Information processing device

Abstract

To generate information useful for management based on information relating to lost business in which reservation requests were made but could not be accepted due to a lack of availability.SOLUTION: An information processing device includes demand forecasting means that, for a given day, generates forecast values of the number of reservations for each day from a day that is a specified number of days before the given day onward, based on reservation results of other days having attributes similar to those of the given day; potential demand forecasting means that, for other days, calculates a potential reservation number representing a magnitude of latent demand for each day from a day that is a specified number of days before onward, based on a number of error responses indicating that no availability exists for reservation requests and a proportion of reservation requests that result in successful reservations; and demand forecast correction means that, for the given day, generates corrected forecast values of the number of reservations by adding the potential reservation number to the forecast values of the number of reservations for each day from the day that is the specified number of days before onward.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] This relates to technology for business management in businesses that have limitations on the number of customers they can accept. [Background technology]

[0002] The accommodation and restaurant industries are businesses that are limited in the number of customers they can accommodate. Facilities and stores have limited capacity, and they must provide services within that limit.

[0003] On the other hand, in these businesses, it is common to adjust prices according to the number of customers they can accept in order to maximize profits. They develop effective management strategies by adjusting supply and demand.

[0004] On the other hand, if you aim to maximize profits through price fluctuations, you need to take into account the size of customer demand, respond sensitively to market trends and consumer preferences, and conduct your business while striking a balance. Summary of the Invention [Problem to be solved by the invention]

[0005] Therefore, the present invention aims to provide an information processing device that generates information useful for business management based on information about lost business where a reservation was made but was lost due to a lack of availability. [Means for solving the problem]

[0006] One form of the information processing device to be disclosed comprises: an error response storage means for storing, as daily time-series information, information regarding an error response in response to a request for reservation of a room or a seat, which is determined to mean that there is no vacancy based on occupancy information for the room or seat; a reservation-related information storage means for storing, as daily time-series information, information regarding the proportion of reservations that are established among the number of reservation requests for the room or seat; a demand forecasting means for generating, for a given day, a predicted value of the number of reservations for each day from the day a predetermined number of days before the given day onwards, based on the reservation records for other days that have similar attributes to the given day; a potential demand forecasting means for calculating, for the other days, a potential number of reservations that represents the magnitude of potential demand, based on the number of error responses and the proportion; and a demand forecast correction means for generating, for the given day, a corrected predicted value that corrects the predicted value of the number of reservations by adding the potential number of reservations to the predicted value of the number of reservations, for each day from the day a predetermined number of days before the given day onwards. [Effects of the Invention]

[0007] The disclosed information processing device generates information useful for business management based on information about lost business where a reservation was made but was lost due to lack of availability. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a diagram showing an overview of an information processing device according to an embodiment of the present invention; [Figure 2] 1 is a functional block diagram of an information processing device according to an embodiment of the present invention; [Figure 3] FIG. 2 is a diagram for explaining processing by the information processing device according to the present embodiment. [Figure 4] FIG. 2 is a diagram for explaining processing by the information processing device according to the present embodiment. [Figure 5] FIG. 2 is a diagram for explaining processing by the information processing device according to the present embodiment. [Figure 6] FIG. 1 is a diagram illustrating an example of a hardware configuration of an information processing device according to an embodiment of the present invention. [Figure 7]10 is a flowchart showing the flow of an example of a reservation process performed by the information processing device according to the present embodiment. [Figure 8] 10 is a flowchart showing a flow of an example of processing at the time of demand forecasting by the information processing device according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described with reference to the drawings. (Operation principle of the information processing device according to this embodiment)

[0010] The operating principle of an information processing device (hereinafter simply referred to as "the device") 100 according to this embodiment will be described with reference to Figures 1 to 5. Figure 1 is a diagram showing the connection relationship between the device 100 and other devices.

