriate as long as the second time point is two or more days before the first time point. Here, the delivery date is July 16, 2022, the first time point is July 15, and the second time point is July 8, 2022. As shown in FIG. 2, the first order quantity calculation unit 32 calculates the first order quantity for product A for each of a plurality of stores (store codes: 101 to 110) at July 8, 2022, which is the second time point, based on the demand forecast. In the example of FIG. 2, the demand forecast quantity is calculated based on the purchasing index (PI) and the predicted number of customers, and the first order quantity is calculated based on the demand forecast quantity, but this is not limited to this. The calculation of the first order quantity based on the demand forecast quantity is calculated based on the error between the past demand forecast quantity and the actual order quantity, but this is not limited to this. As long as the first order quantity can be predicted, various methods can be adopted.

[0017] The total predicted order quantity calculation unit 33 calculates the total predicted order quantity for all stores to be ordered at the first time point based on the total value of the first order quantities for all stores. As shown in FIG. 2, the total value of the first order quantities for all stores is 414.3. Here, because the order unit size for product A is 10, a rounding process is performed and the total predicted order quantity for all stores is determined to be 420. The total predicted order quantity for all stores (420) calculated by the total predicted order quantity calculation unit 33 is sent to the server of the logistics center 4, and is used by the server of the logistics center 4 as the basis for ordering product A. In other words, at the second time point (July 8), it is determined that the total predicted order quantity for all stores (420) is the upper limit of the total order quantity for product A to be ordered at all stores at the first time point (July 15).

[0018] The second order quantity calculation unit 34 calculates the second order quantity of the product to be ordered at each of the multiple stores at a first time point based on a demand forecast using past data up to the first time point. The order quantity calculated by the second order quantity calculation unit 34 is referred to as the second order quantity to distinguish it from the order quantity calculated by the first order quantity calculation unit 32. As shown in FIG. 3, the second order quantity calculation unit 34 calculates the second order quantity for product A at each of the multiple stores (store codes: 101 to 110) at the first time point, July 15, 2022, based on the demand forecast. In the example of FIG. 3, the demand forecast quantity for July 16 is calculated based on the purchasing index (PI) and the predicted number of customers, and the second order quantity is calculated based on the demand forecast quantity, current inventory, and the predicted sales quantity for that day (the 7 / 15 sales forecast quantity). However, this is not limiting. Various methods can be used as long as the second order quantity can be predicted.

[0019] The determination unit 35 determines whether or not an order quantity adjustment is necessary based on the total value of the second order quantity for all stores and the total predicted order quantity for all stores. In the first embodiment, it determines that an order quantity adjustment is necessary when the total value of the second order quantity for all stores exceeds the total predicted order quantity for all stores. In the example of FIGS. 2 and 3, the total value of the second order quantity for all stores is 450 (see FIG. 3, calculated on July 15), and the total predicted order quantity for all stores (see FIG. 2; calculated on July 8) is 420, so it can be determined that an order quantity adjustment is necessary. This is because the logistics center 4 is placing orders with the predicted quantity (420) at the second point in time (July 8) as the upper limit.

[0020] When the determination unit 35 determines that an order quantity adjustment is necessary, the index acquisition unit 37 acquires an index representing the sales likelihood of a product at each of multiple stores. In the example shown in FIG. 3, the number of days in stock is acquired as the index. The number of days in stock indicates how many days of sales the inventory quantity corresponds to, with a larger value indicating a lower sales rate. In this embodiment, the number of days in stock at closing time on July 16 is calculated based on the demand forecast quantity for July 16 and the inventory quantity at closing time on July 16. The number of days in stock at closing time on July 16 can be calculated based on the demand forecast quantity for July 16, the current inventory, the second order quantity, and the sales forecast quantity for July 15, but this is just an example and is not limiting.

