JP2021163003A - Physical distribution base product delivery planning program - Google Patents

Abstract

To automatically and highly accurately plan the delivery of products to a physical distribution base that forms a physical distribution network with each user who buys and sells products by using an EC site.SOLUTION: A physical distribution base product delivery planning program for planning the delivery of products to a physical distribution base that forms a physical distribution network with each user who buys and sells products by using an EC site makes a computer execute: a first information acquisition step of acquiring the demand degree of a product in each user in a network of the physical distribution base; and a proposal step of referring to the first relation degree in three or more steps between the previously-acquired demand degree for reference of the product in each delivery destination in the network of the physical distribution base and the quantity of the actually-delivered products in the physical distribution base, and proposing a plan of the delivery of products to the physical distribution base on the basis of the demand degree acquired in the first information acquisition step.SELECTED DRAWING: Figure 4

Description

The present invention relates to a distribution base product delivery planning program for planning delivery of products to a distribution base that forms a distribution network with each user who buys and sells products using an EC site.

In recent years, electronic commerce through EC (e-commerce) sites has progressed. In fact, as an operator of such an EC site, there is a risk that customers will leave if they run out of inventory and are out of stock, but on the other hand, even if a large amount of inventory is purchased, many will If it is left unsold, it poses a great financial risk. In particular, if many grocery and fashionable products are left unsold, they may not be sold the next day, which may put pressure on management.

That is, in order to make more profits in the transportation business of the EC site, the demand of the product is predicted with high accuracy, the product that meets the demand forecast is ordered from the wholesaler and purchased, and there is almost no inventory left. Ideally, it should be sold out so that it does not run out, and it can attract many customers and increase profits. In order to achieve this, we will deliver an appropriate amount of products to each user's distribution base, which forms a distribution network divided according to the place of residence of each user, without running out of products. It is necessary to make an accurate delivery plan for the goods from.

However, in reality, a distribution base product delivery planning system that plans delivery of products to a distribution base that forms a network with each user has not been proposed in the past.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object thereof is a distribution base that forms a distribution network with each user who buys and sells products by utilizing an EC site. The purpose is to provide a distribution base product delivery planning program that can automatically and accurately plan the delivery of goods to.

In order to solve the above-mentioned problems, the distribution base product delivery planning program according to the present invention delivers products to distribution bases that form a distribution network with each user who buys and sells products using an EC site. In the planned distribution base product delivery planning program, the first information acquisition step for acquiring the demand level of the product for each user in the distribution base network and the product at each delivery destination in the distribution base network acquired in advance. Refer to the first degree of association of three or more stages between the reference demand level and the quantity of products actually delivered for the distribution base, and based on the demand degree acquired in the first information acquisition step above, the distribution base It is characterized by having a computer perform a proposal step of proposing a plan for delivery of goods to.

Even if you do not have any special skills or experience, it is possible to automatically and accurately plan the delivery of products to distribution bases that form a distribution network with each user who buys and sells products using the EC site. It becomes.

It is a block diagram which shows the whole structure of the system to which this invention is applied. It is a figure which shows the specific configuration example of a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the product demand forecast program to which the present invention is applied will be described in detail with reference to the drawings.

FIG. 1 is a block diagram showing an overall configuration of a product demand forecasting system 1 in which a product demand forecasting program to which the present invention is applied is implemented. The product demand forecasting system 1 includes an information acquisition unit 9, a search device 2 connected to the information acquisition unit 9, and a database 3 connected to the search device 2.

The information acquisition unit 9 is a device for a person who uses this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of capturing an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the discrimination device 2 described later. The information acquisition unit 9 outputs the detected information to the determination device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. Further, the information acquisition unit 9 may be composed of an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. Further, the information acquisition unit 9 may be composed of a communication interface for acquiring data on the weather from the Japan Meteorological Agency or a private weather forecast company. Further, the information acquisition unit 9 may be composed of a body sensor that is attached to the body to detect body data, and this body sensor detects, for example, body temperature, heart rate, blood pressure, number of steps, walking speed, and acceleration. It may be composed of a sensor for the purpose. Further, the body sensor may acquire biological data of animals as well as humans. Further, the information acquisition unit 9 may be configured as a device that acquires information such as drawings by scanning or reading it from a database. In addition to these, the information acquisition unit 9 may be configured by an odor sensor that detects an odor or scent.

The database 3 stores various information necessary for forecasting product demand. Information necessary for forecasting product demand includes reference trading history information regarding the past trading history of each product of the user acquired in advance, reference attribute information regarding the user who has acquired the above reference trading history information, and the above reference. The reference trading behavior information regarding the trading behavior of the user who acquired the reference trading history information, the reference area information regarding the area to which the user who acquired the reference trading history information belongs, and the reference trading history information were acquired. Reference sales promotion event information regarding past sales promotion events on the EC site, reference market information regarding market conditions at the time of acquisition of the above reference sales history information, reference external environment regarding the external environment at the time of acquisition of the above reference sales history information There is information.

That is, any of such reference trading history information, reference attribute information, reference trading behavior information, reference area information, reference sales promotion event information, reference market condition information, and reference external environment information is stored in the database 3. The value of 1 or more and the degree of demand for the product according to the subsequent trading behavior are stored in association with each other.

The search device 2 is composed of, for example, an electronic device such as a personal computer (PC), but is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc., in addition to the PC. It may be converted. The user can obtain a search solution by the search device 2.

FIG. 2 shows a specific configuration example of the search device 2. The search device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire search device 2 and an operation unit 25 for inputting various control commands via operation buttons, a keyboard, or the like. A communication unit 26 for the purpose, an estimation unit 27 for making various judgments, and a storage unit 28 for storing a program for performing a search to be executed represented by a hard disk or the like are connected to the internal bus 21, respectively. .. Further, a display unit 23 as a monitor that actually displays information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the search device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 in response to the operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the estimation unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The estimation unit 27 estimates the search solution. The estimation unit 27 reads out various information stored in the storage unit 28 and various information stored in the database 3 as necessary information when executing the estimation operation. The estimation unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any well-known artificial intelligence technique.

The display unit 23 is composed of a graphic controller that creates a display image based on the control by the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 28 is composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program is read and executed by the control unit 24.

The operation in the product demand forecasting system 1 having the above-described configuration will be described.

