JP6945612B2 - Information processing equipment, information processing methods and information processing programs - Google Patents

Description

The present invention relates to an information processing device, an information processing method, and an information processing program.

Conventionally, electronic commerce (EC: Electronic Commerce), which sells products via a network such as an electronic shopping street or an auction site, is known. In such electronic commerce, when a product is purchased or sold, the delivery company that receives the request from the seller who listed the product delivers the product to the user who purchased or won the bid. do. In the delivery of such products, there is known a technique of selecting the cheapest transportation schedule as the optimum transportation schedule based on the delivery cost and the maintenance cost of the delivery schedule.

Japanese Unexamined Patent Publication No. 2004-220234

However, in the above-mentioned conventional technique, it may not be possible to say that an efficient distribution base is selected. For example, in the above-mentioned prior art, the selection of the optimum transportation schedule for the lowest distribution cost is taken into consideration, but the final distribution base (so-called last) that actually delivers the product to the user. Determining an efficient distribution base for a one-mile distribution base) is not considered.

The present application has been made in view of the above, and an object of the present application is to provide an information processing device, an information processing method, and an information processing program for determining a distribution base for actually delivering a product to a user. ..

The information processing device according to the present application includes a storage unit that stores a user's purchase history in electronic commerce, and in addition to the purchase history, road traffic information related to road congestion in the surrounding area at the time of delivery, weather information related to weather, and a delivery destination. Of the distribution bases related to product delivery to the user based on the information on the delivery fee and the real estate information on the property that is a candidate for the distribution base, the final product is actually delivered to the user. The last one mile is given to the decision-making department that calculates the optimum position of the last one mile distribution base indicating the relay point and determines the appropriate property as the last one mile distribution base, and to the business operator involved in the delivery of goods to the user. It is characterized by having a provision department that proposes appropriate properties as a distribution base for the company.

According to one aspect of the embodiment, it is possible to determine the distribution base that actually delivers the product to the user.

FIG. 1 is an explanatory diagram showing an outline of an information processing method according to an embodiment. FIG. 2 is a diagram showing a configuration example of an information processing system according to an embodiment. FIG. 3 is a diagram showing a configuration example of the information processing device according to the embodiment. FIG. 4 is a diagram showing an example of a user information storage unit. FIG. 5 is a diagram showing an example of a store information storage unit. FIG. 6 is a diagram showing an example of a product information storage unit. FIG. 7 is a diagram showing an example of the purchase history information storage unit. FIG. 8 is a diagram showing an example of the proposed information storage unit. FIG. 9 is a flowchart showing a processing procedure according to the embodiment. FIG. 10 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device.

Hereinafter, the information processing apparatus according to the present application, the information processing method, and a mode for carrying out the information processing program (hereinafter, referred to as “the embodiment”) will be described in detail with reference to the drawings. The information processing apparatus, information processing method, and information processing program according to the present application are not limited by this embodiment. Further, in the following embodiments, the same parts are designated by the same reference numerals, and duplicate description is omitted.

[1. Information processing method overview]
First, with reference to FIG. 1, an outline of an information processing method performed by the information processing apparatus according to the embodiment will be described. FIG. 1 is an explanatory diagram showing an outline of an information processing method according to an embodiment. In FIG. 1, the case where the information processing apparatus 100 optimizes the distribution base of the last mile will be described as an example.

The information processing device 100 shown in FIG. 1 is an information processing device that proposes optimization of a distribution base of the last mile, and is realized by, for example, a server device or a cloud system. For example, the information processing device 100 has a function of providing a service related to an electronic shopping district to a store S and a user U, and as a part of a service related to an electronic shopping district, proposes optimization of a distribution base of the last mile. conduct. The last mile distribution base indicates the final relay point where the product is actually delivered to the user. Further, the optimization of the distribution base is to determine the most efficient base, but in reality, it may be at least to determine the more efficient base than the current situation.

The terminal device 10 is an information processing device such as a PC (Personal Computer) or a smartphone used by the user U. The user U is a user of the Web service provided by the information processing device 100, and is a member who has registered user information about himself / herself in the information processing device 100. For example, the terminal device 10 can communicate with the information processing device 100 via a network N such as the Internet (see, for example, FIG. 2). Although not shown in FIG. 1, the information processing device 100 may be capable of communicating with any number of terminal devices 10.

The store S is a store that sells products to the user U through the service of the electronic shopping street provided by the information processing device 100. More specifically, the store S is a store that ships products from a base located in the area A.

The base where the store S ships the products may be the store S itself, or may be a base different from the store S itself, such as a warehouse where the store S manages the products. For example, in the store S, a warehouse for storing and shipping products may be located in the area A, and a business office for accepting orders for the products may be located in an area other than the area A. That is, the location of the store S is a concept that includes not only the location where the store S itself is located but also the base where the store S ships the product, such as the warehouse of the store S.

The distribution base DB is a distribution base of the delivery company DC that has been entrusted with delivery by the store S. More specifically, the distribution base DB is the last one-mile distribution base (final base) to the end user user U among the distribution bases of the delivery company DC that delivers in the area A.

For example, as shown in FIG. 1, the information processing apparatus 100 accepts registration of information (for example, an image, a price, a shipping fee, an inventory quantity, etc.) of a product to be sold in an electronic shopping mall from a store S (step S1).

Then, when the information processing device 100 receives the product search query from the user U, the information processing device 100 extracts the product corresponding to the search query from the exhibited products, and the content indicating the image, price, etc. of the extracted product. Alternatively, the product content, which is the content for purchasing the store exhibited by the store S, is distributed to the user U (step S2).

