[go: up one dir, main page]

WO2020026359A1 - Système informatique, procédé de commercialisation et programme - Google Patents

Système informatique, procédé de commercialisation et programme Download PDF

Info

Publication number
WO2020026359A1
WO2020026359A1 PCT/JP2018/028747 JP2018028747W WO2020026359A1 WO 2020026359 A1 WO2020026359 A1 WO 2020026359A1 JP 2018028747 W JP2018028747 W JP 2018028747W WO 2020026359 A1 WO2020026359 A1 WO 2020026359A1
Authority
WO
WIPO (PCT)
Prior art keywords
product
information
stocked
sales
age
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/JP2018/028747
Other languages
English (en)
Japanese (ja)
Inventor
俊二 菅谷
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Optim Corp
Original Assignee
Optim Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Optim Corp filed Critical Optim Corp
Priority to PCT/JP2018/028747 priority Critical patent/WO2020026359A1/fr
Publication of WO2020026359A1 publication Critical patent/WO2020026359A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • the present invention relates to a computer system, a sales method, and a program for executing inventory management of commodities.
  • Patent Document 1 merely predicts demand during a predetermined period, and does not consider other factors. Therefore, the predicted demand may not always be accurate.
  • the present invention aims to provide a computer system, a sales method, and a program that make it easier to more accurately predict the demand for a product.
  • the present invention provides the following solutions.
  • the present invention provides an acquiring unit for acquiring regional information around a sales store, A prediction unit for predicting an item of a product to be inventoried in the sales store and a necessary inventory amount based on the regional information; A computer system is provided.
  • the computer system acquires regional information around the sales store, and predicts, based on the regional information, items of commodities to be stocked at the sales store and a necessary inventory amount.
  • the present invention is in the category of computer systems.
  • other categories such as methods and programs exhibit the same functions and effects according to the categories.
  • FIG. 1 is a diagram showing an outline of the sales system 1.
  • FIG. 2 is an overall configuration diagram of the sales system 1.
  • FIG. 3 is a flowchart illustrating a first sales process executed by the computer 10.
  • FIG. 4 is a flowchart illustrating a second sales process executed by the computer 10.
  • FIG. 5 is a flowchart illustrating a learning process performed by the computer 10.
  • FIG. 6 is a flowchart illustrating the suggestion processing executed by the computer 10.
  • FIG. 7 is a diagram illustrating an example in which the area information acquired by the computer 10 is schematically illustrated.
  • FIG. 8 is a diagram schematically showing the population distribution by age, the purchase probability, and the required inventory in the area 110 around the sales store 100 for the product A.
  • FIG. 1 is a diagram for explaining an outline of a sales system 1 according to a preferred embodiment of the present invention.
  • the sales system 1 is a computer system that includes a computer 10 and executes inventory management of merchandise in a sales store.
  • the sales system 1 may include, in addition to the computer 10, other terminal devices such as a dealer terminal owned by a dealer who sells commodities and an administrator terminal owned by an administrator of the sales store.
  • the computer 10 manages the item and the stock amount of the product in the sales store, and regional information in a region around the sales store (for example, population distribution by age in this region, SNS (Social Networking Service) in this region). Information, news of this area, medical information of this area, information of an event (concert, athletic meet, outdoor event, etc.) or weather information of this area) is obtained.
  • the computer 10 predicts the item of the product to be stocked at the sales store and the necessary stock amount of the product based on the acquired regional information.
  • the computer 10 notifies the seller of the product by notifying the predicted item of the product to be stocked and the necessary inventory amount of the product to the dealer terminal owned by the seller of the product. , The product item and the required inventory amount are notified.
  • the sales store managed by the computer 10 is a hospital.
  • the medical care information includes the number of patients who have reserved this hospital and their medical conditions, and the computer 10 calculates the items of the products to be inventoried and the necessary amount of the products based on the number and medical conditions of the patients. And predict.
  • the computer 10 manages the product handling status (actual sales) in other regions having characteristics similar to the region information around the sales store (for example, the number of people, the population structure by age, the climate, or illness that has become prevalent in the past). Of the product and the sales quantity of the product). The computer 10 predicts the item of the product to be stocked and the necessary stock amount of the product, taking into account the handling status of the obtained product.
  • sales stores include hospitals, shops, restaurants, and providers of various services (nursing care, cleaning, events, etc.), and products include pharmaceuticals, miscellaneous goods, articles, food and beverages, various services, and the like. It is.
  • the present invention is not limited to this example, and can be applied to various goods and services.
  • the computer 10 acquires regional information on a region around the sales store (step S01).
  • the computer 10 receives location information (location information acquired from a GPS (Global Positioning System) or the like, the address of the sales store, etc.) specifying the area of the sales store managed by the computer 10, and based on the location information, Identify the surrounding area.
  • the computer 10 acquires the area information in the specified area.
  • the regional information is, for example, population distribution by age in the region, SNS information in the region, news in the region, medical information in the region, event information, or weather information in the region.
  • the computer 10 uses at least one of the above-described area information in a process described below.
  • the computer 10 predicts, based on the acquired regional information, the item of the product to be stocked at the sales store and the necessary stock amount of the product (step S02).
  • the computer 10 stores the predetermined keyword, the item of the product, and the required inventory amount of the product in association with each other, and based on the keyword included in the acquired regional information and the stored predetermined keyword. Then, the item of the product associated with the keyword and the necessary inventory amount of the product are predicted.
  • the computer 10 notifies the predicted merchandise item and the required inventory amount of the merchandise to a trader terminal owned by a merchant who sells the merchandise.
  • the computer 10 notifies the trader of the item of the goods and the required stock by displaying the goods and the necessary stock on the trader terminal.
  • FIG. 2 is a diagram showing a system configuration of a sales system 1 according to a preferred embodiment of the present invention.
  • a sales system 1 is a computer system that includes a computer 10 and executes inventory management of merchandise in a sales store.
  • the sales system 1 may include a trader terminal, an administrator terminal, and other terminals.
  • the computer 10 includes a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), and the like as a control unit, and another device (a trader terminal or an administrator terminal not shown) as a communication unit. , And other terminals), for example, a device compatible with Wi-Fi (Wireless-Fidelity) compliant with IEEE 802.11. Further, the computer 10 includes, as a storage unit, a data storage unit such as a hard disk, a semiconductor memory, a recording medium, and a memory card. Further, the computer 10 includes, as a processing unit, various devices that execute various processes.
  • a CPU Central Processing Unit
  • RAM Random Access Memory
  • ROM Read Only Memory
  • the computer 10 includes, as a storage unit, a data storage unit such as a hard disk, a semiconductor memory, a recording medium, and a memory card.