In the product demand forecasting system 1, for example, as shown in FIG. 3, it is premised that the reference trading history information and the degree of association with the degree of demand are set in advance and acquired. The reference trading history information is all information related to the trading history of the user on the EC (e-commerce) site. An EC site includes any information site that allows you to actually purchase a product or sell it for sale via the Internet. From this EC site, keywords searched by using known technology, product browsing history of the above user on the above EC site, history of the page of the clicked product, history of the product specified as the purchase schedule, the history of the ordered product It is configured so that cancellation history, return history of ordered products, etc. can be acquired.

The reference trading history information on the EC site referred to here is the actual product purchased through the EC site, the type, number, price, timing, payment method, destination of the product, and the genre of the product. Any one or more of the groups according to the type are included. Further, as long as it is a trading history, information on all trading, information on all trading in an arbitrary period, and a predetermined period may be included instead of information on only one trading.

Further, the user referred to here includes not only an individual but also a corporation. Both individuals and corporations own their own PCs, smartphones, and tablets to access the website of the EC site via devices such as terminals, and buy and sell products through the site.

Further, the reference trading history information may refer to sales data (including accounting data and financial data) obtained by selling a product. Reference trading history information can also be obtained from quotations, invoices, and invoices. This reference trading history information may be extracted from account deposit / withdrawal details, accounting data, WEB sites, financial statements, and book information. The accounting data may be obtained by discriminating the actual inventory turnover status from the sales asset turnover period, inventory turnover period, and the like.

Further, the reference trading history information may be composed of a transaction history including time-series changes in past product sales.

The degree of demand may be ranked as "large", "very large", "normal", "small", "very small", etc., or displayed as a score, with 1000 points being the most demanded. High, 0 points may be indicated to mean the lowest demand. The most demanded score may be any score other than 1000 points. Further, if this degree of demand is represented by two values, that is, there is demand and that there is no demand, the two values may be trained as a data set of the output solution. This demand level may be obtained by evaluating with a standard provided for each EC site, or may be evaluated with a demand level evaluated by a unified standard. In addition to the artificially evaluated demand, the demand may be calculated based on, for example, subsequent trading behavior. In other words, while there is a history of buying and selling through the EC site, the history of what was newly purchased from the EC site or what was sold is also acquired. That is, it watches the actual trading behavior after that. As a result, the demand level is set higher if the product is actually purchased, and the demand level is set lower if the product is not purchased or sold.

By watching such a trading history for a certain period of time, if the same product is purchased several times, the demand for the product is large by that amount, so the point of demand is high each time the purchase is made. It may be set so as to be. As an alternative to this demand, the number of orders (number of purchases) may be output for each product. In such a case, it is premised that the number of orders for each product is learned as output data.

That is, through this reference trading history information and the demand degree data set, the actual demand degree of each product can be known from various data generated in the reference trading history information. That is, the data described in the reference trading history information and the degree of demand are a data set. For this reason, by collecting the reference trading history information and the data set of the demand level, it is possible to know how the demand level was judged in the examination after what kind of transaction history was in the past. Become. That is, it is possible to learn how the degree of demand increases when the buying and selling behavior is shown as the prior buying and selling behavior. The prior trading behavior is reflected in the reference trading history information, and the actual trading behavior after that is reflected as the demand level.

In the example of FIG. 3, it is assumed that the input data is, for example, reference trading history information P01 to P03. The reference trading history information as such input data is linked to the output. In this output, the degree of demand as an output solution is displayed.

The reference trading history information is associated with each other through three or more levels of association with the demand level as the output solution. The trading history information for reference is arranged on the left side via this degree of association, and the degree of demand is arranged on the right side via this degree of association. The degree of association indicates the degree of demand and the degree of relevance to the reference trading history information arranged on the left side. In other words, this degree of association is an index showing what kind of demand the reference trading history information is likely to be associated with, and is used to select the most probable demand from the reference trading history information. It shows the accuracy in. In the example of FIG. 3, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the degree of demand as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the degree of demand as an output.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference trading history information and the demand level in that case is adopted in determining the actual search solution, and analyzes and analyzes these. By doing so, the degree of association shown in FIG. 3 is created.

By the way, these degree of association may be created for each type of product sold on the EC site, or may be created one by one for a plurality of product groups. In the case of a product group, the trading history information for reference is a desk lamp, mineral water, a calculator, slippers, etc., and the demand level is also the desk lamp, mineral water according to the type of the product. , Calculator, slippers, etc. will be set respectively.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference trading history information P01, analysis is performed from the data of the determination result of the past demand degree. In the case of reference trading history information P01, if there are many cases of demand degree A (for example, the demand degree score is 760 points), the degree of association connected to this demand degree A is set higher, and the demand degree C is set. When there are many cases (for example, the score of the demand degree is 433 points), the degree of association leading to the demand degree C is set higher. For example, in the example of the trading history information P01 for reference, the demand degree A and the demand degree C are linked, but from the previous case, the degree of association of w13 connected to the demand degree A is set to 7 points, and the degree of association of w14 connected to the demand degree C is set to 7. The degree of association is set to 2 points.

Further, the degree of association shown in FIG. 3 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In such a case, as shown in FIG. 4, reference trading history information is input as input data, each demand degree is output as output data, and at least one or more hidden layers are provided between the input node and the output node. It may be made to be machine-learned. On the contrary, the demand level may be input and the reference trading history information may be output.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, the demand level is predicted by using the above-mentioned trained data in actually determining the demand level from now on. In such a case, the latest trading history information of the user of the EC site that actually newly determines the demand degree is acquired. This trading history information is composed of the same type of data as the above-mentioned reference trading history information. The user who is the target of acquiring the trading history information and the user who is the target of acquiring the above-mentioned reference trading history information may be the same or different from each other.

The newly acquired trading history information is input by the above-mentioned information acquisition unit 9. The information acquisition unit 9 may acquire such trading history information as electronic data.

Based on the newly acquired trading history information in this way, the demand degree that is likely to be determined is actually searched for the trading history information. In such a case, the degree of association shown in FIG. 3 (Table 1) acquired in advance is referred to. For example, when the newly acquired trading history information is the same as or similar to P02, the demand degree B is associated with the association degree w15 and the demand degree C is associated with the association degree w16 through the association degree. In such a case, the demand degree B having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the demand degree C, which has a low degree of association but is recognized as having the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In this way, it is possible to search for the demand level to be determined from the newly acquired trading history information and display it to the user who uses the EC site. By looking at the search result, the user can grasp the searched demand level. In addition to this, the operator of the EC site can also determine the degree of demand for each product for each user. In the process of outputting the demand level, in addition to simply displaying the searched demand level, a specific attention may be given or an alarm may be generated based on the demand level.

