Delivery support device, delivery support method, and delivery support system

Abstract

This invention relates to a delivery support device that aims to reduce the number of delivery vehicle trips by improving the efficiency of the load capacity of delivery vehicles that replenish goods to retail stores. This helps to reduce the working hours of logistics personnel and reduce greenhouse gas emissions. The delivery support device includes: a storage means for storing trends in store inventory and sales volume for each store and for each product sold in that store; a storage means for storing a first threshold related to the store's inventory and a second threshold greater than the first threshold for each product; a means for calculating a predicted store inventory for each product after a predetermined period for each store; a means for selecting products whose store inventory is lower than the first threshold as first delivery candidate products; a means for selecting products whose predicted store inventory is lower than the second threshold as second delivery candidate products; and a means for performing delivery scheduling processing to deliver the first and second delivery candidate products to a store using a vehicle.

Description

Technical Field

[0001] This invention relates to a delivery technology for replenishing inventory in a store. Prior Technology

[0002] Manage the inventory of goods sold in retail stores such as drugstores to ensure that there are no stockouts of goods displayed / sold in the store; when the inventory is lower than a fixed amount, replenish the store's stock by sending the goods from manufacturers, wholesalers, or the store's own warehouse.

[0003] In this context, for example, Patent Document 1 proposes an order determination device that can prompt a quantitative standard for judging a reasonable order quantity when ordering goods, thereby verifying the reasonableness of the order quantity. [Known Technical Literature] [Patent Literature]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2024-049832 Summary of the Invention

[0005] [Problem to be solved by the present invention] However, when replenishing goods to a store, vehicles equipped with loading platforms for goods are used. However, in the above-mentioned prior art, since the delivery operation is carried out when there is still space on the loading platform, there are problems such as low vehicle utilization efficiency and the need to use a large number of vehicles.

[0006] Therefore, in view of the above problems, the object of the present invention is to provide a delivery support device that aims to reduce the number of delivery vehicle trips by improving the efficiency of the load capacity of delivery vehicles that replenish goods to retail stores, thereby helping to reduce the working hours of logistics personnel and reduce greenhouse gas emissions. [Technical means to solve the problem]

[0007] One embodiment of the delivery support device disclosed in this invention is characterized by comprising: a product information storage means for storing trends in store inventory and sales volume for each store and for each product sold in that store; a delivery threshold storage means for storing a first threshold related to the store inventory and a second threshold greater than the first threshold for each product; a first inventory prediction means for calculating, based on the information stored in the product information storage means, a predicted value of the store inventory for each product after a predetermined period for each store; a first candidate extraction means for extracting products whose store inventory is lower than the first threshold as first delivery candidate products; a second candidate extraction means for extracting products whose predicted store inventory is lower than the second threshold as second delivery candidate products; and a delivery means for performing delivery scheduling processing to deliver the first and second delivery candidate products to the store using a vehicle. [ ] [Effects of the Invention]

[0008] The delivery support device disclosed in this invention aims to reduce the number of delivery vehicle trips by improving the efficiency of the load capacity of delivery vehicles that replenish goods to retail stores. This helps to reduce the working hours of logistics personnel and reduce greenhouse gas emissions. Simple Explanation of the Diagram

[0009] Figure 1 is a schematic diagram illustrating the delivery support device of this embodiment. Figure 2 is a functional block diagram of the delivery support device in this embodiment. Figure 3 is a diagram showing an example of the hardware configuration of the delivery support device in this embodiment. Figure 4 is a flowchart showing a processing example performed by the delivery support device in this embodiment. Implementation

[0010] Referring to the drawings, the configurations used to implement the present invention are described. (Operating principle of the delivery support device in this embodiment)

[0011] Using Figures 1 and 2, the operating principle of the delivery support device (hereinafter referred to as "the device") 100 of this embodiment will be explained. Figure 1 is a diagram showing the connection relationship between the device 100 and other devices, and Figure 2 is a functional block diagram of the device 100.

[0012] As shown in Figure 1, this device 100 is connected to the store terminal 370 via a communication network 380. The communication network 380 can be either wired or wireless. The store terminal 370 is a device that notifies this device 100 of the sales and inventory status of the goods 240 sold in the store 210; for example, it can be a POS (Point of Sales) system.

[0013] As shown in Figure 2, this device 100 includes a product information storage means 110, a delivery threshold storage means 120, a nearby store storage means 130, a first inventory quantity prediction means 140, a first candidate extraction means 150, a second candidate extraction means 160, a second inventory quantity prediction means 170, a third candidate extraction means 180, a remaining space determination means 190, and a delivery means 200.