[0011] As shown in Fig. 1, the device 100 is connected to a user terminal 340 via a communication network 350. The communication network 350 may be either wired or wireless, or may be a public communication network such as the Internet. The user terminal 340 is a terminal operated by a user 330 who is a potential customer, and may be a mobile information terminal such as a smartphone, or a personal computer such as a desktop or laptop computer.

[0012] Fig. 2 is a functional block diagram of the present device 100. As shown in Fig. 2, the present device 100 includes an error response storage means 110, a reservation-related information storage means 120, a reservation acceptance means 130, a reservation acceptance determination means 140, a demand forecasting means 150, a potential demand forecasting means 160, and a demand forecast correction means 170.

[0013] The error response storage means 110 stores, as daily chronological information, information 240 regarding error responses 230 that are determined to indicate no availability based on room or seat occupancy information 220 in response to a request for room or seat reservation 210. The error response storage means 110 may also be configured to store information 240 regarding error responses 230 for each type of room or seat desired to be reserved.

[0014] A reservation 210 is a reservation to secure a room in a hotel or inn, or a seat in a restaurant, in advance. Vacancy information 220 is information about the daily reservation status of rooms or seats. An error response 230 is intended for cases where there are vacant seats or rooms, but no reservation is made because the offered price is not suitable, and is intended for cases where there are no hits based on the search criteria of the user 330.

[0015] 3 is a diagram showing an example of data for explaining processing by the present device 100. As shown in Fig. 3, for example, the error response storage means 110 stores the number of error responses 240 for 1 day before, 2 days before, 3 days before, ... as 40, 44, 13, ... for October 1, 2022, which has similar attributes to the target date: October 1, 2023.

[0016] The reservation-related information storage means 120 stores information about the ratio 270 of the number 260 of confirmed reservations 210 out of the number 250 of room or seat reservation requests 210 as daily chronological information. The reservation-related information storage means 120 may also store information about the ratio 270 for each type of room or seat desired to be reserved. The number 250 of reservation requests 210 is the number of searches for rooms or seats that meet the conditions desired by the user 330 on a website where the user 330 can reserve a room or seat 210.

[0017] As shown in Figure 3, for example, for October 1, 2022, which has similar attributes to the target date: October 1, 2023, the reservation-related information storage means 120 stores the number of reservation requests (250) for 1 day before, 2 days before, 3 days before, etc. as 50, 51, 52, etc.

[0018] As shown in Figure 3, the reservation-related information storage means 120 stores, for example, the number of completed reservations 260 for 1 day before, 2 days before, 3 days before, etc. for October 1, 2022, which has similar attributes to the target date: October 1, 2023, as 3, 1, 11, etc.

[0019] As shown in Figure 3, the reservation-related information storage means 120 stores, for example, the percentages 270 of 1 day before, 2 days before, 3 days before, etc. for October 1, 2022, which has similar attributes to the target date: October 1, 2023, as 6%, 2%, 21%, etc.

[0020] The reservation receiving means 130 receives requests for room or seat reservations 210. The reservation receiving means 130 may be configured to perform processing for each type of room or seat desired to be reserved.

[0021] The reservation acceptance determination means 140 determines whether or not there is a vacancy for the reservation 210 accepted by the reservation acceptance means 130, based on the room or seat vacancy information 220. The reservation acceptance determination means 140 may be configured to perform processing for each type of room or seat desired to be reserved.

[0022] The demand forecasting means 150 generates a forecast value 300 of the number of reservations for each day from the day a predetermined number of days before the first day 280 onward, based on the reservation records for other days 290 that have similar attributes to the first day 280. "Similar attributes" may mean, for example, that the seasons or times of the year are the same, or that the days of the week are the same, and the administrator of the device 100 can determine the criteria for similarity as appropriate. The other days 290 may be one day or multiple days.

[0023] The processing by the demand forecasting means 150 is not limited to any particular forecasting method, as long as it uses the reservation records for other days 290, and may be, for example, a method using a moving average (for the same day of the week in the past three months), a machine learning method, a deep learning method, or the like.