  • the computer 10 includes, as a processing unit, various devices that execute various processes.
  • the control unit reads a predetermined program, and realizes the acquisition module 20, the notification module 21, the evaluation reception module 22, and the proposal module 23 in cooperation with the communication unit. Further, in the computer 10, the control unit reads a predetermined program, thereby realizing the storage module 30 in cooperation with the storage unit. Also, in the computer 10, the control unit reads a predetermined program, and in cooperation with the processing unit, the area designation module 40, the prediction module 41, the identification module 42, the learning module 43, the evaluation determination module 44, the substitute product module 45 is realized.
  • FIG. 3 is a diagram illustrating a flowchart of the first sales processing executed by the computer 10. The processing executed by each module described above will be described together with this processing.
  • the area specification module 40 receives specification of location information, which is information for specifying an area of a sales store managed by itself (step S10).
  • the area specifying module 40 receives the position information of the sales store (information that can uniquely specify a place such as a latitude / longitude or an address) as the position information.
  • the position information there are a latitude / longitude obtained from a GPS or the like, an address input from an administrator terminal, and the like.
  • the storage module 30 stores the received location information (Step S11).
  • the storage module 30 stores the location information in association with the sales store identifier (store name, management number, manager name, and the like). This is particularly effective when the computer 10 collectively manages a plurality of sales stores.
  • the acquisition module 20 acquires regional information (for example, population distribution by age, SNS information, news, medical information, event information, or weather information) around the sales store based on the location information (step S12).
  • the acquisition module 20 acquires area information corresponding to a predetermined range from the location information. For example, the acquisition module 20 acquires, from the addresses in the location information, the regional information in the same prefecture or the same ward, municipalities.
  • the area information acquired by the acquisition module 20 will be specifically described.
  • examples of the regional information include population distribution by age, SNS information, news, medical information, event information, and weather information.
  • the acquisition module 20 acquires at least one of these area information.
  • the area information acquired by the acquisition module 20 is not limited to the example described above, and may be other information.
  • the acquisition module 20 refers to various databases in which the population distribution by age is stored, using the prefecture or ward as a keyword, among the addresses included in the location information, and acquires the population distribution by age around the sales store. . At this time, the acquisition module 20 acquires the population distribution for each age around the sales store by referring to databases provided by various information agencies and public institutions.
  • the acquisition module 20 searches for posts of various SNSs by using the prefecture or the ward / municipality as a keyword among the addresses included in the location information.
  • the acquisition module 20 acquires a post having this keyword in SNS posts as SNS information around the sales store.
  • the acquisition module 20 searches for articles on various news providing sites by using, as keywords, prefectures or municipalities in the addresses included in the location information.
  • the acquisition module 20 acquires an article having this keyword from various news providing sites as news around the sales store.
  • the sales store is a hospital.
  • the acquisition module 20 acquires past medical records at the hospital serving as a sales store, the number of patients making a reservation, and medical conditions from a hospital database or the like.
  • the acquisition module 20 refers to a database or the like of another hospital that exists in this prefecture or ward, using the prefecture or ward as a keyword, among the addresses included in the location information, and refers to the past information in this other hospital. Get the number of medical records and the number of patients making reservations and medical conditions.
  • the acquisition module 20 searches various event introduction sites by using, as keywords, the prefecture or ward, municipalities, and various event names (concert, athletic meet, outdoor event, etc.) among the addresses included in the location information.
  • the acquisition module 20 acquires events having these keywords on various event introduction sites as event information around the sales store.
  • the acquisition module 20 compares the current date and time with the scheduled date and time of the acquired event information, and cancels the acquisition of the event that has already been completed.
  • the acquisition module 20 searches various weather information providing sites by using a prefecture or a ward, a municipal or the like as a keyword among the addresses included in the location information.
  • the acquisition module 20 acquires the weather information having this keyword from various types of weather information providing sites as weather information around the sales store.
  • the acquisition module 20 may use a keyword other than the prefecture or the ward, municipal, or the like as a keyword.
  • it may be a prefecture and a ward, a municipalities, a region name such as a Kyushu region or a Chugoku region, or a region capable of limiting other regions.
  • the acquisition module 20 may use a module that can limit an area other than the keyword. For example, regional information within a predetermined range (for example, a radius of 5 km and a radius of 10 km) from the address in the location information may be acquired.
  • FIG. 7 is a diagram schematically illustrating an example of the area information acquired by the acquisition module 20.
  • the acquisition module 20 acquires regional information in a region 110 around the sales store 100.
  • the regional information is a population distribution 120 for each age, SNS information 130, news 140, medical treatment information 150, event information 160, and weather information 170.
  • the computer 10 predicts the item of the product to be stocked and the necessary stock amount of the product by using such regional information in a process described later.
  • the acquisition module 20 specifies the area of the sales store based on the predetermined keyword included in the location information, and obtains at least one of the area information in the specified area.
  • the prediction module 41 predicts, based on the acquired area information, the items of the product to be stocked at the sales store and the necessary stock amount of the product (step S13). In step S13, the prediction module 41 predicts an item of a product to be stocked and a necessary stock amount based on a predetermined keyword included in the regional information.
  • the prediction module 41 compares, as a keyword, the previously calculated item of the product by age per unit number, the purchase probability of the product, and the population distribution by age acquired this time. For example, in the product A, the probability that a purchaser of a teenage age purchases the product A is 8%, the probability that a purchaser of a 20s age purchases the product A is 12%, and the purchaser is 30 years of age.
  • the population distribution by age acquired by the acquisition module 20 is 50 people in their teens, 150 people in their 20s, 200 people in their 30s, 700 people in their 40s, 800 people in their 50s, and 900 people in their 60s.
  • the required inventory amount of the product A is the product of the probability of purchasing the product A in each age and the population distribution by age.
  • the required stock of this product A is 4 in the teens, 18 in the 20s, 30 in the 30s, 49 in the 40s, 40 in the 50s, 27 in the 60s, 70 or more Becomes zero.
  • the prediction module 41 predicts 178 pieces, which is the sum total of the calculated required inventory amount for each age, as the required inventory amount. That is, the prediction module 41 predicts that the required inventory amount of the product A is 178. At this time, the prediction module 41 performs the same prediction for all products handled by the sales store.
