JP2005275969A - Method, program and system for optimizing assortment of bookstore - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To efficiently optimize the assortment of a bookstore according to the demands of a region, and to guide a customer who is trying to purchase a book to a store where the book is in stock. <P>SOLUTION: When a customer retrieves information related with a book through a network, which region's bookstore is the customer trying to use is grasped, and not only information related with the book but also the store of the region and the stock status of the book in the bookstore are displayed on a screen. Also, which book is related with the information, and when the information has been referred to, and which region's bookstore is the customer who has referred to the information trying to use are recorded, and the demands of the book in the region where the bookstore exists are predicted by using the records, and the assortment of the bookstore is optimized. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to an assortment optimizing method, an assortment optimizing program, and an assortment optimizing system for managing inventory of books in a bookstore, and in particular, it is possible to efficiently optimize assortment according to local demand. The present invention relates to an assortment optimization method, an assortment optimization program, and an assortment optimization system.

  Traditionally, bookstore inventory management has been based on the experience and intuition of purchasers. Particularly in small bookstores, the area where books can be displayed is limited, and it is very important in terms of management to purchase books that are sold in an appropriate amount and quickly return books that are no longer sold. There are currently over 50,000 new books published annually. It is very difficult to identify the best-selling products, and bookstores often tend to miss sales opportunities or generate excessive inventory.

  The materials used by bookstore purchasers to decide whether or not to purchase a new book at their own store include the name of the book's author and the reputation of the book. Books written by celebrities and popular authors can easily attract customers' attention and can be expected to sell well. In addition, books that have been highly evaluated in the reviews of newspapers and magazines should be highly interested in the customers who read the reviews, and can be expected to sell well.

  In the past, book reviews were mostly published in newspapers and magazines, and due to space limitations, there was a tendency to focus on the publications of major publishers and celebrity works. In recent years, with the spread of the Internet, a service for publishing information related to a large number of books on a network has been realized as disclosed in Patent Document 1, and even for books of small publishers that previously had difficulty obtaining information. Book reviews written by readers can be read. Effective use of information relating to books on these networks is very useful for reducing the burden on the person in charge of purchasing the bookstore and stabilizing the management of the bookstore.

JP 2001-256550 A

  However, when purchasing books, there is a problem that the locality of the bookstore location must be considered. For example, books related to agriculture sell well in rural areas, and academic books sell well around universities. Also, even books that are bestsellers across the country often shift in demand in urban and rural areas. Unless the regional characteristics are fully taken into consideration and reflected in the purchase, the assortment of bookstores cannot be optimized.

  Another problem is that even if books that are of great interest to customers are arranged on the bookshelf in the bookstore, sales opportunities will be missed if the target customers do not know the inventory of the books. . The problem is that from the customer's point of view, even if you have a book you want, you don't know the bookstore that sells it, so you have to spend time and effort searching through several bookstores. It is also a big problem for customers. In order to solve such a problem, it is very important to be able to inform local customers about the inventory of the bookstore in some way.

  The present invention has been made to solve the above-described problems caused by the prior art, and can efficiently optimize the assortment of bookstores according to local demand, and customers who are trying to purchase books. It is an object of the present invention to provide a method for guiding a book to a store where the book is in stock.

  In order to solve the above-described problems and achieve the object, an assortment optimization method according to the invention of claim 1 is an assortment optimization method for managing inventory of books in a bookstore, and is performed by a customer via a network. A reference history recording step (corresponding to the reference history recording unit 14 in FIG. 1) for recording a reference history when information relating to a predetermined book is searched, and the reference history recorded by the reference history recording step An optimal inventory prediction process (corresponding to the optimal inventory prediction unit 15 in FIG. 1) for predicting the optimal stock quantity of a predetermined book at a predetermined bookstore based on the book, and the book at the bookstore predicted by the optimal inventory prediction process Order return processing step (corresponding to the order return processing unit 16 in FIG. 1) which compares the optimum stock quantity of the book and the stock quantity of the book located at the bookstore to determine the order quantity or return quantity of the book And it said that it contained and that).

  According to the first aspect of the present invention, a situation in which a customer refers to information related to a book via a network is recorded, and the demand for the book is predicted by ordering and returning the goods by analyzing the record. Therefore, it is possible to reduce the burden on the person in charge of purchasing the book store related to the purchase of books, and to manage the inventory efficiently.

  The assortment optimizing method according to the invention of claim 2 is the method of optimizing the assortment according to claim 1, wherein the reference history recording step is performed when a customer reviews information on a book review of a predetermined book. Information relating to geographical characteristics is recorded as a reference history in association with information relating to the book, and the optimum inventory forecasting step relates to the location of a predetermined bookstore and the geographical characteristics of the customer recorded in the reference history The optimum inventory quantity of a predetermined book in the bookstore is predicted based on the reference history by the customer including the location of the bookstore in geographical characteristics by collating with the information.