[0014] The product information storage method 110 stores the store inventory 250 and the store sales trend 260 for each of the products 240 sold in store 210. The store sales trend 260 includes, for example, the recent sales situation of each product 240, the seasonal characteristics of sales, and the characteristics of sales due to weather, which is necessary information for predicting the future store inventory 250 of the products 240.

[0015] The delivery threshold storage method 120 stores a first threshold 270 related to the store's inventory 250 and a second threshold 280 that is larger than the first threshold 270 for each of the 240 sold in store 210. Alternatively, the delivery threshold storage method 120 can also store the first threshold 270 and the second threshold 280 for each store 210. This method can address situations where the trend 260 of the store's inventory 250 and sales volume varies depending on the store 210.

[0016] The neighboring store storage method 130 stores store grouping information 330, which groups stores 210 according to predetermined criteria. Here, the predetermined criteria include the concepts of "same delivery route" and "distance between stores 210 is less than a predetermined value". In this way, it is possible to identify stores 210 that can deliver goods efficiently when using the same vehicle for delivery.

[0017] The first inventory forecasting method 140, for a store 220, calculates a forecast value 290 for the future (after a predetermined period) store inventory 250 for each product 240 based on the information 250 and 260 stored in the product information storage method 110. The forecast value 290 is, for example, the store inventory 250 for 3 days or 1 week later. Furthermore, the forecast value 290 is calculated using a general forecasting method, which is not particularly limited.

[0018] The first candidate extraction method 150 involves extracting 240 items whose store inventory 250 associated with a store 220 is lower than the first threshold 270 as the first delivery candidate items 300. This is a general process for selecting the items 240 that should be replenished to a store 220.

[0019] The second candidate extraction method 160 involves extracting products 240 whose predicted store inventory value 290 related to a store 220 is lower than the second threshold 280 as second delivery candidate products 310. Additionally, products 240 extracted as first delivery candidate products 300 by the first candidate extraction method 150 are excluded from the second delivery candidate products 310. This is a process of selecting products that "have a surplus of store inventory 250 compared to the first delivery candidate products 300, but are nearing the replenishment period (imminent delivery)."

[0020] In this way, if the delivery times of each of the goods 240 are made as consistent as possible, the number of goods 240 that can be loaded onto a delivery vehicle 340 can be increased, thus ultimately reducing the number of delivery vehicles 340 in operation. This also helps to reduce the working hours of logistics personnel and reduce greenhouse gas emissions accompanying the operation of delivery vehicles 340.

[0021] The second inventory forecasting method 170 calculates a forecast value 290 for the future (after a predetermined period) store inventory 250 for each of the other stores 230 based on the information 250 and 260 stored in the product information storage method 110. The forecast value 290 is, for example, the store inventory 250 for 3 days or 1 week later. Furthermore, the forecast value 290 is calculated using a general forecasting method, which is not particularly limited.

[0022] The second inventory forecasting method 170 can also take the following form: for other stores 230 that belong to the same group as a store 220 in the store grouping information 330 stored in the neighboring store storage method 130, the predicted value 290 of the store inventory 250 is calculated. From the perspective of improving logistics efficiency, by screening other store 230 as candidates, the operational efficiency of the delivery vehicle 340 is improved. This can also help reduce the working hours of logistics-related personnel and reduce the greenhouse gas emissions accompanying the operation of the delivery vehicle 340.

[0023] The third candidate extraction method 180 involves extracting products 240 whose predicted inventory 290 of stores related to other stores 230 is lower than the second threshold 280 as third delivery candidate products 320.

[0024] In this way, if the delivery times of each of the goods 240 are made as consistent as possible, the number of goods 240 that can be loaded onto a delivery vehicle 340 can be increased, thus ultimately reducing the number of delivery vehicles 340 in operation. This also helps to reduce the working hours of logistics personnel and reduce greenhouse gas emissions accompanying the operation of delivery vehicles 340.

[0025] The remaining space determination method 190 confirms / determines whether there is remaining space 350 on the platform when the first delivery candidate product 300 and the second delivery candidate product 310 are placed on the platform of a delivery vehicle 340.

[0026] Furthermore, the remaining space determination means 190 confirms the size 360 ​​of the remaining space on the platform of a delivery vehicle 340 when the first delivery candidate product 300 and the second delivery candidate product 310 are placed on the platform. Then, if the remaining space determination means 190 confirms that the size 360 ​​of the remaining space is larger than the size of the third delivery candidate product 320, it determines that the third delivery candidate product 320 can be delivered by a delivery vehicle 340.