  • the purchase probability of a product is calculated by learning the approximate age of the purchaser and the purchased product when the product has been sold at this sales store or another sales store in the past. It is possible.
  • the prediction module 41 may execute prediction on some products instead of all products.
  • FIG. 8 is a diagram schematically showing the population distribution by age, the purchase probability, and the required inventory in the area 110 around the sales store 100 for the product A.
  • the population distribution by age is 50 people in their teens, 150 people in their 20s, 200 people in their 30s, 700 people in their 40s, 800 people in their 50s, and 900 people in their 60s. There are 400 people in their 70s and over.
  • the purchase probability of product A by age is 8% for teens, 12% for 20s, 15% for 30s, 7% for 40s, 5% for 50s, 3% for 60s, 70s
  • the above is 0.1%.
  • the required inventory is the product of the population distribution for each age and the purchase probability. Therefore, four teenagers, 18 teenagers, 30 teenagers, 30 teenagers, 49 teenagers, and 50 teenagers take 40. The number is 27 for the 60s and zero for the 70s and above. As a result, the required inventory amount of the product A is 178 which is the total of the required inventory amount for each age.
  • the prediction module 41 compares the purchase probability of a product associated with a predetermined keyword set in advance with the SNS post acquired this time. When “asthma” is set as a predetermined keyword, the keyword of “asthma” is extracted by performing text recognition on the acquired SNS post. The prediction module 41 predicts “product B” associated with this “asthma” as an item of a product to be stocked.
  • the probability of purchase of this product B by a purchaser of age 10s is 8%
  • the probability of purchase of this product B by a purchaser of age 20s is 12%
  • purchasers of age 30s Has a 15% probability of purchasing this product B, a 7% probability that a buyer in their forties will purchase this product B, a 5% probability of a buyer in their 50s purchasing this product B
  • the probability that a buyer in his 60s will purchase this product B is 3%
  • the probability that a buyer in his 70s will purchase this product B is 0.1%, based on an example in which it is calculated in advance. Will be explained.
  • the required inventory amount of this product B is the probability that each age will purchase this product B at this sales store in response to the SNS post.
  • the population distribution by age The population distribution for each age is the same as that obtained as the above-mentioned area information.
  • the population distribution by age is 50 people in their teens, 150 people in their 20s, 200 people in their 30s, 700 people in their 40s, 800 people in their 50s, 900 people in their 60s, Assuming that there are 400 people in their 70s or more, the required inventory of this product B is 4 in their teens, 18 in their 20s, 30 in their 30s, 49 in their 40s, 40 in their 50s, There are 27 in 60s and 0 in 70s and above.
  • the prediction module 41 predicts 178 pieces, which is the sum total of the calculated required inventory amount for each age, as the required inventory amount. That is, the prediction module 41 predicts that the required inventory amount of the product B is 178. Also, at this time, the prediction module 41 performs the same prediction for all products associated with the predetermined keyword included in the acquired SNS post.
  • the purchase probability of a product in this sales store in response to an SNS post is calculated by learning the change in the number of sales with respect to the past SNS post when the product has been sold by this sales store or another sales store in the past. It is possible to For example, when the keyword “asthma” is extracted in the SNS post, the fluctuation of the sales of the product B in the sales store is learned.
  • the prediction module 41 performs the prediction in the SNS posting using the population distribution for each age, but may perform the prediction only in the SNS posting. In this case, the prediction module 41 changes the probability of purchasing this product at this sales store in response to the SNS post, and associates the number of items to be stocked at this sales store with this keyword in response to this SNS post. It is possible to deal with it by doing so.
  • the prediction module 41 compares the purchase probability of a product associated with a predetermined keyword set in advance with the news acquired this time. When “asthma” is set as the predetermined keyword, the keyword of “asthma” is extracted by performing text recognition on the acquired news. The prediction module 41 predicts “product C” associated with “asthma” as an item of a product to be stocked.
  • the probability of purchase of this product C by a purchaser of age 10s is 8%
  • the probability of purchase of this product C by a purchaser of age 20s is 12%
  • the purchaser of age 30s Has a 15% probability of purchasing this product C, a 7% probability that a buyer in their 40s will purchase this product C, a 5% probability of a buyer in their 50s purchasing this product C
  • the probability that a buyer in his 60s will purchase this product C is 3%
  • the probability that a buyer in his 70s will purchase this product C is 0.1% Will be explained.
  • the stock requirement of this product C is determined by the probability and age of purchasing this product C at this sales store in each age in response to the news. It is the product of each population distribution.
  • the population distribution for each age is the same as that obtained as the above-mentioned area information.
  • the population distribution by age is 50 people in their teens, 150 people in their 20s, 200 people in their 30s, 700 people in their 40s, 800 people in their 50s, 900 people in their 60s, Assuming that there are 400 people in their 70s or more, the required inventory of this product C is 4 in their 10s, 18 in their 20s, 30 in their 30s, 49 in their 40s, 40 in their 50s, There are 27 in 60s and 0 in 70s and above.
  • the prediction module 41 predicts 178 pieces, which is the sum total of the calculated required inventory amount for each age, as the required inventory amount. That is, the prediction module 41 predicts the required inventory amount of the product C to be 178. In addition, at this time, the prediction module 41 executes the same prediction for all products associated with a predetermined keyword included in the acquired news.
  • the probability of purchasing a product in this sales store in response to news should be calculated by learning the change in the number of sales for past news when selling at this sales store or another sales store in the past. Is possible. For example, when a keyword of “asthma” is extracted in news, the fluctuation of the sales of the product C in the sales store is learned.
  • the prediction module 41 uses the population distribution for each age to execute prediction in news, the prediction module 41 may execute prediction only in news. In this case, the prediction module 41 changes the probability of purchasing this product at this sales store in response to the news, and associates the number of items to be stocked at this sales store in response to this news with the keyword. And so on.
  • the prediction module 41 compares the prescription probability of a medicine associated with a predetermined keyword set in the past medical chart of the hospital with the medical condition and the number of patients acquired this time as medical treatment information.
  • “asthma” is set as the predetermined keyword
  • the acquired past medical record is recognized as a text to extract the keyword of “asthma”.
  • the prediction module 41 predicts “medicine D” associated with “asthma” as an item of a product to be stocked.
  • the probability that a patient in his teens is prescribed this medicine D is 8%
  • the probability that a patient in his 20s is prescribed this medicine D is 12%
  • the age of a patient in his thirties is 15% probability of prescribing this drug D
  • 7% probability of prescribing this drug D for patients in their 40s is 15% probability of prescribing this drug D
  • 5% probability of prescribing this drug D for patients in their 50s Based on an example in which the probability that a patient in his sixth generation is prescribed this drug D is 3%, and the probability that a patient in his 70s is prescribed this drug D is 0.1% in advance. Will be explained.