[0021] The adjustment unit 36 ​​selects at least one store from the multiple stores according to the sales likelihood indicated by the indicator, and adjusts the second order quantity by increasing or decreasing the selected store's second order quantity so that the total value of the adjusted second order quantity is equal to or less than the total predicted order quantity for all stores. In the first embodiment, the adjustment is performed by decreasing the second order quantity for a store whose sales are poor, indicated by the indicator, so that the total value of the adjusted second order quantity is equal to or less than the total predicted order quantity for all stores. This will be explained using the example of FIG. 3. In the example of FIG. 3, stars are added to the adjusted portion for easy viewing. Since the total value of the second order quantities before adjustment is 450 and the total predicted order quantity for all stores is 420, the second order quantity needs to be reduced by 30 or more. Of all stores, the two stores with store codes 103 and 110 have the second order quantity (second order quantity > 0) and the longest inventory days. Therefore, the second order quantities for these two stores are reduced by 10 (order unit) each. The second order quantity needs to be further reduced by 10 or more. Of all the stores, the only store with a second order quantity (second order quantity > 0) and the next longest inventory days is the one store with store code 107. Therefore, the second order quantity for this one store is decreased by 10. This reduces the second order quantity by a total of 30, and the adjustment process ends. Even if the second order quantity is decreased, the store does not sell product A as easily as other stores, so the risk of immediate stockout can be kept low. In the first embodiment, the adjustment of the second order quantity is performed by subtraction only.

[0022] In this way, the first order quantity to be placed at the first time point (July 15) is calculated at the second time point (July 8), the total predicted order value for all stores is determined, and the actual second order quantity to be placed at the first time point is adjusted so as not to exceed the total predicted order value for all stores. This prevents or reduces stockouts from being sent from the distribution center 4 to each store. At the same time, the second order quantity for each store is reduced preferentially for stores with poor sales indicated by the index, thereby reducing the risk of immediate stockouts at stores where the second order quantity is reduced. Because the second order quantity uses more recent data than the first order quantity, prediction accuracy is higher. However, by aggregating data from all stores, the prediction accuracy of the total first order quantity improves due to economies of scale. By utilizing this and adjusting the prediction error using the index, it is possible to achieve both optimal inventory management at the distribution center 4 and reduction of stockouts at stores.

[0023] [Second embodiment] A second embodiment will now be described. In the first embodiment, if the total value of the second order quantities for all stores exceeds the predicted total order quantity for all stores, it is determined that an order quantity adjustment is necessary, and the second order quantities for stores where sales are slower, as indicated by the index, are adjusted by decreasing them. On the other hand, in the second embodiment, if the total value of the second order quantities for all stores is equal to or less than a predetermined threshold value that is lower than the predicted total order quantity for all stores, it is determined that an order quantity adjustment is necessary, and the second order quantities for stores where sales are faster, as indicated by the index, are adjusted by increasing them. In the second embodiment, the predicted total order quantity for all stores shown in FIG. 2 is the same as in the first embodiment.

[0024] Specifically, the determination unit 35 in the second embodiment determines that an order quantity adjustment is necessary when the total value of the second order quantity for all stores is equal to or less than a predetermined threshold value that is lower than the total predicted order quantity for all stores. In the example of FIG. 4, the total value of the second order quantity for all stores is 400, and the total predicted order quantity for all stores (see FIG. 2; calculated on July 8) is 420. Here, the predetermined threshold is set to a value (410) obtained by subtracting a predetermined number (10) from the total predicted order quantity for all stores. In this case, the determination unit 35 determines that an order quantity adjustment is necessary. This is because the logistics center 4 places orders with the predicted quantity (420) at the second time point (July 8) as the upper limit, but because the upper limit has a margin relative to the total second order quantity, it can be determined that there is room for adjustment. The predetermined threshold can be set to any value lower than the total predicted order quantity for all stores.

[0025] The adjustment unit 36 ​​of the second embodiment adjusts the second order quantities of stores indicated by the indicators by increasing them so that the total value of the second order quantities after adjustment is equal to or less than the total predicted order quantities of all stores. This will be described using the example of FIG. 4. In the example of FIG. 4, stars are added to the adjusted portions for easy viewing. Since the total value of the second order quantities before adjustment is 400 and the total predicted order quantities of all stores is 420, the second order quantity can be increased up to 20. Of all stores, the store with the second order quantity (second order quantity > 0) and the shortest inventory days (the store with the easiest inventory) is the store with store code 105. Therefore, the second order quantity for this store (store code 105) is increased by 10 (order unit). It is preferable to further increase the second order quantity by 10. Of all stores, the store with the second order quantity (second order quantity > 0) and the next shortest inventory days (the store with the next easiest inventory) is the store with store code 106. Therefore, the second order quantity for this store is increased by 10. This increases the second order quantity by a total of 20, so the adjustment process ends. Even if the second order quantity is increased, the risk of overstocking can be reduced because product A sells more easily at that store than at other stores. In the second embodiment, the adjustment is performed so that the total value of the second order quantities after adjustment matches the total predicted order quantity for all stores. However, as long as the total value of the second order quantities after adjustment is equal to or less than the total predicted order quantity for all stores, the adjustment quantity can be changed arbitrarily as long as it complies with the order unit. In the second embodiment, the adjustment of the second order quantity is performed by adding only.

