Recommended workforce planning methods

Abstract

To automatically generate a manpower plan in a drinking-eating establishment on the basis of sales track record of a store.SOLUTION: In a system where a server terminal, a plurality of store terminals, a store register terminal (POS terminal), and a management system are respectively connected via a network, a server terminal 100 includes a storage unit 120 and a control unit 130. An information reception unit 131 of the control unit 130 receives information on a store, such as sales track record data, etc., from a store user terminal, and stores it in a store data storage unit 121 of the storage unit 120. A store information processing unit 132 of the control unit is trained by machine learning on the basis of stored information, and generates a learning model for performing sales prediction, etc., that are described later. The store information processing unit 132 references the sales track record data and reservation data of a store, and generates future sales prediction data of the store on the basis of the learning model. A prediction calculation processing unit 133 of the control unit references the sales prediction data and generates necessary working time prediction for the personnel of the store on the basis of the learning model.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a method for recommending staffing plans for a restaurant establishment. [Background technology]

[0002] Traditionally, restaurants have implemented POS systems to consistently manage everything from order processing to accounting.

[0003] For example, as shown in the technology disclosed in Patent Document 1, by using a POS system in a restaurant, the entire process from order processing to accounting can be managed electronically, making it possible to easily grasp at a later date the daily sales figures and the characteristics of customers visiting the restaurant, which can be useful for the management of the restaurant. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2005-234782 Summary of the Invention [Problem to be solved by the invention]

[0005] However, as mentioned above, POS systems used in restaurants are limited to managing sales and reservation records, and it is difficult to add other functions. In particular, when it comes to personnel planning, such as creating actual shifts, they rely heavily on the experience of the store manager, resulting in variations between stores and a lack of accuracy.

[0006] Therefore, the present invention aims to provide a method for predicting sales based on store sales records, etc., and then automatically generating a forecast of required working hours by time period, which serves as the basis for creating personnel plans, based on the forecast. [Means for solving the problem]

[0007] In one aspect of the present invention, there is provided a method for recommending staffing plans for a restaurant, in which a control unit of a server terminal references the store's sales record and other data stored in a memory unit of the server terminal, predicts sales, and generates a forecast of the required working hours for store staff, such as a shift schedule, based on the forecast. [Effects of the Invention]

[0008] According to the present invention, it is possible to provide a method for automatically generating a forecast of required working hours by time period, which serves as the basis for personnel planning, based on store sales records and the like. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a block diagram showing a system for executing a method for automatically generating a time-zone-specific required work hour prediction according to a first embodiment of the present invention. FIG. [Figure 2] FIG. 2 is a functional block diagram showing the server terminal 100 of FIG. [Figure 3] FIG. 2 is a functional block diagram showing the store terminal 200 of FIG. 1. [Figure 4] FIG. 2 is a diagram showing an example of store data stored in the server 100. [Figure 5] 1 is a flowchart showing a method for learning data such as store sales results according to the first embodiment of the present invention. [Figure 6] 1 is a flowchart illustrating a method for generating a sales forecast and a reservation forecast for a store according to a first embodiment of the present invention. [Figure 7] 10 is an example of a screen displaying a daily sales forecast for a store according to the first embodiment of the present invention. [Figure 8] 1 is a flowchart illustrating a method for generating a time-of-day required labor hour forecast for a store according to a first embodiment of the present invention. [Figure 9] 10 is an example of a screen displaying hourly sales forecasts for a store according to the first embodiment of the present invention. [Figure 10] 10 is an example of a screen displaying hourly sales forecasts and required staff numbers in a store according to the first embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that the embodiments described below do not unduly limit the content of the present invention described in the claims. Furthermore, not all of the components shown in the embodiments are necessarily essential components of the present invention.