  • the stock requirement of this medicine D is the product of the probability of being prescribed this medicine D in each age and the number of patients for each age. Becomes The number of patients for each age is the number of patients who have reserved this hospital. The number of patients by age is 5 in their teens, 15 in their 20s, 20 in their 30s, 70 in their 40s, 80 in their 50s, 90 in their 60s, and 40 in their 70s and above Assuming that there is, the necessary inventory amount of this medicine D is 1 in 10s, 2 in 20s, 3 in 30s, 5 in 40s, 4 in 50s, 3 in 60s, 70 No more than generations.
  • the prediction module 41 predicts 18 pieces, which is the sum total of the calculated required inventory for each age, as the required inventory. That is, the prediction module 41 predicts that the necessary inventory amount of the medicine D is 18 pieces. In addition, at this time, the prediction module 41 performs the same prediction for all medicines associated with the predetermined keyword included in the acquired medical information.
  • the prescription probability of a medicine can be calculated by learning the change in the number of prescriptions with respect to past medical information when prescriptions have been made by this hospital or another hospital in the past. For example, when the keyword “asthma” is extracted from the medical care information, the fluctuation in the number of prescriptions of the medicine D in the hospital is learned.
  • the prediction module 41 compares the purchase probability of a product associated with a predetermined keyword set in advance with the event information acquired this time.
  • the keyword of the “athletic meet” is extracted by performing text recognition on the acquired event information.
  • the prediction module 41 predicts “product E” associated with this “athletic meet” as an item of a product to be stocked.
  • the probability that a purchaser of a teenage age purchases the product E is 8%
  • the probability of a purchaser of a twenties age purchasing this product E is 12%
  • a purchaser of an age 30s Has a 15% probability of purchasing this product E, a 7% probability that a buyer in their 40s will purchase this product E, a 5% probability that a buyer in their 50s will purchase this product E
  • Based on an example in which the probability that a buyer in his 60s will purchase this product E is 3%, and the probability that a buyer in his 70s will purchase this product E is 0.1%. Will be explained.
  • the stock requirement of this product E is the probability that each age will purchase this product E at this sales store in response to the event information.
  • the population distribution by age The population distribution for each age is the same as that obtained as the above-mentioned area information.
  • the population distribution by age is 50 people in their teens, 150 people in their 20s, 200 people in their 30s, 700 people in their 40s, 800 people in their 50s, 900 people in their 60s, Assuming that there are 400 people in their 70s or more, the required inventory of this product E is 4 in their teens, 18 in their 20s, 30 in their 30s, 49 in their 40s, 40 in their 50s, There are 27 in 60s and 0 in 70s and above.
  • the prediction module 41 predicts 178 pieces, which is the sum total of the calculated required inventory amount for each age, as the required inventory amount. That is, the prediction module 41 predicts that the required inventory amount of the product E is 178. At this time, the prediction module 41 performs the same prediction for all products associated with a predetermined keyword included in the acquired event information.
  • the purchase probability of the product in this sales store in response to the event information is calculated by learning the change in the number of sales with respect to the past event information when selling at this sales store or another sales store in the past. It is possible to For example, when the keyword of “athletic meet” is extracted from the event information, the fluctuation of the sales of the product E in the sales store is learned.
  • the prediction module 41 performs the prediction in the event information using the population distribution for each age, the prediction module 41 may perform the prediction using only the event information. In this case, the prediction module 41 changes the probability of purchasing this product at this sales store in response to the event information, and associates the number of items to be stocked at this sales store with this keyword in response to this event information. It is possible to deal with it by doing so.
  • the prediction module 41 compares the purchase probability of a product associated with a predetermined keyword set in advance with the weather information acquired this time. When “rain” is set as the predetermined keyword, the keyword of “rain” is extracted by performing text recognition on the acquired weather information. The prediction module 41 predicts “product F” associated with this “rain” as a product item to be stocked.
  • the probability of purchase of this product F by a purchaser of a teenage age is 8%
  • the probability of purchase of this product F by a purchaser of a age of 20 is 12%
  • the purchaser of age 30s Has a 15% probability of purchasing this product F, a 7% probability that a buyer in their forties will purchase this product F, a 5% probability of a buyer in their 50s purchasing this product F
  • the probability that a purchaser in his 60s will purchase this product F is 3%
  • the probability that a buyer in his 70s will purchase this product F is 0.1%, based on an example that is calculated in advance. Will be explained.
  • the necessary inventory amount of the product F is the probability that each age will purchase this product F at this sales store in response to the weather information.
  • the population distribution by age The population distribution for each age is the same as that obtained as the above-mentioned area information.
  • the population distribution by age is 50 people in their teens, 150 people in their 20s, 200 people in their 30s, 700 people in their 40s, 800 people in their 50s, 900 people in their 60s, Assuming that there are 400 people in their 70s or more, the required inventory of this product F is 4 in their teens, 18 in their 20s, 30 in their 30s, 49 in their 40s, 40 in their 50s, There are 27 in 60s and 0 in 70s and above.
  • the prediction module 41 predicts 178 pieces, which is the sum total of the calculated required inventory amount for each age, as the required inventory amount. That is, the prediction module 41 predicts the required inventory amount of the product F to be 178. At this time, the prediction module 41 performs the same prediction for all products associated with a predetermined keyword included in the acquired weather information.
  • the purchase probability of a product in this sales store in response to weather information is calculated by learning the change in the number of sales with respect to past weather information when selling from this sales store or another sales store in the past It is possible to For example, when the keyword “rain” is extracted from the weather information, the fluctuation of the sales of the product F in the sales store is learned.
  • the prediction module 41 executes the prediction in the weather information using the population distribution for each age, the prediction module 41 may execute the prediction only in the weather information. In this case, the prediction module 41 changes the probability of purchasing this product in this sales store in response to the weather information, and associates the number of items to be stocked in this sales store in response to this weather information with the keyword. It is possible to deal with it by doing so.
  • the notification module 21 notifies the predicted merchandise items to be stocked and the required inventory amount of the merchandise to the trader terminal owned by the trader who sells the merchandise (step S14). In step S14, the notification module 21 notifies the seller of the sale by displaying the item of the product and the necessary stock amount on the dealer terminal.
  • FIG. 4 is a diagram illustrating a flowchart of the second sales process executed by the computer 10. The processing executed by each module described above will be described together with this processing.
  • the region designation module 40 accepts designation of location information, which is information for specifying the region of the sales store managed by itself (step S20).