Further, in the information processing device 100, when the user U purchases the product of the store S through the product content, the address to be the delivery destination (shipping destination) of the product, the name of the recipient, and the delivery method of the product (for example). , Airmail or land delivery, etc.), accept registration of delivery information such as desired delivery date and time (step S3).

In such a case, the information processing device 100 notifies the store S where the user U has purchased the product of the fact that the product has been purchased and the above delivery information (step S4).

For example, the information processing device 100 notifies the store S of the home address of the user U and instructs the store S to ship the product to the user U. The information processing device 100 may charge the user U for the purchased product and provide the store S for the product. Further, the information processing device 100 may provide the store S with a charge obtained by subtracting an advertising cost and a predetermined fee from the product price.

As a result, the store S ships the product to the user U via the delivery company DC that delivers in the area A (step S5).

At this time, the store S requests the delivery company DC to deliver the product to the user U. The delivery company DC collects the products ordered by the user U from the store S from the store S located in the area A. The delivery company DC may belong to the store S or may be affiliated with the store S.

The delivery company DC aggregates the products ordered by the user U from the store S and the products to be delivered to other users located in the same area A in the distribution base DB located in the area A (step S6).

The product to be delivered to another user may be the same product as the product ordered by the user U from the store S. For example, it may be a product ordered from the store S by a user other than the user U. Here, the user U and other users located in the same area A belong to the same delivery destination group DL. The delivery destination group DL is a group of delivery destinations to which the delivery company DC delivers the goods within the same day.

The delivery company DC delivers the products collected in the distribution base DB to the delivery destination group DL as the last mile delivery (step S7).

That is, the delivery company DC delivers the products ordered by each customer under each customer belonging to the delivery destination group DL. Along with this, the delivery company DC delivers the products ordered by the user U to the store S from the distribution base DB to the bottom of the user U (for example, at home).

In addition, the delivery company DC processes information on road congestion at the time of delivery, weather, surrounding environment, etc. when delivering the products ordered by each customer under each customer belonging to the delivery destination group DL. The device 100 may be notified.

The information processing device 100 optimizes the last mile distribution base (final base) from the purchase logs (purchase history) of the user U and other users belonging to the same delivery destination group DL (step S8).

In the purchase history of the user U, the address of the user U, the desired delivery time zone, and the like are stored as the delivery destination of the product. Therefore, from the huge purchase history (so-called big data) held by electronic shopping streets and auction sites, we analyze the distribution and uneven distribution of user U who became the delivery destination, the time zone when delivery requests are concentrated, etc. for each region. However, these will be used to improve distribution and optimize distribution bases.

For example, the information processing device 100 is a candidate for a distribution base, in addition to the purchase history of the purchaser, road traffic information related to road congestion in the surrounding area at the time of delivery, weather information related to the weather, information related to the delivery destination and the delivery fee, and distribution bases. The location of the optimum distribution base (hereinafter referred to as the optimum distribution base) OPDB is calculated from the empty tenant information (real estate information) related to the property. The information processing device 100 extracts a property appropriate as the optimum distribution base OPDB from the calculated real estate information about the property such as the position of the optimum distribution base OPDB or its surrounding vacant lots, vacant stores, and vacant warehouses. At this time, the information processing device 100 estimates the delivery schedule and the delivery fee when the optimum distribution base OPDB is set up at that position, and further determines the appropriate price (acquisition price, rent, etc.) of the property that becomes the optimum distribution base OPDB. Calculate and comprehensively judge to extract properties that are appropriate as the optimal distribution base OPDB.

Further, when a warehouse is newly constructed in a vacant lot or the like, the information processing apparatus 100 also optimizes the arrangement and the number of warehouses. That is, the information processing device 100 also optimizes the arrangement of warehouses and the number of buildings of the optimum distribution base OPDB. In addition, the information processing apparatus 100 also optimizes the personnel allocation (number of personnel, personnel composition, allocation status, etc.) of the optimum distribution base OPDB.

The optimum distribution base OPDB is not limited to a permanent one, but may be a temporary one.

In recent years, physical distribution facilities (logistics real estate) such as distribution centers and warehouses tend to be concentrated in super-large distribution facilities in order to reduce distribution costs. This consolidation into super-large logistics facilities has created vacancy in medium- and small-scale logistics facilities. In particular, most small facilities are old storage warehouses, and it is difficult to find new shippers / tenants. Therefore, it is expected that the last mile distribution base will be optimized by temporarily using such an empty warehouse.

In addition, there are concerns about the increase in vacant houses and rooms due to the declining population, and the increase in closed / closed shops and offices such as the so-called shutter town. Therefore, it is expected that the last mile distribution base will be optimized by temporarily using such vacant houses, vacant rooms, and vacant stores.

In addition, services for renting vacant spaces on an hourly basis are increasing in offices, stores, warehouses, and privately owned land (private homes, parking lots, etc.) in central Tokyo. It is also expected that the last mile distribution base will be optimized by temporarily using such hourly rental properties.

In addition, by partnering with nearby weekly condominiums and hotels, it is expected that these rooms, collection areas, luggage storage areas, etc. will be temporarily used to optimize the last mile distribution base. In this case, if it is a general delivery company DC, it is possible to deliver goods to weekly condominiums, hotels, etc., and conversely, to ship packages from these at any time, which is mutually beneficial. be. In addition, the company may cooperate not only with weekly condominiums and hotels, but also with large customers such as companies that have offices in the neighborhood.

Furthermore, in recent years, there has been a movement to build a distribution system in condominiums and to create a distribution center in which delivery staff are stationed in the condominium. It is also expected that the last mile distribution base will be optimized by temporarily using such an in-condominium distribution system.

