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] It relates to a technology for business management in an industry with a limit on the number of acceptable customers.

Background Art

[0002] The accommodation and food service industries are industries with constraints on the number of customers that can be accepted. The capacity of facilities and stores is limited, and it is necessary to provide services within that framework.

[0003] On the other hand, in these industries, it is common to vary the price according to the margin of the number of customers that can be accepted in order to maximize profits. While adjusting demand and supply, an effective business strategy is developed.

[0004] On the other hand, when aiming to maximize profits through price fluctuations, it is necessary to consider the magnitude of demand by customers, respond sensitively to market trends and consumer preferences, and conduct business while maintaining a balance.

Summary of the Invention

Problems to be Solved by the Invention

[0005] Therefore, an object of the present invention is to provide an information processing apparatus that generates information useful for business management based on information regarding lost business where a reservation offer has been made but there is no availability and the order has been lost.

Means for Solving the Problems

[0006] One form of the disclosed information processing device includes: an error response storage means that stores, as daily time-series information, information regarding error responses that determine that there is no availability based on the availability information of the room or seat in response to a reservation request for the room or seat; a reservation-related information storage means that stores, as daily time-series information, information regarding the ratio of the number of reservations that are made out of the number of reservation requests for the room or seat; a demand forecasting means that, for a given day, generates a predicted value for the number of reservations for each day from a predetermined number of days prior to the given day, based on the reservation record of other days with similar attributes to the given day; a potential demand forecasting means that, for each day from a predetermined number of days prior to the given day, calculates a potential number of reservations that represents the magnitude of potential demand based on the number of error responses and the ratio; and a demand forecasting correction means that, for each day from a predetermined number of days prior to the given day, generates 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. The potential demand forecasting means calculates the number of potential reservations for each day following a predetermined number of days prior to the other day by multiplying the number of error responses by the ratio. It is characterized by the following: [Effects of the Invention]

[0007] The disclosed information processing device generates information useful for business management based on information about lost business—businesses where a reservation request was made but no availability was found and the deal was lost. [Brief explanation of the drawing]

[0008] [Figure 1] This figure shows an overview of the information processing device according to this embodiment. [Figure 2] This is a functional block diagram of the information processing device according to this embodiment. [Figure 3] This is a diagram illustrating the processing performed by the information processing device according to this embodiment. [Figure 4] This is a diagram illustrating the processing performed by the information processing device according to this embodiment. [Figure 5] This is a diagram illustrating the processing performed by the information processing device according to this embodiment. [Figure 6]This figure shows an example of the hardware configuration of the information processing device according to this embodiment. [Figure 7] This flowchart shows an example of the reservation processing flow using the information processing device according to this embodiment. [Figure 8] This flowchart shows an example of the processing flow when demand forecasting is performed using the information processing device according to this embodiment. [Modes for carrying out the invention]

[0009] The embodiments for carrying out the present invention will be described with reference to the drawings. (Operating principle of the information processing device according to this embodiment)

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

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

[0012] Figure 2 is a functional block diagram of the device 100. As shown in Figure 2, the device 100 includes an error response storage means 110, a reservation-related information storage means 120, a reservation acceptance means 130, a reservation feasibility 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 information 240 regarding error responses 230 that determine there is no availability based on room or seat availability information 220 in response to a request for a room or seat reservation 210, as daily time-series information. The error response storage means 110 may also store information 240 regarding error responses 230 for each type of room or seat that the user wishes to reserve.

[0014] Reservation 210 is a reservation to secure a room at a hotel or inn, or a seat at a restaurant, in advance. Availability information 220 is information regarding the daily reservation status of rooms or seats. Error response 230 applies when there are available seats or rooms, but the user does not make a reservation because the quoted price is unsuitable, and when there are no matches in the user's search criteria.

[0015] Figure 3 is a diagram illustrating an example of data used to explain the processing performed by the device 100. As shown in Figure 3, the error response storage means 110 stores, for example, the number of error responses (240) for October 1, 2022, which has similar attributes to the target date: October 1, 2023, as 40, 44, 13, ... for 1 day prior, 2 days prior, 3 days prior, ...

[0016] The reservation-related information storage means 120 stores information regarding the ratio 270 of the number of reservations 210 that are actually made out of the total number of room or seat reservation requests 250, as daily time-series information. The reservation-related information storage means 120 may also store information regarding the ratio 270 for each type of room or seat that the user wishes to reserve. The number of reservation requests 250 is the number of times a user 330 has searched for a room or seat that meets their desired conditions on a website where room or seat reservations 210 are possible.