In the example of FIG. 5, it is assumed that the input data is, for example, reference trading history information P01 to P03 and reference attribute information P14 to 17. The intermediate node shown in FIG. 5 is a combination of reference attribute information and reference trading history information as such input data. Each intermediate node is further linked to the output. In this output, each demand degree as an output solution is displayed.

In the example of FIG. 5, it is premised that the combination of the reference trading history information and the reference attribute information is formed. The reference attribute information is all information related to the personal attributes of the user of the EC site. Reference attribute information includes the individual's age and health status, annual income, date of birth, birthplace, and family environment (whether the wife is pregnant, the family is pursuing a disability or illness, whether or not they live with their grandparents, etc. Whether or not the caregiver is at home, whether or not the child is affected, whether or not there is DV or abuse, whether or not the child is divorced, whether or not the household income, whether or not there is livelihood protection, whether or not there is a wife and child, and their age structure, etc. If the child is in school, the school expenses and the expected school expenses in the future, whether the individual is currently in employment or working, etc. are also included. Regarding the state of health, whether or not the individual is in perfect health or has some kind of congenital disorder and the degree of the disorder, and whether or not there is an acquired disorder after birth and the degree of the disorder. Also, whether or not you have some kind of illness after birth, whether or not the illness continues at present and its degree, allergic condition, inflammatory condition, injured condition, chronic condition, medication status, etc. , Contains all information that indicates health status. The health condition included in the reference attribute information may be derived from medical data itself such as heart rate, pulse rate, blood data, electrocardiogram data, and X-ray image. All of these may be obtained through declarations and documents submitted by each household, medical certificates of doctors, and the like. This reference attribute information is, for example, "Age 46 years old, Birthplace: Shizuoka, Annual income of ¥ 100,000, 〇 × Working at a company, Family environment: Wife and eldest son, eldest daughter's family of four, eldest son is high school, eldest daughter is Junior high school student, health condition: "Has been operated on for gastric ulcer in the past", "Age 38 years old, Birthplace: Tokyo, Annual income of ¥ 100,000, ▲ 〇 Worked at a company, Family environment: Family of three, wife and eldest daughter, eldest daughter Is an elementary school student, health condition: good "etc.

If the user is a corporation, it is all information about the attributes of the corporation of the past lender. The attributes here include, but are not limited to, the type of business, technical field, history, history, capital, scale, number of employees, years of establishment, etc. of the corporation, but are not limited to this, such as corporate culture and employee morale. The number of hires is also included.

In the example of FIG. 5, it is assumed that the input data is, for example, reference trading history information P01 to P03 and reference attribute information P14 to 17. The intermediate node shown in FIG. 5 is a combination of reference attribute information and reference trading history information as such input data. Each intermediate node is further linked to the output. In this output, the degree of demand as an output solution is displayed.

Each combination (intermediate node) of the reference trading history information and the reference attribute information is associated with each other through three or more levels of association with the demand as the output solution. The reference trading history information and the reference attribute information are arranged on the left side via the degree of association, and the demand degree is arranged on the right side via the degree of association. The degree of association indicates the degree of high relevance to each demand degree with respect to the reference trading history information and the reference attribute information arranged on the left side. In other words, this degree of association is an index indicating what kind of demand the reference trading history information and the reference attribute information are likely to be associated with, and the reference trading history information and the reference attribute information. It shows the accuracy in selecting each demand level that is most probable from. In addition to the trading history information, the degree of demand may change according to the actual geographical situation, and it is possible to make a judgment based on this. Therefore, the optimum demand level is searched for by combining the reference trading history information and the reference attribute information.

In the example of FIG. 5, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference trading history information, the reference attribute information, and the degree of demand in that case is suitable for determining the actual search solution, and these By analyzing and analyzing the above, the degree of association shown in FIG. 5 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference trading history information P01 and the reference attribute information P16, the degree of demand is analyzed from the past data. When there are many cases of demand degree A, the degree of association leading to this degree of demand A is set higher, and when there are many cases of degree of demand B and there are few cases of degree of demand A, the degree of association leading to degree of demand B is set. The degree is set high, and the degree of association that leads to the demand level A is set low. For example, in the example of the intermediate node 61a, the output of the demand degree A and the demand degree B is linked. The degree is set to 2 points.

Further, the degree of association shown in FIG. 5 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 5, the node 61b is a node in which the reference attribute information P14 is combined with the reference trading history information P01, and the degree of association of the demand degree C is w15 and the degree of association of the demand degree E. The degree is w16. The node 61c is a node in which the reference attribute information P15 and P17 are combined with the reference trading history information P02, and the degree of association of the demand degree B is w17 and the degree of association of the demand degree D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually performing a search for determining the demand degree from now on, the above-mentioned learned data will be used. In such a case, attribute information is acquired in addition to the trading history information from the user who newly determines the demand level. This attribute information corresponds to the above-mentioned reference attribute information, and the acquisition method thereof is also the same. The user who acquires this attribute information and the user who acquires the above-mentioned reference attribute information may be the same or different from each other.

Based on the newly acquired trading history information and attribute information in this way, the optimum demand level is searched for. In such a case, the degree of association shown in FIG. 5 (Table 1) acquired in advance is referred to. For example, when the newly acquired trading history information is the same as or similar to P02 and the attribute information is the same as or similar to P17, the node 61d is associated via the degree of association. The node 61d is associated with a demand degree C of w19 and a demand degree D of a degree of association w20. In such a case, the demand degree C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the demand degree D in which the degree of association itself is recognized although the degree of association is low may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In addition, Table 2 below shows examples of the degrees of association w1 to w12 extending from the input.

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the degrees of association w1 to w12 may all have the same value, and the weights in the selection of the intermediate node 61 may all be the same.

FIG. 6 shows an example in which the combination of the above-mentioned reference trading history information, the reference sales promotion event information, and the degree of demand for the combination are set to three or more levels of association.