The information processing device 100 proposes a property appropriate as the determined optimum distribution base OPDB to the store S or the delivery company DC (step S9).

For example, the information processing device 100 narrows down the positions of properties that are appropriate as the optimum distribution base OPDB in consideration of road congestion, the surrounding environment, weather (precipitation, snowfall, typhoons, etc.), topography (whether or not it is a flooded area), and the like. .. The information processing device 100 is used as an optimal distribution base OPDB for a reasonable property location, a reasonable price (acquisition price, rent, etc.), and a reason why the location and price are appropriate (reduction of distribution cost, shortening of delivery schedule). , Optimization of staffing, etc.) to the store S or the delivery company DC.

When the store S or the delivery company DC sets up the optimum distribution base OPDB in response to the proposal, it leads to reduction of distribution cost, shortening of delivery schedule, optimization of staffing, and the like. Dynamic pricing of shipping charges may be adopted in order to set up the optimum distribution base OPDB.

The information processing device 100 may match the store S or the delivery company DC with the property from the viewpoint of the property that is appropriate as the optimum distribution base OPDB. At this time, the information processing device 100 may acquire information about the property from the real estate information that is open to the public, or may acquire information about the property from the real estate information registered in the information processing device 100.

In addition, the store S may change the contractor of product delivery from the current delivery company DC to another delivery company having a distribution base DB at an appropriate position as the optimum distribution base OPDB. Corresponds to having set up.

Logistics conditions and demand are changing from time to time. For example, when a large-scale detached residential area or a large-scale condominium is completed and residents move in, the distribution of the area may increase rapidly. In such a case, the distribution base corresponding to the existing condominium or the like may not be optimal when the distribution to the new condominium or the like is also considered. In addition, due to the relocation or new construction / closure of business establishments and factories, a large number of employees may flow in / out, and the distribution in the area may increase / decrease. In addition, the delivery destination of a large number of products may change due to the movement of a large customer who has purchased the product frequently or in large quantities due to moving or the like. In addition, due to factors such as regional characteristics, seasons, and local trends, the demand for specific products may temporarily increase in a specific area or time, and the distribution may increase sharply. It is desired to optimize the last mile distribution base by appropriately changing it in response to such changes in distribution.

According to this embodiment, the last mile distribution base can be optimized as appropriate according to the distribution situation and changes in product demand. In addition, by setting up a distribution base near the user, it is possible to promptly respond to redelivery notifications (requests). For example, when delivering products that require cash on delivery or identity verification (including address), large products, high-priced products, etc., there is a limited gap time (short time) within the delivery time zone due to various errands. ) Can only be used at home to immediately redeliver the product upon contact. Further, even in the case of normal delivery, it becomes easier for the user to pick up the product, which improves convenience.

[2. Information processing system configuration example]
Next, the configuration of the information processing system 1 including the information processing device 100 according to the embodiment will be described with reference to FIG. FIG. 2 is a diagram showing a configuration example of the information processing system 1 according to the embodiment. As shown in FIG. 2, the information processing system 1 according to the embodiment includes a terminal device 10 and an information processing device 100. These various devices are communicably connected via network N by wire or wirelessly. The network N is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network) such as the Internet.

Further, the number of each device included in the information processing system 1 shown in FIG. 2 is not limited to that shown in the figure. For example, in FIG. 2, for simplification of the illustration, only one terminal device 10 is shown, but this is merely an example and is not limited, and may be two or more.

The terminal device 10 is an information processing device used by the user U. For example, the terminal device 10 is a smart device such as a smartphone or a tablet terminal, a feature phone, a PDA (Personal Digital Assistant), a smart watch, a wearable device (Wearable Device), a PC (Personal Computer), or the like.

Further, the terminal device 10 includes a wireless communication network such as LTE (Long Term Evolution), 4G (4th Generation), 5G (5th Generation: 5th generation mobile communication system), Bluetooth (registered trademark), and wireless LAN (Local). It can be connected to the network N via short-range wireless communication such as Area Network) and communicate with the information processing apparatus 100.

The information processing device 100 is, for example, a PC, a server, a mainframe, a workstation, or the like. The information processing device 100 may be realized by cloud computing.

[3. Information processing device configuration example]
Next, the configuration of the information processing apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram showing a configuration example of the information processing device 100 according to the embodiment. As shown in FIG. 3, the information processing device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

(About communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card) or the like. Further, the communication unit 110 is connected to the network N by wire or wirelessly.

(About storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory (Flash Memory), or a storage device such as a hard disk or an optical disk. As shown in FIG. 3, the storage unit 120 includes a user information storage unit 121, a store information storage unit 122, a product information storage unit 123, a purchase history information storage unit 124, and a proposal information storage unit 125.

(About user information storage unit 121)
The user information storage unit 121 stores various information about the user U. For example, the user information storage unit 121 stores various information such as the attributes of the user U. FIG. 4 is a diagram showing an example of a user information storage unit. In the example shown in FIG. 4, the user information storage unit 121 has items such as "user ID (Identifier)", "age", "gender", "home", "work location", and "interest".

The "user ID" indicates identification information for identifying the user U. Further, "age" indicates the age of the user U identified by the user ID. The "age" may be the specific age of the user U identified by the user ID, such as 35 years old. Further, "gender" indicates the gender of the user U identified by the user ID.

Further, "home" indicates the location information of the home of the user U identified by the user ID. In the example shown in FIG. 4, "home" is illustrated with an abstract reference numeral such as "LC11", but may be latitude / longitude information or the like. Further, for example, "home" may be an area name or an address.