[0027] Delivery method 200 performs delivery scheduling processing to deliver the first delivery candidate item 300 and the second delivery candidate item 310 to a store 220 using a delivery vehicle 340. Furthermore, delivery method 200 may also be configured to perform delivery scheduling processing to deliver the third delivery candidate item 320 to other stores 230 using a delivery vehicle 340, in addition to the above processing.

[0028] In this way, if the delivery times of each product 240 and different stores 220 and 230 are made as consistent as possible, the number of products 240 that can be loaded onto a delivery vehicle 340 can be increased, thus ultimately reducing the number of delivery vehicles 340 in operation. This also helps to reduce the working hours of logistics personnel and reduce greenhouse gas emissions accompanying the operation of delivery vehicles 340.

[0029] In addition, the delivery method 200 may also take the form of: in addition to performing the above-mentioned processing, if the remaining space determination method 190 determines that there is remaining space 350, a delivery scheduling process is also performed to deliver the third delivery candidate product 320 to other stores 230 using a delivery vehicle 340.

[0030] In addition, in addition to the above processing, if the remaining space determination method 190 determines that the third delivery candidate product 320 can be delivered by a delivery vehicle 340, the delivery method 200 also performs delivery scheduling processing to deliver the third delivery candidate product 320 to other stores 230 using a delivery vehicle 340.

[0031] Based on the above operating principle, this device 100 aims to reduce the number of trips of delivery vehicles 340 by improving the efficiency of the carrying capacity of delivery vehicles 340 that replenish goods 240 to retail stores 210. This helps to reduce the working hours of logistics personnel and reduce greenhouse gas emissions. (Hardware configuration of the delivery support device in this embodiment)

[0032] Using FIG3, an example of the hardware configuration of the present device 100 will be described. FIG3 is a diagram showing an example of the hardware configuration of the present device 100. As shown in FIG3, the present device 100 includes: CPU (Central Processing Unit) 510, ROM (Read-Only Memory) 520, RAM (Random Access Memory) 530, auxiliary storage device 540, communication interface (I / F) 550, input device 560, display device 570, and storage media interface 580.

[0033] CPU 510 is a device that executes programs stored in ROM 520. Following the program's instructions, it processes the data loaded into RAM 530 and controls the entire device 100. ROM 520 stores the programs and data executed by CPU 510. RAM 530, when CPU 510 executes the programs stored in ROM 520, loads the executed programs and data and temporarily stores the processing data during the operation.

[0034] The auxiliary storage device 540 is a device that stores the basic software, i.e., the OS (Operating System), the applications of this embodiment, and related data. For example, it includes a product information storage device 110, a delivery threshold storage device 120, and a nearby store storage device 130. The auxiliary storage device 540 may be, for example, an HDD (Hard Disk Drive) or flash memory.

[0035] The communication interface 550 is used to connect to a wired / wireless LAN (Local Area Network), Internet or other communication network 380, and to send and receive data with other devices (POS system, etc.) 370 that provide communication functions.

[0036] Input device 560 is a device such as a keyboard used for inputting data into this device 100. Display device (output device) 570 is a device such as an LCD (Liquid Crystal Display) that functions as a user interface when the user utilizes the functions of this device 100 or makes various settings. Storage media interface 580 is an interface used for receiving and sending data to and from storage media 590 such as CD-ROM, DVD-ROM, and USB memory.

[0037] The various means provided by this device 100 can be configured such that the CPU 510 executes the program corresponding to each means stored in the ROM 520 or the auxiliary storage device 540. Furthermore, the various means provided by this device 100 can also be configured to implement the related processing of each means as hardware. Additionally, it can be configured such that the program of this invention is read from an external server device via the communication interface 550, or the program of this invention is read from the storage medium 590 via the storage medium interface 580, causing the device 100 to execute the program. (A processing example using the delivery support device in this embodiment) Using Figure 4, a processing example performed by this apparatus 100 will be described. Figure 4 is a flowchart showing the process of the processing example performed by this apparatus 100.

[0038] In S10, the first candidate extraction method 150 extracts the goods 240 whose store inventory 250 associated with a store 220 is lower than the first threshold 270 as the first delivery candidate goods 300. This is the general process for selecting the goods 240 that should be replenished to a store 220.