  According to the invention of claim 2, only records of references made by customers who may use a bookstore to be predicted from a geographical viewpoint, among records of situations in which customers refer to information related to books via a network. Thus, it is possible to predict the optimal inventory of books, so that it is possible to predict the demand of each book in the bookstore area and optimize the bookstore inventory according to the local demand.

  According to a third aspect of the present invention, there is provided the assortment optimization method according to the first or second aspect of the invention, wherein the search processing step is performed when the customer searches for information related to a predetermined book. A bookstore located in an area suitable for geographical characteristics and a stock status of the book in the bookstore are displayed on the terminal of the customer.

  According to the invention of claim 3, when the customer searches for information, in addition to the information related to the book, it exists in the area where the customer lives, the area visited frequently, the area where he / she is going, or the area where he / she wants to go Since the information related to the bookstores and the stock status of the books at those bookstores are also displayed, the customer can be guided to the store where the books are stocked.

  A program according to the invention of claim 4 is an assortment optimization program for managing inventory of books in a bookstore, and a reference history when information relating to a predetermined book is searched by a customer via a network. A reference history recording step of recording a record, an optimal inventory prediction step of predicting an optimal stock quantity of a predetermined book in a predetermined bookstore based on the reference history recorded by the reference history recording step, and the optimal inventory prediction step An order return processing step of determining the number of orders or returns of the book by comparing the optimal stock quantity of the book at the bookstore predicted by the above and the inventory quantity of the book located at the bookstore. Features.

  According to the invention of claim 4, a situation in which a customer refers to information related to a book via a network is recorded, and the demand for the book is predicted by ordering and returning by analyzing the record. Therefore, it is possible to reduce the burden on the person in charge of purchasing the book store related to the purchase of books, and to manage the inventory efficiently.

  An assortment optimizing system according to the invention of claim 5 is an assortment optimizing system for managing inventory of books in a bookstore, and a customer searches for information relating to a predetermined book via a network. Reference history recording means for recording a reference history, optimal stock prediction means for predicting an optimal stock quantity of a predetermined book in a predetermined bookstore based on the reference history recorded by the reference history recording means, Order return processing means for comparing the optimal stock quantity of the book in the bookstore predicted by the optimal stock prediction means and the inventory quantity of the book located in the bookstore to determine the number of orders or returns of the book. It is characterized by having.

  According to the invention of claim 5, the situation in which the customer refers to the information related to the book via the network is recorded, and the demand for the book is predicted by ordering and returning the goods by analyzing the record. Therefore, it is possible to reduce the burden on the person in charge of purchasing the book store related to the purchase of books, and to manage the inventory efficiently.

  As described above, according to the invention of claim 1, a situation in which a customer refers to information related to a book via a network is recorded, and the demand of the book is predicted by analyzing the record. Therefore, it is possible to obtain an assortment optimization method capable of reducing the burden on the person in charge of purchasing a bookstore related to purchasing books and efficiently performing inventory management.

  Further, according to the invention of claim 2, the record of the reference by the customer who may use the bookstore to be predicted from the geographical viewpoint among the records of the situation in which the customer referred to the information related to the book through the network. Is extracted to predict the optimal inventory of books, so it is possible to predict the demand for each book in a bookstore area and optimize the bookstore inventory to the local demand There is an effect that an assortment optimization method can be obtained.

  According to the invention of claim 3, when the customer searches for information, in addition to the information related to the book, the bookstore is located in the area where the customer often visits, the area where the customer is present, or the area where he / she is going. Since the information and the inventory status of the books at those bookstores are also displayed, there is an effect that an assortment optimization method capable of guiding the customer to the store where the books are stocked can be obtained.

  Further, according to the invention of claim 4, the situation in which the customer refers to the information related to the book via the network is recorded, and the demand of the book is predicted by ordering and returning the goods by analyzing the record. As a result, it is possible to obtain a program that can reduce the burden on the person in charge of purchasing a bookstore related to the purchase of books, and can efficiently manage inventory.

  According to the invention of claim 5, the situation in which the customer refers to the information related to the book via the network is recorded, and the demand of the book is predicted by ordering and returning the goods by analyzing the record. Therefore, it is possible to obtain an assortment optimization system that can reduce the burden on the person in charge of purchasing a book store related to the purchase of books, and can efficiently perform inventory management.

  Exemplary embodiments of a bookstore assortment optimization method, an assortment optimization program, and an assortment optimization system according to the present invention will be described below in detail with reference to the accompanying drawings.

  First, the system configuration of the bookstore assortment optimization system according to the present embodiment will be described. FIG. 1 is a functional block diagram showing a system configuration of a bookstore assortment optimization system according to the present embodiment.