Further, the "work location" indicates the location information of the work location of the user U identified by the user ID. In the example shown in FIG. 4, the “work location” is illustrated by an abstract code such as “LC12”, but may be latitude / longitude information or the like. Further, for example, the "work location" may be a region name or an address.

Further, "interest" indicates the interest of the user U identified by the user ID. That is, "interest" indicates an object that the user U identified by the user ID is highly interested in. In the example shown in FIG. 4, one "interest" is shown for each user U, but there may be a plurality of "interests".

For example, in the example shown in FIG. 4, the age of the user U identified by the user ID "U1" is "20's", and the gender is "male". Further, for example, the user U identified by the user ID "U1" indicates that his / her home is "LC11". Further, for example, the user U identified by the user ID "U1" indicates that the work location is "LC12". Further, for example, the user U identified by the user ID "U1" indicates that he / she is interested in "sports".

In the example shown in FIG. 4, for convenience of understanding, abstract codes such as "U1", "LC11" and "LC12" are used for illustration, but "U1", "LC11" and "LC12" are concrete. Information shall be stored. Hereinafter, abstract codes may be illustrated also in figures relating to other information.

The user information storage unit 121 is not limited to the above, and may store various information depending on the purpose. For example, the user information storage unit 121 may store information regarding the demographic attribute and information regarding the psychographic attribute of the user U. For example, the user information storage unit 121 may store information such as name, family structure, occupation, position, income, qualification, living style, presence / absence of a car, commuting time, hobbies, and lifestyle. Further, the user information storage unit 121 may store information such as the type (smartphone, desktop PC, etc.) of the terminal device 10 used by the user U, identification information, address information, and the like.

(About store information storage unit 122)
The store information storage unit 122 stores various information related to the store S. FIG. 5 is a diagram showing an example of the store information storage unit 122. In the example shown in FIG. 5, the store information storage unit 122 has items such as “store ID”, “warehouse ID”, “location location”, “product ID”, and “inventory quantity”.

The "store ID" indicates identification information for identifying the store S. The "warehouse ID" is an identifier of a warehouse owned by the store S indicated by the associated "store ID", that is, a facility whose store S indicated by the associated "store ID" is the shipping source of the product. Further, the "location location" indicates the location location of the warehouse indicated by the associated "warehouse ID", that is, the area where the facility whose shipping source is the store S indicated by the associated "store ID" is located. ..

For example, in the example shown in FIG. 5, the store ID “S1”, the warehouse ID “M1”, the location position “A1”, the product ID “C1”, and the inventory quantity “5” are registered in association with each other. Such information indicates that the store S indicated by the store ID “S1” has the facility indicated by the warehouse ID “M1” as the shipping source facility in the area indicated by the location “A1”. Further, such information indicates that the product indicated by the product ID "C1" is stored in the facility indicated by the warehouse ID "M1" and the number of the products in stock is "5".

The store information storage unit 122 is not limited to the above, and may store various information depending on the purpose. For example, the store information storage unit 122 may store information about the corporate group to which the store S belongs.

(About product information storage unit 123)
The product information storage unit 123 stores various information related to the products exhibited by each store S. FIG. 6 is a diagram showing an example of the product information storage unit 123. In the example shown in FIG. 6, the product information storage unit 123 stores items such as "store ID", "product ID", "identification information", "inventory information", "price", "shipping cost", and "state information". Have.

The "store ID" is an identifier that identifies each store S. Further, the "product ID" is an identifier that identifies the product exhibited by each store S for each product. The "identification information" is an identifier that identifies a product, such as a JAN code. Further, "inventory information" is information indicating the number of inventories of products. The "price" is information indicating the selling price of the product. The "shipping fee" is information indicating the fee required for delivery of the product. Further, the "state information" is information indicating the state of the product, and is information indicating whether the product is new or used.

For example, in the example shown in FIG. 6, the product information storage unit 123 has the store ID "S1", the product ID "C1", the identification information "B1", the inventory information "15", the price "2600", and the shipping fee "500". , And the status information "new" are registered in association with each other. Such information indicates that the product identified by the product ID "C1" in the electronic shopping district by the store S indicated by the store ID "S1" and the product indicated by the identification information "B1" is exhibited. show. Further, in such information, the number of inventories of the product identified by the product ID "C1" is "15", the selling price is "2600" yen, the shipping fee is "500" yen, and the state. Indicates that is "new".

The product information storage unit 123 is not limited to the above, and may store various information depending on the purpose. For example, the product information storage unit 123 may store information regarding the type / category of the product.

(About the purchase history information storage unit 124)
The purchase history information storage unit 124 stores various information related to the purchase history of products in the electronic shopping district by each user U. FIG. 7 is a diagram showing an example of the purchase history information storage unit 124. In the example shown in FIG. 7, the purchase history information storage unit 124 has items such as "order ID", "product ID", "shipping source", "delivery destination", and "shipping flag".

The "order ID" is an identifier that identifies the order contents by the user U. The "shipping source" is information indicating the base of each store S that is the shipping source of the product, and is, for example, the "warehouse ID" shown in FIG. The "delivery destination" is information indicating each delivery destination of the product, and is, for example, the "user ID" shown in FIG. This group of delivery destinations in the same area A becomes the delivery destination group DL. Further, the "shipping flag" is a flag indicating whether or not the product indicated by the associated "product ID" has been shipped. The "shipping flag" is, for example, "0" if it has not been shipped, and "1" if it has been shipped.