The reference sales promotion event information is information about the sales promotion event held on the EC site. The sales promotion event here is any event for promoting sales, such as a bargain sale, a bargain, and an inventory disposal sale. In addition to this, sales for various events such as Christmas, the end of the year, entrance celebrations, Tanabata, and Halloween are also included in this promotional event. The reference sales promotion event includes information on the type, scale, and period of the actual sales promotion event, as well as information on the scale of the advertising cost carried out when the event is held.

In the example of FIG. 6, it is assumed that the input data is, for example, reference trading history information P01 to P03 and reference sales promotion event information P18 to 21. The intermediate node shown in FIG. 6 is a combination of the reference sales promotion event information and the reference sales promotion history information as such input data. Each intermediate node is further linked to the output. In this output, the degree of demand as an output solution is displayed.

Each combination (intermediate node) of the reference trading history information and the reference sales promotion event information is associated with each other through three or more levels of association with the demand level as this output solution. The reference trading history information and the reference sales promotion event information are arranged on the left side via this degree of association, and the degree of demand is arranged on the right side via this degree of association. The degree of association indicates the degree of high relevance to the degree of demand with respect to the reference trading history information and the reference sales promotion event information arranged on the left side. In other words, this degree of association is an index indicating what kind of demand the reference trading history information and the reference sales promotion event information are likely to be associated with, and the reference trading history information and the reference sales promotion event. It shows the accuracy in selecting each demand level that is most likely from the information. In addition to trading history information, the degree of demand changes depending on the type and scale of promotional events and the scale of advertising. Therefore, the optimum demand level is searched for by combining the reference trading history information and the reference sales promotion event information.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference trading history information, the reference sales promotion event information, and the demand level in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 6 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, when the reference trading history information P01 is the reference sales promotion event information P20, the demand degree is analyzed from the past data. Further, the degree of association shown in FIG. 6 may be composed of the nodes of the neural network in artificial intelligence.

In the example of the degree of association shown in FIG. 6, the node 61b is a node in which the reference sales promotion event information P18 is combined with the reference sales history information P01, and the association degree C of the demand degree C is w15 and the demand degree E. The degree of association is w16. The node 61c is a node that is a combination of the reference sales promotion event information P19 and P21 with respect to the reference trading history information P02, and the degree of association of the demand degree B is w17 and the degree of association of the demand degree D is w18. ..

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned trading history information, sales promotion event information of the EC site targeted for new demand forecast is acquired. The sales promotion event information corresponds to the reference sales promotion event information.

The demand level is searched based on the newly acquired trading history information and sales promotion event information in this way. In such a case, the degree of association shown in FIG. 6 (Table 1) acquired in advance is referred to. For example, when the newly acquired trading history information is the same as or similar to P02 and the sales promotion event information is P21, the node 61d is associated via the degree of association. The node 61d is associated with a demand degree C of w19 and a demand degree D of a degree of association w20. In such a case, the demand degree C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the demand degree D in which the degree of association itself is recognized although the degree of association is low may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

FIG. 7 shows an example in which the combination of the above-mentioned reference trading history information, the reference area information, and the degree of demand for the combination are set to three or more levels of association.

The reference area information is information on the area to which the actual user belongs, and may be designated and classified in units of country, region, and city. The reference area information may include information on various events, events, and the environment associated with the location of the area, such as closer rivers, stronger winds, more snow in winter, and more yellow sand. .. Further, when there is an event unique to the area such as the Aomori Nebuta Festival, such a regional event may be added to the reference area information. In addition, traffic information and natural environment information in this area are also included in this reference area information.

In the example of FIG. 7, it is assumed that the input data is, for example, reference trading history information P01 to P03 and reference area information P18 to 21. The intermediate node shown in FIG. 7 is a combination of the reference trading history information and the reference area information as such input data. Each intermediate node is further linked to the output. In this output, the degree of demand as an output solution is displayed.

Each combination (intermediate node) of the reference trading history information and the reference area information is associated with each other through three or more levels of association with the demand level as this output solution. The reference trading history information and the reference area information are arranged on the left side via the degree of association, and the demand degree is arranged on the right side via the degree of association. The degree of association indicates the degree of high relevance to the degree of demand with respect to the reference trading history information and the reference area information arranged on the left side. In other words, this degree of association is an index indicating what kind of demand the reference trading history information and the reference area information are likely to be associated with, and from the reference trading history information and the reference area information. It shows the accuracy in selecting each of the most probable demand levels. In addition to trading history information, the degree of demand changes depending on the various natural environments in the area and the type and scale of local events. Therefore, the optimum demand level is searched for by combining the reference trading history information and the reference area information.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 7. That is, the search device 2 accumulates past data as to which of the reference trading history information, the reference area information, and the demand level in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 7 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference trading history information P01 and the reference area information P20, the degree of demand is analyzed from the past data. Further, the degree of association shown in FIG. 7 may be composed of the nodes of the neural network in artificial intelligence.

In the example of the degree of association shown in FIG. 7, the node 61b is a node in which the reference area information P18 is combined with the reference trading history information P01, and the degree of association of the demand degree C is w15 and the degree of association of the demand degree E. The degree is w16. The node 61c is a node that is a combination of the reference area information P19 and P21 with respect to the reference trading history information P02, and the degree of association of the demand degree B is w17 and the degree of association of the demand degree D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned trading history information, the user's regional information is acquired. The area information corresponds to the reference area information. The user who acquires this area information and the user who acquires the above-mentioned reference area information may be the same or different from each other.

The demand level is searched based on the newly acquired trading history information and regional information. In such a case, the degree of association shown in FIG. 7 (Table 1) acquired in advance is referred to. For example, when the newly acquired trading history information is the same as or similar to P02 and the area information is P21, the node 61d is associated via the degree of association. The node 61d is associated with a demand degree C of w19 and a demand degree D of a degree of association w20. In such a case, the demand degree C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the demand degree D in which the degree of association itself is recognized although the degree of association is low may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

FIG. 8 shows an example in which the combination of the above-mentioned reference trading history information, the reference trading behavior information, and the degree of demand for the combination are set to three or more levels of association.

