Recommended methods for staffing planning

Abstract

This invention provides a method for forecasting sales based on store sales performance and other data, and then automatically generating forecasts of required working hours by time of day, which serve as the basis for creating staffing plans. [Solution] A method for generating a staffing plan for a restaurant, wherein the control unit of a server terminal refers to the sales performance of the restaurant stored in the memory unit of the server terminal, predicts sales based on the sales performance, and generates a prediction of the required working hours for the staff of the restaurant.

Description

Technical Field

[0001] The present invention relates to a method for recommending a staffing plan for a restaurant store.

Background Art

[0002] Conventionally, in a restaurant, a POS system has been introduced to manage the process from order processing to accounting in a consistent manner.

[0003] For example, as shown in the technology disclosed in Patent Document 1, in a restaurant, by using a POS system, the process from order processing to accounting can be managed electronically in a consistent manner, and it becomes possible to easily grasp the daily sales performance and the attributes of customers who visit the store at a later date, which is useful for the management of the restaurant.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, in the POS system used in a restaurant, as described above, it only manages sales performance and reservation performance, and it is difficult to add other functions. In particular, regarding the staffing plan such as actual shift creation, it largely depends on the experience of the store manager, and there are problems such as variations among stores and lack of accuracy.

[0006] Therefore, an object of the present invention is to provide a method for predicting sales based on the sales performance of a store and the like, and further automatically generating a predicted required working time by time zone that serves as a basis for creating a staffing plan based on them.

Means for Solving the Problems

[0007] A method for recommending staffing plans for a restaurant, according to one aspect of the present invention, wherein the control unit of a server terminal refers to the store's sales performance and the like stored in the server terminal's memory, predicts sales, and generates a prediction of the required working hours for the store's staff, such as a shift schedule, based on the prediction. [Effects of the Invention]

[0008] According to the present invention, it is possible to provide a method for automatically generating forecasts of required working hours by time of day, which serve as the basis for personnel planning, based on store sales performance and other factors. [Brief explanation of the drawing]

[0009] [Figure 1] This is a block diagram showing a system for performing a method for automatically generating time-of-day labor time predictions according to the first embodiment of the present invention. [Figure 2] Figure 1 is a functional block diagram showing the server terminal 100. [Figure 3] Figure 1 is a functional block diagram showing the store terminal 200. [Figure 4] This figure shows an example of store data stored in server 100. [Figure 5] This is a flowchart showing a method for learning data such as store sales performance according to the first embodiment of the present invention. [Figure 6] This is a flowchart showing a method for generating store sales forecasts and reservation forecasts according to a first embodiment of the present invention. [Figure 7] This is an example of a screen displaying daily sales forecasts in a store according to the first embodiment of the present invention. [Figure 8] This is a flowchart showing a method for generating a forecast of required working hours for a store by time of day, according to a first embodiment of the present invention. [Figure 9] This is an example of a screen displaying hourly sales forecasts in a store according to the first embodiment of the present invention. [Figure 10] This is an example of a screen displaying hourly sales forecasts and required personnel in a store, according to the first embodiment of the present invention. [Modes for carrying out the invention]

[0010] Embodiments of the present invention will be described below with reference to the drawings. The embodiments described below are not intended to unduly limit the scope of the present invention as described in the claims. Furthermore, not all components shown in the embodiments are necessarily essential components of the present invention.

[0011] <Structure> Figure 1 is a block diagram showing a system that implements a method for automatically generating a staffing plan according to the first embodiment of the present invention. This system 1 includes, for example, a server terminal 100, store terminals 200A and 200B managed by store users, a store register terminal (POS terminal) 300A, and a store management system 300B, which receive daily sales performance information and shift information from the store's cash register or a management system installed by the store, perform sales forecasts and reservation forecasts based on the acquired information using a predetermined method, and generate the necessary staffing forecasts based on these to create a staffing plan for the store. For the sake of explanation, each terminal is described as a single or specific number, but there is no limit to the number of each.

[0012] The server terminal 100, store terminals 200A and 200B, and store register terminal (POS terminal) 300A and management system 300B are each connected via network NW1. Network NW consists of the Internet, intranet, wireless LAN (Local Area Network), WAN (Wide Area Network), etc.

[0013] The server terminal 100 may be a general-purpose computer such as a workstation or personal computer, or it may be logically implemented through cloud computing.

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

[0015] In this embodiment, the system 1 includes a server terminal 100, store terminals 200A and 200B, a store register terminal (POS terminal) 300A, and a management system 300B. It will be described as a configuration in which users of each terminal operate on the server terminal 100 using their respective terminals. However, the server terminal 100 may be configured as a stand-alone device, and the server terminal itself may be provided with a function for each user to directly perform operations.

[0016] FIG. 2 is a functional block configuration diagram of the server terminal 100 in FIG. 1. The server terminal 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

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

[0018] The storage unit 120 stores programs for executing various control processes and each function in the control unit 130, input data, etc., and is composed of a RAM (Random Access Memory), a ROM (Read Only Memory), etc. The storage unit 120 also has a store data storage unit 121 for storing 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 memory unit 120, and is composed of a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), etc. The 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 store-related data, and a predictive calculation processing unit 133 that refers to and processes various store-related data. The information receiving unit 131, the store information processing unit 132, and the predictive calculation processing unit 133 are started by programs stored in the memory unit 120 and executed by the server terminal 100, which is a computer (electronic computer).