[0039] In S20, the first inventory forecasting method 140, for a store 220, calculates a predicted value 290 for the future (after a predetermined period) store inventory 250 for each product 240 based on the information 250 and 260 stored in the product information storage method 110. The predicted value 290 is, for example, the store inventory 250 for 3 days or 1 week later. Furthermore, the predicted value 290 is calculated using a general forecasting method, which is not particularly limited.

[0040] Furthermore, in S20, the second candidate extraction method 160 extracts products 240 whose predicted store inventory value 290 related to a store 220 is lower than the second threshold 280 as second delivery candidate products 310. Additionally, products 240 extracted in S10 as first delivery candidate products 300 are excluded from the second delivery candidate products 310. This is a process of selecting products that "have a surplus of store inventory 250 compared to the first delivery candidate products 300, but are close to the replenishment period (about to be delivered)."

[0041] In this way, if the delivery times of each of the goods 240 are made as consistent as possible, the number of goods 240 that can be loaded onto a delivery vehicle 340 can be increased, thus ultimately reducing the number of delivery vehicles 340 in operation. This also helps to reduce the working hours of logistics personnel and reduce greenhouse gas emissions accompanying the operation of delivery vehicles 340.

[0042] In S30, the second inventory forecasting method 170, for other stores 230, calculates a forecast value 290 for the future (after a predetermined period) store inventory 250 for each product 240 based on the information 250 and 260 stored in the product information storage method 110. The forecast value 290 is, for example, the store inventory 250 3 days or 1 week later. Furthermore, the forecast value 290 is calculated based on a general forecasting method, and the forecasting method is not particularly limited.

[0043] The second inventory forecasting method 170 can also take the following form: for other stores 230 that belong to the same group as a store 220 in the store grouping information 330 stored in the neighboring store storage method 130, the predicted value 290 of the store inventory 250 is calculated. From the perspective of improving logistics efficiency, by screening other store 230 as candidates, the operational efficiency of the delivery vehicle 340 is improved. This can also help reduce the working hours of logistics-related personnel and reduce the greenhouse gas emissions accompanying the operation of the delivery vehicle 340.

[0044] Furthermore, in S30, the third candidate extraction method 180 extracts the goods 240 whose predicted value 290 of the store inventory 250 related to other stores 230 is lower than the second threshold 280 as the third delivery candidate goods 320.

[0045] In this way, if the delivery times of each of the goods 240 are made as consistent as possible, the number of goods 240 that can be loaded onto a delivery vehicle 340 can be increased, thus ultimately reducing the number of delivery vehicles 340 in operation. This also helps to reduce the working hours of logistics personnel and reduce greenhouse gas emissions accompanying the operation of delivery vehicles 340.

[0046] In S40, the remaining space determination means 190 confirms / determines whether there is remaining space 350 on the platform when the first delivery candidate product 300 and the second delivery candidate product 310 are placed on the platform of a delivery vehicle 340.

[0047] In another embodiment, in S40, the remaining space determination means 190 determines the size 360 ​​of the remaining space on the platform of a delivery vehicle 340 when the first delivery candidate product 300 and the second delivery candidate product 310 are placed on the platform. Then, if the remaining space determination means 190 can confirm that the size 360 ​​of the remaining space is larger than the size of the third delivery candidate product 320, it determines that the third delivery candidate product 320 can be delivered by a delivery vehicle 340.

[0048] In S50, delivery means 200 performs delivery scheduling processing to deliver the first delivery candidate item 300 and the second delivery candidate item 310 to a store 220 using a delivery vehicle 340. Alternatively, in addition to the above processing, delivery means 200 also performs delivery scheduling processing to deliver the third delivery candidate item 320 to another store 230 using a delivery vehicle 340.

[0049] In another form, in S50, the delivery means 200, in addition to performing the above-mentioned processing, also performs delivery scheduling processing in S40 if it is determined that there is remaining space 350, to deliver the third delivery candidate product 320 to other stores 230 using a delivery vehicle 340.

[0050] As another form, in addition to the above processing, if it is determined in S40 that the third delivery candidate product 320 can be delivered by a delivery vehicle 340, the delivery method 200 also performs delivery scheduling processing to deliver the third delivery candidate product 320 to other stores 230 by using a delivery vehicle 340.

[0051] In this way, if the delivery times of each product 240 and different stores 220 and 230 are made as consistent as possible, the number of products 240 that can be loaded onto a delivery vehicle 340 can be increased, thus ultimately reducing the number of delivery vehicles 340 in operation. This also helps to reduce the working hours of logistics personnel and reduce greenhouse gas emissions accompanying the operation of delivery vehicles 340.