The reference trading behavior information includes all actions and behaviors for the user to buy and sell on the EC site as information. The reference trading behavior information is one or more of the product browsing history on the EC site, the history of the page of the clicked product, the history of the product specified as the purchase schedule, the cancellation history of the ordered product, and the return history of the ordered product. It may be composed of. For the product browsing history on the EC site, for example, a search keyword may be acquired. Further, it may be acquired via the browsing time of each product, the bounce rate (the rate of jumping to another site without seeing other incidental information), the repeat browsing rate, and the like. As the history of the page of the clicked product, the history of the page of the actually searched product or the page of the actually clicked product may be acquired. The history of the product designated as the purchase schedule may be acquired from the contents of the so-called shopping cart designated by the user as the purchase schedule and the history thereof. In addition, the cancellation history of the ordered product is acquired from the history of subsequently canceling the ordered product. In addition, the return history is acquired from the history of returning the product once purchased. On the contrary, such cancellation history and return history may be scored and learned in the direction of lower demand.

The reference trading behavior information is any one of the product browsing history on the EC site, the page history of the clicked product, the history of the product specified as the purchase schedule, the cancellation history of the ordered product, and the return history of the ordered product. It may be indicated by a score obtained by weighting and adding one or more explanatory variables.

In addition, the reference trading behavior information may be included in the information when the behavior of selling for sale is taken through the EC site. In such a case, it is possible to make the degree of association work so as to lower the demand level by assuming that the demand is low by that amount.

In the example of FIG. 8, it is assumed that the input data is, for example, reference trading history information P01 to P03 and reference trading behavior information P18 to 21. The intermediate node shown in FIG. 8 is a combination of reference trading history information and reference trading behavior information as such input data. Each intermediate node is further linked to the output. In this output, the degree of demand as an output solution is displayed.

Each combination (intermediate node) of the reference trading history information and the reference trading behavior information is associated with each other through three or more levels of association with the demand level as this output solution. The reference trading history information and the reference trading behavior information are arranged on the left side via the degree of association, and the demand degree is arranged on the right side via the degree of association. The degree of association indicates the degree of high relevance to the degree of demand with respect to the reference trading history information and the reference trading behavior information arranged on the left side. In other words, this degree of association is an index indicating what kind of demand the reference trading history information and the reference trading behavior information are likely to be associated with, and the reference trading history information and the reference trading behavior. It shows the accuracy in selecting each demand level that is most likely from the information. In addition to trading history information, the degree of demand changes depending on what the trading behavior is. Therefore, the optimum demand level is searched for by combining the reference trading history information and the reference trading behavior information.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference trading history information, the reference trading behavior information, and the demand level in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 8 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference trading history information P01 and the reference trading behavior information P20, the degree of demand is analyzed from the past data. Further, the degree of association shown in FIG. 8 may be composed of the nodes of the neural network in artificial intelligence.

In the example of the degree of association shown in FIG. 8, the node 61b is a node that is a combination of the reference trading history information P01 and the reference trading behavior information P18, and the degree of association of the demand degree C is w15 and the degree of demand E. The degree of association is w16. The node 61c is a node that is a combination of the reference trading behavior information P19 and P21 with respect to the reference trading history information P02, and the degree of association of the demand degree B is w17 and the degree of association of the demand degree D is w18. ..

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned trading history information, the user's trading behavior information is acquired. The trading behavior information corresponds to the trading behavior information for reference. The user who acquires the trading behavior information and the user who acquires the above-mentioned reference trading behavior information may be the same or different from each other.

The demand level is searched based on the newly acquired trading history information and trading behavior information. In such a case, the degree of association shown in FIG. 8 (Table 1) acquired in advance is referred to. For example, when the newly acquired trading history information is the same as or similar to P02 and the trading behavior information is P21, the node 61d is associated via the degree of association. The node 61d is associated with a demand degree C of w19 and a demand degree D of a degree of association w20. In such a case, the demand degree C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the demand degree D in which the degree of association itself is recognized although the degree of association is low may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In the present invention, as an alternative to the reference sales promotion event information shown in FIG. 6, the following reference external environment information is learned by being associated with the degree of association by a combination of reference trading history information. There may be.

The reference external environment information referred to here is various information related to the external environment information. The external environmental information here is economic data (GDP, employment statistics, mining and industry production index, capital investment, labor force survey, etc.), household data (household consumption situation survey, household data, average working hours per week, savings amount). Statistical data, annual income statistical data, etc.), real estate data (office vacancy rate, unit price per tsubo, rent market, land price, vacant house data, etc.), natural environment data (disaster data, temperature data, precipitation data, wind direction data, humidity data, etc.) Etc.). The external environment information includes all information external to the user, in addition to the information that reflects a part or all of these data. The external environment information for reference may be categorized as the external environment itself. For example, the classification may be performed by separating the data in the employment statistics. Further, it may be categorized by a pattern (for example, a pattern of whether the growth rate of GDP increases rapidly or gradually) or the like.

In such a case, a combination having the reference trading history information and the reference external environment information, and the degree of association with the demand degree in three or more stages are acquired in advance. At the time of actual judgment, the external environment information about the outside of the user who is the target of the new demand forecast is acquired. This external environment information corresponds to the above-mentioned reference external environment information, and the acquisition method is also the same. Then, after referring to the degree of association, the degree of demand is determined in the same manner as described above based on the newly acquired trading history information and the external environment information.

In the present invention, as an alternative to the reference sales promotion event information shown in FIG. 6, the reference market condition information shown below is learned by being associated with the degree of association by a combination of reference trading history information. You may.

Reference market information is various information related to market conditions. The market conditions referred to here may target any range including one company, the entire industry including the company, the entire Japan, and the entire world. Examples of this reference market information include interest rates, exchange rates, stock prices of each stock, crude oil, futures, precious metals, and price movements of Bitcoin. This reference market information may be displayed as a time-series chart, a line graph, or the like for these targets. Further, information such as Bollinger band, MACD, moving average line and the like may be attached. In addition, this market information may include fundamental indicators of each brand's company, and in addition to annual sales, profits, and costs, PER (price-earnings ratio), PBR (price-to-book value ratio), ROE ( Indicators such as return on equity) may be included. Information such as a chart showing price movements between currencies, Bollinger Bands, MACD, and moving averages may be attached to the exchange rate. The reference market information may categorize the market potential itself. The reference market information may be classified by type. For example, the market information may be classified according to whether or not the growth rate of the stock price is 0% or more per year. Further, it may be categorized by a pattern (for example, a pattern of whether the growth rate of the stock price increases rapidly or gradually) or the like.

In addition, this reference market condition information may include the market price of the product traded on the EC site. For example, when a used item is put up for sale on an EC site and sold, the price and market price are included in this reference market information.