[0011] <Configuration> FIG. 1 is a block diagram showing a system for automatically generating a staffing plan according to a first embodiment of the present invention. This system 1 receives daily sales performance information and shift information from, for example, the store's cash register or a management system implemented by the store, performs sales forecasts and reservation forecasts using a predetermined method based on the acquired information, and generates the necessary staffing forecasts and creates a staffing plan for the store. The system includes a server terminal 100, store terminals 200A and 200B managed by a store user, a store cash register terminal (POS terminal) 300A, and a management system 300B implemented by the store. For ease of explanation, each terminal is described as being a single terminal or a specific number of terminals, but the number of each is not limited.

[0012] The server terminal 100, the store terminals 200A and 200B, the store register terminal (POS terminal) 300A, and the management system 300B are all connected via a network NW1. The network NW is configured by the Internet, an intranet, a wireless LAN (Local Area Network), a WAN (Wide Area Network), or the like.

[0013] The server terminal 100 may be, for example, a general-purpose computer such as a workstation or a personal computer, or may be logically realized by cloud computing.

[0014] The store terminals 200A, 200B, the store's cash register terminal (POS terminal) 300A, and the management system 300B introduced by the store are, for example, information processing devices such as personal computers and tablet terminals, but may also be configured as smartphones, mobile phones, PDAs, etc.

[0015] In this embodiment, the system 1 is described as comprising a server terminal 100, store terminals 200A and 200B, a store register terminal (POS terminal) 300A, and a management system 300B, and a configuration in which users of each terminal use their respective terminals to perform operations on the server terminal 100; however, the server terminal 100 may be configured as a standalone unit, and the server terminal itself may have the function of allowing each user to perform operations directly.

[0016] Fig. 2 is a functional block configuration diagram of the server terminal 100 of Fig. 1. The server terminal 100 includes a communication unit 110, a storage unit 120, and a control unit .

[0017] The communication unit 110 is a communication interface for communicating with the store terminal 200 and the company terminal 300 via the network NW1, and communication is performed according to a communication protocol such as TCP / IP (Transmission Control Protocol / Internet Protocol).

[0018] The storage unit 120 stores input data, programs for executing various control processes and functions in the control unit 130, and is composed of RAM (Random Access Memory), ROM (Read Only Memory), etc. The storage unit 120 also has a store data storage unit 121 that stores various data related to the store. Note that a database (not shown) storing various data may be constructed outside the storage unit 120 or the server terminal 100.

[0019] The control unit 130 controls the overall operation of the server terminal 100 by executing programs stored in the storage unit 120, and is composed of a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), etc. Functions of the control unit 130 include an information receiving unit 131 that receives information from each store terminal, a store information processing unit 132 that refers to and processes various data related to the store, and a prediction calculation processing unit 133 that refers to and processes various data related to the store. The information receiving unit 131, store information processing unit 132, and prediction calculation processing unit 133 are activated by programs stored in the storage unit 120 and executed by the server terminal 100, which is a computer (electronic calculator).

[0020] The information receiving unit 131 receives information from the store terminal 200, the cash register terminal (POS terminal) 300A, and the management system terminal 300B via the communication unit 110. For example, from the store terminal 200, information relating to the store's daily sales performance is received.

[0021] The store information processing unit 132 performs predetermined processing by referring to various data related to the store (for example, store data 1000, which will be described later) received from the store terminal 200, the cash register terminal 300A, and the management system terminal 300B.

[0022] The predictive calculation processing unit 133 refers to various data related to the store (for example, store data 1000, which will be described later) and performs predetermined processing such as predictive calculation.

[0023] The control unit 130 may also have a screen generation unit (not shown), which generates screen information to be displayed via the user interface of the store terminal 200 upon request. For example, the control unit 130 generates a user interface by using image and text data (not shown) stored in the storage unit 120 as material and arranging various images and text in predetermined areas of the user interface based on predetermined layout rules. Processing related to the image generation unit may also be executed by a GPU (Graphics Processing Unit).

[0024] Fig. 3 is a functional block diagram showing the store terminal 200 of Fig. 1. The store terminal 200 includes a communication unit 210, a display operation unit 220, a storage unit 230, and a control unit 240.