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

[0018] As shown in FIG. 3, for example, with respect to October 1, 2022 which has similar attributes to the target date of October 1, 2023, the reservation-related information storage means 120 stores the number of reservation completions 260 one day before, two days before, three days before, etc. as 3 cases, 1 case, 11 cases, etc.

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

[0020] The reservation reception means 130 receives offers for room or seat reservations 210. The reservation reception means 130 may be configured to perform processing for each type of room or seat for which a reservation is desired.

[0021] Based on the occupancy information 220 of the room or seat, the reservation availability determination means 140 determines whether there is availability for the reservation offer 210 received by the reservation reception means 130. The reservation availability determination means 140 may be configured to perform processing for each type of room or seat for which a reservation is desired.

[0022] Based on the reservation performance of other days 290 that are similar in attributes to a certain day 280, the demand prediction means 150 generates predicted values 300 for the number of reservations for each day after the day a predetermined number of days before the certain day 280. "Similar in attributes" is assumed to mean, for example, that the season or time period is the same, the day of the week is the same, etc., and the criteria for similarity can be appropriately determined by the administrator of the apparatus 100. The other days 290 may be one day or multiple days.

[0023] The processing by the demand prediction means 150 is not limited to prediction methods such as, for example, a method using a moving average (of the same day of the week in the past three months), a method using machine learning, a method using deep learning, etc., as long as the reservation performance of other days 290 is used.

[0024] Figures 4 and 5 show the daily trends of the predicted number of reservations 300 and the revised predicted number of reservations 320, which will be described later. As shown in Figure 3, the demand forecasting means 150 generates daily predicted numbers of reservations 300 for August 2, 2023, which is 60 days prior to October 1, 2023, based on reservation data for other days 290: October 1, 2022, for example, Day 1 280: October 1, 2023. As shown in Figures 4 and 5, the demand forecasting means 150 calculates, for example, for Day 1 280: October 1, 2023, the predicted number of reservations 300 for 1 day, 2 days, 3 days, ..., 60 days prior to October 1, 2023, as 202, 200, 200, ..., 152.

[0025] The potential demand forecasting means 160 calculates the number of potential reservations 310, which represents the magnitude of potential demand, for each day from a predetermined number of days prior to the other day 290, based on the ratio 270 of the number of error responses 230 240 and the number of reservation requests 250 to the number of reservations that are actually made 260. The ratio 270 is calculated as the number of reservations that are actually made 260 ÷ the number of reservation requests 250.

[0026] The potential demand forecasting means 160 calculates the number of potential reservations 310 for each day from a predetermined number of days prior to the other day 290 by calculating the number of error responses 240 × the number of reservations that are made 260 ÷ the number of reservation requests 250.

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

[0028] The demand forecast correction means 170, with respect to a day 280, generates a corrected forecast value 320 by adding the number of potential reservations 310 to the predicted number of reservations 300 for each day starting from a predetermined number of days prior to the day 280.

[0029] As shown in Figures 3 to 5, the demand forecast correction means 170 adds the number of potential reservations 310 for 1 day, 2 days, 3 days, ..., 60 days prior to the predicted number of reservations 300 for 1 day, 2 days prior, 3 days prior, ..., 60 days prior, respectively, for a day 280: October 1, 2023. In this way, the demand forecast correction means 170 calculates the corrected predicted values ​​320 for 1 day, 2 days prior, 3 days prior, ..., 60 days prior as follows: 202 + 2.4 = 204.2, 200 + 0.9 = 200.9, 200 + 2.8 = 202.8, ..., 152 + 0.8 = 152.8.

[0030] Furthermore, the demand forecasting means 150, the potential demand forecasting means 160, and the demand forecast correction means 170 may be configured to process each type of room or seat that the user wishes to reserve.

[0031] The accommodation and food service industries cannot accept more bookings than they are fully booked, so their booking curves tend to be lower than their demand. In such cases, by recording information about lost business (240) that was lost due to a lack of available rooms or seats, it is possible to calculate the potential demand (310) and the number of potential bookings.

[0032] By analyzing the reservation curve 320, which incorporates the potential number of reservations 310 calculated by this device 100, managers and other operators in the lodging and food service industries will be able to analyze management indicators that are in line with actual demand.