[0026] <Modification> (A) In the above embodiment, the indicator is the number of days of inventory, but this is not limiting as long as it indicates the difficulty of selling the product. For example, sales rank may be used as the indicator. The sales rank is evaluated for each store based on sales for product A over the past few to several tens of days, and one of multiple ranks, such as A-rank store or B-rank store, is associated with the store and stored as rank data. The indicator acquisition unit 37 acquires the rank of each store by referring to the rank data and the store code. For example, the adjustment unit 36 ​​prioritizes low ranks (low sales) and reduces the second order quantity.

[0027] (B) In the above embodiment, the number of order units is set to 10, but this is not limited to this and various values ​​can be used. Also, in the above embodiment, the number of days of inventory after closing is used as the index, but the number of days of inventory at any point in time can also be used as the index. For example, the number of days of inventory after delivery can also be used as the index.

[0028] (C) In the above embodiment, the method for adjusting the second order quantity involves reducing one order unit (order unit of 10) for each store targeted for reduction, but this is not limited to this. The order quantity may also be reduced by an amount corresponding to the difference from a statistical value (e.g., average) of the number of days of inventory. Specifically, the order quantity for the first store may be reduced by 20, and the order quantity for the second store may be reduced by 10.

[0029] (D) In ​​the above embodiment, the ordering server 11 is installed at the headquarters, but this is not limited to this. Various servers may be cloud servers, and may be installed anywhere on the network. A server that realizes one function may be distributed across multiple resources, or may change dynamically over time.

[0030] [1] As described above, in the first and second embodiments, the method for determining the order quantity of products is a method executed by one or more processors, and includes the steps of: calculating a first order quantity of products to be ordered at a first time point (July 15th) for each of a plurality of stores based on a demand forecast using past data up to a second time point (July 8th) several days before the first time point (July 15th); calculating a predicted total order quantity (420) for all stores to be ordered at the first time point (July 15th) based on the total value (414.3) of the first order quantities for all stores; and calculating a second order quantity of products to be ordered at the first time point (July 15th) for each of the plurality of stores based on the total value (414.3) of the first order quantities for all stores. The method may include calculating the second order quantity based on a demand forecast using past data up to a first point in time (July 15th); determining whether or not an order quantity adjustment is necessary based on the total value of the second order quantity for all stores and the total predicted order quantity for all stores (420); and, if it is determined that an order quantity adjustment is necessary, obtaining an index representing the sellability of the product for each of the multiple stores; selecting at least one store from the multiple stores according to the sellability represented by the index, and adjusting the second order quantity for the selected store by increasing or decreasing it so that the total value of the second order quantity after adjustment is less than or equal to the total predicted order quantity for all stores (420).

[0031] This configuration makes it possible to prevent or reduce the occurrence of shortages of products delivered to each store from the logistics center 4. At the same time, it is possible to reduce the risk of immediate shortages at the store that has adjusted the second order quantity.

[0032] [2] As in the first embodiment, in the method for determining the order quantity of a product described in [1] above, if the total value (450) of the second order quantities for all stores exceeds the total predicted order quantity (420) for all stores, it may be determined that an order quantity adjustment is necessary, and the second order quantities for stores with poor sales indicated by the indicator may be reduced and adjusted so that the total value of the second order quantities after adjustment is equal to or less than the total predicted order quantity (420) for all stores. This configuration makes it possible to prevent or reduce the occurrence of shortages of products delivered to each store from the logistics center 4. At the same time, it is possible to reduce the risk of immediate shortages at the store that has adjusted the second order quantity.