For example, in the example shown in FIG. 7, the order ID “D1”, the product ID “C1”, the shipping source “M1”, the delivery destination “U2”, and the shipping flag “0” are registered in association with each other. Such information is such that the content of the order identified by the order ID "D1" is the product indicated by the product ID "C1" from the base indicated by the shipping source "M1" to the user indicated by the delivery destination "U2". It is delivered to the address, and as indicated by the shipping flag "0", it indicates that the shipping has not been performed yet.

The purchase history information storage unit 124 is not limited to the above, and may store various information depending on the purpose. For example, the purchase history information storage unit 124, in addition to the address to which the product is delivered and the name of the recipient, the delivery method of the product (for example, whether it is airmail or land delivery), the desired delivery date and time, etc. Etc. may be stored in the delivery information. Further, the purchase history information storage unit 124 may store information regarding the fact that the product is a large-sized product and that the product requires refrigeration / freezing.

(About the proposal information storage unit 125)
The proposal information storage unit 125 stores various information regarding the content of the proposal of the property that is appropriate as the optimum distribution base OPDB. For example, in the proposal information storage unit 125, FIG. 8 is a diagram showing an example of the proposal information storage unit. In the example shown in FIG. 8, the proposal information storage unit 125 has items such as "proposal ID", "region", "reasonable position", "reasonable price", "reasonable reason", and "property candidate".

The "proposal ID" indicates identification information for identifying the proposal to the delivery company DC. Further, the “region” indicates the region A where the store S and the distribution base DB are located. The "region" may be a predetermined region such as a prefecture or a municipality, or may be an region whose range is arbitrarily specified.

In addition, the "reasonable position" indicates a position that is appropriate for setting up the optimum distribution base OPDB. The "reasonable position" may be, for example, a coordinate position acquired by GPS (Global Positioning System) or other positioning means, or a position designated on a map as a POI (Point of Interest).

In addition, "reasonable price" indicates a reasonable price (acquisition price, rent, etc.) of the property that is the optimum distribution base OPDB. The "reasonable price" is not limited to the predetermined amount of money, and may be information indicating the range of the predetermined amount of money.

In addition, the "reasonable reason" indicates the reason why the position and price are reasonable for setting up the optimum distribution base OPDB. The "reasonable reason" indicates that, for example, when the optimum distribution base OPDB is set up at that position and price, it is possible to reduce the distribution cost, shorten the delivery schedule, optimize the staffing, and the like. In addition, the "reasonable reason" is the specific distribution cost that is expected to be reduced compared to the current situation, the transportation time that is expected to be shortened (time required for delivery), and the minimum number of people required. It may be a numerical value.

In addition, "property candidate" indicates a property that is a candidate for the optimum distribution base OPDB. The “property candidate” is, for example, a property that is located at an appropriate position as an optimal distribution base OPDB and is presumed to be available for acquisition / rent at an appropriate price.

For example, in the example shown in FIG. 8, the content of the proposal identified by the proposal ID "PR1" is obtained by setting the optimum distribution base OPDB at the appropriate position "PT1" and the appropriate price "VL1" in the area "AR1". , It is shown that the last one mile distribution base can be optimized because of the valid reason "RS1", and that the property candidates "ES11, ES12, ..." Are listed as the properties that can be the optimum distribution base OPDB.

The proposed information storage unit 125 is not limited to the above, and various information may be stored depending on the purpose. For example, the proposal information storage unit 125 may store information about major delivery destinations in the vicinity.

(About control unit 130)
Returning to FIG. 3, the explanation will be continued. The control unit 130 is a controller, and is an information processing device 100 using, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like. It is realized by executing various programs (corresponding to an example of an information processing program) stored in an internal storage device using a storage area such as a RAM as a work area. In the example shown in FIG. 3, the control unit 130 includes a reception unit 131, an analysis unit 132, an optimization unit 133, a provision unit 134, and a learning unit 135.

(About reception desk 131)
The reception unit 131 receives the registration of various information about the user U from the terminal device 10 of the user U via the communication unit 110, and stores the information in the user information storage unit 121.

Further, the reception unit 131 receives registration of various information related to the store S from each store S via the communication unit 110, and stores the registration in the store information storage unit 122. Further, the reception unit 131 receives registrations of various information related to the products exhibited by the store S from each store S via the communication unit 110, and stores the registration in the product information storage unit 123.

In addition, when the user U purchases the product of the store S through the product content, the reception unit 131 provides the address to be the delivery destination of the product from the terminal device 10 of the user U via the communication unit 110. It accepts registration of delivery information such as the name of the recipient, the delivery method of the product, and the desired delivery date and time, and stores it in the purchase history information storage unit 124 as purchase history information. Then, the reception unit 131 notifies the store S where the user U has purchased the product via the communication unit 110 that the product has been purchased and the above-mentioned delivery information.

(About analysis unit 132)
The analysis unit 132 analyzes the current state of physical distribution from the purchase history information stored in the purchase history information storage unit 124. For example, the analysis unit 132 analyzes the current distribution cost, delivery schedule, and personnel assigned to the distribution base. The analysis unit 132 also analyzes the distribution and uneven distribution of delivery destinations.

In addition, the analysis unit 132 verifies the effect of establishing the optimum distribution base OPDB after newly establishing the optimum distribution base OPDB. For example, the location and price of the optimal distribution base OPDB and the change in the distribution status before and after the establishment of the optimum distribution base OPDB will be verified. That is, the analysis unit 132 also functions as a verification unit. The verification unit may be provided independently of the analysis unit 132.