[0024] 4 and 5 are diagrams showing daily trends in the predicted value 300 of the number of reservations and the revised predicted value 320 of the number of reservations, which will be described later. As shown in FIG. 3, for example, for one day 280: October 1, 2023, the demand forecasting means 150 generates a predicted value 300 of the number of reservations for each day from August 2, 2023 onwards, which is 60 days before October 1, 2023, based on the reservation record for another day 290: October 1, 2022. As shown in FIGS. 4 and 5, for example, for one day 280: October 1, 2023, the demand forecasting means 150 calculates the predicted values ​​300 of the number of reservations 1 day before, 2 days before, 3 days before, ..., 60 days before as 202, 200, 200, ..., 152.

[0025] The potential demand forecasting means 160 calculates the number of potential reservations 310, which indicates the size of potential demand, for each day from the day a predetermined number of days ago onwards for other days 290, based on the number of error responses 230 240 and the ratio 270 of the number of successful reservations 260 out of the number of reservation requests 250. The ratio 270 is calculated by dividing the number of successful reservations 260 by the number of reservation requests 250.

[0026] For other days 290, the potential demand forecasting means 160 calculates the potential number of reservations 310 by calculating the number of error responses 240 × the number of confirmed reservations 260 ÷ the number of reservation requests 250 for each day from the day a specified number of days ago.

[0027] As shown in Figure 3, for example, the potential demand forecasting means 160 calculates the number of potential reservations 310 for another day 290: October 1, 2022, 1 day before, 2 days before, 3 days before, etc., as follows: 40 x 3 ÷ 50 = 2.4, 44 x 1 ÷ 51 = 0.9, 13 x 11 ÷ 52 = 2.8, etc.

[0028] The demand forecast correction means 170 generates a corrected forecast value 320 that corrects the forecast value 300 of the number of reservations by adding the number of potential reservations 310 to the forecast value 300 of the number of reservations for each day from the day a predetermined number of days before the day 280 onwards.

[0029] 3 to 5, for example, for one day 280 (October 1, 2023), the demand forecast correction means 170 adds the potential reservations 310 for one day, two days, three days, ..., and 60 days before to the predicted values ​​300 of the number of reservations for one day before, two days before, three days before, ..., and 60 days before, respectively. In this way, the demand forecast correction means 170 calculates the corrected predicted values ​​320 for one day before, two days before, three days before, ..., and 60 days before as follows: 202 + 2.4 = 204.2, 200 + 0.9 = 200.9, 200 + 2.8 = 202.8, ..., 152 + 0.8 = 152.8.

[0030] The demand forecasting means 150, potential demand forecasting means 160, and demand forecast correcting means 170 may be configured to perform processing for each type of room or seat desired to be reserved.

[0031] In the accommodation and restaurant industries, it is not possible to make reservations beyond the full capacity, so the last minute reservation curve tends to be lower than the demand. In such cases, by recording information 240 on lost business where there were no rooms or seats available, it is possible to calculate the number of potential reservations 310, which represents potential demand.

[0032] By analyzing the reservation curve 320 that takes into account the potential number of reservations 310 calculated by this device 100 in this way, managers of the accommodation and restaurant industries will be able to analyze business management indicators that are in line with actual demand.

[0033] Based on the above-described operating principle, the device 100 generates information useful for business management based on information 240 about lost business where a reservation was made but was lost due to lack of availability. (Hardware configuration of the information processing device according to this embodiment)

[0034] An example of the hardware configuration of the device 100 will be described using Fig. 6. Fig. 6 is a diagram showing an example of the hardware configuration of the device 100. As shown in Fig. 6, the device 100 has a CPU (Central Processing Unit) 410, a ROM (Read-Only Memory) 420, a RAM (Random Access Memory) 430, an auxiliary storage device 440, a communication I / F 450, an input device 460, a display device 470, and a recording medium I / F 480.