[0025] The communication unit 210 is a communication interface for communicating with the server terminal 100 via the network NW, and communication is performed according to a communication protocol such as TCP / IP.

[0026] The display operation unit 220 is a user interface used by the provider to input instructions and display text, images, etc. in accordance with input data from the control unit 240, and is composed of a display, keyboard, and mouse if the store terminal 200 is configured as a personal computer, and is composed of a touch panel, etc. if the store terminal 200 is configured as a smartphone or tablet terminal. This display operation unit 220 is started up by a control program stored in the storage unit 230 and executed by the store terminal 200, which is a computer (electronic calculator).

[0027] The storage unit 230 stores input data, programs for executing various control processes and functions in the control unit 240, and is composed of RAM, ROM, etc. The storage unit 230 also temporarily stores the contents of communication with the server terminal 100.

[0028] The control unit 240 controls the overall operation of the store terminal 200 by executing the programs stored in the storage unit 230, and is composed of a CPU, a GPU, and the like.

[0029] Although the configuration of the store terminal 200 has been described using FIG. 3, the store register terminal 300A and the management system 300B can also be configured in substantially the same manner, and therefore description thereof will be omitted.

[0030] FIG. 4 is a diagram showing an example of user data stored in the server 100. As shown in FIG.

[0031] The store data 1000 shown in Fig. 4 stores various data related to store users. For convenience of explanation, Fig. 4 shows an example of one store user (for example, a store user unit) (a user identified by a user ID "10001"), but information on multiple store users can be stored.

[0032] Various data related to store users may include, but are not limited to, basic information about the store user (e.g., store name, company name, store name, address, contact information, person in charge, etc.), personnel data such as past shift performance information contained in the shift management system, sales performance data contained in the POS register and sales management data, number of reservations and reservation amount data contained in the seat reservation system, shift performance data and attendance performance data contained in the attendance management system, weather data and event data around the store, sales forecast data, reservation forecast data, and shift plan data.

[0033] <Processing flow> The flow of processing for the method of automatically generating a required work hour prediction, which is executed by the system 1 of this embodiment, will be described with reference to FIG. 5 and subsequent figures. FIG. 5 is a flowchart showing a method for learning data such as store sales results according to the first embodiment of the present invention.

[0034] First, in the processing of step S101, the information receiving unit 131 of the control unit 130 of the server terminal 100 receives from the store terminal 200 basic information about the store (for example, store name, company name, store name, address, contact information, person in charge, etc.), personnel data such as past shift performance information contained in the shift management system, sales performance data contained in the POS register and sales management data, number of reservations and reservation amount data contained in the seat reservation system, shift performance data and attendance performance data contained in the attendance management system, store sales information, reservation information, etc. The information receiving unit 131 of the control unit 130 stores the received information in the store data storage unit 121 of the memory unit 120.

[0035] Next, in the processing of step S102, the store information processing unit 132 of the control unit 130 of the server terminal 100 performs machine learning based on the information received in the processing of S101, and generates a learning model for performing sales forecasting, etc., as described below.

[0036] FIG. 6 is a flowchart showing a method for generating a sales forecast and a reservation forecast for a store according to the first embodiment of the present invention.

[0037] First, in the process of S201, the store information processing unit 132 of the server terminal 100 refers to the store's sales performance data, reservation data, etc. acquired in step S101. Here, the data to be referred to also includes information about events taking place near the store and weather information in the store's area. In addition, the store information processing unit 132 refers to the learning model generated in step S102.

[0038] Next, in step S202, the forecast calculation processing unit 133 of the server terminal 100 generates a future sales forecast for the store based on the data referenced in step S201. Here, the forecast to be generated can be generated for a predetermined period, such as one hour. Furthermore, it is also possible to generate a forecast for the next few months. The forecast calculation processing unit 132 of the control unit 130 stores the generated sales forecast as store data 1000 in the store data storage unit 121 of the memory unit 120.