[0033] [3] As in the second embodiment, in the method for determining the order quantity of a product described in [1] above, if the total value (400) of the second order quantities for all stores is equal to or less than a predetermined threshold (410) that is lower than the total predicted order quantity (420) for all stores, it may be determined that an order quantity adjustment is necessary, and the second order quantities for stores that are likely to sell well, as indicated by the indicator, may be increased and adjusted so that the total value of the second order quantities after adjustment is equal to or less than the total predicted order quantity (420) for all stores. This configuration makes it possible to prevent or reduce stockouts from occurring when products are sent from the logistics center 4 to each store. At the same time, it is possible to reduce the risk of immediate stockouts at stores that have adjusted their second order quantities. In addition, by increasing the second order quantity for stores where sales are high, it is possible to reduce the risk of excess inventory.

[0034] [4] In the method for determining the order quantity of a product according to any one of the above [1] to [3], the index may be the number of days of inventory or sales rank. These are preferred examples of the index.

[0035] [5] As in the first and second embodiments, the system for determining the order quantity of a commodity includes a first order quantity calculation unit 32 that calculates a first order quantity of a commodity to be ordered at a first time point (July 15th) for each of a plurality of stores based on a demand forecast using past data up to a second time point (July 8th) several days before the first time point (July 15th), a predicted total order quantity calculation unit 33 that calculates a predicted total order quantity (420) for all stores to be ordered at the first time point (July 15th) based on the total value (414.3) of the first order quantities for all stores, and a second order quantity of a commodity to be ordered at the first time point (July 15th) for each of a plurality of stores based on the total value (414.3) of the first order quantities for all stores. The system may include a second order quantity calculation unit 34 that calculates the quantity based on a demand forecast using past data, a determination unit 35 that determines whether or not an order quantity adjustment is necessary based on the total value of the second order quantity for all stores (450) and the total predicted order quantity for all stores, an index acquisition unit 37 that acquires an index representing the sellability of the product for each of the multiple stores when it is determined that an order quantity adjustment is necessary, and an adjustment unit 36 ​​that selects at least one store from the multiple stores according to the sellability represented by the index and adjusts the second order quantity for the selected store by increasing or decreasing it so that the total value of the second order quantity after adjustment is less than or equal to the total predicted order quantity for all stores (420).

[0036] [6] As in the first embodiment, in a system for determining the order quantity of a product described in [5] above, the determination unit 35 may determine that an order quantity adjustment is necessary when the total value (450) of the second order quantities for all stores exceeds the total predicted order quantity (420) for all stores, and the adjustment unit 36 ​​may adjust the second order quantities for stores with poor sales indicated by the indicators by reducing them so that the total value of the second order quantities after adjustment is equal to or less than the total predicted order quantity (420) for all stores.

[0037] [7] As in the second embodiment, in a system for determining the order quantity of a product described in [5] above, the determination unit 35 may determine that an order quantity adjustment is necessary when the total value (400) of the second order quantities for all stores is equal to or less than a predetermined threshold (410) that is lower than the predicted total order quantity (420) for all stores, and the adjustment unit 36 ​​may adjust the order quantity by increasing the second order quantities for stores that are likely to sell well, as indicated by the indicator, so that the total value of the second order quantities after adjustment is equal to or less than the predicted total order quantity (420) for all stores.

[0038] [8] In the system for determining the order quantity of a product described in any one of [5] to [7] above, the index may be the number of days of inventory or sales rank.

[0039] [9] As in the first and second embodiments, the program may cause one or more processors to execute the method described in any one of [1] to [4] above.

[0040] Although the embodiments of the present disclosure have been described above with reference to the drawings, the specific configurations should not be considered to be limited to these embodiments. The scope of the present disclosure is defined not only by the description of the above embodiments but also by the claims, and further includes all modifications within the meaning and scope of the claims.

[0041] The structures employed in the above-described embodiments can be employed in any other embodiment. The specific configurations of the components are not limited to the above-described embodiments, and various modifications are possible within the scope of the present disclosure.

[0042] For example, the order of execution of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings, can be implemented in any order, as long as the output of a previous process is not used in a subsequent process. Even if the flow in the claims, specifications, and drawings is explained using terms such as "first" and "next" for convenience, this does not mean that the processes must be executed in this order.

[0043] 1 are realized by executing a predetermined program on one or more processors, but each unit may also be configured with a dedicated memory or dedicated circuit. In the system of the above embodiment, each unit is implemented in the processor of one computer, but each unit may be distributed and implemented on multiple computers or in the cloud. In other words, the above method may be executed on one or more processors.