(About optimization unit 133)
The optimization unit 133 optimizes the last mile distribution base based on the purchase history information stored in the purchase history information storage unit 124 and the analysis result of the current state of distribution. Specifically, the optimization unit 133 determines the optimum distribution base for the last mile. That is, the optimization unit 133 corresponds to a determination unit that determines the final relay point for actually delivering the product to the user. Further, the optimization unit 133 stores information about the optimum distribution base OPDB, which is an optimized distribution base, in the proposal information storage unit 125.

The optimization unit 133 optimizes the last mile distribution base when the total number of purchase history information stored in the purchase history information storage unit 124 exceeds a predetermined number (for example, 10,000). It may be. In addition, the optimization unit 133 optimizes the last mile distribution base when the number of purchase history information of users located in a certain area A exceeds a predetermined number (for example, 100). May be good. Alternatively, the optimization unit 133 may optimize the last mile distribution base when the number of users belonging to the same delivery destination group DL exceeds a predetermined number (for example, 100). ..

(About the provider 134)
The providing unit 134 provides the store S or the delivery company DC with the proposal information regarding the optimum distribution base OPDB based on the proposal information stored in the proposal information storage unit 125.

For example, the providing unit 134 may provide the proposal information regarding the optimum distribution base OPDB as a report (report) to the store S or the delivery company DC on a regular basis (every month or the like). Alternatively, the providing unit 134 may post the proposal information regarding the optimum distribution base OPDB on a dedicated Web page or the like that can be accessed only by the store S or the delivery company DC. The store S or the delivery company DC acquires the proposal information regarding the optimum distribution base OPDB by browsing this dedicated Web page or the like.

(About Learning Department 135)
The learning unit 135 performs machine learning on the optimization of the last mile distribution base. Here, the learning unit 135 performs machine learning about the position and price of the optimum distribution base OPDB and the change in the distribution status before and after the establishment of the optimum distribution base OPDB. The learning unit 135 also performs machine learning on the distribution and uneven distribution of delivery destinations.

For example, when the delivery company DC sets up the optimum distribution base OPDB in response to the proposal, the learning unit 135 performs machine learning related to reduction of distribution cost, shortening of delivery schedule, optimization of staffing, and the like. That is, the learning unit 135 performs machine learning on the effect of reducing the actual distribution cost and the effect of shortening the transportation time before and after the establishment of the optimum distribution base OPDB.

Further, the learning unit 135 may perform machine learning on the determination criteria when the delivery company DC selects the optimum distribution base OPDB. For example, the learning unit 135 performs machine learning on the points that the delivery company DC attaches importance to, the points that are the final deciding factor, the tendency of the selected property, and the like.

Based on the above learning results, the learning unit 135 creates a learning model (optimization model) for optimizing the last mile distribution base. For example, the optimization unit 133 extracts properties that are candidates for the optimum distribution base OPDB based on this learning model (optimization model).

Machine learning is, for example, deep learning using a deep neural network. Data mining and other machine learning algorithms may also be used.

[4. Processing procedure]
Next, the processing procedure by the information processing apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 9 is a flowchart showing a processing procedure according to the embodiment. The processing procedure shown below is repeatedly executed by the control unit 130 of the information processing apparatus 100.

When the user U purchases the product of the store S via the product content, the control unit 130 of the information processing device 100 serves as the delivery destination of the product from the terminal device 10 of the user U via the communication unit 110. Accepts registration of delivery information such as address, recipient's name, product delivery method (for example, airmail or land delivery, etc.), desired delivery date and time (step S101).

The control unit 130 stores the product information regarding the purchased product and the above-mentioned delivery information in the purchase history information storage unit 124 (step S102).

The control unit 130 notifies the store S where the user U has purchased the product via the communication unit 110 that the product has been purchased and the above delivery information, and delivers the product to the user U. Request (step S103).

As a result, the store S ships the product to the user U via the delivery company DC that delivers in the area A. The delivery company DC collects the products ordered by the user U from the store S and the products to be delivered to other users located in the same area A in the distribution base DB located in the area A, and delivers the last mile. As a result, the products collected in the distribution base DB are delivered to the delivery destination group DL.

The control unit 130 analyzes the current status of physical distribution based on the purchase history information stored in the purchase history information storage unit 124 (step S104).

As a result of analyzing the current state of physical distribution, the control unit 130 determines whether or not the current last mile physical distribution base DB is optimal (step S105).

When the control unit 130 determines that the current last mile distribution base DB is optimal (step S105; Yes), the control unit 130 maintains the current situation.

When the control unit 130 determines that the current last mile distribution base DB is not optimal (step S105; No), the control unit 130 optimizes the last one mile distribution base (step S106).

As a result of the optimization of the last mile distribution base, the control unit 130 provides the store S or the delivery company DC with the proposal information regarding the optimum distribution base OPDB (step S107).

The store S or the delivery company DC newly establishes the optimum distribution base OPDB based on the proposal information (step S108).

The control unit 130 verifies the effect of establishing the optimum distribution base OPDB from changes in distribution costs, transportation times, etc. before and after the establishment of the optimum distribution base OPDB (step S109).

For example, the control unit 130 verifies the position and price of the optimum distribution base OPDB and the change in the distribution status before and after the establishment of the optimum distribution base OPDB, and machine-learns the verification result.

[5. Modification example]
The terminal device 10 and the information processing device 100 described above may be implemented in various different forms other than the above-described embodiment. Therefore, a modified example of the embodiment will be described below.

In the above embodiment, the optimization of the last mile distribution base is described as an example in normal times, but in reality, it may be used to temporarily change the last mile distribution base in an emergency. .. For example, according to the above embodiment, when a neighboring area or a road is temporarily congested due to a festival, cherry blossom viewing, or other event, it is possible to avoid this and optimize the distribution base of the last mile. In addition, when some roads are interrupted due to the effects of typhoons, floods, earthquakes, etc., it is possible to avoid this and optimize the distribution base for the last mile.