[0035] The CPU 410 is a device that executes a program stored in the ROM 420, performs arithmetic processing on data loaded into the RAM 430 in accordance with instructions from the program, and controls the entire device 100. The ROM 420 stores the programs and data to be executed by the CPU 410. When the CPU 410 executes a program stored in the ROM 420, the programs and data to be executed are loaded into the RAM 430, and the RAM 430 temporarily holds the arithmetic data during the calculation.

[0036] The auxiliary storage device 440 is a device that stores the OS (Operating System), which is basic software, the application program according to this embodiment, and other related data. The auxiliary storage device 440 includes, for example, an error response storage means 110 and a reservation-related information storage means 120, and is an HDD (Hard Disk Drive) or flash memory.

[0037] The communication I / F 450 is an interface for connecting to a communication network 350 such as a wired or wireless LAN (Local Area Network) or the Internet, and for transmitting and receiving data to and from other devices 340 that provide communication functions.

[0038] The input device 460 is a device such as a keyboard for inputting data to the device 100. The display device (output device) 470 is a device formed of an LCD (Liquid Crystal Display) or the like, and functions as a user interface when the user uses the functions of the device 100 or when making various settings. The recording medium I / F 480 is an interface for sending and receiving data to and from a recording medium 490 such as a CD-ROM, DVD-ROM, or USB memory.

[0039] Each of the means included in the device 100 may be realized by the CPU 410 executing a program corresponding to each of the means stored in the ROM 420 or the auxiliary storage device 440. Each of the means included in the device 100 may also be realized by hardware that performs the processing of the respective means. Alternatively, the device 100 may be caused to execute the program by reading the program according to the present invention from an external server device via the communication I / F 450 or by reading the program according to the present invention from the recording medium 490 via the recording medium I / F 480. (Example of processing by the information processing device according to this embodiment) (1) Reservation Processing by the Device 100 An example of the flow of reservation processing by the device 100 will be described with reference to Fig. 7. Fig. 7 is a flowchart showing the flow of reservation processing by the device 100.

[0040] In S10, the reservation receiving means 130 receives a request for a room or seat reservation 210. The reservation receiving means 130 may be configured to process the reservation 210 for each type of room or seat.

[0041] In S20, the reservation acceptance determination means 140 determines whether or not there is a vacancy for the reservation 210 accepted in S10, based on the room or seat vacancy information 220. The reservation acceptance determination means 140 may be configured to perform processing for each type of room or seat desired for reservation 210.

[0042] In S30, the reservation acceptance determination means 140 responds to the reservation 210 accepted in S10 by stating that there is availability. Thereafter, the reservation application operation on the user terminal 340 establishes the reservation 210 of the room or seat. In S40, the reservation acceptance / non-acceptance determination means 140 responds to the reservation 210 accepted in S10 by returning an error response 230 indicating that there is no availability.

[0043] In the error response storage means 110, the device 100 stores information 240 regarding an error response 230 in response to a request for a room or seat reservation 210, which is determined to be unavailable based on room or seat occupancy information 220, as daily time-series information.

[0044] Furthermore, the device 100 stores, as daily time-series information in the reservation-related information storage means 120, information on the ratio 270 of the number 260 of confirmed reservations 210 out of the number 250 of room or seat reservations 210 requested. The number 250 of reservation 210 requests is the number of searches for rooms or seats that meet the conditions desired by the user 330 on a website where the room or seat reservation 210 can be made. (2) Processing during demand forecasting by the device 100 An example of the flow of processing performed by the device 100 at the time of reservation will be described with reference to Fig. 8. Fig. 8 is a flowchart showing an example of the flow of processing performed by the device 100 at the time of demand forecasting.

[0045] In S110, the demand forecasting means 150 generates a forecast value 300 of the number of reservations for each day from the day a predetermined number of days before the first day 280 onward, based on the reservation records for other days 290 that have similar attributes to the first day 280. "Similar attributes" may mean, for example, that the seasons or times of the year are the same, that the days of the week are the same, or that a specific event is taking place close by, and the administrator of the device 100 can determine the criteria for similarity as appropriate. The other days 290 may be one day or multiple days.