[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, it receives information regarding the store's daily sales performance from the store terminal 200.

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

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

[0023] Furthermore, the control unit 130 may also have a screen generation unit (not shown) that generates screen information to be displayed via the user interface of the store terminal 200 as needed. For example, using image and text data (not shown) stored in the storage unit 120 as material, the user interface is generated by arranging various images and text in predetermined areas of the user interface according to predetermined layout rules. Processing related to the image generation unit can also be performed by a GPU (Graphics Processing Unit).

[0024] Figure 3 is a functional block diagram showing the store terminal 200 in Figure 1. The store terminal 200 comprises a communication unit 210, a display and 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 using a communication protocol such as TCP / IP.

[0026] The display operation unit 220 is a user interface used to display text, images, etc., in response to input data from the control unit 240, based on instructions from the provider. If the store terminal 200 is a personal computer, it consists of a display and a keyboard or mouse; if the store terminal 200 is a smartphone or tablet, it consists of a touch panel, etc. This display operation unit 220 is activated 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 memory unit 230 stores programs for executing various control processes and functions within the control unit 240, input data, etc., and is composed of RAM, ROM, etc. The memory unit 230 also temporarily stores the contents of communications with the server terminal 100.

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

[0029] Although the configuration of the store terminal 200 has been explained using Figure 3, the store register terminal 300A and the management system 300B can be essentially configured the same way, so their explanation will be omitted.

[0030] Figure 4 shows an example of user data stored in server 100.

[0031] The store data 1000 shown in Figure 4 stores various data related to store users. In Figure 4, for the sake of explanation, an example of one store user (for example, a store user unit) (a user identified by user ID "10001") is shown, but information for multiple store users can be stored.

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

[0033] <Processing flow> Referring to Figures 5 and onward, the processing flow of the method for automatically generating the required working hours forecast executed by System 1 of this embodiment will be explained. Figure 5 is a flowchart showing a method for learning data such as store sales performance according to the first embodiment of the present invention.

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

[0035] Next, in 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 S101 process and generates a learning model for sales forecasting and other purposes described later.

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

[0037] First, as part of the S201 process, the store information processing unit 132 of the server terminal 100 refers to the store's sales performance data and reservation data, etc., acquired in step S101. The data referred to here also includes information on events held near the store and weather information for the store's area. The store information processing unit 132 also refers to the learning model generated in step S102.

[0038] Next, as part of step S202, the prediction calculation processing unit 133 of the server terminal 100 generates a forecast of future sales for the store based on the data referenced in step S201. Here, the forecast can be generated for predetermined periods, such as hourly intervals. Furthermore, it is also possible to generate a forecast for the next few months. The prediction 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 storage unit 120.

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

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

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

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

[0043] As part of step S304, the prediction calculation processing unit 133 of the server terminal 100 generates a required labor time forecast based on the sales forecast, reservation forecast, shift, attendance data referenced in steps S301 to S303, and the learning model generated in step S102. Here, the forecast can be generated for predetermined periods, such as in one-hour increments. Furthermore, it is also possible to generate forecasts for the next few months. The store information processing unit 132 of the control unit 130 stores the generated required labor time forecast as store data 1000 in the store data storage unit 121 of the storage unit 120. Here, the required labor time forecast displays, for example, the number of personnel needed every hour. Furthermore, the forecast takes into account times when the number of customers visiting the restaurant is particularly low, so-called idle time, making it possible to propose a personnel plan that reduces labor costs. Furthermore, the processes described above in steps 101-S103, S201-S202, and S301-304 are updated in real time, allowing stores to obtain the latest information.

[0044] Figure 9 is an example screen showing hourly sales forecasts and customer count forecasts in a store according to the first embodiment of the present invention. For example, along with date information and weather forecast, the estimated number of customers and estimated sales amount for each hour are displayed.

[0045] Figure 10 shows an example screen displaying hourly sales forecasts, customer count forecasts, and required staffing levels for a store, according to the first embodiment of the present invention. For example, along with date information, hourly sales forecasts, customer count forecasts, and required staffing levels for the store are displayed, and can be compared with actual data for that time period as needed. This allows for more accurate and real-time forecasts of sales, customer counts, and required staffing levels based on a wide range of data, compared to forecasts based on human experience.

[0046] Although embodiments of the invention have been described above, these can be implemented in various other forms, and can be carried out by various omissions, substitutions, and modifications. These embodiments and variations, as well as those with omissions, substitutions, and modifications, are included within the technical scope of the claims and their equivalents. [Explanation of Symbols]

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

Claims

1. A method for recommending staffing plans for restaurant stores, The control unit of the server terminal is: Referencing the sales performance of the store stored in the storage unit of the server terminal, A method for predicting sales based on the aforementioned sales performance and generating a prediction of the required working hours for the personnel of the store.

2. A method according to claim 1, comprising generating a prediction of the required working hours for the staff of the store based on the staff shift data of the store.

3. A method according to claim 1, comprising generating a prediction of the required working hours for the staff of the store based on the store's reservation history.

4. A method according to claim 1, comprising generating a prediction of the required working hours for personnel at the store based on event data around the store.

5. A method according to claim 1, comprising generating a prediction of the required working hours for the staff of the store based on weather data around the store.

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