Further, in the above embodiment, the optimization of the last mile distribution base is described as an example, but in reality, it may be used to optimize the parking place and the stop place of the delivery vehicle. For example, when the driver of a delivery vehicle delivers products to a customer in a residential area (residential area) using a cart or the like, or when a customer or a housing complex living in the same apartment or condominium follows the same building or the same staircase. When delivering products to customers who live in a room, the delivery vehicle itself becomes the last one-mile distribution base. Therefore, optimizing the parking place and stopping place of the delivery vehicle will optimize the distribution base of the last mile.

Further, in the above embodiment, the optimization of the last mile distribution base for the purchaser such as online shopping is described as an example, but in reality, the last mile distribution for the person who wants to deliver the product in the actual store is explained. It may be used to optimize the site. It may also be used to optimize the last mile distribution base for users of regular home delivery services such as consumer co-ops (co-ops) and home delivery milk. It may also be used to optimize the last mile distribution base for people who are inconvenient to shop (weak shopping, etc.) in depopulated areas.

Further, in the above embodiment, the optimization of the last mile distribution base is described as an example, but in reality, the candidate sites for new stores and temporary stores of courier services and courier chain stores are optimized. May be used to do so. Similarly, it may be used to optimize the candidate sites for new stores such as restaurants and temporary stores that provide home delivery services such as delivery services and lunch boxes.

[6. effect〕
As described above, the information processing device 100 according to the present application includes a storage unit 120 and an optimization unit 133 (determination unit). The storage unit 120 stores the purchase history of the user U in the electronic commerce. The optimization unit 133 (decision unit) determines the distribution base for delivering the product to the user U based on the purchase history. As a result, it is possible to determine the distribution base for delivering the product to the user based on the huge purchase history held by the electronic shopping street, the auction site, or the like.

In addition, the optimization unit 133 (decision unit) determines the distribution base for delivering the product to the user U based on the purchase history and the road traffic information related to the road congestion. As a result, it is possible to determine the distribution base for delivering the product to the user in consideration of the road congestion in the surrounding area.

In addition, the optimization unit 133 (decision unit) determines the distribution base for delivering the product to the user U based on the purchase history and the weather information related to the weather. As a result, it is possible to determine the distribution base for delivering products to users in consideration of the tendency of the weather (precipitation, snow cover, typhoon, etc.) in the surrounding area.

In addition, the optimization unit 133 (decision unit) determines the distribution base for delivering the product to the user U based on the information regarding the delivery destination and the delivery fee in the purchase history. As a result, it is possible to determine the distribution base for delivering the product to the user in consideration of the delivery fee and dynamic pricing.

In addition, the optimization unit 133 (decision unit) determines the distribution base related to the delivery of goods to the user U based on the purchase history and the vacant tenant information regarding the property that is a candidate for the distribution base. As a result, it is possible to determine a distribution base related to product delivery to users in consideration of vacant properties that are candidates for distribution bases.

In addition, the optimization unit 133 (decision unit) calculates the purchase history and the amount of money that can be used to acquire the property at the distribution base. As a result, it is possible to calculate a reasonable amount of money for acquiring the property of the distribution base.

In addition, the optimization unit 133 (decision unit) determines how to place the warehouse of the distribution base related to the delivery of goods to the user U. This makes it possible to determine the contents of the distribution base related to the delivery of goods to the user.

In addition, the optimization unit 133 (decision unit) determines the staffing of the distribution base related to the delivery of goods to the user U. This makes it possible to determine the required number of distribution bases for delivering products to users.

In addition, the optimization unit 133 (decision unit) determines the final relay point for actually delivering the product to the user as a distribution base for delivering the product to the user U. This makes it possible to determine the last mile distribution base.

Further, the information processing apparatus 100 according to the present application further includes a providing unit 134. The providing unit 134 provides the business operator having the distribution base related to the product delivery to the user U with the proposal information regarding the distribution base determined by the optimization unit 133 (decision unit). As a result, it is possible to propose the optimum distribution base to the business operator.

[7. Hardware configuration]
The information processing device 100 and the terminal device 10 according to the above-described embodiment are realized by, for example, a computer 1000 having a configuration as shown in FIG. Hereinafter, the information processing apparatus 100 will be described as an example. FIG. 10 is a hardware configuration diagram showing an example of a computer 1000 that realizes the functions of the information processing device 100 and the terminal device 10. The computer 1000 includes a CPU 1100, a RAM 1200, a ROM (Read Only Memory) 1300, an HDD (Hard Disk Drive) 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F). ) Has 1700.

The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400, and controls each part. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started, a program that depends on the hardware of the computer 1000, and the like.

The HDD 1400 stores a program executed by the CPU 1100, data used by such a program, and the like. The communication interface 1500 receives data from another device via the communication network 500 (corresponding to the network N shown in FIG. 2) and sends the data to the CPU 1100, and the data generated by the CPU 1100 is transmitted to the other device via the communication network 500. Send to the device.

The CPU 1100 controls an output device such as a display or a printer, and an input device such as a keyboard or a mouse via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. Further, the CPU 1100 outputs the data generated via the input / output interface 1600 to the output device.

The media interface 1700 reads a program or data stored in the recording medium 1800 and provides the program or data to the CPU 1100 via the RAM 1200. The CPU 1100 loads the program from the recording medium 1800 onto the RAM 1200 via the media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase change rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. And so on.