  As shown in the figure, the bookstore assortment optimization system includes a server 10, customer terminals 21 to 24, bookstore terminals 31 to 32, a book wholesale center 40, and a book return center 50. Connected through. Note that the bookstore terminals 31 to 32, the book wholesale center 40, and the book return center 50 do not necessarily need to be connected to the server 10 via the Internet line, but are unique networks such as VAN for the publishing and distribution industry. You may be connected through.

  The server 10 is roughly divided into two different functions for bookstores and customers. Specifically, the server 10 has a function of inventory management for bookstores and a function of providing information related to books and bookstores for customers. The server 10 includes an interface unit 11, a storage unit 12, a search processing unit 13, a reference history recording unit 14, an optimal inventory prediction unit 15, an order return processing unit 16 and a sales management processing unit 17.

  The interface unit 11 is a network interface for performing data communication with customer terminals 21 to 24 and the like connected via a network. The storage unit 12 is a storage unit that stores information relating to books, bookstores, etc., and includes a customer DB 12a, a bookstore DB12b, a book DB12c, a book review DB12d, a reference history 12e, a bookstore inventory DB12f, and a purchase history 12g. The bookstore customer DB 12h is stored.

  The search processing unit 13 is a processing unit that accepts browsing requests for information relating to books from the customer terminals 21 to 24, searches the storage unit 12 for requested information, and returns search results to the requesting terminal as a response. . Information searched by the search processing unit 13 includes the book name, author, publisher, price, book review, and the like of the book.

  The reference history recording unit 14 is a processing unit that stores, in the reference history 12 e of the storage unit 12, when information related to which book is browsed by the customer terminals 21 to 24. The information stored in the reference history recording unit 14 includes information related to the geographical characteristics of the referred customer.

  The optimal stock prediction unit 15 is a processing unit that analyzes the reference history accumulated by the reference history recording unit 14 and predicts the optimal stock of each book in each bookstore. The optimal inventory predicting unit 15 extracts the reference history of a customer who may use the bookstore to be predicted from the reference history 12e, performs aggregation processing, adds various weights to the aggregation result, and calculates each book in the bookstore. Predict the optimal inventory.

  The order return processing unit 16 compares the optimum stock of each book predicted by the optimum stock prediction unit 15 with the stock in the actual bookstore, and when the actual stock does not exist or the actual stock is insufficient. A processing unit that performs ordering processing to the book wholesale center and performs return processing to the book return center when the actual stock is excessive.

  In addition, although it is an actual ordering and return method, the server 10 creates ordering information and return information, and directly transmits the information to the book center and the book return center. A semi-automatic way to create ordering information and return information and provide it to the purchasers at each bookstore, and make corrections to the provided information, and the purchasers at each bookstore place orders and returns Either method may be used. Further, two methods may be used in combination, such as classifying books according to the number of sales, books with high sales being processed fully automatically, and books with low sales being processed semi-automatically.

  The sales management processing unit 17 is a processing unit that stores the inventory status and sales status of each book at each bookstore in the storage unit 12. The sales management processing unit 17 reflects the information received from the order return processing unit 16 and each bookstore in the bookstore inventory DB 12f, the purchase history 12g, and the bookstore customer DB 12h. The sales management processing unit 17 may acquire any stock status and sales status from each bookstore.

  Next, information stored in the storage unit 12 illustrated in FIG. 1 will be described in detail. FIG. 7 is a diagram illustrating an example of a data structure of the customer DB 12a illustrated in FIG. The customer DB 12a is for registering customer information for searching for information related to books via a network. In addition to an ID (customer number), name, address, etc. for identifying a customer, It is characterized by holding information (candidate sites 1 to n) relating to the geographical characteristics of customers. In candidate sites 1 to n, one or more regions where the customer is likely to use the bookstore are registered, such as the customer's residence, work location, commuting route, and frequently visited commercial location.

  A customer's geographic characteristics refer to the area in which the customer lives, the area they visit frequently, the area they are currently in, or the area they are about to go to. This geographic characteristic can identify bookstores available to customers. Conversely, customers who may use a bookstore can be identified by the geographical characteristics of the customers.

  In the system, it is not necessary for all customers to be registered in the customer DB 12a, and a customer who is not registered in the DB may be able to search for information related to books via the network.

  FIG. 8 shows an example of the data structure of the bookstore DB 12b shown in FIG. The bookstore DB 12b is for registering information relating to the bookstore. In addition to the ID (bookstore number), bookstore name, address, etc. for identifying the bookstore, information related to the bookstore location (location 1 to location) n) is a feature. Since the location of the bookstore may span a plurality of trade areas, it is preferable that the data structure be such that a plurality of locations can be registered for one bookstore.

  FIG. 9 is a diagram illustrating an example of a data structure of the book DB 12c illustrated in FIG. The book DB 12c is for registering information other than book reviews among information related to books, and holds information such as an ID (book number) for identifying a book, a book name, and an author.

  FIG. 10 is a diagram showing an example of the data structure of the book review DB 12d shown in FIG. The book review DB 12d is for registering information relating to a book review, and includes information such as an ID (book review number) for identifying a book review, a book number of a book targeted by the book review, an evaluation score, and a text of the book review. Hold.