[0033] Based on the above operating principle, the device 100 generates information useful for business management based on information 240 regarding lost businesses where a reservation request was made but no availability was found. (Hardware configuration of the information processing device according to this embodiment)

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

[0035] The CPU 410 is a device that executes programs stored in the ROM 420. It processes data loaded into the RAM 430 according to program instructions and controls the entire device 100. The ROM 420 stores the programs and data that the CPU 410 will execute. When the CPU 410 executes a program stored in the ROM 420, the RAM 430 loads the programs and data to be executed and temporarily holds the calculation data during the calculation.

[0036] The auxiliary storage device 440 is a device that stores the operating system (OS), which is the basic software, and application programs related to this embodiment, along with 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 may be an HDD (Hard Disk Drive) or flash memory.

[0037] The communication interface 450 is an interface for exchanging data with other devices 340 that provide communication functions, by connecting to a communication network 350 such as a wired or wireless LAN (Local Area Network) or the Internet.

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

[0039] Each of the means of this device 100 may be realized by the CPU 410 executing a program corresponding to each means stored in the ROM 420 or auxiliary storage device 440. Alternatively, each of the means of this device 100 may be realized by the processing related to each means being implemented as hardware. Furthermore, the program according to the present invention may be read from an external server device via the communication I / F 450, or read from the recording medium 490 via the recording medium I / F 480, and the device 100 may execute the program. (Example of processing by the information processing device according to this embodiment) (1) Reservation processing by this device 100 The flow of a reservation processing example using this device 100 will be explained using Figure 7. Figure 7 is a flowchart showing the flow of a reservation processing example using this device 100.

[0040] In S10, the reservation acceptance means 130 accepts requests for room or seat reservations 210. The reservation acceptance means 130 may also process the requests for reservations 210 for each type of room or seat that is desired.

[0041] In S20, the reservation availability determination means 140 determines whether there is availability for the reservation request 210 received in S10, based on the room or seat availability information 220. The reservation availability determination means 140 may also perform processing for each type of room or seat for which the reservation 210 is requested.

[0042] In S30, the reservation availability determination means 140 responds to the reservation request 210 received in S10, indicating that there is availability. Subsequently, the reservation 210 for a room or seat is completed through a reservation request operation on the user terminal 340. In S40, the reservation availability determination means 140 responds to the reservation request 210 received in S10 by indicating that there are no available slots, i.e., an error response 230.

[0043] The device 100 stores, in its error response storage means 110, information 240 regarding error responses 230 that are determined to be unavailable based on room or seat availability information 220 in response to a request for a room or seat reservation 210, as daily time-series information.

[0044] Furthermore, the device 100 stores information regarding the ratio 270 of the number of reservations 210 that are actually made out of the total number of room or seat reservation requests 250, as daily time-series information in the reservation-related information storage means 120. The number of reservation requests 250 is the number of times a user 330 has searched for a room or seat on a website where room or seat reservations 210 are possible, based on the conditions desired by the user 330. (2) Processing during demand forecasting by this device 100 Figure 8 illustrates the flow of a reservation processing example using this device 100. Figure 8 is a flowchart showing the flow of a demand forecasting processing example using this device 100.

[0045] In S110, the demand forecasting means 150 generates a predicted number of daily reservations 300 for a given day 280, based on the reservation history of other days 290 that have similar attributes to day 280. "Similar attributes" could mean, for example, that the seasons / times are the same, that the days of the week are the same, or that the dates of specific events are close together. The criteria for similarity can be determined as appropriate by the administrator of this device 100. Other days 290 can be one day or multiple days.

[0046] The processing by the demand forecasting means 150 is not limited to any forecasting method, as long as it uses the reservation data for 290 other days. For example, it can use methods such as moving averages (for the same day of the week in the past three months), machine learning methods, or deep learning methods.

[0047] Figures 4 and 5 show the daily trends of the predicted number of reservations 300 and the revised predicted number of reservations 320, which will be described later. As shown in Figure 3, the demand forecasting means 150 generates daily predicted numbers of reservations 300 for August 2, 2023, which is 60 days prior to October 1, 2023, based on reservation data for other days 290: October 1, 2022, for example, Day 1 280: October 1, 2023. As shown in Figures 4 and 5, the demand forecasting means 150 calculates, for example, for Day 1 280: October 1, 2023, the predicted number of reservations 300 for 1 day, 2 days, 3 days, ..., 60 days prior to October 1, 2023, as 202, 200, 200, ..., 152.

[0048] In S120, the potential demand forecasting means 160 calculates the number of potential reservations 310, which represents the magnitude of potential demand, for each day from a predetermined number of days prior to the other day 290, based on the ratio 270 of the number of reservations that are actually made out of the number of reservation offers 250 and the number of error responses 230 240. The ratio 270 is calculated as the number of reservations that are actually made 260 ÷ the number of reservation offers 250.