[0039] FIG. 7 is an example of a screen displaying daily sales forecasts for a store according to the first embodiment of the present invention. Actual sales forecasts for the most recent month are listed. For example, the daily sales forecast for June 9th is 388,000 yen, and the forecast weather for that day is also listed.

[0040] 8 is a flowchart showing a method for generating a store staffing plan according to the first embodiment of the present invention. First, in the process of step S301, the store information processing unit 132 of the server terminal 100 refers to the shift data acquired in step S101.

[0041] Subsequently, in the process of step S302, the forecast calculation processing unit 133 of the control unit 130 of the server terminal 100 refers to the sales forecast data generated by the processes of steps S201 and S202.

[0042] In the process of step S303, the store information processing unit 132 of the server terminal 100 refers to the attendance data acquired in step S101.

[0043] In step S304, the prediction calculation processing unit 133 of the server terminal 100 generates a required work hour prediction based on the sales prediction, reservation prediction, shift and attendance data referenced in steps S301 to S303, and the learning model generated in step S102. The generated prediction can be made for a predetermined period, such as one hour. It can also be generated for the most recent several months. The store information processing unit 132 of the control unit 130 stores the generated required work hour prediction as store data 1000 in the store data storage unit 121 of the memory unit 120. The required work hour prediction indicates, for example, the number of staff required per hour. Furthermore, the prediction takes into account times when the number of customers at a restaurant is particularly low, known as idle time, making it possible to propose a staffing plan that further reduces labor costs. The above-mentioned processes of steps S101 to S103, S201 to S202, and S301 to S304 are updated in real time as needed, allowing the store to obtain the latest information.

[0044] 9 is an example of a screen displaying hourly sales forecasts and customer numbers for a store according to the first embodiment of the present invention. For example, the predicted number of customers who will visit the store and the predicted sales amount for each hour are displayed along with date information and weather forecast.

[0045] 10 is an example of a screen displaying hourly sales forecasts, customer numbers, and required staff numbers for a store according to the first embodiment of the present invention. For example, along with date information, the store's hourly sales forecast, the estimated number of customers, and the required staff numbers are displayed, and, if necessary, are displayed so that they can be compared with actual results for that time period. This allows for more accurate and real-time predictions of sales, customer numbers, and required staff numbers based on a wide range of data, compared to predictions based on human experience.

[0046] Although the embodiments of the present invention have been described above, they can be embodied in various other forms, and various omissions, substitutions, and modifications can be made. These embodiments, modifications, and omissions, substitutions, and modifications are included in the technical scope of the claims and their equivalents. [Explanation of symbols]

[0047] 1 System 100 Server terminal, 110 Communication unit, 120 Storage unit, 130 Control unit, 200 Store terminal, NW1 Network

Claims

1. A method for recommending staffing plans for a restaurant establishment, comprising: The control unit of the server terminal Refer to performance data stored in a storage unit of the server terminal, including sales performance data, reservation performance data, past attendance information of the staff, weather information around the store, and shift performance data of the staff, generating a learning model for sales forecasting using the performance data as input data; referencing the actual sales data, the actual reservation data, and the weather information of the restaurant for each time slot corresponding to a predetermined day, which are acquired from a store terminal of the restaurant, and generating a sales forecast for each time slot on the predetermined day using the actual sales data, the actual reservation data, the weather information, and the learning model; generating a required work time prediction for staff at the store for each time slot of the specified day using the learning model by referring to the generated sales forecast, the actual data of the attendance information, and the actual shift data, wherein the required work time prediction includes predicting the required number of staff for each time slot of the specified day, and predicting the required number of staff to be lower for time slots of the specified day that correspond to idle times in the sales forecast than for other time slots; A method for displaying the sales forecast, the required number of staff, and the weather information for each time period on the specified day together with date information on the same screen of the store terminal of the store, so as to be comparable with the performance data.

2. The method according to claim 1 , wherein the control unit generates a second sales forecast by referring to information about events taking place near the store.

3. 3. The method of claim 2, The control unit A method for generating a required labor hour forecast by referring to the second sales forecast and using the learning model.

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