  FIG. 11 is a diagram illustrating an example of a data structure of the reference history 12e illustrated in FIG. The reference history 12e is history information that is added one by one each time a customer refers to information related to a book via the network. The book number of the book referred to by the customer, the book review number of the book review referred to by the customer, In addition to the customer number of the referenced customer, the date of reference, etc., the information (region 1 to region n) relating to the geographical characteristics of the referenced customer is retained. In areas 1 to n, the area where the customer lives, the frequently visited area, the current area, or the area to be visited is stored. When a customer who is not registered in the customer DB 12a refers to information related to a book, a reference history with a blank customer number is created.

  FIG. 12 is a diagram showing an example of the data structure of the bookstore inventory DB 12f shown in FIG. The bookstore inventory DB 12f is for managing the number of books in each bookstore, and includes information such as the bookstore number, the book number, the number of books in the bookstore indicated by the bookstore number, and the latest arrival date. Hold. This information is mainly updated by the sales management processing unit 17.

  FIG. 13 is a diagram showing an example of the data structure of the purchase history 12g shown in FIG. The purchase history 12g is history information that is added one by one each time a customer registered in the customer DB 12a purchases a book, and the bookstore number of the bookstore where the customer purchased the book, the customer number of the purchased customer, It holds information such as the book number and purchase date of the book. This information is mainly updated by the sales management processing unit 17.

  FIG. 14 is a diagram illustrating an example of a data structure of the bookstore customer DB 12h illustrated in FIG. The bookstore customer DB 12h is for registering information on customers who have purchased books at the bookstore. The bookstore number of the bookstore where the customer purchased the book, the customer number of the purchased customer, and finally the book. Holds information such as date of purchase. This information is updated mainly by the sales management processing unit 17 based on the purchase history 12g.

  Next, a processing procedure for assortment optimization of the server 10 shown in FIG. 1 will be described. FIG. 2 is a flowchart showing a processing procedure for assortment optimization of the server 10 shown in FIG. As shown in the figure, the server 10 first acquires information related to the bookstore to be processed from the bookstore DB 12b (step S201). The information acquired here includes information on the location of the bookstore.

  Subsequently, the server 10 acquires, from the reference history 12e, a situation in which the customer refers to information related to the book via the network (step S202). Although the reference history of customers in various regions is recorded in the reference history 12e, only the reference history of customers who may use the bookstore to be processed is extracted here. Specifically, a reference history in which at least one of the regions (region 1 to region n) recorded as the geographical characteristics of the referred customer matches the bookstore location is extracted.

  Then, the server 10 predicts the optimum stock of each book in the bookstore to be processed from the acquired reference history (step S203). This prediction processing procedure will be described in another section with reference to FIG. Further, the server 10 acquires the stock status of each book at the bookstore to be processed (step S204), compares the predicted optimum stock with the stock, determines the number of orders or returns (step S205), and returns the order. Processing is executed (step S206).

  Next, the processing procedure for predicting optimum stock in the processing procedure for assortment optimization of the server 10 shown in FIG. 2 will be described. FIG. 3 is a flowchart and sample data showing a processing procedure for predicting the optimal stock in the processing procedure for assortment optimization of the server 10 shown in FIG. Note that the formulas and coefficients used here are used as an example for convenience of explanation, and in fact, customers who search for information related to books via the bookstore sales scale or network It is necessary to be able to set flexibly in consideration of the increase or decrease of

  As shown in the figure, the server 10 first obtains an evaluation score of a book review from the book review DB 12d as a parameter for weighting the reference count (step S301), and further purchases a book at a bookstore to be predicted. Is acquired from the bookstore customer DB 12h (step S302).

  Then, the server 10 classifies the reference history acquired in step S202 of FIG. 2 according to the time when the reference is made and the presence / absence of purchase in the bookstore, and counts the number of references (step S303). If the customer recorded in the reference history exists among the customers with the purchase record acquired in step S302, the reference history is determined to be from the customer with the purchase record, and if not, the reference is made. It is determined that the history is from a customer who has no purchase record. When the customer number is not recorded in the reference history, it is assumed that the reference history is from a customer who has no purchase record. 303a is a sample of the result of the counting, and the number of reference histories is divided into two periods, the last 30 days (hereinafter referred to as “current month”) and the previous 30 days (hereinafter referred to as “previous month”). It is a count for each presence or absence.

  Subsequently, the server 10 weights the reference count by increasing or decreasing the reference count (step S304). Books whose number of references has increased are also in demand for customers, and conversely, books whose numbers of references have decreased are also considered to have a decrease in customer demand. To reflect this assumption and get more accurate prediction results, books with an increased number of references will evaluate more times, and books with a reduced number of references will evaluate less. . 304a is a weighting sample by increasing / decreasing the number of reference times, and compares the previous month's reference number with the current month's reference number. If the reference number has increased by 20% or more, the current month's reference number is multiplied by 1.2. If the number is reduced by more than 20%, the reference count for the current month is multiplied by 0.8.