  • the processing in step S20 is the same as the processing in step S10 described above.
  • Step S21 The storage module 30 stores the received location information (Step S21).
  • the processing in step S21 is the same as the processing in step S11 described above.
  • the acquisition module 20 acquires regional information around the sales store based on the location information (Step S22).
  • the processing in step S22 is the same as the processing in step S12 described above.
  • the specifying module 42 specifies a region having a region characteristic (for example, population number, population structure by age, climate (weather information and weather conditions), or a disease that has become widespread in the past) similar to the obtained region information (step S23). ).
  • the specifying module 42 refers to various databases and various websites based on the acquired region information and specifies a region having a region characteristic similar to the region information around the sales store.
  • the identification module 42 identifies an area having an area characteristic similar to at least one of the acquired area information (having similar numerical values, close conditions, etc.) as an area having an area characteristic similar to the acquired area information.
  • the identification module 42 extracts a predetermined keyword included in the acquired area information, and identifies an area having an area characteristic including a keyword similar to the extracted keyword as an area having a similar area characteristic.
  • the identification module 42 may include a population distribution for each age in which the population distribution for each age falls within a predetermined range, or a population distribution for each age.
  • An area having the number of populations whose total sum of the population is within a predetermined range is specified as an area having similar area characteristics.
  • the acquired regional information is an SNS post, weather information, or news
  • a keyword related to climate included in the SNS post, a keyword related to climate in weather information, and a keyword related to climate included in news are extracted and extracted.
  • a region having a keyword related to climate similar to the keyword is specified as a region having similar region characteristics.
  • the acquired area information is medical information
  • a keyword relating to a medical condition or a disease name included in the medical information is extracted, and an area having a keyword relating to a disease similar to the extracted keyword is converted to an area having similar regional characteristics. To be specified.
  • the identification module 42 may identify an area having similar area characteristics by combining a plurality of the above-described examples. For example, an area that satisfies both the conditions of the age-specific population structure and the disease that has prevailed in the past may be specified as an area having similar area characteristics. Further, a region having a similar region characteristic may be specified by combining other region characteristics.
  • the acquisition module 20 acquires the handling status of the product in the specified area (Step S24).
  • the handling status is the item of the product that was actually sold and the handling amount of the product.
  • the acquisition module 20 acquires the handling status of the product in the specified area from the computer that manages the sales store in the specified area.
  • the acquisition module 20 may acquire the handling status of the product in the specified area by another method. For example, by referring to various databases and various websites, the handling status of the product in the specified area may be acquired.
  • the prediction module 41 in addition to the above-described area information, based on the obtained product handling status in a region having similar regional characteristics, the item of the product to be stocked at the sales store, the necessary inventory amount of this product, Is predicted (step S25).
  • step S25 the prediction module 41 predicts the item of the product to be stocked at the sales store and the necessary stock amount of the product in the process of step S13 described above, and further takes into account the handling status of the obtained product. Then, the item of the product to be stocked at the sales store and the necessary stock amount of this product are predicted.
  • the prediction module 41 specifies the medicine D as the item of the commodity to be stocked, and predicts 18 pieces of the necessary stock of the medicine D as the stock.
  • the prediction module 41 determines the prediction result based on the regional information and the product of the product in the region having the similar regional characteristics.
  • the average value with the handling status is predicted as the necessary inventory amount of the product to be inventoried. By using the average value, it can be expected that the required inventory amount can be more accurately predicted than a prediction result based on one information.
  • the handling status of the product for each age is obtained, and the required inventory amount of the product to be stocked for each age at the forecast time and the average value of the handling status of the product for each age are calculated. It is also possible to predict the sum of the values as the required inventory of the goods to be inventoried.
  • the prediction module 41 is not limited to the above-described average value, and may predict other numerical values. For example, if there is a difference equal to or greater than a predetermined threshold between the prediction result based on the regional information and the handling status of this product in a region having similar regional characteristics, the prediction result based on the regional information is prioritized. Then, based on the result of the prediction, it may be predicted that the required quantity of the product should be stored. Further, a configuration is adopted in which a predetermined coefficient is set according to the handling situation of this product in an area having similar area characteristics, not an average value, and a prediction result based on area information is corrected based on the coefficient. Is also good.
  • the prediction module 41 is similar to the prediction result of the item of the product to be stocked and the required inventory amount of this product, as in the case of the medical information described above, even if the regional information is the other example described above. By taking into account the handling status of this product in the region having the regional characteristics, the items of the product to be stocked and the necessary inventory amount of this product are predicted.
  • the prediction module 41 corrects the item of the product and the required inventory amount predicted based on the region information based on the handling status of the product in a region having similar region characteristics, thereby obtaining the item of the product to be stocked. And the inventory requirement for this product.
  • the computer 10 refers to the sales results in another sales store having a regional characteristic similar to the location of the sales store, and performs sales prediction in this sales store.
  • step S26 The notification module 21 notifies the trader terminal owned by the trader who sells the product of the predicted item of the product to be stocked and the required inventory amount of the product (step S26).
  • the process in step S26 is the same as the process in step S14 described above.
  • FIG. 5 is a diagram illustrating a flowchart of the learning process executed by the computer 10. The processing executed by each module described above will be described together with this processing.
  • the learning module 43 learns the predicted items to be stocked in the past predetermined period or the same day and the necessary stock amount of the products (step S30). In step S30, the learning module 43 determines, for example, in the past month, the past week, or the same day in the past, the item of the product predicted in the first sales process or the second sales process described above and the inventory of this product. Learn the amount.
  • Step S31 the storage module 30 stores the learned date and the learning result in association with each other.