[0044] The system includes a processor. For example, the processor may be a central processing unit (CPU), microprocessor, or other processing unit capable of executing computer-executable instructions. The system also includes memory for storing data for the system. In one example, memory includes computer storage media, including RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, DVD or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired data and that can be accessed by the system. [Explanation of symbols]

[0045] 3: System 32: First order quantity calculation unit 33: Total predicted order quantity calculation section 34: Second order quantity calculation unit 35: Judgment section 36: Adjustment section 37: Indicator acquisition part A:Product

Claims

1. 1. A method executed by one or more processors, comprising: calculating a first order quantity of the product to be ordered at a first time point in each of a plurality of stores based on a demand forecast using past data up to a second time point that is several days before the first time point; calculating a predicted total number of orders to be placed at all stores at the first time point based on the total value of the first order quantity for all stores; calculating a second order quantity of the product to be ordered at each of the plurality of stores at the first time point based on a demand forecast using past data up to the first time point; determining whether or not an order quantity adjustment is necessary based on a total value of the second order quantity for all stores and the predicted total order quantity for all stores; When it is determined that the order quantity adjustment is necessary, acquiring an index representing the sales rate of the product at each of the plurality of stores; selecting at least one store from the plurality of stores in accordance with the sales potential represented by the indicator, and adjusting the second order quantity of the selected store by increasing or decreasing it so that the total value of the second order quantity after adjustment is equal to or less than the total predicted order quantity of all the stores; A method for determining the number of items to order, including:

2. determining that an order quantity adjustment is necessary when the total value of the second order quantity for all stores exceeds the predicted total order quantity for all stores; The method according to claim 1, wherein the second order quantity of the store with poor sales represented by the indicator is adjusted by decreasing it so that the total value of the second order quantities after adjustment is equal to or less than the total predicted order quantity of all the stores.

3. determining that an order quantity adjustment is necessary when the total value of the second order quantity for all stores is equal to or less than a predetermined threshold value that is lower than the predicted total order quantity for all stores; The method according to claim 1, wherein the second order quantity of the store represented by the indicator that is likely to sell well is increased and adjusted so that the total value of the second order quantities after adjustment is equal to or less than the total predicted order quantity of all the stores.

4. The method according to any one of claims 1 to 3, wherein the indicator is inventory days or sales rank.

5. a first order quantity calculation unit that calculates a first order quantity of a product to be ordered at a first time point in each of a plurality of stores based on a demand forecast using past data up to a second time point that is several days before the first time point; a predicted total order quantity calculation unit that calculates a predicted total order quantity for all stores to be ordered at the first time point based on the total value of the first order quantity for all stores; a second order quantity calculation unit that calculates a second order quantity of the product to be ordered at each of the plurality of stores at the first time point based on a demand forecast using past data up to the first time point; a determination unit that determines whether or not an order quantity adjustment is necessary based on a total value of the second order quantity for all stores and the predicted total order quantity for all stores; an index acquisition unit that acquires an index representing the sales potential of the product at each of the plurality of stores when it is determined that the order quantity adjustment is necessary; an adjustment unit that selects at least one store from the plurality of stores in accordance with the sales potential represented by the index, and adjusts the second order quantity of the selected store by increasing or decreasing it so that the total value of the second order quantity after adjustment is equal to or less than the predicted total order quantity of all the stores; A system for determining the order quantity of a product, comprising:

6. the determination unit determines that an order quantity adjustment is necessary when a total value of the second order quantities for all stores exceeds a predicted total order quantity for all stores; The system described in claim 5, wherein the adjustment unit adjusts the second order quantity of the store with poor sales represented by the indicator by reducing it so that the total value of the second order quantities after adjustment is less than or equal to the predicted total order quantity of all the stores.

7. the determination unit determines that an order quantity adjustment is necessary when a total value of the second order quantities for all stores is equal to or less than a predetermined threshold value that is lower than the predicted total order quantity for all stores; The system described in claim 5, wherein the adjustment unit adjusts the second order quantity of the store represented by the indicator as being a good seller by increasing it so that the total value of the second order quantities after adjustment is equal to or less than the predicted total order quantity of all the stores.

8. The system according to any one of claims 5 to 7, wherein the indicator is inventory days or sales rank.

9. A program that causes one or more processors to execute the method according to any one of claims 1 to 3.

Images