  Further, the server 10 adds weighting based on the evaluation score of the book review (step S305). Books that are highly evaluated in book reviews are highly motivated to purchase by customers, and conversely, books that are rated low in book reviews are considered to be less motivated to purchase by customers. In order to reflect this assumption and obtain a more accurate prediction result, a book with a high evaluation score evaluates the reference count more, and a book with a low evaluation score evaluates the reference count less. 305a is a weighted sample based on the evaluation score of the book review, and the reference number of the month is multiplied by a coefficient corresponding to the evaluation score given between 5 and 1 in descending order of evaluation. The coefficients used in this sample are 1.5 when the evaluation score is 5, 1.2 when the evaluation score is 4, 1.0 when the evaluation score is 3, and 2 when the evaluation score is 2. When the evaluation score is 1, it is 0.5.

  Finally, the server 10 obtains the average of the number of references for each book (step S306), calculates the predicted number of purchases according to the presence / absence of purchase (step S307), and sums both to make the predicted optimum stock ( Step S308). Customers who have purchased books at bookstores in the past are more likely to purchase books at bookstores than customers who have never purchased books, so the number of purchases multiplied by a larger number of references Predict. Reference numeral 306a is a sample obtained by calculating the average of the number of times of reference for each book. Reference numeral 307a is a sample for predicting the number of purchases, and the number of purchases is predicted by multiplying the number of times of reference by a customer with a purchase history by 0.5 and the number of times of reference by a customer without a purchase history by 0.1. Reference numeral 308a is a sample of the optimal stock of each book predicted through steps S301 to S308.

  As described above, in the first embodiment, out of the records of the situation in which the customer refers to the information related to the book via the network, the reference by the customer who may use the bookstore to be predicted from the geographical viewpoint. Since only the records are extracted, the optimal inventory of the books is predicted using the records, and the forecast results are used to place orders and return items. Can be well optimized.

  In the first embodiment, it is assumed that information relating to a customer who has purchased a book at a bookstore is registered in the storage unit 12, but in order to register such information, the customer When purchasing books at a bookstore, they need to present their customer number to the bookstore in some way. This problem can be solved, for example, by distributing a membership card in which a customer number is recorded to customers registered in the customer DB 12a so that the membership card is presented when purchasing a book. Offering services such as giving a point to a customer each time a book is purchased with a membership card and offering discounts based on the accumulated points gives the customer the advantage of presenting a customer number, and the book purchase history Can be recorded more reliably.

  By the way, in said Example 1, although the function of inventory management with respect to a bookstore was demonstrated among the functions which the server 10 has, the server 10 is provided with the information which concerns on a book and a bookstore with respect to a customer. There is another function. Therefore, in the second embodiment, the information provision function of the server 10 will be described. Here, a case where a member who has registered customer information in advance searches for information related to a book via a network will be described as an example.

  Since the system configuration of the second embodiment is the same as that of the first embodiment, the processing procedure will be mainly described here. FIG. 4 is a flowchart showing a processing procedure when a customer who has been registered in the customer DB 12a operates the customer terminal 21 shown in FIG. 1 to connect to the server 10 and browses information related to a book. The same processing procedure is used when the customer operates the customer terminals 22 to 24 and browses information related to books.

  As shown in the figure, first, the customer terminal 21 requests connection to the server 10 (step S401). When the server 10 confirms the connection request from the customer terminal 21 (step S402), the server 10 transmits an authentication screen requesting at least the customer number to be input to the customer terminal 21 (step S403).

  The customer terminal 21 displays the received authentication screen to the customer (step S404), accepts input of authentication information from the customer (step S405), and transmits the input information to the server 10 (step S406). The server 10 confirms that a customer that matches the transmitted information is registered in the customer DB 12a (step S407), and acquires information related to the customer from the customer DB 12a (step S408). This information includes information related to a region registered as a candidate site where the customer uses the bookstore.

  Subsequently, the server 10 transmits a search condition screen to the customer terminal 21 (step S409). This screen is for inputting a search condition for searching for a book desired by a customer, and a book name, author name, and the like can be designated. The customer terminal 21 displays the transmitted search condition screen (step S410), accepts input of the search condition from the customer (step S411), and transmits the input information to the server 10 (step S412).

  The server 10 that has received the search condition searches the book DB 12c for a book that satisfies the search condition (step S413), and transmits a list of search results to the customer terminal 21 (step S414). Upon receiving the list of search results, the customer terminal 21 displays it on the screen (step S415). This list of search results is composed of information on a relatively small number of items such as book titles, and serves as an index for customers to find out the desired book from these and browse more detailed information. .