In such a case, the combination of the reference trading history information and the reference market condition information and the degree of association with the demand degree in three or more stages are acquired in advance. At the time of actual determination, the market condition information at the time of acquisition of the trading history information is acquired. This market condition information corresponds to the above-mentioned reference market condition information, and the acquisition method is also the same. Then, after referring to the degree of association, the degree of demand is determined in the same manner as described above based on the newly acquired trading history information and the market condition information.

In addition to the trading history information, if any two or more of attribute information, sales promotion event information, trading behavior information, market condition information, external environment information, regional information, etc. are acquired, the two or more information to be acquired will be used. Reference trading history information for two or more reference information (reference attribute information, reference sales promotion event information, reference market condition information, reference external environment information, reference area information, reference trading behavior information, etc.) By creating learning data consisting of three or more levels of association between the combination of and the demand for the combination, it is possible to search for a solution of the credit rating in the same manner.

In addition to the trading history information, in addition to any one or more of attribute information, sales promotion event information, trading behavior information, market condition information, external environment information, regional information, etc., the same applies when acquiring other information. , Reference attribute information according to the acquired information, reference sales promotion event information, reference market condition information, reference external environment information, reference area information, reference trading behavior information, etc., according to other information to be acquired The solution search can be performed in the same manner by creating learning data consisting of three or more levels of association between the combination with the reference information and the demand for the combination.

Further, as shown in FIG. 9, the present invention determines each demand degree based on the degree of association of two or more types of information, the reference information U and the reference information V. This reference information U is reference trading history information, and reference information V is other reference information (reference attribute information, reference sales promotion event information, reference market information, reference external environment information, reference area). Information, trading behavior information for reference, etc.).

At this time, as shown in FIG. 9, the output obtained for the reference information U may be used as the input data as it is, and may be associated with the output via the intermediate node 61 in combination with the reference information V. For example, for the reference information U, after the output solution is output, this may be used as an input as it is, and the output may be searched by using the degree of association with other reference information V.

Second Embodiment Hereinafter, the second embodiment will be described. In executing this second embodiment, the distribution base product delivery planning system 1, the information acquisition unit 9, the search device 2, and the database 3 used in the first embodiment are similarly used. The description of each of these configurations will be omitted below by quoting the description of the first embodiment.

In the second embodiment, the delivery of the product to the distribution base forming the distribution network 100 with the user who uses the EC site is planned.

At the distribution base, the demand level searched for each user is collected. As a result, as shown in FIG. 10, it is possible to acquire the degree of demand for each product in the entire network 100 centered on the distribution base. By the way, this network 100 may be divided into regions, and delivers products to users who have a place of residence or an address in the region. The demand level in each user may be obtained based on the method described in the first embodiment, or may be obtained based on other well-known methods without being limited to this. good. In this way, demand is collected from all or some users in the network 100.

In this way, as shown in FIG. 11, the demand degree of the entire network is acquired for each of the networks 100a to 100d of each distribution base. Data on the demand level of the networks 100a to 100d of each distribution base is collected. The demand level collected in advance in this way is called the reference demand level. At the same time, the quantity of the goods actually delivered from the wholesaler 111 is measured at the distribution base. In the example of FIG. 11, the quantity of goods actually delivered from the wholesaler 111 to the distribution base having each network 100a to 100d is measured. The measurement of delivery of this product may be configured in any time unit such as daily unit, monthly unit, weekly unit, and the like. In the second embodiment, the quantity of each product delivered to each distribution base is described in this delivery plan. In other words, a data set is created between the reference demand level and the delivery plan (quantity of delivery of each product to each distribution base).

In the second embodiment, for example, as shown in FIG. 12, it is premised that three or more levels of association between the reference demand degree and the delivery plan of the product to the distribution base are set and acquired in advance. ..

The reference demand may be ranked as "large", "very large", "normal", "small", "very small", etc., or displayed as a score, with 1000 points being the most. It may be indicated that the demand is high and 0 point is the lowest demand. The most demanded score may be any score other than 1000 points. Further, if the reference demand degree is represented by two values of demand and no demand, the two values may be trained as a data set of the output solution. The reference demand may be obtained by evaluating with a standard provided for each network 100, or may be evaluated with a demand evaluated by a unified standard. In addition to the artificially evaluated demand, the reference demand may be calculated based on, for example, the number of subsequent orders. In other words, it can be determined that the larger the number of orders placed after the product is sold or after it remains in inventory, the higher the demand, while the smaller the number of orders placed at that timing, the lower the demand. Can be determined. As an alternative to this reference demand, the total number of orders from each user for the entire network 100 may be output for each product. In such a case, it is premised that the number of orders for each product is learned as output data.

In other words, through this reference demand and the data set of the delivery plan, the actual quantity of the delivered goods of each product can be known from various data generated in the reference demand.

In the example of FIG. 12, it is assumed that the input data is, for example, reference demand α to β. The reference demand as such input data is linked to the output. In this output, the delivery plan as the output solution is displayed.

The reference demands are associated with each other through three or more levels of association with the delivery plan as the output solution. Reference demand is arranged on the left side through this degree of association, and delivery plans are arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to which delivery plan is higher than the degree of demand for reference arranged on the left side. In other words, this degree of association is an indicator of what delivery plan each reference demand is likely to be associated with, and is used to select the most probable delivery plan from the reference demand. It shows the accuracy. In the example of FIG. 11, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the delivery plan as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the delivery plan as an output.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to whether the reference demand degree or the delivery plan in that case is adopted in determining the actual search solution, and analyzes and analyzes these. By doing so, the degree of association shown in FIG. 11 is created.

This analysis and analysis may be performed by artificial intelligence. The degree of association shown in FIG. 12 may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In such a case, as shown in FIG. 13, the reference demand degree is input as input data, each delivery plan is output as output data, and at least one or more hidden layers are provided between the input node and the output node. , Machine learning may be made. On the contrary, the demand level may be input and the reference sales history information may be output.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, in actually planning a new delivery plan from now on, the delivery plan will be proposed using the above-mentioned learned data. In such a case, the recent demand level of each network 100 that actually newly determines the delivery plan is acquired. This demand level is composed of the same type of data as the reference demand level described above, and the acquisition method is also the same.

The newly acquired demand level is input by the information acquisition unit 9 described above. The information acquisition unit 9 may acquire such a demand degree as electronic data. That is, the latest demand level is obtained when formulating a delivery plan, and the method for acquiring the demand level may be the method of the first embodiment or any other method. good.