  • the prediction module 41 predicts an item of a product to be stocked and a necessary stock amount of the product after taking the learning result into consideration (step S32). This processing will be described using the first sales processing as an example.
  • the prediction module 41 should stock the item in the sales store in consideration of the learning result, in addition to the item of the product to be stocked, which is predicted based on the acquired area information, and the necessary stock amount of the product. The item of the product and the required inventory of the product are predicted.
  • the prediction module 41 specifies the medicine D as the item of the commodity to be stocked, and predicts 18 pieces of the necessary stock of the medicine D as the stock.
  • the prediction module 41 stores the average value of the prediction result based on the regional information and the learning result in inventory. Predict the required inventory of goods. By using the average value, it can be expected that the required inventory amount can be more accurately predicted than a prediction result based on one information.
  • the prediction module 41 is not limited to the above-described average value, and may predict other numerical values. For example, if there is a difference equal to or greater than a predetermined threshold between the prediction result based on the region information and the learning result, the prediction result based on the region information is prioritized and the inventory is determined based on the prediction result. It may be estimated as the necessary inventory amount of the product to be manufactured. Further, a configuration may be adopted in which a predetermined coefficient is set according to the learning result instead of the average value, and the prediction result based on the area information is corrected based on the coefficient.
  • the prediction module 41 adds the learning result to the prediction result of the item of the product to be stocked and the necessary inventory amount of this product, similarly to the case of the medical information described above. Is added, the item of the commodity to be inventoried and the necessary inventory amount of this commodity are predicted.
  • the prediction module 41 corrects the item of the product and the required inventory amount predicted based on the regional information based on the learning result, thereby predicting the item of the product to be inventoried and the required inventory amount of the product.
  • the prediction module 41 specifies the medicine D as an item of the commodity to be inventoried, predicts 18 medicines as the necessary inventory amount of the medicine D, and predicts 18 medicines in an area having similar regional characteristics.
  • the handling status of this medicine D is 15.
  • the prediction module 41 determines that the prediction result based on the region information and the prediction result based on the region information indicate that this product in the region having similar region characteristics is obtained.
  • the average value of the handling status and the average value of the learning result are predicted as the required inventory amount of the product to be inventoried. By using the average value, it can be expected that the required inventory amount can be more accurately predicted than a prediction result based on the regional information and the handling status of products having similar regional characteristics.
  • the prediction module 41 is not limited to the above-described average value, and may predict other numerical values. For example, if there is a difference equal to or more than a predetermined threshold between the prediction result based on the regional information, the average value of the handling status of this product in a region having similar regional characteristics, and the learning result, A prediction result based on the handling situation of the product in a region having a region characteristic similar to the information may be prioritized, and the necessary inventory amount of the product to be stocked may be predicted based on the prediction result. Also, a predetermined coefficient is set according to the learning result instead of the average value, and based on the coefficient, the prediction result based on the area information and the handling situation of this product in a region having a similar regional characteristic is corrected. It may be a configuration.
  • the prediction module 41 determines the area similar to the prediction result of the item of the product to be stocked and the necessary inventory amount of this product, similarly to the case of the medical information described above. By adding the learning result to the handling situation of the product in the region having the characteristic, the items of the product to be stocked and the necessary stock amount of the product are predicted.
  • the prediction module 41 corrects, based on the learning result, the prediction based on the item of the product predicted based on the region information and the handling status of the product in a region having a region characteristic similar to the required inventory amount, based on the learning result.
  • the item of the product to be performed and the required inventory of the product are predicted.
  • step S33 The notification module 21 notifies the trader terminal owned by the trader who sells the product of the predicted item of the product to be stocked and the necessary inventory amount of the product (step S33).
  • the processing in step S33 is the same as the processing in step S14 described above.
  • FIG. 6 is a diagram illustrating a flowchart of the suggestion processing executed by the computer 10. The processing executed by each module described above will be described together with this processing.
  • the evaluation receiving module 22 obtains, from the user who has been provided with the product, the evaluation of the item of the product predicted in the first sales process, the second sales process, or the learning process (Step S40). In step S40, the evaluation module 22 evaluates the product (for example, texts such as good or bad, numbers, etc.) from a user terminal owned by the user via a dedicated application, a website, or the like. Symbol).
  • the evaluation determination module 44 determines whether the received evaluation is equal to or smaller than a predetermined threshold (Step S41). In step S41, the evaluation determination module 44 determines whether or not the received evaluations are equal to or more than a predetermined number and whether the evaluations are equal to or lower than a predetermined threshold (meaning that the evaluations are low). When the evaluation determination module 44 determines that the difference is not equal to or smaller than the predetermined threshold (step S41: NO), the present processing ends.
  • step S41 when the evaluation determination module 44 determines that the value is equal to or less than the predetermined threshold value (step S41 YES), the substitute product module 45 creates another product similar to this product as a substitute product plan. (Step S42).
  • the alternative product module 45 creates this alternative product plan based on the regional information acquired by the acquisition module 20 (for example, a test result of a newly released drug or a result of an actual prescription).
  • the proposal module 23 proposes the created alternative product plan to the manager terminal owned by the manager of the sales store (step S43).
  • the suggestion module 23 causes the manager terminal to display a notification for excluding items with low evaluations from the item of the item, and a notification for proposing an alternative item similar to the low evaluation.
  • the means and functions described above are implemented when a computer (including a CPU, an information processing device, and various terminals) reads and executes a predetermined program.
  • the program is provided, for example, in the form of being provided from a computer via a network (SaaS: Software as a Service).
  • the program is provided in a form recorded on a computer-readable recording medium such as a flexible disk, a CD (eg, a CD-ROM), and a DVD (eg, a DVD-ROM, a DVD-RAM).
  • the computer reads the program from the recording medium, transfers the program to an internal recording device or an external recording device, records the program, and executes the program.
  • the program may be recorded in advance on a recording device (recording medium) such as a magnetic disk, an optical disk, or a magneto-optical disk, and may be provided to the computer from the recording device via a communication line.