  Here, when the customer requests to display more detailed information of a specific book (step S416), the customer terminal 21 requests the server 10 to transmit detailed information related to the book (step S417). The server 10 that has received the request acquires information related to the requested book from the book DB 12c and the book review DB 12d (steps S418 and S419). Further, the server 10 acquires the bookstores existing in the customer candidate sites acquired in step S408 from the bookstore DB 12b (step S420), and acquires the inventory status of the corresponding books at those bookstores from the bookstore inventory DB 12f ( Step S421). Then, the server 10 edits the acquired information as a book details screen and transmits it to the customer terminal 21 (step S422), and the customer terminal 21 displays this on the screen (step S423).

  This detailed screen displays, in addition to detailed information related to the book, the bookstore existing in the region registered by the customer as a bookstore use candidate site and the inventory status of the book at the bookstore. Therefore, if the customer actually wants to purchase the referenced book, the customer can almost certainly purchase the book by visiting one of the bookstores in stock.

  Further, this detailed screen displays a part of each book review about the book. If the customer is interested in the book review, it can be instructed to display the entire book review. When the customer terminal 21 receives an instruction to display the book review information from the customer (step S424), the customer terminal 21 requests the server 10 to transmit the book review information (step S425).

  Upon receiving the request, the server 10 acquires the book review information from the book review DB 12d (step S426). At this time, the server 10 records the requested book review information and the requested customer information in the reference history 12e (step S427). In the reference history 12e, as the geographical characteristics of the referenced customer, the use candidate site of the customer bookstore acquired in step S408 is set. As described in the first embodiment, the reference record recorded here is used as basic information for predicting the optimum stock of books.

  Then, the server 10 transmits the acquired book review information to the customer terminal 21 (step S428), and the customer terminal 21 displays this on the screen (step S429).

  As described above, in the second embodiment, when the customer refers to the information related to the book via the network, in addition to the information related to the book, the bookstore in the region registered in advance by the customer and the bookstore Since it is configured to display the stock, the customer can be guided to the store where the books are stocked.

  The customer browses the book related to the book via the network and decides to purchase the book, but there may be cases where no book is available in the bookstore in the area where the customer has registered in advance. In such a case, it is possible to prevent the bookstore and the customer from being disadvantaged by using a system in which the book can be ordered as it is and the book can be received at the bookstore designated by the customer. .

  By the way, in the second embodiment described above, the case where a member who has registered customer information in advance searches for information related to a book via a network has been described. However, the service can be used without registering customer information in advance. May be more convenient for the user. In the third embodiment, a case where a non-member customer who has not registered customer information searches for information related to a book via a network will be described.

  Since the system configuration of the present embodiment is the same as that of the first embodiment, description thereof is omitted. In addition, since the processing procedure of the present embodiment has many parts in common with the second embodiment, only differences from the second embodiment will be described. FIG. 5 is a flowchart showing a processing procedure when a customer who is not registered in the customer DB 12a operates the customer terminal 21 shown in FIG. 1 to connect to the server 10 and browses information related to a book. The same processing procedure is used when the customer operates the customer terminals 22 to 24 and browses information related to books.

  As shown in the figure, after confirming the connection request from the customer terminal 21, the server 10 transmits a screen requesting input of a bookstore candidate site to the customer terminal 21 (step S501).

  The customer terminal 21 displays the received bookstore candidate site input screen to the customer (step S502), accepts input of the bookstore candidate site from the customer (step S503), and transmits the input information to the server 10 (step S503). Step S504).

  Then, when the server 10 acquires the bookstore candidate site information, the server 10 temporarily stores it for use in the subsequent processing (step S505).

  In the second embodiment, the bookstore candidate location is acquired from the customer DB 12a, whereas in this embodiment, the bookstore candidate location is input from the screen. For this reason, what is acquired from the bookstore DB 12b in step S420 is the bookstore existing in the candidate site input from the screen, and the book detail screen displays the bookstore existing in the candidate site input from the screen and the inventory status of the bookstore. Will be displayed. In the reference history recorded in step S427, candidate sites input from the screen are set as the geographical characteristics of the customer. Note that since the referred customer cannot be specified, the customer number in this reference history is empty. Other processing procedures in the present embodiment are the same as those in the second embodiment.

  As described above, in the third embodiment, when the customer refers to the information related to the book via the network, in addition to the information related to the book, the bookstore in the area designated by the customer and the stock in the bookstore Since it was comprised so that it might display, a customer can be guided to the store where books are stocked.

  By the way, in the above-described third embodiment, a configuration is adopted in which a customer inputs a candidate location of a bookstore. However, in recent years, a device such as a mobile phone having a function of acquiring a current location using information of a satellite or a base station is used. It has become widespread. When a customer searches for information related to a book using such a device, it is used by automatically acquiring the customer's current location by skipping the candidate location input step in the third embodiment. User convenience can be improved. Thus, in the fourth embodiment, a case where a customer searches for information related to a book from a terminal having a position information acquisition function via a network will be described.