For example, when the computer 1000 functions as the information processing device 100 according to the embodiment, the CPU 1100 of the computer 1000 realizes the function of the control unit 130 by executing the program loaded on the RAM 1200. Further, the data in the storage unit 120 is stored in the HDD 1400. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, these programs may be acquired from another device via the communication network 500.

[8. others〕
Although the embodiments of the present application have been described above, the present invention is not limited to the contents of these embodiments. In addition, the above-mentioned components include those that can be easily assumed by those skilled in the art, those that are substantially the same, that is, those in a so-called equal range. Furthermore, the components described above can be combined as appropriate. Further, various omissions, replacements or changes of the components can be made without departing from the gist of the above-described embodiment.

Further, among the processes described in the above-described embodiment, all or a part of the processes described as being automatically performed can be manually performed, or the processes described as being manually performed can be performed. All or part of it can be done automatically by a known method. In addition, the processing procedure, specific name, and information including various data and parameters shown in the above document and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the illustrated information.

Further, each component of each of the illustrated devices is a functional concept, and does not necessarily have to be physically configured as shown in the figure. That is, the specific form of distribution / integration of each device is not limited to the one shown in the figure, and all or part of the device is functionally or physically dispersed / physically distributed in arbitrary units according to various loads and usage conditions. Can be integrated and configured.

For example, the information processing device 100 described above may be realized by a plurality of server computers, or may be realized by calling an external platform or the like by API (Application Programming Interface), network computing, or the like depending on the function. Can be changed flexibly.

Further, the above-described embodiments and modifications can be appropriately combined as long as the processing contents do not contradict each other.

Further, the above-mentioned "section, module, unit" can be read as "means" or "circuit". For example, the reception unit can be read as a reception means or a reception circuit.

1 Information processing system 10 Terminal device 100 Information processing device 110 Communication unit 120 Storage unit 121 User information storage unit 122 Store information storage unit 123 Product information storage unit 124 Purchase history information storage unit 125 Proposal information storage unit 130 Control unit 131 Reception unit 132 Analysis department 133 Optimization department (decision department)
134 Providing Department 135 Learning Department

Claims (12)

A storage unit that stores the purchase history of users in electronic commerce,
In addition to the purchase history, the user is based on road traffic information on road congestion in the surrounding area at the time of delivery, weather information on weather, information on delivery destinations and delivery charges, and real estate information on properties that are candidates for distribution bases. Among the distribution bases related to the delivery of goods to the user, the optimum position of the last one mile distribution base indicating the final relay point for actually delivering the goods to the user is calculated, and the last one mile distribution base is calculated. and a determining section for determining a reasonable property as,
An information processing device characterized in that the business operator involved in the delivery of goods to the user is provided with a providing unit that proposes an appropriate property as the distribution base of the last mile. The information processing device according to claim 1, wherein the determination unit determines the distribution base related to the delivery of goods to the user based on the purchase history and road traffic information related to road congestion. .. The information processing device according to claim 1 or 2, wherein the determination unit determines the distribution base related to the delivery of goods to the user based on the purchase history and weather information related to the weather. .. Any of claims 1 to 3, wherein the determination unit determines the distribution base related to the delivery of goods to the user based on the information regarding the delivery destination and the delivery fee in the purchase history. The information processing device according to one. Claim 1 is characterized in that the determination unit determines the distribution base related to product delivery to the user based on the purchase history and vacant tenant information regarding a property that is a candidate for the distribution base. The information processing apparatus according to any one of ~ 4. The information processing device according to any one of claims 1 to 5, wherein the determination unit calculates the purchase history and a reasonable price of the property of the distribution base. The information processing device according to any one of claims 1 to 6, wherein the determination unit determines how to place a warehouse of the distribution base related to delivery of goods to the user. The information processing device according to any one of claims 1 to 7, wherein the determination unit determines the staffing of the distribution base related to the delivery of goods to the user. Claims 1 to 8 are characterized in that the determination unit determines a final relay point for actually delivering the product to the user as the distribution base for delivering the product to the user. The information processing apparatus according to any one of the above. The first to ninth aspects of claim 1 to 9, further comprising a providing unit that provides the proposal information regarding the distribution base determined by the determination unit to the business operator having the distribution base related to the delivery of goods to the user. The information processing device according to any one of them. It is an information processing method executed by an information processing device.
A storage unit that stores the purchase history of users in electronic commerce,
In addition to the purchase history, the user is based on road traffic information on road congestion in the surrounding area at the time of delivery, weather information on weather, information on delivery destinations and delivery charges, and real estate information on properties that are candidates for distribution bases. Among the distribution bases related to the delivery of goods to the user, the optimum position of the last one mile distribution base indicating the final relay point for actually delivering the goods to the user is calculated, and the last one mile distribution base is calculated. a determination step of determining a reasonable property as,
An information processing method including a providing process of proposing a property appropriate as a distribution base for the last mile to a business operator involved in delivering goods to the user. A storage procedure for storing a user's purchase history in electronic commerce,
In addition to the purchase history, the user is based on road traffic information on road congestion in the surrounding area at the time of delivery, weather information on weather, information on delivery destinations and delivery charges, and real estate information on properties that are candidates for distribution bases. Among the distribution bases related to the delivery of goods to the user, the optimum position of the last one mile distribution base indicating the final relay point for actually delivering the goods to the user is calculated, and the last one mile distribution base is calculated. and the determination procedure for determining a reasonable property as,
An information processing program for causing a computer to execute a provision procedure for proposing an appropriate property as a distribution base for the last mile to a business operator involved in delivering goods to the user.

Images