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Strategic Management (AREA)
  • Finance (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

La présente invention a pour objet de fournir un système informatique, un procédé de commercialisation et un programme qui permettent de prédire avec plus de précision une demande pour un produit. Ce système informatique acquiert des informations régionales se rapportant à une région entourant un magasin de vente (comprenant la distribution de population par âge dans la région entourant le magasin et/ou des informations de SNS se rapportant à cette région et/ou des nouvelles concernant cette région et/ou des informations cliniques se rapportant à cette région et/ou des informations météorologiques concernant cette région), et prédit des articles de produit à stocker dans le magasin de vente, et la quantité de chaque article qui doit être stockée, sur la base des informations régionales acquises. Le système informatique acquiert également des volumes de renouvellement de produits dans une région ayant des caractéristiques régionales (comprenant la population totale et/ou la population par âge et/ou le climat et/ou des maladies prédominantes passées) similaires auxdites informations régionales, et prédit des articles de produits à stocker dans le magasin de ventes, et la quantité de chaque article qui doit être stockée, sur la base des volumes de renouvellements acquis.
PCT/JP2018/028747 2018-07-31 2018-07-31 Système informatique, procédé de commercialisation et programme Ceased WO2020026359A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/JP2018/028747 WO2020026359A1 (fr) 2018-07-31 2018-07-31 Système informatique, procédé de commercialisation et programme