  Since the system configuration of the present embodiment is the same as that of the first embodiment, description thereof is omitted. In addition, since the processing procedure of the present embodiment has many parts in common with the second embodiment, only differences from the second embodiment will be described. FIG. 6 is a flowchart showing a processing procedure when a customer operates the customer terminal 23 shown in FIG. 1 to connect to the server 10 and browses information related to a book. Here, it is assumed that the customer terminal 23 is a terminal having a position information acquisition function. Note that the same processing procedure is used when the customer browses information related to a book by operating the customer terminal 24 having a position information acquisition function.

  As shown in the figure, after confirming the connection request from the customer terminal 23, the server 10 transmits a screen requesting transmission of position information to the customer terminal 23 (step S601).

  The customer terminal 23 displays the received location information transmission request screen to the customer (step S602), acquires the location information after obtaining customer approval (step S603), and transmits the acquired information to the server 10. (Step S604).

  Then, when the server 10 obtains the position information, the server 10 converts it so that it can be easily used in the subsequent processing and temporarily stores it (step S605).

  In the second embodiment, the candidate location of the bookstore is acquired from the customer DB 12a, whereas in the present embodiment, the current location of the customer is acquired from the terminal and set as the candidate location. Therefore, what is acquired from the bookstore DB 12b in step S420 is the bookstore in the region where the customer is currently located, and the bookstore in the region where the customer is currently located and the inventory status of the bookstore are displayed on the book detail screen. In the reference history recorded in step S427, the customer's current location is set as the customer's geographical characteristics. Note that since the referred customer cannot be specified, the customer number in this reference history is empty. Other processing procedures in the present embodiment are the same as those in the second embodiment.

  As described above, in the fourth embodiment, when the customer refers to the information related to the book via the network, in addition to the information related to the book, the bookstore in the area where the customer is present and the stock in the bookstore are displayed. Since it comprised so, a customer can be induced | guided | derived to the store where books are stocked.

(Supplementary note 1) An assortment optimization method for inventory management of books in a bookstore,
A reference history recording step of recording a reference history when a customer searches for information on a predetermined book via a network;
An optimal inventory prediction step of predicting an optimal inventory number of a predetermined book in a predetermined bookstore based on the reference history recorded by the reference history recording step;
An order return processing step of determining the number of orders or returns of the book by comparing the optimal stock quantity of the book in the bookstore predicted by the optimal inventory prediction step with the stock quantity of the book located in the bookstore; Assortment optimization method characterized by including

(Supplementary note 2) When the reference history recording process refers to information related to book reviews of a predetermined book by a customer, the reference history recording step associates information related to the geographical characteristics of the customer with information related to the book as a reference history. Record,
The optimal inventory forecasting step compares the location of a predetermined bookstore with information on the geographical characteristics of the customer recorded in the reference history, and thus the reference by the customer including the location of the bookstore in the geographical characteristics. 2. The assortment optimization method according to appendix 1, wherein an optimum stock quantity of a predetermined book in the bookstore is predicted based on a history.

(Additional remark 3) The said optimal stock prediction process estimates the optimal stock quantity of the predetermined book in the said bookstore by adding the weight by the presence or absence of the purchase performance in the predetermined bookstore of the customer recorded on the said reference history. The assortment optimization method according to Supplementary Note 1 or 2.

(Additional remark 4) The said optimal stock prediction process adds the weighting by the evaluation score contained in the information which concerns on the said book, and predicts the optimal stock quantity of the said book in a predetermined bookstore, The additional remark 1 or 2 characterized by the above-mentioned. Assortment optimization method described in 1.

(Additional remark 5) When the search of the information which concerns on a predetermined book is made | formed by the customer, the said search process process is the bookstore located in the area suitable for the geographical characteristic of the said customer, and the stock status of the said book in the said bookstore Is displayed on the customer's terminal. 3. The assortment optimization method according to appendix 1 or 2, wherein:

(Additional remark 6) When the search of the information which concerns on a predetermined book is made | formed from the terminal with the positional information notification function by the customer, the said search process process is the bookstore located in the area where the said customer is located, and the said store in the said bookstore 3. The assortment optimization method according to appendix 1 or 2, wherein the inventory status of books is displayed on the terminal of the customer.