Based on the newly acquired demand level in this way, a delivery plan that is likely to be actually determined for the demand level is searched for. In such a case, the degree of association shown in FIG. 12 (Table 1) acquired in advance is referred to, and the method for deriving a specific search solution with reference to this is the same as in the first embodiment.

In this way, it is possible to search for a delivery plan to be determined from the newly acquired demand level and display it to the user (logistics system operator, user's shop owner, employee, consultant, logistics person). By looking at this search result, the user can grasp the delivery quantity of each product to each distribution base included in the delivery plan. If the delivery quantity of each product to each distribution base can be grasped, the wholesaler can grasp what kind of product should be delivered to each distribution base in what quantity, and via the delivery vehicle. It can be used as the basis for planning for delivery. In practice, this plan may also be created automatically, between the delivery quantity of each product to the distribution base and the delivery schedule information of the past delivery vehicle, which is output through the output delivery plan. A data set may be created in the above, and a solution search may be performed in the same manner as in FIG. 12 based on the degree of association between these three or more levels.

Further, in the second embodiment, the delivery plan of the product from the distribution base may be made to each user in the network 100. In such a case, as shown in FIG. 14, the demand for reference of the product by each user in the network of the distribution base acquired in advance and the delivery history of the delivery vehicle actually delivered from the distribution base to each user are obtained. Obtain 3 or more levels of association.

That is, in FIG. 14, as shown in FIG. 10, when focusing on one network 100, a delivery plan for products to each user in the network 100 is formulated. And this delivery history shows the delivery history by the actual delivery vehicle from the distribution base to each user, and shows what kind of product was delivered at what frequency and in what quantity. be.

In FIG. 14, the solution search is performed in the same manner. As a result, the delivery history can be obtained as an output solution. The delivery history shows the delivery history of the actual delivery vehicle from the distribution base to each user, and shows what kind of product was delivered at what frequency and in what quantity. Therefore, referring to this will help in formulating an actual delivery plan from the distribution base to each user.

FIG. 15 shows an example in which the delivery plan shown in FIG. 11 is determined by adding the reference demand degree and the reference market condition information in combination. That is, an example is shown in which a combination of the above-mentioned reference demand level, reference market condition information, and a delivery plan for the combination are set to three or more levels of association. The details of the reference market information are the same as those of the first embodiment. As shown in FIG. 12, the delivery plan referred to here is for the purpose of grasping the delivery quantity of each product to each distribution base, and if the delivery quantity of each product to each distribution base can be grasped, The wholesaler can grasp what kind of product should be delivered to each distribution base in what quantity, and can be used as a basis for making a plan for delivery via a delivery vehicle.

In the example of FIG. 15, it is assumed that the input data is, for example, reference demand α to γ and reference market information P18 to 21. The intermediate node shown in FIG. 14 is a combination of the reference market condition information with respect to the reference demand level as such input data. Each intermediate node is further linked to the output. In this output, the delivery plan as the output solution is displayed.

Each combination (intermediate node) of the reference demand level and the reference market condition information is linked to each other through three or more levels of linking to the delivery plan as this output solution. Reference demand and reference market information are arranged on the left side via this degree of association, and delivery plans are arranged on the right side via this degree of association. The degree of association indicates the degree of demand for reference arranged on the left side and the degree of relevance to the delivery plan with respect to the market information for reference. In other words, this degree of association is an index showing what kind of delivery plan the reference demand level and the reference market condition information are likely to be associated with, and the reference demand level and the reference market condition information. It shows the accuracy in selecting each delivery plan that is most probable from. In addition to the reference demand, the amount of goods required will change depending on the market conditions at that time, and the delivery plan will need to be changed accordingly. Therefore, the optimum delivery plan will be searched for by combining the reference demand level and the reference market condition information.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned demand level, the market condition information regarding the market condition at the time when the demand level is acquired is acquired. Then, the delivery plan is searched based on the newly acquired demand level and market condition information. Since this search method is the same as that of the first embodiment described above, the description of the first embodiment will be cited and the description below will be omitted.

In the second embodiment, as an alternative to the reference market condition information shown in FIG. 15, the reference external environment information shown below is learned by being associated with the degree of association by a combination of reference demand degrees. There may be. The reference external environment information referred to here is the same as that of the first embodiment.

In such a case, the degree of association with the reference demand degree, the combination having the reference external environment information, and the delivery plan in three or more stages is acquired in advance. At the time of actual judgment, external environment information is acquired. This external environment information corresponds to the above-mentioned reference external environment information, and the acquisition method is also the same. Then, after referring to the degree of association, the delivery plan is determined in the same manner as described above based on the newly acquired demand degree and the external environment information.

In the above-mentioned degree of association, the degree of association is expressed by a 10-step evaluation, but it is not limited to this, and it may be expressed by a degree of association of 3 or more levels, and conversely, it may be expressed by 3 or more levels. For example, 100 steps or 1000 steps may be used. On the other hand, this degree of association does not include those expressed in two stages, that is, whether or not they are related to each other, either 1 or 0.

According to the present invention having the above-described configuration, anyone can easily search for the demand level of a company considering financing without any special skill or experience. Further, according to the present invention, it is possible to determine the search solution with higher accuracy than that performed by a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Since the above-mentioned input data and output data often do not exist exactly the same in the process of learning, the input data and the output data may be classified by type. That is, the information P01, P02, ... P15, 16, ... That constitute the input data are classified according to the criteria classified in advance on the system side or the user side according to the content of the information, and the classified inputs. A data set may be created between the data and the output data and trained.

Further, according to the present invention, there is a feature that an optimum solution search is performed through the degree of association set in three or more stages. The degree of association can be described by, for example, a numerical value from 0 to 100% in addition to the above-mentioned 10 steps, but the degree of association is not limited to this, and any step can be described as long as it can be described by a numerical value of 3 or more steps. It may be configured.

Under the circumstances where there are multiple possible candidates for search solutions by determining the demand level with higher credibility and lower misunderstanding based on the degree of association expressed by such numerical values of three or more levels. In, it is also possible to search and display in descending order of the degree of association.

In addition to this, according to the present invention, it is possible to judge without overlooking the discrimination result of the output having an extremely low degree of association such as 1%. It warns the user that even a judgment result with an extremely low degree of association is connected as a slight sign and may be useful as the judgment result once every tens or hundreds of times. be able to.