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/JP2018/028747 WO2020026359A1 (fr) 2018-07-31 2018-07-31 Système informatique, procédé de commercialisation et programme

Publications (1)

Publication Number Publication Date
WO2020026359A1 true WO2020026359A1 (fr) 2020-02-06

Family

ID=69231590

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2018/028747 Ceased WO2020026359A1 (fr) 2018-07-31 2018-07-31 Système informatique, procédé de commercialisation et programme

Country Status (1)

Country Link
WO (1) WO2020026359A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115994788A (zh) * 2023-03-20 2023-04-21 北京永辉科技有限公司 一种数据处理分析方法和装置

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002140592A (ja) * 2000-10-30 2002-05-17 Nippon Medical School ネットワークを利用した医療支援システム
JP2004310556A (ja) * 2003-04-08 2004-11-04 Aos:Kk 販売支援システム、販売支援プログラム及び販売支援方法
JP2004326411A (ja) * 2003-04-24 2004-11-18 Life Business Weather:Kk 需要動向予測装置及び需要動向予測システム
JP2005539330A (ja) * 2002-09-18 2005-12-22 ミツイ ブッサン ロジスティックス，インコーポレイテッド 配送チェーンの管理システム及び方法
JP2016024542A (ja) * 2014-07-17 2016-02-08 オリンパス株式会社 医療業務支援装置
JP2017027434A (ja) * 2015-07-24 2017-02-02 トヨタホーム株式会社 冷暖房機器選定支援システム
JP2018036969A (ja) * 2016-09-01 2018-03-08 株式会社博伸社 販路開拓支援システム

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002140592A (ja) * 2000-10-30 2002-05-17 Nippon Medical School ネットワークを利用した医療支援システム
JP2005539330A (ja) * 2002-09-18 2005-12-22 ミツイ ブッサン ロジスティックス，インコーポレイテッド 配送チェーンの管理システム及び方法
JP2004310556A (ja) * 2003-04-08 2004-11-04 Aos:Kk 販売支援システム、販売支援プログラム及び販売支援方法
JP2004326411A (ja) * 2003-04-24 2004-11-18 Life Business Weather:Kk 需要動向予測装置及び需要動向予測システム
JP2016024542A (ja) * 2014-07-17 2016-02-08 オリンパス株式会社 医療業務支援装置
JP2017027434A (ja) * 2015-07-24 2017-02-02 トヨタホーム株式会社 冷暖房機器選定支援システム
JP2018036969A (ja) * 2016-09-01 2018-03-08 株式会社博伸社 販路開拓支援システム

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115994788A (zh) * 2023-03-20 2023-04-21 北京永辉科技有限公司 一种数据处理分析方法和装置

Similar Documents

Publication Publication Date Title
Luo et al. The impact of platform protection insurance on buyers and sellers in the sharing economy: A natural experiment
Moriuchi et al. Satisfaction trust and loyalty of repeat online consumer within the Japanese online supermarket trade
Anderson et al. Advertising in a competitive market: The role of product standards, customer learning, and switching costs
Kalwani et al. A price expectations model of customer brand choice
Elfenbein et al. Market structure, reputation, and the value of quality certification
Che et al. Investigating effects of out-of-stock on consumer stockkeeping unit choice
US20120253828A1 (en) System and method for sensitivity or nutritional factor exposure monitoring
US20140279208A1 (en) Electronic shopping system and service
Calvo-Porral et al. Private label brands: Major perspective of two customer-based brand equity models
Siu et al. Service quality in grocery retailing: The study of a Japanese supermarket in Hong Kong
US20110093401A1 (en) Recall exchange platform
Gaupholm et al. The influence of food environments on dietary behaviour and nutrition in Southeast Asia: A systematic scoping review
JP2018139036A (ja) 分析装置
Coleman et al. Access to allergen-free food among Black and White children with food allergy in the FORWARD study
JP3667726B2 (ja) 販売管理装置及び方法
Ehsani et al. Consumer segmentation based on location and timing dimensions using big data from business-to-customer retailing marketplaces
Herbig et al. Design guidelines for assistance systems supporting sustainable purchase decisions
Ali et al. Evaluating closures of fresh fruit and vegetable vendors during the COVID-19 pandemic: methodology and preliminary results using omnidirectional street view imagery
CN111083211A (zh) 一种基于大数据平台的用户触达方法与系统
US20220138683A1 (en) Property Inventory Tracking
Bendall‐Lyon et al. The influence of mass communication and time on satisfaction and loyalty
WO2020026359A1 (fr) Système informatique, procédé de commercialisation et programme
Zhuo Do low‐price guarantees guarantee low prices? Evidence from competition between Amazon and Big‐Box stores
CN114862508A (zh) 目标产品的展示方法、系统、电子设备和存储介质
JP2010157122A (ja) サンプル商品アンケート管理システム

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18928984

Country of ref document: EP

Kind code of ref document: A1

DPE1 Request for preliminary examination filed after expiration of 19th month from priority date (pct application filed from 20040101)
NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 18928984

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: JP