[0052] By performing the processes described above, this device 100 aims to reduce the number of trips of delivery vehicles 340 by improving the efficiency of the loading rate of delivery vehicles 340 that replenish goods 240 to retail stores 210. This helps to reduce the working hours of logistics personnel and reduce greenhouse gas emissions.

[0053] While the embodiments of the present invention have been described in detail above, the present invention is not limited to these specific embodiments. Various modifications and alterations can be made within the scope of the spirit of the present invention as described in the claims.

[0054] 100: Delivery Support Device 110: Product Information Storage Methods 120: Delivery threshold storage method 130: Storage methods for nearby stores 140: First Inventory Forecasting Method 150: Method for selecting the first candidate 160: Second candidate extraction method 170: Second Inventory Forecasting Method 180: Third candidate extraction method 190: Remaining Space Determination Method 200: Delivery Methods 210: Shop 220: A shop 230: Other shops 240:Commodity 250: Store inventory 260: Trends in Store Sales Volume 270: First threshold 280: Second threshold 290: Forecast of store inventory levels after a given period 300: First delivery candidate item 310: Second delivery candidate item 320: Third delivery candidate item 330: Store Segmentation Information 340: Vehicles 350: Remaining space on the platform 360: Dimensions of the remaining space on the platform 370: Shop Terminal (POS System) 380: Communication Network 510: CPU 520:ROM 530: RAM 540: Auxiliary storage device 550: Communication Interface 560: Input device 570: Output device 580: Storage Media Interface 590: Storage Media

Claims

1. A delivery support device, characterized by comprising: a product information storage means for storing trends in store inventory and sales volume for each store and for each product sold in that store; a delivery threshold storage means for storing a first threshold related to the store inventory and a second threshold greater than the first threshold for each product; a first inventory prediction means for calculating, based on the information stored in the product information storage means, a predicted value of the store inventory after a predetermined period for each product in that store; a first candidate extraction means for extracting products whose store inventory is lower than the first threshold as first delivery candidate products; a second candidate extraction means for extracting products whose predicted store inventory is lower than the second threshold as second delivery candidate products; and a delivery means for performing delivery scheduling processing to deliver the first delivery candidate products and the second delivery candidate products to the store using a vehicle.

2. As in request item 1, the delivery support device, wherein, It also has: a second inventory forecasting method, which calculates the predicted inventory level of each store for a given period based on the information stored in the product information storage method for other stores. And a third candidate extraction method, which extracts goods whose predicted inventory value of the other store is lower than the second threshold as third delivery candidate goods; the delivery method performs delivery scheduling processing to deliver the third delivery candidate goods to the other store using the vehicle.

3. As in request item 2, the delivery support device, wherein, It also has a nearby store storage method, which stores are grouped based on a predetermined standard including stores with the same delivery route or those located within a predetermined distance; the second inventory prediction method calculates the predicted inventory value of other stores that belong to the same group as the store in the store grouping information.

4. A delivery support method, implemented in a computer equipped with a product information storage means and a delivery threshold storage means: the product information storage means stores trends in store inventory and sales volume for each store and for each product sold in that store; and the delivery threshold storage means stores a first threshold related to the store inventory and a second threshold greater than the first threshold for each product; the delivery support method includes the following steps: a first inventory prediction means, for a store, calculates a predicted value of the store inventory for each product after a predetermined period based on the information stored in the product information storage means; a first candidate extraction means extracts products whose store inventory is lower than the first threshold as first delivery candidate products; The second candidate extraction means to extract goods whose predicted inventory value related to the store is lower than the second threshold as second delivery candidate goods; and the delivery means to perform delivery scheduling processing to deliver the first delivery candidate goods and the second delivery candidate goods to the store using a vehicle.

5. As per request item 4 regarding delivery support methods, where, It further includes the following steps: A second inventory forecasting method calculates a predicted inventory level for each of the other stores based on information stored in the product information storage method, after a predetermined period; a third candidate extraction method extracts products whose predicted inventory levels for the other stores are lower than the second threshold as third delivery candidate products; and the delivery method performs delivery scheduling processing to deliver the third delivery candidate product to the other stores using the same vehicle.

6. As per request item 5 regarding delivery support methods, where, The computer has a nearby store storage method, which stores the stores based on a predetermined standard that includes stores with the same delivery route or whose location is within a predetermined distance. The second inventory prediction method calculates the predicted inventory value of the other stores that belong to the same group as the store in the store grouping information.

7. A delivery support program for enabling a computer to perform a delivery support method for any one of requests 4 to 6.

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