[0049] The potential demand forecasting means 160 calculates the number of potential reservations 310 for each day from a predetermined number of days prior to the other day 290 by calculating the number of error responses 240 × the number of reservations that are made 260 ÷ the number of reservation requests 250.

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

[0051] In S130, the demand forecast correction means 170 generates a corrected forecast value 320 by adding the number of potential reservations 310 to the predicted number of reservations 300 for each day from a predetermined number of days prior to the first day 280, with respect to a day 280.

[0052] As shown in Figures 3 to 5, the demand forecast correction means 170 adds the number of potential reservations 310 for 1 day, 2 days, 3 days, ..., 60 days prior to the predicted number of reservations 300 for 1 day, 2 days prior, 3 days prior, ..., 60 days prior, respectively, for a day 280: October 1, 2023. In this way, the demand forecast correction means 170 calculates the corrected predicted values ​​320 for 1 day, 2 days prior, 3 days prior, ..., 60 days prior 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 food service industries, reservations cannot be made beyond full occupancy or capacity, so the reservation curve for the last minute tends to be lower than the demand. In such cases, by recording information on lost business (240) that was not made due to a lack of available rooms or seats, it is possible to calculate the potential demand (310) and the number of potential reservations.

[0054] Furthermore, the demand forecasting means 150, the potential demand forecasting means 160, and the demand forecast correction means 170 may be configured to process each type of room or seat that the user wishes to reserve.

[0055] By analyzing the reservation curve 320, which incorporates the potential number of reservations 310 calculated by this device 100, managers and other operators in the lodging and food service industries will be able to analyze 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 operating principle, using information 240 about lost businesses where a reservation request 210 was made but was lost due to lack of availability.

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

[0058] 100 Information Processing Devices 110 Error response storage means 120 Reservation-related information storage means 130 Reservation methods 140 Reservation Availability Determination Method 150 Demand forecasting methods 160 Potential Demand Forecasting Methods 170 Demand forecast correction methods 210 Room or seat reservation 220 Room or seat availability information 230 Error Response 240 Error Responses 250 reservation requests 260 confirmed bookings 270 Percentage of confirmed reservations out of total reservation requests 280 Day 1 290 Other days Predicted number of reservations: 300 310 potential bookings 320 Revised forecast for number of reservations 330 users 340 user terminals 350 Communication Networks 410 CPU 420 ROM 430 RAM 440 Auxiliary storage 450 communication interfaces 460 Input Devices 470 Output device 480 Recording Media Interfaces 490 recording media

Claims

1. An error response storage means stores information regarding error responses that determine, based on the availability information of the room or seat, are unavailable in response to a request for a room or seat reservation, as daily time-series information. A reservation-related information storage means that stores information regarding the ratio of reservations that are actually made out of the number of reservations made for the aforementioned room or seat as daily time-series information, A demand forecasting means that generates a predicted number of daily reservations for a given day, based on reservation records for other days with similar attributes to that day, starting from a predetermined number of days prior to that day. Regarding the other days, a potential demand forecasting means calculates a potential number of reservations representing the magnitude of potential demand for each day from the day prior to the predetermined number of days prior, based on the number of error responses and the percentage; With respect to the aforementioned day, the system includes a demand forecast correction means that generates a corrected forecast value by adding the number of potential reservations to the predicted number of reservations for each day starting from a predetermined number of days prior to the aforementioned day, thereby correcting the predicted number of reservations. The information processing device is 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 following a predetermined number of days prior to the other day.

2. An information processing method in an information processing device comprising: an error response storage means that stores information regarding error responses that determine, based on the availability information of the room or seat, are unavailable in response to a request for a room or seat reservation, as daily time-series information; and a reservation-related information storage means that stores information regarding the ratio of the number of room or seat reservation requests to the number of reservations that are actually made, as daily time-series information; The demand forecasting means includes the step of generating a predicted number of daily reservations for a given day, based on reservation records for other days with similar attributes to that day, starting from a predetermined number of days prior to that day. The potential demand forecasting means calculates the number of potential reservations, which represents the magnitude of potential demand, for each day from a predetermined number of days prior to the other day, based on the number of error responses and the percentage. The demand forecast correction means includes the step of generating a corrected forecast value by adding the number of potential reservations to the predicted number of reservations for each day from a predetermined number of days prior to the aforementioned day, with respect to the aforementioned day, An information processing method 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 following a predetermined number of days prior to the other day.

3. An information processing program for causing a computer to perform the method described in claim 2.

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