[0046] The processing by the demand forecasting means 150 is not limited to any particular forecasting method, as long as it uses the reservation records for other days 290, and may be, for example, a method using a moving average (for the same day of the week in the past three months), a machine learning method, a deep learning method, or the like.

[0047] 4 and 5 are diagrams showing daily trends in the predicted value 300 of the number of reservations and the revised predicted value 320 of the number of reservations, which will be described later. As shown in FIG. 3, for example, for one day 280: October 1, 2023, the demand forecasting means 150 generates a predicted value 300 of the number of reservations for each day from August 2, 2023 onwards, which is 60 days before October 1, 2023, based on the reservation record for another day 290: October 1, 2022. As shown in FIGS. 4 and 5, for example, for one day 280: October 1, 2023, the demand forecasting means 150 calculates the predicted values ​​300 of the number of reservations 1 day before, 2 days before, 3 days before, ..., 60 days before as 202, 200, 200, ..., 152.

[0048] In S120, the potential demand forecasting means 160 calculates the number of potential reservations 310, which indicates the size of potential demand, for each day from the day a predetermined number of days ago onwards, for other days 290, based on the number of error responses 230 240 and the ratio 270 of the number of successful reservations 260 out of the number of reservation requests 250. The ratio 270 is calculated by dividing the number of successful reservations 260 by the number of reservation requests 250.

[0049] For other days 290, the potential demand forecasting means 160 calculates the potential number of reservations 310 by calculating the number of error responses 240 × the number of confirmed reservations 260 ÷ the number of reservation requests 250 for each day from the day a specified number of days ago.

[0050] As shown in Figure 3, for example, the potential demand forecasting means 160 calculates the number of potential reservations 310 for another day 290: October 1, 2022, 1 day before, 2 days before, 3 days before, etc., as follows: 40 x 3 ÷ 50 = 2.4, 44 x 1 ÷ 51 = 0.9, 13 x 11 ÷ 52 = 2.8, etc.

[0051] In S130, the demand forecast correction means 170 generates a corrected forecast value 320 that corrects the forecast value 300 of the number of reservations by adding the number of potential reservations 310 to the forecast value 300 of the number of reservations for each day from the day a predetermined number of days before the day 280 onwards.

[0052] 3 to 5, for example, for one day 280 (October 1, 2023), the demand forecast correction means 170 adds the potential reservations 310 for one day, two days, three days, ..., and 60 days before to the predicted values ​​300 of the number of reservations for one day before, two days before, three days before, ..., and 60 days before, respectively. In this way, the demand forecast correction means 170 calculates the corrected predicted values ​​320 for one day before, two days before, three days before, ..., and 60 days before as follows: 202 + 2.4 = 204.2, 200 + 0.9 = 200.9, 200 + 2.8 = 202.8, ..., 152 + 0.8 = 152.8.

[0053] In the accommodation and restaurant industries, reservations cannot be made when the hotel or restaurant is fully booked, so the last-minute reservation curve tends to be lower than the demand. In such cases, by recording information 240 about lost business where there were no rooms or seats available, it is possible to calculate the number of potential reservations 310, which represents potential demand.

[0054] The demand forecasting means 150, potential demand forecasting means 160, and demand forecast correcting means 170 may be configured to perform processing for each type of room or seat desired to be reserved.

[0055] By analyzing the reservation curve 320 that takes into account the potential number of reservations 310 calculated by this device 100 in this way, managers of the accommodation and restaurant industries will be able to analyze business management indicators that are in line with actual demand.

[0056] By performing the above-described processing, the device 100 generates information useful for business management based on the above-described operating principle, based on information 240 about lost business where a reservation 210 was made but was lost due to lack of availability.