Further, according to the present invention, there is an advantage that the search policy can be determined by the method of setting the threshold value by performing the search based on the degree of association of three or more stages. If the threshold value is lowered, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but it is unlikely that a more appropriate discrimination result can be detected favorably, and a lot of noise may be picked up. be. On the other hand, if the threshold value is raised, there is a high possibility that the optimum search solution can be detected with a high probability. Sometimes the solution is overlooked. It is possible to decide which one to prioritize based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided via a public communication network such as the Internet. In addition to market information, when event information, external environmental information, household information, real estate information, expert opinion information, and natural environment information knowledge, information, and data are acquired, the degree of association is increased or decreased accordingly. Let me.

In other words, this update corresponds to learning in the sense of artificial intelligence. It can be said that it is a learning act because it acquires new data and reflects it in the learned data.

In addition, this update of the degree of association is not based on the information that can be obtained from the public communication network, but is also updated by the system side or the user side based on the contents of research data, papers, conference presentations, newspaper articles, books, etc. by experts. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

Further, the process of first creating the trained model and the above-mentioned update may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of reading and learning the data set of input data and output data, information corresponding to the input data is read and trained, and the degree of association related to the output data is self-formed from there. You may let it.

1 Logistics base product delivery planning system 2 Search device 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimate unit 28 Storage unit 61 node

Claims (11)

In the distribution base product delivery planning program, which plans the delivery of products to distribution bases that form a distribution network with each user who buys and sells products using the EC site.
The first information acquisition step to acquire the demand level of the product by each of the above users in the network of the distribution base, and
Refer to the first degree of association of three or more stages between the reference demand for products at each delivery destination in the network of the distribution bases acquired in advance and the quantity of products actually delivered for the distribution base, and the above-mentioned first 1 A distribution base product delivery planning program characterized by having a computer execute a proposal step of proposing a plan for delivery of products to the distribution base based on the degree of demand acquired in the information acquisition step. In the distribution base product delivery planning program, which plans to deliver products from the distribution base to each user who buys and sells products using the EC site in the distribution network.
The first information acquisition step to acquire the demand level of products for each user in the network of the above distribution bases,
Refer to the first degree of association of three or more stages between the reference demand for products by each user in the network of the above distribution bases acquired in advance and the delivery history of the delivery vehicle actually delivered to each user from the distribution base. , A distribution base product characterized by having a computer execute a proposal step of proposing a delivery plan of the product from the distribution base to each user by a delivery vehicle based on the degree of demand acquired in the first information acquisition step. Delivery planning program. The second information acquisition step of acquiring the transaction history information regarding the transaction history of the product by the user, and
Referencing the second degree of association of three or more stages between the reference trading history information regarding the past trading history of the product by the user acquired in advance and the demand degree according to the number of subsequent orders for the product, the second Based on the trading history information acquired in the information acquisition step, it further has a determination step of determining the degree of demand for the product.
The distribution base product delivery planning program according to claim 1 or 2, wherein in the first information acquisition step, the demand degree of the product for each user determined in the determination step is acquired. The second information acquisition step of acquiring the transaction history information regarding the transaction history of the product by the user, and
Referencing the second degree of association of three or more stages between the reference trading history information regarding the past trading history of the product by the user acquired in advance and the demand degree according to the number of subsequent orders for the product, the second Based on the trading history information acquired in the information acquisition step, it further has a determination step of determining the degree of demand for the product.
The distribution base product delivery planning program according to claim 1 or 2, wherein in the above proposal step, the reference demand based on the demand of the product by each user determined in the determination step is used. In the second information acquisition step, the attribute information related to the user's attribute is further acquired.
In the determination step, the second degree of association of three or more stages of the combination having the reference trading history information and the reference attribute information regarding the user who has acquired the reference trading history information is referred to, and the second The distribution base product delivery planning program according to claim 3 or 4, wherein the degree of demand for the product is determined based on the sales history information and the attribute information acquired in the information acquisition step. In the second information acquisition step, the market information regarding the market conditions at the time when the trading history information is acquired is further acquired.
In the determination step, the second degree of association of three or more stages of the combination having the reference trading history information and the reference market condition information regarding the market condition at the time when the reference trading history information is acquired is referred to, and the above is described. The distribution base product delivery planning program according to claim 3 or 4, wherein the degree of demand for the product is determined based on the sales history information and the market condition information acquired in the second information acquisition step. In the second information acquisition step, the external environment information regarding the external environment at the time when the trading history information is acquired is further acquired.
In the determination step, the second degree of association of three or more stages of the combination having the reference trading history information and the reference external environment information regarding the external environment at the time when the reference trading history information is acquired is referred to. The distribution base product delivery planning program according to claim 3 or 4, wherein the degree of demand for the product is determined based on the trading history information acquired in the second information acquisition step and the external environment information. In the second information acquisition step, the trading behavior information regarding the trading behavior of the user on the EC site is further acquired.
In the determination step, the second association of three or more stages of the combination having the reference trading history information and the reference trading behavior information regarding the trading behavior of the user who has acquired the reference trading history information on the EC site. The distribution base product delivery according to claim 3 or 4, wherein the degree of demand for the product is determined based on the trading history information and the trading behavior information acquired in the second information acquisition step. Planning program. In the first information acquisition step, the market information regarding the market conditions at the time when the demand level is acquired is further acquired.
In the above proposal step, the first connection degree of three or more stages of the combination having the reference demand degree and the reference market condition information regarding the market condition at the time when the reference demand degree is acquired is referred to, and the first The distribution base product delivery planning program according to claim 1, wherein a plan is proposed for delivery of products to the distribution base based on the demand level and market condition information acquired in the information acquisition step. In the first information acquisition step, the external environment information regarding the external environment at the time when the demand degree is acquired is further acquired.
In the above proposal step, the first degree of association of three or more stages of the combination having the reference demand degree and the reference external environment information regarding the external environment at the time when the reference demand degree is acquired is referred to, and the above is described. The distribution base product delivery planning program according to claim 1, wherein a plan is proposed for delivery of products to the distribution base based on the demand level acquired in the first information acquisition step and external environmental information. The distribution base product according to any one of claims 1 to 8, wherein in the above proposed step, the first degree of association corresponding to the weighting coefficient of each output of the node of the neural network in artificial intelligence is used. Delivery planning program.

Images