(Supplementary note 7) An assortment optimization program for managing inventory of books in a bookstore,
A reference history recording step of recording a reference history when a customer searches for information on a predetermined book via a network;
An optimal inventory prediction step of predicting an optimal inventory number of a predetermined book in a predetermined bookstore based on the reference history recorded by the reference history recording step;
An order return processing step of determining the number of orders or returns of the book by comparing the optimal stock quantity of the book in the bookstore predicted by the optimal inventory prediction step with the stock quantity of the book located in the bookstore; Assortment optimization program characterized by including

(Appendix 8) An assortment optimization system for managing inventory of books at a bookstore,
Reference history recording means for recording a reference history when a customer searches for information related to a predetermined book via a network;
Optimal stock prediction means for predicting the optimal stock quantity of a predetermined book in a predetermined bookstore based on the reference history recorded by the reference history recording means;
Order return processing means for comparing the optimum stock quantity of the book in the bookstore predicted by the optimum stock prediction means with the stock quantity of the book located in the bookstore to determine the order quantity or return quantity of the book; Assortment optimization system characterized by having

  As described above, the bookstore assortment optimization method, the assortment optimization program, and the assortment optimization system according to the present invention are useful for inventory management in bookstores. It is suitable when you want to optimize efficiently according to your needs.

It is a functional block diagram for demonstrating the system configuration | structure of the assortment optimization system of the bookstore which concerns on the present Example 1. FIG. 3 is a flowchart illustrating a processing procedure for assortment optimization according to the first embodiment. It is a flowchart which shows the process sequence of prediction of the optimal stock in FIG. 10 is a flowchart illustrating a processing procedure for providing information according to the second embodiment. 12 is a flowchart illustrating a processing procedure for providing information according to the third embodiment. 14 is a flowchart illustrating a processing procedure for providing information according to the fourth embodiment. It is a data structure figure which shows the data structure of customer DB which concerns on the present Example 1. FIG. It is a data structure figure which shows the data structure of bookstore DB which concerns on the present Example 1. FIG. It is a data structure figure which shows the data structure of book DB which concerns on the present Example 1. FIG. It is a data structure figure which shows the data structure of book review DB which concerns on the present Example 1. FIG. FIG. 3 is a data structure diagram illustrating a data structure of a reference history according to the first embodiment. It is a data structure figure which shows the data structure of the bookstore inventory DB which concerns on the present Example 1. FIG. It is a data structure figure which shows the data structure of the purchase history which concerns on the present Example 1. FIG. It is a data structure figure which shows the data structure of the bookstore customer DB which concerns on the present Example 1. FIG.

Explanation of symbols

10 Server 11 Interface unit 12 Storage unit 12a Customer DB
12b Bookstore DB
12c Book DB
12d Book Review DB
12e Reference history 12f Bookstore inventory DB
12g Purchase history 12h Bookstore customer DB
DESCRIPTION OF SYMBOLS 13 Search processing part 14 Reference history recording part 15 Optimal stock prediction part 16 Order return processing part 17 Sales management processing part 18 Control part 21, 21, 23, 24 Customer terminal 31, 32 Bookstore terminal 40 Book wholesale center 50 Book return center 60 the Internet

Claims (5)

An assortment optimization method for inventory management of books at a bookstore,
A reference history recording step of recording a reference history when a customer searches for information on a predetermined book via a network;
An optimal inventory prediction step of predicting an optimal inventory number of a predetermined book in a predetermined bookstore based on the reference history recorded by the reference history recording step;
An order return processing step of determining the number of orders or returns of the book by comparing the optimal stock quantity of the book in the bookstore predicted by the optimal inventory prediction step with the stock quantity of the book located in the bookstore; Assortment optimization method characterized by including In the reference history recording step, when information related to a book review of a predetermined book is referred to by a customer, information related to the geographical characteristics of the customer is recorded as a reference history in association with the information related to the book,
The optimal inventory forecasting step compares the location of a predetermined bookstore with information on the geographical characteristics of the customer recorded in the reference history, and thus the reference by the customer including the location of the bookstore in the geographical characteristics. 2. The assortment optimization method according to claim 1, wherein an optimal stock quantity of a predetermined book in the bookstore is predicted based on the history.   In the search processing step, when information related to a predetermined book is searched by a customer, a bookstore located in an area that matches the geographical characteristics of the customer and an inventory status of the book in the bookstore are displayed. 3. The assortment optimization method according to claim 1 or 2, wherein display is performed on a terminal. An assortment optimization program for inventory management of books at bookstores,
A reference history recording step of recording a reference history when a customer searches for information on a predetermined book via a network;
An optimal inventory prediction step of predicting an optimal inventory number of a predetermined book in a predetermined bookstore based on the reference history recorded by the reference history recording step;
An order return processing step of determining the number of orders or returns of the book by comparing the optimal stock quantity of the book in the bookstore predicted by the optimal inventory prediction step with the stock quantity of the book located in the bookstore; Assortment optimization program characterized by including An assortment optimization system for inventory management of books at bookstores,
Reference history recording means for recording a reference history when a customer searches for information related to a predetermined book via a network;
Optimal stock prediction means for predicting the optimal stock quantity of a predetermined book in a predetermined bookstore based on the reference history recorded by the reference history recording means;
Order return processing means for comparing the optimum stock quantity of the book in the bookstore predicted by the optimum stock prediction means with the stock quantity of the book located in the bookstore to determine the order quantity or return quantity of the book; Assortment optimization system characterized by having

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