[0057] Although the embodiments of the present invention have been described in detail above, the present invention is not limited to such specific embodiments, and various modifications and variations are possible within the scope of the gist of the present invention as defined in the claims. [Explanation of symbols]

[0058] 100 Information processing device 110 Error response storage means 120 Reservation-related information storage means 130 Reservation acceptance method 140 Reservation availability determination means 150 Demand Forecasting Methods 160 Potential demand forecasting methods 170 Demand forecast correction methods 210 Room or seat reservations 220 Room or seat availability information 230 Error Response 240 Error Responses 250 Reservation Requests 260 successful reservations 270 Percentage of successful reservations out of total reservation requests 280 First Day 290 other days 300 Reservations Forecast 310 potential reservations 320 Revised forecast for number of reservations 330 users 340 user terminals 350 Communication Network 410 CPU 420 ROM 430 RAM 440 Auxiliary storage 450 communication interface 460 Input Device 470 Output Device 480 Recording Media Interface 490 Recording Media

Claims

1. an error response storage means for storing, as daily time-series information, information regarding an error response in response to a request for reservation of a room or a seat, in which it is determined that there is no vacancy based on the occupancy information of the room or the seat; a reservation-related information storage means for storing information relating to the ratio of the number of successful reservations among the number of reservations for the room or seat as daily time-series information; a demand forecasting means for generating a forecast value of the number of reservations for each day from a day a predetermined number of days before a certain day onward, based on the reservation records for other days having attributes similar to those of the certain day; a potential demand forecasting means for calculating a potential number of reservations representing the magnitude of potential demand based on the number of error responses and the ratio for each day from the day before the predetermined number of days before the other days; and a demand forecast correction means for generating a corrected forecast value that corrects the forecast value of the number of reservations by adding the number of potential reservations to the forecast value of the number of reservations for each day from the day a predetermined number of days before the one day onwards.

2. The information processing device according to claim 1, characterized in that the potential demand forecasting means calculates the number of potential reservations by multiplying the number of error responses by the ratio for each day after the day that is the specified number of days before the other days.

3. 2. The information processing apparatus according to claim 1, wherein the demand forecasting means, potential demand forecasting means, and demand forecast correcting means perform processing for each type of room or seat desired to be reserved.

4. a reservation receiving means for receiving a request for reservation of the room or seat; 2. The information processing apparatus according to claim 1, further comprising a reservation acceptance / rejection determination means for determining whether or not a room or seat is available in response to the reservation request, based on the room or seat availability information.

5. An information processing method for an information processing device comprising: an error response storage means for storing, as daily time-series information, information regarding an error response in response to a request for reservation of a room or a seat, in which it is determined that there is no vacancy based on occupancy information for the room or the seat; and a reservation-related information storage means for storing, as daily time-series information, information regarding the ratio of the number of reservation requests for the room or the seat that are established, a step in which a demand forecasting means generates, for a given day, a forecast value of the number of reservations for each day from a day a predetermined number of days before the given day onward, based on the reservation records of other days having attributes similar to those of the given day; a step in which potential demand forecasting means calculates a number of potential reservations representing the magnitude of potential demand for each of the other days onward from the day before the predetermined number of days ago, based on the number of error responses and the ratio; a step in which a demand forecast correction means generates a corrected forecast value that corrects the forecast value of the number of reservations by adding the number of potential reservations to the forecast value of the number of reservations for each day from the day a predetermined number of days before the one day onwards.

6. The information processing method according to claim 5, characterized in that the potential demand forecasting means calculates the number of potential reservations by multiplying the number of error responses by the ratio for each day after the day that is the specified number of days before the other days.

7. 6. The information processing method according to claim 5, wherein the demand forecasting means, potential demand forecasting means, and demand forecast correcting means perform processing for each type of room or seat desired to be reserved.

8. A reservation receiving means receives a request for reservation of the room or seat; 6. The information processing method according to claim 5, further comprising a step in which reservation availability determining means determines whether or not there is a vacancy in response to said reservation request based on the vacancy information of said room or seat.

9. An information processing program for causing a computer to execute the method according to any one of claims 5 to 8.

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